{"as_of":"2026-08-07T15:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ab8ce066f495a481edef60475ce11141dd44ee37a943378689c0db3ad531ff22","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-15T05:25:59.964619Z","state":"measured"},{"denominator":107,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":107,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":82,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":82,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:52:15.371607Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":10,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T14:52:15.371607Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.17296","last_updated":"2025-05-22T21:23:20Z","snapshot_observed_at":"2026-08-07T14:47:09.821079Z","submitted_at":"2025-05-22T21:23:20Z","title":"SELF: Self-Extend the Context Length With Logistic Growth Function","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T14:52:15.371607Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2505.17296"},"observation_digest":"sha256:56038574900e4757c2c926d6320dafe5a9989ba5889b9547e9c6b74902516fb4","observation_id":"b5436054-3fed-4c0b-af5d-43e70cda53d2","resolution":{"observed_at":"2026-08-07T14:52:15.371607Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T14:40:55.657678Z","title":"Qwen2.5-1m technical report, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.18092","last_updated":"2025-05-27T09:42:25Z","snapshot_observed_at":"2026-08-07T14:33:20.156940Z","submitted_at":"2025-05-23T16:47:00Z","title":"QwenLong-CPRS: Towards $\\infty$-LLMs with Dynamic Context Optimization","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:40:55.657678Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2505.18092"},"observation_digest":"sha256:4d6cb50b24cafd7a6092fc06253a310ab7301b77fa21144a87fca8b7791de29b","observation_id":"edfdad66-48f7-4157-a520-a156c793946d","resolution":{"observed_at":"2026-08-07T14:40:55.657678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T13:59:15.247478Z","title":"Qwen2.5 technical report.arXiv preprint arXiv:2501.15383, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20285","last_updated":"2025-05-27T06:46:24Z","snapshot_observed_at":"2026-08-07T13:53:20.076311Z","submitted_at":"2025-05-26T17:58:50Z","title":"MaskSearch: A Universal Pre-Training Framework to Enhance Agentic Search Capability","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:59:15.247478Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2505.20285"},"observation_digest":"sha256:4fa1a5a30bad81718be103e3e6850a83e235fa0761b3c1c905cfb89a1e6167cc","observation_id":"293450c9-9e25-44f5-9a7f-64bffeda2323","resolution":{"observed_at":"2026-08-07T13:59:15.247478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T12:53:26.847399Z","title":"Qwen2.5-1m technical report.CoRR, abs/2501.15383, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.23380","last_updated":"2025-05-29T12:00:15Z","snapshot_observed_at":"2026-08-07T12:44:53.093273Z","submitted_at":"2025-05-29T12:00:15Z","title":"UniRL: Self-Improving Unified Multimodal Models via Supervised and Reinforcement Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T12:53:26.847399Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2505.23380"},"observation_digest":"sha256:ac0eeea0c41321aaadc85963bef98ed9df6dcae65cc5fca032c5b143de966154","observation_id":"17d2601d-6d50-4659-876f-f6998775a666","resolution":{"observed_at":"2026-08-07T12:53:26.847399Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T12:37:49.263799Z","title":"Qwen2.5-1m technical report.arXiv preprint arXiv:2501.15383,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.24164","last_updated":"2025-05-30T03:11:46Z","snapshot_observed_at":"2026-08-07T12:29:46.881822Z","submitted_at":"2025-05-30T03:11:46Z","title":"Mixed-R1: Unified Reward Perspective For Reasoning Capability in Multimodal Large Language Models","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T12:37:49.263799Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2505.24164"},"observation_digest":"sha256:d671229df08fdd10f4d36d6f1d88779da6669dcb40d49290e127875193ab6ae4","observation_id":"9b330a79-d238-4eb8-889c-aa84aefb3fce","resolution":{"observed_at":"2026-08-07T12:37:49.263799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T12:39:02.628737Z","title":"Qwen2. 5-1m technical report,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24179","last_updated":"2025-05-30T03:40:24Z","snapshot_observed_at":"2026-08-07T12:28:26.705118Z","submitted_at":"2025-05-30T03:40:24Z","title":"SALE : Low-bit Estimation for Efficient Sparse Attention in Long-context LLM Prefilling","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:39:02.628737Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2505.24179"},"observation_digest":"sha256:055a185ebffbba3c708e5bf78bd0eaeaae7317f2a40611262b30423bc4ce2c09","observation_id":"3dbd470f-d605-4360-a5f6-439816f638b5","resolution":{"observed_at":"2026-08-07T12:39:02.628737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T12:35:40.930281Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24782","last_updated":"2025-06-06T16:42:11Z","snapshot_observed_at":"2026-08-07T12:11:05.144228Z","submitted_at":"2025-05-30T16:43:28Z","title":"Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:40.930281Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2505.24782"},"observation_digest":"sha256:2536f067e0894173816551091b82ceb9664232cb9bc017c4c9baf5778fbce3dc","observation_id":"eb2866fd-216e-4769-af7b-4f543dece219","resolution":{"observed_at":"2026-08-07T12:35:40.930281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T10:52:48.981099Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.04108","last_updated":"2025-06-05T05:39:48Z","snapshot_observed_at":"2026-08-07T10:45:12.972483Z","submitted_at":"2025-06-04T16:01:48Z","title":"Rectified Sparse Attention","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T10:52:48.981099Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2506.04108"},"observation_digest":"sha256:9337bc8f309909516e2612bbcaef74c765c9ce83f64352f49527c9d235251a4f","observation_id":"afb1e160-cf1b-40f4-8c89-1d923ac34cf3","resolution":{"observed_at":"2026-08-07T10:52:48.981099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T04:55:28.915211Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11144","last_updated":"2025-06-11T05:33:03Z","snapshot_observed_at":"2026-08-07T04:46:32.123425Z","submitted_at":"2025-06-11T05:33:03Z","title":"AlignHuman: Improving Motion and Fidelity via Timestep-Segment Preference Optimization for Audio-Driven Human Animation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T04:55:28.915211Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2506.11144"},"observation_digest":"sha256:fcf1bd8e9aadda0748d4f4db339207e2db500c4b1357fac6e5453099fe90031a","observation_id":"f8b87c68-d613-4395-a5e6-d84a82c53b51","resolution":{"observed_at":"2026-08-07T04:55:28.915211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T12:38:40.663165Z","title":"Qwen2.5-1m technical report","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.15704","last_updated":"2025-05-30T02:35:59Z","snapshot_observed_at":"2026-08-07T12:30:17.322980Z","submitted_at":"2025-05-30T02:35:59Z","title":"Learn from the Past: Fast Sparse Indexing for Large Language Model Decoding","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:38:40.663165Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2506.15704"},"observation_digest":"sha256:7f3932480f6db36ca3187fbc8775e3ff1f273ceff41f3b4607628e305e6222fb","observation_id":"23115589-32d9-4e42-b217-8997271304a3","resolution":{"observed_at":"2026-08-07T12:38:40.663165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T12:19:58.792545Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.15710","last_updated":"2025-05-30T17:57:08Z","snapshot_observed_at":"2026-08-07T12:10:14.942012Z","submitted_at":"2025-05-30T17:57:08Z","title":"RAST: Reasoning Activation in LLMs via Small-model Transfer","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T12:19:58.792545Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2506.15710"},"observation_digest":"sha256:2025d7f78ad4c7e18dd747bb1f30c6a87e7d7254a764cc9c7528f11f7296c0ca","observation_id":"f2c8036e-f9b9-454e-a544-10e25ef41675","resolution":{"observed_at":"2026-08-07T12:19:58.792545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-06T23:21:09.462136Z","title":"Available: https://arxiv.org/abs/2501.15383","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18532","last_updated":"2025-06-23T11:40:04Z","snapshot_observed_at":"2026-08-07T14:14:59.106543Z","submitted_at":"2025-06-23T11:40:04Z","title":"End-to-End Spoken Grammatical Error Correction","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T23:21:09.462136Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2506.18532"},"observation_digest":"sha256:f7c0261759d6c80a99bd4a4ec7c71580e2cec8c98f0f35da608ba124f227407a","observation_id":"39798cae-acae-4f3f-a087-f20ce54a86f5","resolution":{"observed_at":"2026-08-06T23:21:09.462136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2507.02259","last_updated":"2026-07-29T12:55:39Z","snapshot_observed_at":"2026-08-06T20:31:23.587108Z","submitted_at":"2025-07-03T03:11:50Z","title":"MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-15T11:17:24.406028Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2507.02259"},"observation_digest":"sha256:c4afaa469bc97f7ee0d4c005e444e2d0a01ef666329e1b5f3b0da31a4520f2c3","observation_id":"fa07207a-f609-4cec-9d07-23165b5b4aaf","resolution":{"observed_at":"2026-05-15T11:17:24.504450Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-06T20:40:13.824597Z","title":"Qwen2.5-1m technical report.arXiv preprint arXiv:2501.15383, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.02259","last_updated":"2026-07-29T12:55:39Z","snapshot_observed_at":"2026-08-06T20:31:23.587108Z","submitted_at":"2025-07-03T03:11:50Z","title":"MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T20:40:13.824597Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2507.02259"},"observation_digest":"sha256:74d98b5d39bc27db58521729fe5e7099a6e7d3dda091a51efeacd0d39bc50c88","observation_id":"2c0e9604-26ab-4316-a09d-b10c18b8df11","resolution":{"observed_at":"2026-08-06T20:40:13.824597Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-06T15:43:39.500934Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15224","last_updated":"2025-07-21T03:55:41Z","snapshot_observed_at":"2026-08-07T02:44:19.282770Z","submitted_at":"2025-07-21T03:55:41Z","title":"SimdBench: Benchmarking Large Language Models for SIMD-Intrinsic Code Generation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T15:43:39.500934Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2507.15224"},"observation_digest":"sha256:f775b6b8dfa4c77bd0637ada93ed4de3bf65632264d2fe817df2f1c90d14b571","observation_id":"6b0709c7-7350-4f7d-8c2c-4e5c7d516b36","resolution":{"observed_at":"2026-08-06T15:43:39.500934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-06T12:55:58.331495Z","title":"arXiv preprint arXiv:2501.15383","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.21406","last_updated":"2025-07-29T00:26:33Z","snapshot_observed_at":"2026-08-06T12:55:56.526336Z","submitted_at":"2025-07-29T00:26:33Z","title":"Shapley Uncertainty in Natural Language Generation","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T12:55:58.331495Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2507.21406"},"observation_digest":"sha256:fbe6ac116151c78ce5dadfa5933286abcec38f6401359b646971f4820be54f5b","observation_id":"31aeb051-80ed-42d9-b2cd-c9f9c9a4a1e7","resolution":{"observed_at":"2026-08-06T12:55:58.331495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-06T11:46:27.658253Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.22411","last_updated":"2026-06-20T07:40:02Z","snapshot_observed_at":"2026-08-06T11:46:25.999796Z","submitted_at":"2025-07-30T06:29:50Z","title":"NeedleChain: Measuring Intact Context Comprehension Capability of Large Language Models","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T11:46:27.658253Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2507.22411"},"observation_digest":"sha256:efc262b55ff8df9fb72f2ce15e9ca381365c2c7cbfd58ab4d6084979b7470106","observation_id":"74fd94d5-9007-46ef-a4ca-df8740aed517","resolution":{"observed_at":"2026-08-06T11:46:27.658253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T20:04:50.570486Z","title":"Qwen2.5-1m technical report","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.11326","last_updated":"2025-08-15T08:53:56Z","snapshot_observed_at":"2026-08-07T04:02:54.878172Z","submitted_at":"2025-08-15T08:53:56Z","title":"MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T20:04:50.570486Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2508.11326"},"observation_digest":"sha256:4bbf444f50d8743f4d01e4fc19ce9b1a49239f240a3a188f4772246ed78210f1","observation_id":"cf0c18c0-5f8a-4972-b220-3ebce6c1707b","resolution":{"observed_at":"2026-08-05T20:04:50.570486Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-04T21:46:08.742538Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.07730","last_updated":"2025-09-10T04:50:56Z","snapshot_observed_at":"2026-08-04T21:45:55.515666Z","submitted_at":"2025-09-09T13:32:29Z","title":"M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-04T21:46:08.742538Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2509.07730"},"observation_digest":"sha256:651f255041a7fab85486fe251894876e29a60ca9ca401b79a202249b97c89e28","observation_id":"45440354-4c53-4f9e-a0d4-7a73aae2f8e9","resolution":{"observed_at":"2026-08-04T21:46:08.742538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-04T14:43:28.822086Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.24372","last_updated":"2026-07-14T15:11:12Z","snapshot_observed_at":"2026-08-06T13:33:46.319626Z","submitted_at":"2025-09-29T07:19:34Z","title":"Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning","version":3},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-04T14:43:28.822086Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2509.24372"},"observation_digest":"sha256:19d2bd005482d9461efa23cd84484d40b152e022f903d66ec0f3679fd0443e93","observation_id":"09d98d04-fe78-4afc-86f1-89a15c6fd4f5","resolution":{"observed_at":"2026-08-04T14:43:28.822086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2509.24765","last_updated":"2026-04-22T06:35:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-09-29T13:31:22Z","title":"Semantic-Aware Logical Reasoning via a Semiotic Framework","version":8},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-18T12:57:45.584017Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2509.24765"},"observation_digest":"sha256:360f00555ddd263fc0115f6fb88847b5607d584013366617954a08f88fc5e550","observation_id":"8bca3dd6-3f35-4423-93cd-2a10c859a70e","resolution":{"observed_at":"2026-05-18T13:01:24.283099Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-04T13:22:35.773784Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.01009","last_updated":"2026-03-30T19:53:43Z","snapshot_observed_at":"2026-08-06T16:42:01.247661Z","submitted_at":"2025-10-01T15:15:36Z","title":"POVQA: Preference-Optimized Video Question Answering with Rationales for Data Efficiency","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T13:22:35.773784Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2510.01009"},"observation_digest":"sha256:0e2ce059e24ebd69c691424e322c009b1cea369b9d87c3978bfcd7365e2093ad","observation_id":"6d6a8ca9-fca7-4d50-a6d2-bf099951be2a","resolution":{"observed_at":"2026-08-04T13:22:35.773784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2510.10129","last_updated":"2026-05-21T08:20:19Z","snapshot_observed_at":"2026-07-06T22:32:22.953630Z","submitted_at":"2025-10-11T09:28:26Z","title":"CacheClip: Accelerating RAG with Effective KV Cache Reuse","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-22T12:36:41.630618Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2510.10129"},"observation_digest":"sha256:f4148434f7c8fd3e840cf073045ee1ec1f9d026cb937d6b4f2a1f7795633e16f","observation_id":"329903ad-81c7-446f-acf0-c83f97c7abe3","resolution":{"observed_at":"2026-05-22T12:41:33.686672Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2510.18830","last_updated":"2026-05-19T17:27:20Z","snapshot_observed_at":"2026-07-06T22:33:43.999976Z","submitted_at":"2025-10-21T17:25:32Z","title":"MTraining: Distributed Dynamic Sparse Attention for Efficient Ultra-Long Context Training","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-21T19:44:04.833504Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2510.18830"},"observation_digest":"sha256:3d27cd91676a62850b826594dfee9a8eadba76ca7749f1584fb2e6c3c6d4ed98","observation_id":"365dd40c-f9b7-4744-b502-aafdfd5e2728","resolution":{"observed_at":"2026-05-21T19:44:19.692165Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-03T20:26:58.746500Z","title":"Qwen2.5-1m technical report, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.20102","last_updated":"2026-06-04T11:55:53Z","snapshot_observed_at":"2026-08-06T20:52:02.892575Z","submitted_at":"2025-11-25T09:21:57Z","title":"SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T20:26:58.746500Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2511.20102"},"observation_digest":"sha256:37153dd23c0f3d6c0bd4bee4df42f985f433132fde8e5f76dcbdc5ef1b11c207","observation_id":"c23ae301-0286-43d0-98de-a0abe18a4c88","resolution":{"observed_at":"2026-08-03T20:26:58.746500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-03T18:28:20.718184Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.05277","last_updated":"2026-06-02T23:07:19Z","snapshot_observed_at":"2026-08-07T00:48:11.221345Z","submitted_at":"2025-12-04T21:57:10Z","title":"From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models","version":4},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:20.718184Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2512.05277"},"observation_digest":"sha256:a64d1334391ac6f888f13ce1c86ca42d6b2e4422962add96c2325e21a3f4725e","observation_id":"b06c060d-57e4-4008-97a0-c0e3e13fa17a","resolution":{"observed_at":"2026-08-03T18:28:20.718184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-03T11:35:39.931339Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.05751","last_updated":"2026-06-05T07:06:47Z","snapshot_observed_at":"2026-08-04T17:00:12.286204Z","submitted_at":"2026-01-09T12:07:38Z","title":"Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns","version":2},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-03T11:35:39.931339Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2601.05751"},"observation_digest":"sha256:b04c9d92b398e342363fc657d95d019ad2b0f42364846d97ab8d4137b87b43e1","observation_id":"aa298461-e5a5-41fb-9a0e-e1383f94df3f","resolution":{"observed_at":"2026-08-03T11:35:39.931339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2601.20309","last_updated":"2026-05-18T19:51:16Z","snapshot_observed_at":"2026-07-06T22:43:17.026472Z","submitted_at":"2026-01-28T07:01:46Z","title":"SuperInfer: SLO-Aware Rotary Scheduling and Memory Management for LLM Inference on Superchips","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-21T15:26:01.283448Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2601.20309"},"observation_digest":"sha256:a051f9340c23dfc966ecb96ae93d1b5e9b7a4865945df95831cc02977b804365","observation_id":"ac17edcb-ccac-4c09-a9a9-6fff4ea82b18","resolution":{"observed_at":"2026-05-21T15:30:17.999845Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2601.21468","last_updated":"2026-05-18T07:50:29Z","snapshot_observed_at":"2026-08-04T01:48:38.560710Z","submitted_at":"2026-01-29T09:47:17Z","title":"MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning","version":5},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-21T15:15:22.055616Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2601.21468"},"observation_digest":"sha256:5a2a1e5f8f128aafeb5530fe9c73278bad07470e5bb377bfb539c5893122ae2b","observation_id":"19a4315e-727f-485e-83c5-5174dd4e1d40","resolution":{"observed_at":"2026-05-21T15:20:17.544758Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-03T06:24:06.208463Z","title":"Qwen2. 5-1m technical report,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.00202","last_updated":"2026-01-30T12:09:38Z","snapshot_observed_at":"2026-08-06T23:33:23.215005Z","submitted_at":"2026-01-30T12:09:38Z","title":"Vision-Language Model Purified Semi-Supervised Semantic Segmentation for Remote Sensing Images","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-03T06:24:06.208463Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2602.00202"},"observation_digest":"sha256:e5cb21c0d3103d0bb97a749b5d2c8b875d1c6a67ceb398435bf9e6aafc0cf8ee","observation_id":"c29c1330-ff6b-4ef9-83ce-0539ec589ebd","resolution":{"observed_at":"2026-08-03T06:24:06.208463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-03T05:45:43.533522Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.02600","last_updated":"2026-06-05T12:49:59Z","snapshot_observed_at":"2026-08-03T05:45:40.908563Z","submitted_at":"2026-02-01T17:41:32Z","title":"Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T05:45:43.533522Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2602.02600"},"observation_digest":"sha256:97b9bc4f6324d93fe353016a8242a6de8b4cc5177160e9430a535954c5e7099f","observation_id":"4ec446d6-0ff9-4998-95de-1f29e5e44e6d","resolution":{"observed_at":"2026-08-03T05:45:43.533522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.06600","last_updated":"2026-04-16T02:46:44Z","snapshot_observed_at":"2026-07-31T17:58:04.679740Z","submitted_at":"2026-04-08T02:34:58Z","title":"IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-05-10T17:34:56.485882Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.06600"},"observation_digest":"sha256:bc364d4a2eccbe7043f76701dff383dd0622c181cb326775c8cfac1d18fff585","observation_id":"33f2a49f-daab-4865-9b75-cf4d6f2f809a","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.08362","last_updated":"2026-05-21T16:20:11Z","snapshot_observed_at":"2026-08-02T15:20:13.753843Z","submitted_at":"2026-04-09T15:26:21Z","title":"Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-10T18:20:56.661012Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.08362"},"observation_digest":"sha256:fa1a9d77a59f372c19c73af6606fda49d892998b2f9dd89c8e82a7a5112c8d7f","observation_id":"e2bb1548-49a2-454a-a3a3-6bedb0c6477b","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.08362","last_updated":"2026-05-21T16:20:11Z","snapshot_observed_at":"2026-08-02T15:20:13.753843Z","submitted_at":"2026-04-09T15:26:21Z","title":"Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-22T10:30:28.404222Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.08362"},"observation_digest":"sha256:cd7865031058afbeca811e0338736a9f6350f2a671a4171522a0de5f6fb41ca0","observation_id":"41d582aa-440e-4cbe-b264-9d76fc2f00aa","resolution":{"observed_at":"2026-05-22T10:31:24.819197Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.09021","last_updated":"2026-04-10T06:35:46Z","snapshot_observed_at":"2026-08-03T17:17:39.935538Z","submitted_at":"2026-04-10T06:35:46Z","title":"Noise-Aware In-Context Learning for Hallucination Mitigation in ALLMs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-10T17:41:59.761237Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.09021"},"observation_digest":"sha256:aa9bbd4020893d3d12cd71fa64536a0cb8db515620887b40842c8a9db4008eca","observation_id":"ad3f3a54-7a91-4420-bac6-78772016598a","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.14325","last_updated":"2026-04-15T18:32:32Z","snapshot_observed_at":"2026-07-06T23:02:09.426599Z","submitted_at":"2026-04-15T18:32:32Z","title":"Faithfulness Serum: Mitigating the Faithfulness Gap in Textual Explanations of LLM Decisions via Attribution Guidance","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T13:45:51.417645Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.14325"},"observation_digest":"sha256:2f5839579434c8cb31d1e1513d15d4916bc2a51a2881105250ae692d442c58d1","observation_id":"0e5d7719-beb1-4b5c-8b3a-78e24d7ed215","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.16007","last_updated":"2026-04-17T12:29:54Z","snapshot_observed_at":"2026-07-06T23:03:26.308324Z","submitted_at":"2026-04-17T12:29:54Z","title":"MemExplorer: Navigating the Heterogeneous Memory Design Space for Agentic Inference NPUs","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T07:45:17.043107Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.16007"},"observation_digest":"sha256:33236e859503f22df1c0d84b77f43ba82ca3c9e7964c0eb8fc901050b35ed997","observation_id":"400687cb-9eee-4426-a291-d8b491ee9354","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.20685","last_updated":"2026-04-22T15:33:45Z","snapshot_observed_at":"2026-07-06T23:07:14.243878Z","submitted_at":"2026-04-22T15:33:45Z","title":"MGDA-Decoupled: Geometry-Aware Multi-Objective Optimisation for DPO-based LLM Alignment","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-05-10T01:38:49.892824Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.20685"},"observation_digest":"sha256:6d4cb0059c859d1242feaa7261b6e67d40c26b5a4f82dceb18366ea97eeb04ec","observation_id":"491954c6-159d-4031-83e5-b6a4ff36d75b","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.20983","last_updated":"2026-04-22T18:12:07Z","snapshot_observed_at":"2026-07-31T08:01:41.105010Z","submitted_at":"2026-04-22T18:12:07Z","title":"Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T00:20:11.425885Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.20983"},"observation_digest":"sha256:11a05fff5f5e628314edae9f29ada1ce28eb8a4495108b1ffe429ad90617fde4","observation_id":"41e03522-9abd-4c98-a6c1-2d3b5fd12ffa","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.22906","last_updated":"2026-04-24T16:56:53Z","snapshot_observed_at":"2026-08-06T08:01:38.096740Z","submitted_at":"2026-04-24T16:56:53Z","title":"Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities","version":1},"reference_index":182,"source":"pdf_text","source_observed_at":"2026-05-08T09:45:57.201837Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.22906"},"observation_digest":"sha256:1858ef9bcbe9512fe00b72c2d2e99a9d09e4299722e1ac2d76fb472a1730e67c","observation_id":"09076c60-edaf-49fe-81fa-9cff4179c4ed","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.23156","last_updated":"2026-04-25T06:05:41Z","snapshot_observed_at":"2026-07-06T23:09:23.591544Z","submitted_at":"2026-04-25T06:05:41Z","title":"Birds of a Feather Cluster Nearby: a Proximity-Aware Geo-Codebook for Local Service Recommendation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-08T07:32:27.258441Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.23156"},"observation_digest":"sha256:e4f43db398bc0e7c3ecef4a29419dfd8997c719d92b9418c2ece0c0365909082","observation_id":"0eb09ddd-3a07-4865-87bc-8451794b7c3d","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2604.27723","last_updated":"2026-04-30T11:15:37Z","snapshot_observed_at":"2026-08-02T22:18:26.414883Z","submitted_at":"2026-04-30T11:15:37Z","title":"Optimized Deferral for Imbalanced Settings","version":1},"reference_index":125,"source":"arxiv_source","source_observed_at":"2026-05-07T07:44:35.524601Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2604.27723"},"observation_digest":"sha256:af84b5b9add8d63eeaa18fd01102216b5fdc2d561a646fbf5eae263b3132cc64","observation_id":"6f7d2da4-f1cf-4964-8e2d-91334d28a958","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.01394","last_updated":"2026-05-02T11:31:33Z","snapshot_observed_at":"2026-08-06T01:33:30.617883Z","submitted_at":"2026-05-02T11:31:33Z","title":"LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-09T14:35:14.357256Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.01394"},"observation_digest":"sha256:7816bf58bdef7508aea88fc10b0df6b791ca346e224d5934883b7d9cd319527f","observation_id":"a7ad512e-978c-4eee-ae22-d8022af86faf","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.03058","last_updated":"2026-06-06T22:46:37Z","snapshot_observed_at":"2026-07-06T23:16:00.796105Z","submitted_at":"2026-05-04T18:27:37Z","title":"Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-08T18:55:24.214794Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.03058"},"observation_digest":"sha256:6a7f384c86681cdaba2d79b8a49fd7493ff92e86c3cb8cb7c2ae0990ba30f77f","observation_id":"4d4c9918-12ba-48df-b227-a8a5876894d2","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.03058","last_updated":"2026-06-06T22:46:37Z","snapshot_observed_at":"2026-07-06T23:16:00.796105Z","submitted_at":"2026-05-04T18:27:37Z","title":"Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-01T00:06:52.820343Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.03058"},"observation_digest":"sha256:5b387077a7fe94d3a5ffe7f60a4b9c6448cd36008f5e306e2cbce3934eb46b63","observation_id":"e4b9bd56-18ac-44eb-8f24-c5de96e66abb","resolution":{"observed_at":"2026-07-01T00:15:09.500046Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.06055","last_updated":"2026-05-08T01:46:43Z","snapshot_observed_at":"2026-07-06T23:18:36.537416Z","submitted_at":"2026-05-07T11:41:18Z","title":"Relay Buffer Independent Communication over Pooled HBM for Efficient MoE Inference on Ascend","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-08T05:24:57.000485Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.06055"},"observation_digest":"sha256:bdca49a10c672e9dd2e64a814fd6fdf53768e8e1193ba044fabad8363bf39eec","observation_id":"0ebe77b2-f020-4fae-873a-365f80746cdc","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.06055","last_updated":"2026-05-08T01:46:43Z","snapshot_observed_at":"2026-07-06T23:18:36.537416Z","submitted_at":"2026-05-07T11:41:18Z","title":"Relay Buffer Independent Communication over Pooled HBM for Efficient MoE Inference on Ascend","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-11T00:49:02.180778Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.06055"},"observation_digest":"sha256:11a827de726a7f9dda4ad0a9b9890ff85f64f3df4b54108f3a0e5e7a6f2e0fca","observation_id":"6fb90cbf-b5d1-46db-8e77-1be9be682e47","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.06221","last_updated":"2026-05-07T13:18:08Z","snapshot_observed_at":"2026-08-04T15:36:16.480613Z","submitted_at":"2026-05-07T13:18:08Z","title":"UniPrefill: Universal Long-Context Prefill Acceleration via Block-wise Dynamic Sparsification","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-08T10:43:01.724760Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.06221"},"observation_digest":"sha256:bb5d03fcd45786ac8a9083fb68fd34861aec3d91cccc2a45a26ec9225b060d5f","observation_id":"a7008323-cc3b-4023-9cfd-876888e98485","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.07075","last_updated":"2026-05-08T00:49:05Z","snapshot_observed_at":"2026-07-06T23:19:30.387067Z","submitted_at":"2026-05-08T00:49:05Z","title":"ModelLens: Finding the Best for Your Task from Myriads of Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-11T01:55:57.223673Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.07075"},"observation_digest":"sha256:036f8474d9fa4879d8abb47c07de75bbe4a9e3047d70c52c00af98f7aa4dd1a6","observation_id":"8458ad03-9514-453d-9ff7-d17078feaa75","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.07985","last_updated":"2026-05-21T13:49:15Z","snapshot_observed_at":"2026-08-02T04:20:16.015867Z","submitted_at":"2026-05-08T16:44:47Z","title":"Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-11T02:42:32.739352Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.07985"},"observation_digest":"sha256:c55d725263e3f0cf94b471cae9e5dfc2b8aed64f2f44f11fd18c15a5a5a7be9a","observation_id":"3dadb320-ce23-470b-abce-f65410a4eb4e","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.07985","last_updated":"2026-05-21T13:49:15Z","snapshot_observed_at":"2026-08-02T04:20:16.015867Z","submitted_at":"2026-05-08T16:44:47Z","title":"Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-22T10:20:45.375375Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.07985"},"observation_digest":"sha256:833173f5e872c90e97ff68d10e948e8cbbad46fbfdafd5b92e48b8a692f2f99e","observation_id":"be340fb5-53e1-4131-a294-6e289c2d1de8","resolution":{"observed_at":"2026-05-22T10:21:23.926791Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.09225","last_updated":"2026-05-09T23:51:18Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T23:51:18Z","title":"The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-12T02:42:08.565972Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.09225"},"observation_digest":"sha256:b45bccd42a01a608fede909b866607751360c5223721ca3a8f1446189332e979","observation_id":"14a34b0c-a6c2-4edd-b015-9afc7a84e4ee","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.09778","last_updated":"2026-07-08T15:49:43Z","snapshot_observed_at":"2026-07-12T17:10:10.625140Z","submitted_at":"2026-05-10T21:51:36Z","title":"Nectar: Neural Estimation of Cached-Token Attention via Regression","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-12T02:42:44.058913Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.09778"},"observation_digest":"sha256:d48e664a4b70df8454e8c7558d6bff4b4daf001b01c7d123580f84ae57f3de54","observation_id":"44c17f05-f684-4dbb-a53b-52a25ea45302","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.13831","last_updated":"2026-05-13T17:52:53Z","snapshot_observed_at":"2026-08-06T00:20:13.394323Z","submitted_at":"2026-05-13T17:52:53Z","title":"Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-14T19:16:07.851098Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.13831"},"observation_digest":"sha256:6a1f6810e725d399f90e0ea53dd0c9683d0473725610dcfaf92e91103c25e9cf","observation_id":"30cb9f43-f131-42dd-bcbc-91ae82b5b2c3","resolution":{"observed_at":"2026-05-15T05:26:00.189704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.15615","last_updated":"2026-05-15T04:54:08Z","snapshot_observed_at":"2026-07-06T23:26:51.110846Z","submitted_at":"2026-05-15T04:54:08Z","title":"Neutral-Reference Prompting for Vision-Language Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-20T19:15:54.152498Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.15615"},"observation_digest":"sha256:1842f74827201bb9dacacd36d01e5346aa2dceab157dd95c1865327fcfb4945f","observation_id":"a254cde5-99dc-4849-b22d-fca2f9626411","resolution":{"observed_at":"2026-05-20T19:18:54.579556Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.16928","last_updated":"2026-06-08T02:38:05Z","snapshot_observed_at":"2026-08-02T02:41:46.596895Z","submitted_at":"2026-05-16T10:51:58Z","title":"Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-19T20:51:29.740033Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.16928"},"observation_digest":"sha256:3e516cd3d571f4b7ba34ece034c99866d0001fa05a7bfb3155eb500d76b98632","observation_id":"ce4e3c57-df34-4581-9f31-8b4592f0facd","resolution":{"observed_at":"2026-05-19T20:52:46.080668Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.16928","last_updated":"2026-06-08T02:38:05Z","snapshot_observed_at":"2026-08-02T02:41:46.596895Z","submitted_at":"2026-05-16T10:51:58Z","title":"Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-30T19:17:28.300961Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.16928"},"observation_digest":"sha256:6e35a4ca3192178afaa7bc2fc94c4350e4daa0facc845d91795fc7b51b10b6fa","observation_id":"5bd9f627-5181-471d-a199-a20e736edca4","resolution":{"observed_at":"2026-07-01T14:55:47.423892Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.18071","last_updated":"2026-05-18T08:54:16Z","snapshot_observed_at":"2026-08-02T15:32:20.092680Z","submitted_at":"2026-05-18T08:54:16Z","title":"KVDrive: A Holistic Multi-Tier KV Cache Management System for Long-Context LLM Inference","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-20T11:13:46.098095Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.18071"},"observation_digest":"sha256:5372127b9eb75b532d5de59f73b93fb53de4b6f56473a41499d535768fb1a1a7","observation_id":"f7c9d456-837e-484c-8019-eb6747c3711a","resolution":{"observed_at":"2026-05-20T11:18:13.976207Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.18607","last_updated":"2026-05-18T16:17:15Z","snapshot_observed_at":"2026-07-06T23:29:29.105126Z","submitted_at":"2026-05-18T16:17:15Z","title":"Forecasting Downstream Performance of LLMs With Proxy Metrics","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-05-20T10:30:20.575552Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.18607"},"observation_digest":"sha256:a97d7dffdf8b79fdfa17074cfe875184bd625b73e6c4e32ee8ca91c46e0ef3ce","observation_id":"c6d89ad7-41ef-4488-ad03-70fce3f2102c","resolution":{"observed_at":"2026-05-20T10:33:12.960266Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.20201","last_updated":"2026-05-22T07:16:53Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-06T16:44:17Z","title":"Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-21T09:42:35.699237Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.20201"},"observation_digest":"sha256:683b10ad9b58481492b441eef789acb0ce84bbfc5026da5d7e9dac1ec125eaed","observation_id":"304cc578-e237-4c0f-91f8-85c5a7fb69d8","resolution":{"observed_at":"2026-05-21T09:44:05.472883Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.20201","last_updated":"2026-05-22T07:16:53Z","snapshot_observed_at":"2026-07-06T23:30:49.211483Z","submitted_at":"2026-04-06T16:44:17Z","title":"Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-25T07:03:35.679259Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.20201"},"observation_digest":"sha256:e6a5d1fcf5d4c7c354689d7e1ae995491b02b41e2434a887ecadcd8a687da0ef","observation_id":"8a9f01f9-d37d-476a-a601-d5af0c9d292f","resolution":{"observed_at":"2026-05-25T07:05:26.217472Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.25618","last_updated":"2026-05-25T09:18:59Z","snapshot_observed_at":"2026-08-06T19:08:22.954882Z","submitted_at":"2026-05-25T09:18:59Z","title":"Symbolic-Neural Soft-Logic Reasoning: Towards Robust and Verifiable Thinking Chains via Cooperative Evolution","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T19:46:46.452126Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.25618"},"observation_digest":"sha256:a01f4e9b2a08bdee096741d7018afff132959c1cc9e961270e4c7487e20a01dd","observation_id":"dc1f6f1d-4e63-42ff-b505-c705ddca04dc","resolution":{"observed_at":"2026-06-29T19:53:55.844831Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2605.31025","last_updated":"2026-05-29T08:57:06Z","snapshot_observed_at":"2026-08-07T02:59:29.426661Z","submitted_at":"2026-05-29T08:57:06Z","title":"TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-28T23:13:14.912686Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2605.31025"},"observation_digest":"sha256:fd4b7624ea14b47b38288e93d66a180cec8779283f12722ed9712b185b5dd9ed","observation_id":"7b7ce563-c91a-4273-8a5b-afa6d7269793","resolution":{"observed_at":"2026-06-29T00:12:50.615204Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2606.00079","last_updated":"2026-05-22T13:05:53Z","snapshot_observed_at":"2026-08-05T07:27:21.222843Z","submitted_at":"2026-05-22T13:05:53Z","title":"BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-30T15:38:18.616792Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2606.00079"},"observation_digest":"sha256:27cde0eaf8ddf234b99d1d618f0120bb77360c9cf69401a7a1e9cfa416f748c9","observation_id":"0eba6870-bc2e-4c7b-8729-f663b3c72d9e","resolution":{"observed_at":"2026-06-30T15:44:48.471564Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2606.00640","last_updated":"2026-05-30T09:30:30Z","snapshot_observed_at":"2026-08-06T02:39:29.306378Z","submitted_at":"2026-05-30T09:30:30Z","title":"An Attribute-Based Measure of Video Complexity","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-28T19:00:54.718177Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2606.00640"},"observation_digest":"sha256:d60ac69c4ece8a1276576f46f92eb27ca50244c0a6510eabb59fec52de9c85d9","observation_id":"80b730af-970e-451a-96af-efc9b870d538","resolution":{"observed_at":"2026-06-28T19:02:34.104198Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2606.01751","last_updated":"2026-06-07T12:43:36Z","snapshot_observed_at":"2026-08-05T18:02:40.039582Z","submitted_at":"2026-06-01T06:12:55Z","title":"SparseX: Efficient Segment-Level KV Cache Sharing for Interleaved LLM Serving","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-28T11:45:04.176857Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2606.01751"},"observation_digest":"sha256:c96156f6c2c296a6c07d8ad8a8c71a3ec03441ed9784ddcba5c20d7a0ae8ecfd","observation_id":"f55f8782-c9e3-4ac3-a3bd-99798bc5b6f9","resolution":{"observed_at":"2026-07-02T01:36:25.587508Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2606.07706","last_updated":"2026-06-05T10:10:53Z","snapshot_observed_at":"2026-08-06T18:43:42.284133Z","submitted_at":"2026-06-05T10:10:53Z","title":"MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-06-27T21:47:22.295896Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2606.07706"},"observation_digest":"sha256:e17df5f3e688d2ac81c5b317a6de50aac5e245f3ce7a95da37fa45e5159b6e8f","observation_id":"0567c1e9-9387-42e7-b03e-7d3c10b22169","resolution":{"observed_at":"2026-06-27T21:51:18.130326Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2606.09092","last_updated":"2026-06-08T06:42:12Z","snapshot_observed_at":"2026-07-06T23:48:30.569726Z","submitted_at":"2026-06-08T06:42:12Z","title":"From Shortcuts to Reasoning: Robust Post-Training of Theory of Mind with Reinforcement Learning","version":1},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-06-27T17:42:38.122144Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2606.09092"},"observation_digest":"sha256:fc01d3ccffe1f0bf719fabcc9779cdcf75beddc90766cd7150959eb9b430590b","observation_id":"08ac6c56-5e61-4eec-a2a5-5e6827267d43","resolution":{"observed_at":"2026-07-02T23:57:28.263486Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2606.27500","last_updated":"2026-06-25T19:36:38Z","snapshot_observed_at":"2026-08-07T13:35:50.634455Z","submitted_at":"2026-06-25T19:36:38Z","title":"Aloe-Vision: Robust Vision-Language Models for Healthcare","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T02:02:47.472868Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2606.27500"},"observation_digest":"sha256:6cc2d83feb226afbbea75f86552bf5d7bc76ec896dacf4b06b801f1c69f7ea40","observation_id":"5c51c2cc-ec29-4833-820a-ec271962b7f5","resolution":{"observed_at":"2026-07-01T18:25:57.934055Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2606.30887","last_updated":"2026-06-29T20:22:25Z","snapshot_observed_at":"2026-07-07T00:04:39.472457Z","submitted_at":"2026-06-29T20:22:25Z","title":"Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-07-01T01:57:54.065453Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2606.30887"},"observation_digest":"sha256:2ea4217694c4de79ff5da24e199fb76552c7ea5b1393c11e5fb6e45740d2b12b","observation_id":"740ce3eb-209d-46ed-a6ef-1c3c9326c0fc","resolution":{"observed_at":"2026-07-01T12:35:43.871092Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2606.31519","last_updated":"2026-06-30T11:32:14Z","snapshot_observed_at":"2026-08-03T23:10:23.282240Z","submitted_at":"2026-06-30T11:32:14Z","title":"RaBitQCache: Rotated Binary Quantization for KVCache in Long Context LLM Inference","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-07-01T06:34:15.790154Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2606.31519"},"observation_digest":"sha256:c76526e5b0c1d15550c0419f82c575107228fca6446c2ce381960f51ab430bab","observation_id":"2e1f852a-e657-4e5d-beac-fa4ab9f33b72","resolution":{"observed_at":"2026-07-01T06:35:29.400610Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2607.01523","last_updated":"2026-07-01T22:38:54Z","snapshot_observed_at":"2026-07-07T00:07:04.211507Z","submitted_at":"2026-07-01T22:38:54Z","title":"Multi-Head Recurrent Memory Agents","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-03T20:51:19.184959Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2607.01523"},"observation_digest":"sha256:f3e87095dd65711f7570cb03620eabc5dda840d62d028d4266eb9ad813cd2589","observation_id":"4048cd83-162e-49a7-a702-4db543f728f3","resolution":{"observed_at":"2026-07-03T20:58:57.347529Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2607.01938","last_updated":"2026-07-02T09:32:39Z","snapshot_observed_at":"2026-07-07T00:07:28.231599Z","submitted_at":"2026-07-02T09:32:39Z","title":"PhysMani: Physics-principled 3D World Model for Dynamic Object Manipulation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-07-03T12:05:35.255381Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2607.01938"},"observation_digest":"sha256:31b49a58500d51cf8e6bb44bbd4d10d4b2e7b73017f30ccc2a44e3e25ae15006","observation_id":"186a1f28-0b6a-4c1a-8f21-1f7f8e7e0707","resolution":{"observed_at":"2026-07-03T12:08:06.361798Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-07-12T06:58:07.886642Z","title":"arXiv preprint arXiv:2501.15383 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.02802","last_updated":"2026-07-02T22:25:35Z","snapshot_observed_at":"2026-08-07T13:08:33.135602Z","submitted_at":"2026-07-02T22:25:35Z","title":"Seduced by the Narrative: Assessing Rule Adherence in Semi-Open Textual Sandboxes","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-07-12T06:58:07.886642Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2607.02802"},"observation_digest":"sha256:718ff1e5903fa879961e6677f7badf54a8645398acf55c47d4e5c38066e166b3","observation_id":"49bba021-8303-4a8c-b1d7-9e64da11a870","resolution":{"observed_at":"2026-07-12T06:58:07.886642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-07-12T01:10:52.578408Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03615","last_updated":"2026-07-10T17:26:34Z","snapshot_observed_at":"2026-08-02T12:29:59.349636Z","submitted_at":"2026-07-03T22:06:20Z","title":"The Powerless Noise: How Experimental Settings Shape the Reported Power of Noise","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-12T01:10:52.578408Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2607.03615"},"observation_digest":"sha256:7e1f798eb3449023998472e1870e3e359e2408e22e69072940a35d55d612c234","observation_id":"b88bf84b-8706-41e6-9a3b-4102f34caf96","resolution":{"observed_at":"2026-07-12T01:10:52.578408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-07-13T07:04:18.645464Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03615","last_updated":"2026-07-10T17:26:34Z","snapshot_observed_at":"2026-08-02T12:29:59.349636Z","submitted_at":"2026-07-03T22:06:20Z","title":"The Powerless Noise: How Experimental Settings Shape the Reported Power of Noise","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-13T07:04:18.645464Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2607.03615"},"observation_digest":"sha256:2afba39585adf3bfcc7aa9bfa738ddc94bda382d2cd12522f2e458ae0edf9114","observation_id":"32649bac-d827-4b5b-9aa6-8cf9a1373764","resolution":{"observed_at":"2026-07-13T07:04:18.645464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":"2501.15383","doi":"10.48550/arxiv.2501.15383","metadata_source":"pith","pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-1M Technical Report","venue":"cs.CL","work_id":"397e3b66-80bd-403f-bcf9-9a907eed1830","year":2025},"citing_paper":{"arxiv_id":"2607.05863","last_updated":"2026-07-07T05:41:54Z","snapshot_observed_at":"2026-07-10T23:17:29.985243Z","submitted_at":"2026-07-07T05:41:54Z","title":"Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-07-08T22:26:44.052574Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2607.05863"},"observation_digest":"sha256:46d2842372d6f8a22ab676bc87100b600aadbf7eb1ad4395efff44eb97796c1e","observation_id":"ea6b76ea-b9b3-4557-bb15-5402c19f6751","resolution":{"observed_at":"2026-07-08T22:35:40.671283Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-01T17:16:38.767293Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.17715","last_updated":"2026-07-20T09:09:23Z","snapshot_observed_at":"2026-08-06T08:28:52.422763Z","submitted_at":"2026-07-20T09:09:23Z","title":"C$^2$KV: Compressed and Composable KV Cache Reuse for Efficient LLM Inference","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T17:16:38.767293Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2607.17715"},"observation_digest":"sha256:413fe0aa26b3d11f4c90a9b23a3ed9671b463b78fbe8ac66133c59f95ed9835a","observation_id":"a4fdbbb4-a77e-44f7-9015-d31b1c5635dc","resolution":{"observed_at":"2026-08-01T17:16:38.767293Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-01T01:12:54.888311Z","title":"arXiv preprint arXiv:2501.15383 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.25903","last_updated":"2026-07-28T16:00:40Z","snapshot_observed_at":"2026-08-06T18:21:31.634480Z","submitted_at":"2026-07-28T16:00:40Z","title":"CARE: A Multimodal Corpus for Studying Speech and Non-Verbal Communication Across Multiple Medical Conditions","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-01T01:12:54.888311Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2607.25903"},"observation_digest":"sha256:7a60c55be394b39056af357123a068b80ba0ebcb61d4ada70116265899b85526","observation_id":"107f099a-6cde-4fc3-af74-fd8561f0bbfb","resolution":{"observed_at":"2026-08-01T01:12:54.888311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-01T03:16:00.717109Z","title":"arXiv preprint arXiv:2501.15383 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27692","last_updated":"2026-07-30T05:25:23Z","snapshot_observed_at":"2026-08-02T23:38:26.154373Z","submitted_at":"2026-07-30T05:25:23Z","title":"Recall Before You Rank: Similarity-Guided Top-$K$ Reuse for Efficient Long-Context Attention","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T03:16:00.717109Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2607.27692"},"observation_digest":"sha256:3c80124ff2bf88ee20f6d77fe165e263dca1ede60f51a7ccafbc345014394cbc","observation_id":"2190ae60-f75b-4f09-9b8e-6341835856d4","resolution":{"observed_at":"2026-08-01T03:16:00.717109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T00:45:48.538584Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.04405","last_updated":"2026-08-05T03:19:21Z","snapshot_observed_at":"2026-08-07T15:05:45.101616Z","submitted_at":"2026-08-05T03:19:21Z","title":"Training-Free Hashing-Based Attention via Binary Principal Components","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-07T00:45:48.538584Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2608.04405"},"observation_digest":"sha256:fdebd94d4d065bec486a1648b6eed5407ceb1c0518395a321f9d4d213d374997","observation_id":"de6a2277-4edb-46d0-8692-8e3ee5b02e44","resolution":{"observed_at":"2026-08-07T00:45:48.538584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15383","snapshot_observed_at":"2026-08-07T05:49:21.792007Z","title":"arXiv preprint arXiv:2501.15383 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06312","last_updated":"2026-08-06T17:27:23Z","snapshot_observed_at":"2026-08-07T15:35:20.065809Z","submitted_at":"2026-08-06T17:27:23Z","title":"Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T05:49:21.792007Z"},"links":{"cited_paper":"/paper/2501.15383","citing_paper":"/paper/2608.06312"},"observation_digest":"sha256:8e5fe7aeb747cb26f6fb9875f4ff26e6123bd4465ad30f5c08b2ff7beaf840dc","observation_id":"92fb491b-8fd4-4a51-979f-0f82a19fcf67","resolution":{"observed_at":"2026-08-07T05:49:21.792007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2501.15383/citation-record","integrity":"/paper/2501.15383/integrity","json":"/paper/2501.15383/citation-record.json","paper":"/paper/2501.15383"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.17463","last_updated":"2024-05-29T05:44:58Z","snapshot_observed_at":"2026-08-06T08:11:58.702104Z","submitted_at":"2024-02-27T12:39:23Z","title":"Training-Free Long-Context Scaling of Large Language Models","version":2},"cited_work":{"arxiv_id":"2402.17463","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.17463","snapshot_observed_at":"2026-07-10T20:07:33.544111Z","title":"Training-free long-context scaling of large language models","venue":"cs.CL","work_id":"a17fa8b4-29df-4838-80b2-c9d3440e8ed5","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2402.17463","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:3e7af36bad4c74ba7081f350660e43a78db92259fffca1572839507f72d59f59","observation_id":"7560798f-5b87-4622-b7d4-45885d797702","resolution":{"observed_at":"2026-05-15T05:26:00.053816Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07732","last_updated":"2021-08-16T03:57:30Z","snapshot_observed_at":"2026-08-02T19:23:53.535075Z","submitted_at":"2021-08-16T03:57:30Z","title":"Program Synthesis with Large Language Models","version":1},"cited_work":{"arxiv_id":"2108.07732","doi":"10.1007/s11390-025-5518-5","metadata_source":"pith","pith_arxiv_id":"2108.07732","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Program Synthesis with Large Language Models","venue":"cs.PL","work_id":"fd241a05-03b9-4de2-9588-9d77ce176125","year":2021},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2108.07732","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:d0d6fa89834729128f1d5446bbbee0046f80616921fd0ac7365c50148d9a0f88","observation_id":"ea6819ea-3a35-4825-8ada-a5ebe53f64b6","resolution":{"observed_at":"2026-05-15T05:26:00.008301Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16609","last_updated":"2023-09-28T17:07:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-28T17:07:49Z","title":"Qwen Technical Report","version":1},"cited_work":{"arxiv_id":"2309.16609","doi":"10.48550/arxiv.2309.16609","metadata_source":"pith","pith_arxiv_id":"2309.16609","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen Technical Report","venue":"cs.CL","work_id":"bb1fd52f-6b2f-437c-9516-37bdf6eb9be8","year":2023},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2309.16609","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:d206ffcfc6e8a0e900967eb41b7ea322d36481b49142b269f7968cfda951068e","observation_id":"802e5399-0a75-4cb8-8e27-65c25c3dc272","resolution":{"observed_at":"2026-05-15T05:26:00.015528Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-15T23:50:15.620681+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-15T23:50:15.620681+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.14255","last_updated":"2022-07-28T17:40:47Z","snapshot_observed_at":"2026-08-07T11:38:07.397956Z","submitted_at":"2022-07-28T17:40:47Z","title":"Efficient Training of Language Models to Fill in the Middle","version":1},"cited_work":{"arxiv_id":"2207.14255","doi":"10.48550/arxiv.2207.14255","metadata_source":"pith","pith_arxiv_id":"2207.14255","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Efficient Training of Language Models to Fill in the Middle","venue":"cs.CL","work_id":"54afe4f8-4d93-4829-99ae-2a27143a9641","year":2022},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2207.14255","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:29c93a77784f3bc1f0d6b5b8a7f92dc4c70b35a55090c73a555017e7e5fd467e","observation_id":"a68db182-1bc5-4ab1-b2f5-6e8e0b87ac33","resolution":{"observed_at":"2026-05-18T00:40:42.183417Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.14255","last_updated":"2022-07-28T17:40:47Z","snapshot_observed_at":"2026-08-07T11:38:07.397956Z","submitted_at":"2022-07-28T17:40:47Z","title":"Efficient Training of Language Models to Fill in the Middle","version":1},"cited_work":{"arxiv_id":"2207.14255","doi":"10.48550/arxiv.2207.14255","metadata_source":"pith","pith_arxiv_id":"2207.14255","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Efficient Training of Language Models to Fill in the Middle","venue":"cs.CL","work_id":"54afe4f8-4d93-4829-99ae-2a27143a9641","year":2022},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2207.14255","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:3ca892899465594bf034d5d2c794bbd1b18c602c40edac4607c99ac7e0951ffa","observation_id":"c950b955-9b25-43a9-b223-88ebd1bfb553","resolution":{"observed_at":"2026-05-18T00:40:42.183417Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":"2107.03374","doi":"10.48550/arxiv.2107.03374","metadata_source":"pith","pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Evaluating Large Language Models Trained on Code","venue":"cs.LG","work_id":"042493e9-b26f-4b4e-bbde-382072ca9b08","year":2021},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:30386d1fb10cf800d627cbff621621d48545f94e4dd539d9e53b785ce2395ea2","observation_id":"4544b6b9-2ff9-4467-afa2-f9ee6074578a","resolution":{"observed_at":"2026-05-15T05:26:00.029260Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-01T08:08:23.404839+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T08:08:23.404839+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-07T01:45:38.840969Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":"2110.14168","doi":"10.1002/j.1545-","metadata_source":"pith","pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Training Verifiers to Solve Math Word Problems","venue":"cs.LG","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","year":2021},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:8651ccca74c0e6e3dfc2dcb869ff8c0e6b62ae09173fed40d1e6ced6095e4e7a","observation_id":"4eaa24fa-2b4f-4623-8687-25dba0fb4445","resolution":{"observed_at":"2026-05-15T05:26:00.037580Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"21f7dce1-5226-4fbb-9cfd-57c2e017fda6","year":2022},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:cbe65c9d2e1e0765e95ef3041c6896ed8b9459e705000c06c07a71c9aa4d2b39","observation_id":"cbc37800-cd94-454c-b2c7-a5ae420d3774","resolution":{"observed_at":"2026-05-15T05:26:00.187864Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":"2407.21783","doi":"10.1016/s0749-0720(15","metadata_source":"pith","pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"The Llama 3 Herd of Models","venue":"cs.AI","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:290e247d5f8afc283f3a997d8278381866155bbf1f36a1a86ddc47d36d0cbeb3","observation_id":"fae579fe-9dcf-4500-aba4-943296f3f903","resolution":{"observed_at":"2026-05-15T05:26:00.060858Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04127","last_updated":"2025-01-10T14:31:21Z","snapshot_observed_at":"2026-08-02T00:54:17.243656Z","submitted_at":"2024-06-06T14:49:06Z","title":"Are We Done with MMLU?","version":3},"cited_work":{"arxiv_id":"2406.04127","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.04127","snapshot_observed_at":"2026-07-04T19:20:05.653512Z","title":"Are we done with mmlu? CoRR, abs/2406.04127","venue":null,"work_id":"ed71a0fb-b1cc-4fbc-a1d5-01a096a4e957","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2406.04127","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:a3dd01f40580d535e7fcd5d184f952cbebcaff7b1e6b2ddea96a0d2a82731082","observation_id":"1bc2a4a8-1bae-43a1-bbdc-60a90eea74a2","resolution":{"observed_at":"2026-05-15T05:26:00.070159Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.06654","last_updated":"2024-08-06T21:48:58Z","snapshot_observed_at":"2026-07-06T17:58:00.820879Z","submitted_at":"2024-04-09T23:41:27Z","title":"RULER: What's the Real Context Size of Your Long-Context Language Models?","version":3},"cited_work":{"arxiv_id":"2404.06654","doi":"10.48550/arxiv.2404.06654","metadata_source":"pith","pith_arxiv_id":"2404.06654","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"RULER: What's the Real Context Size of Your Long-Context Language Models?","venue":"cs.CL","work_id":"c0bc4689-3ce8-4e3d-9442-bd74869445bb","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2404.06654","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:5a556fc7e63941cb2d65ea2a8dafdfe3db8f76f5780f0b27d4c6509de04dd6b8","observation_id":"9f523472-7434-48e6-a85f-3f195607d5f5","resolution":{"observed_at":"2026-05-15T05:26:00.078471Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12186","last_updated":"2024-11-12T13:24:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-18T17:57:57Z","title":"Qwen2.5-Coder Technical Report","version":3},"cited_work":{"arxiv_id":"2409.12186","doi":"10.48550/arxiv.2409.12186","metadata_source":"pith","pith_arxiv_id":"2409.12186","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2.5-Coder Technical Report","venue":"cs.CL","work_id":"09ba463d-6377-4017-9801-444ffb94b056","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2409.12186","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:b41aad2a96546f25d574494b5cbf284bdd0c8509da679f5721fa616522f3a8ac","observation_id":"4d8f280d-863c-463c-9ea1-44d650c4d19d","resolution":{"observed_at":"2026-05-15T05:26:00.086283Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-11T05:18:55.772185+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T05:18:55.772185+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07974","last_updated":"2024-06-06T17:41:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-03-12T17:58:04Z","title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","version":2},"cited_work":{"arxiv_id":"2403.07974","doi":"10.1109/icsme52107.2021.00025","metadata_source":"pith","pith_arxiv_id":"2403.07974","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","venue":"cs.SE","work_id":"ea9e51ce-1e75-4182-92d8-4d25f70d2ee4","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2403.07974","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:586a98894c3de0d7d9aeb7e7e8b468a014705efe2c6a659da8bb4d3e569acc21","observation_id":"f62e0984-3322-43a7-9c97-1b4f74bc32bb","resolution":{"observed_at":"2026-05-15T05:26:00.093880Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":"2310.06825","doi":"10.48550/arxiv.2310.06825","metadata_source":"pith","pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mistral 7B","venue":"cs.CL","work_id":"eb5e1305-ad11-4875-ad8d-ad8b8f697599","year":2023},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:2a8a07c9e31e49002751db48610de49bdf697de12c04be67ba615395322030db","observation_id":"0056f237-87c7-44b2-a067-96ad4924e9cf","resolution":{"observed_at":"2026-05-15T05:26:00.101437Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-02T03:08:12.282824+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-02T03:08:12.282824+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11939","last_updated":"2024-10-14T18:11:58Z","snapshot_observed_at":"2026-08-02T11:33:41.297984Z","submitted_at":"2024-06-17T17:26:10Z","title":"From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline","version":2},"cited_work":{"arxiv_id":"2406.11939","doi":"10.48550/arxiv.2406.11939","metadata_source":"pith","pith_arxiv_id":"2406.11939","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline","venue":"cs.LG","work_id":"ad4ca175-a846-44ce-add1-5fd69a8d5c41","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2406.11939","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:e096d490d32ecec35c0e488ae036a719a5fdc59f1fe17c179dddf90821cf6e1f","observation_id":"bfffb73e-a91b-4d2c-9bf9-2f37f60fd8bc","resolution":{"observed_at":"2026-05-15T05:26:00.110871Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-20T23:23:28.73804+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-20T23:23:28.73804+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":"2303.08774","doi":"10.1002/tea.20265","metadata_source":"pith","pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GPT-4 Technical Report","venue":"cs.CL","work_id":"b928e041-6991-4c08-8c81-0359e4097c7b","year":2023},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:d5df07c54d1fba450efd94633f40fdcba75fb90352bef5dbd68859f74617980c","observation_id":"d506ee28-71cc-48f5-b553-a21c3949b46c","resolution":{"observed_at":"2026-05-15T05:26:00.118080Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.00071","last_updated":"2026-02-06T19:40:50Z","snapshot_observed_at":"2026-08-01T02:15:47.181936Z","submitted_at":"2023-08-31T18:18:07Z","title":"YaRN: Efficient Context Window Extension of Large Language Models","version":3},"cited_work":{"arxiv_id":"2309.00071","doi":"10.48550/arxiv.2309.00071","metadata_source":"pith","pith_arxiv_id":"2309.00071","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"YaRN: Efficient Context Window Extension of Large Language Models","venue":"cs.CL","work_id":"31f454a9-7de2-4696-86f2-9bfa1410f80d","year":2023},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2309.00071","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:96321d8b20b185cb32c8e49f74a6323a2f7f966e730c0f388d142edce0edc8d2","observation_id":"447efaaf-418a-4341-b8f1-8143b8675e21","resolution":{"observed_at":"2026-05-15T05:26:00.125071Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12022","last_updated":"2023-11-20T18:57:34Z","snapshot_observed_at":"2026-08-04T22:55:15.345443Z","submitted_at":"2023-11-20T18:57:34Z","title":"GPQA: A Graduate-Level Google-Proof Q&A Benchmark","version":1},"cited_work":{"arxiv_id":"2311.12022","doi":"10.48550/arxiv.2311.12022","metadata_source":"pith","pith_arxiv_id":"2311.12022","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GPQA: A Graduate-Level Google-Proof Q&A Benchmark","venue":"cs.AI","work_id":"9e2a976b-f5ad-4aee-af5c-243fe0fe75d2","year":2023},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2311.12022","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:26087319795d6a1b6f74c9f699debb7e66b9f893595390c0e398a71516c44322","observation_id":"8239196b-28d9-4fdf-b2fa-d50595c8b090","resolution":{"observed_at":"2026-05-15T05:26:00.133025Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-01T04:38:46.941438+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T04:38:46.941438+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":"2302.13971","doi":"10.48550/arxiv.2302.13971","metadata_source":"pith","pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"LLaMA: Open and Efficient Foundation Language Models","venue":"cs.CL","work_id":"c018fc23-6f3f-4035-9d02-28a2173b2b9d","year":2023},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:64e0eb5be8bf5f46e88120aa814ebfda77dc814c3034ff5a4684e844ed4df25f","observation_id":"06baa121-3f68-4a25-b361-e48c6a63515d","resolution":{"observed_at":"2026-05-15T05:26:00.140924Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-01T11:08:05.851253+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-01T11:08:05.851253+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19314","last_updated":"2025-04-18T19:36:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-27T16:47:42Z","title":"LiveBench: A Challenging, Contamination-Limited LLM Benchmark","version":2},"cited_work":{"arxiv_id":"2406.19314","doi":"10.48550/arxiv.2406.19314","metadata_source":"pith","pith_arxiv_id":"2406.19314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"LiveBench: A Challenging, Contamination-Limited LLM Benchmark","venue":"cs.CL","work_id":"6b2b33bf-350e-4ee2-b8a7-f011e53384e7","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2406.19314","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:d72a14b1a9bb44de6b7c1029754275640c15444a37582ba1076ce1c392e023ab","observation_id":"79c9293a-89b2-4a6c-9c14-e79f5df8666d","resolution":{"observed_at":"2026-05-15T05:26:00.148493Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16039","last_updated":"2023-11-14T01:40:13Z","snapshot_observed_at":"2026-07-06T16:24:38.375055Z","submitted_at":"2023-09-27T21:41:49Z","title":"Effective Long-Context Scaling of Foundation Models","version":3},"cited_work":{"arxiv_id":"2309.16039","doi":"10.48550/arxiv.2309.16039","metadata_source":"arxiv_reference","pith_arxiv_id":"2309.16039","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A., Oguz, B., et al","venue":"arXiv (Cornell University)","work_id":"100ca4eb-be6f-4510-9c7f-bbb24f10d946","year":2023},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2309.16039","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:e32e33fa86f953e19bbffea6024275428a092c4b217ccd681ebed6a6623d00dc","observation_id":"95c1ff53-a00f-4553-93c3-e7972323f675","resolution":{"observed_at":"2026-05-15T05:26:00.157889Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10671","last_updated":"2024-09-10T13:25:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-15T12:35:42Z","title":"Qwen2 Technical Report","version":4},"cited_work":{"arxiv_id":"2407.10671","doi":"10.18653/v1/2024.naacl-long.246","metadata_source":"pith","pith_arxiv_id":"2407.10671","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Qwen2 Technical Report","venue":"cs.CL","work_id":"a1857881-ab9b-4b80-9b5f-9ae4b5c2566d","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2407.10671","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:05bc700654053cb5beb06883cde344be388840d8b4a134a84e8916a8d56c9d94","observation_id":"972c9ce4-2cf1-4130-b6b6-96d9b5322bec","resolution":{"observed_at":"2026-05-15T05:26:00.165007Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2402.05136","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T15:25:48.110956Z","title":"LV-Eval: A balanced long-context benchmark with 5 length levels up to 256K","venue":null,"work_id":"f9d0736d-ad1a-4059-aaec-2fde79a17bb8","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:85ed80bf2d0084857759bf51b075bcb20103b7b53cac38b50304c57e023dba57","observation_id":"7c3e12ba-f684-429b-bda3-bdb148be12f4","resolution":{"observed_at":"2026-05-15T05:26:00.174742Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12793","last_updated":"2024-07-30T03:58:11Z","snapshot_observed_at":"2026-08-07T13:56:34.167869Z","submitted_at":"2024-06-18T16:58:21Z","title":"ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools","version":2},"cited_work":{"arxiv_id":"2406.12793","doi":"10.48550/arxiv.2406.12793","metadata_source":"pith","pith_arxiv_id":"2406.12793","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools","venue":"cs.CL","work_id":"de9ce5af-0d8d-4b94-9793-64968d9bc06d","year":2024},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2406.12793","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:37778ef92dc17b7bc4f91accfb0da109cb6c8020e3664a4977feecb4f8cb1806","observation_id":"aeb9d781-359c-4fdd-9b7d-5637e2644450","resolution":{"observed_at":"2026-05-15T05:26:00.182339Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.07911","last_updated":"2023-11-14T05:13:55Z","snapshot_observed_at":"2026-07-06T16:47:08.877195Z","submitted_at":"2023-11-14T05:13:55Z","title":"Instruction-Following Evaluation for Large Language Models","version":1},"cited_work":{"arxiv_id":"2311.07911","doi":"10.48550/arxiv.2311.07911","metadata_source":"pith","pith_arxiv_id":"2311.07911","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Instruction-Following Evaluation for Large Language Models","venue":"cs.CL","work_id":"3aa06177-125a-4f5a-8f4a-8070c5986c26","year":2023},"citing_paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-15T05:25:59.964619Z"},"links":{"cited_paper":"/paper/2311.07911","citing_paper":"/paper/2501.15383"},"observation_digest":"sha256:16ac08d0715e640af89eeb3393fb871faf12a7d1408eb6ec7b54649ecf6db289","observation_id":"c8e34144-5b5a-44fb-97b4-1835ec3bf0d4","resolution":{"observed_at":"2026-05-15T05:26:00.045477Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.15383","last_updated":"2025-01-26T03:47:25Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-31T01:49:29.562668Z","submitted_at":"2025-01-26T03:47:25Z","title":"Qwen2.5-1M Technical Report"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":5,"parse_uncertain":0,"unresolved":1,"verified_exact":19,"verified_fuzzy":0},"total_outbound_references":25},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 82 inbound Pith citation observations for arXiv:2501.15383."}