{"as_of":"2026-08-23T18:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7481e54ca464341d002f1837e8085f20e7bc0776f792b1a2221046c57826d3ec","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":29,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T16:49:21.976799Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-10T00:56:40.961590Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-12T16:49:21.976799Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.13157","last_updated":"2024-11-27T03:25:44Z","snapshot_observed_at":"2026-08-16T10:20:38.532309Z","submitted_at":"2024-11-20T09:46:30Z","title":"Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-12T16:49:21.976799Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2411.13157"},"observation_digest":"sha256:f102f629df1d32fcf1f2aeec2e065d0aace9b3784f13f0d7c7bc208460b2c940","observation_id":"f1e8f27e-4c36-4d5b-a8dd-e8e522e1cc1b","resolution":{"observed_at":"2026-08-12T16:49:21.976799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-12T14:40:23.633017Z","title":"Lazyllm: Dynamic token pruning for efficient long context llm inference","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.15024","last_updated":"2025-03-28T14:11:37Z","snapshot_observed_at":"2026-08-19T01:50:35.848234Z","submitted_at":"2024-11-22T15:55:19Z","title":"DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Models","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T14:40:23.633017Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2411.15024"},"observation_digest":"sha256:bcf1348e0ccdd1c049ca07f692b7817014201b4226a0bf6bcf0447885119e8db","observation_id":"e8b7dab3-7667-4bb2-91bf-6d44d6c44b0b","resolution":{"observed_at":"2026-08-12T14:40:23.633017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-12T12:13:55.187485Z","title":"Lazyllm: Dynamic token pruning for efficient long context llm inference","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17426","last_updated":"2025-01-31T14:13:49Z","snapshot_observed_at":"2026-08-19T07:19:32.477239Z","submitted_at":"2024-11-26T13:34:02Z","title":"CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:13:55.187485Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2411.17426"},"observation_digest":"sha256:49dfebe3a274dc92103f3204e859815c46ca99728b26f7e893c11e76f6ee9555","observation_id":"b58ca8af-8e8a-47d0-b4fc-e6a66fc6ec52","resolution":{"observed_at":"2026-08-12T12:13:55.187485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-11T23:46:01.998574Z","title":"Lazyllm: Dynamic token pruning for efficient long context llm inference, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02252","last_updated":"2025-08-04T02:17:56Z","snapshot_observed_at":"2026-08-16T15:44:22.755394Z","submitted_at":"2024-12-03T08:29:27Z","title":"Compressing KV Cache for Long-Context LLM Inference with Inter-Layer Attention Similarity","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T23:46:01.998574Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2412.02252"},"observation_digest":"sha256:bbee13fafa08835f7c495247ebee8fd13b91b51ad663d395025bb9fcf82d9299","observation_id":"d6cf978e-0eb9-44c4-9592-8fb91282da87","resolution":{"observed_at":"2026-08-11T23:46:01.998574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-11T20:54:16.056197Z","title":"Lazyllm: Dynamic token pruning for efficient long context llm inference","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05185","last_updated":"2024-12-11T14:43:02Z","snapshot_observed_at":"2026-08-15T18:10:10.082702Z","submitted_at":"2024-12-06T17:04:42Z","title":"LinVT: Empower Your Image-level Large Language Model to Understand Videos","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T20:54:16.056197Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2412.05185"},"observation_digest":"sha256:ea4b667489bd2a925d8bbffcb9722856bfcda1cfdbbf0b9c54dfa52207fe9b49","observation_id":"88ca61d6-2467-463d-9ec8-119d1d6d4566","resolution":{"observed_at":"2026-08-11T20:54:16.056197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-11T11:57:17.905006Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.14838","last_updated":"2025-05-27T03:08:57Z","snapshot_observed_at":"2026-08-21T02:11:05.377768Z","submitted_at":"2024-12-19T13:28:42Z","title":"DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs","version":4},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T11:57:17.905006Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2412.14838"},"observation_digest":"sha256:6390ec4d778f0c97495d0c917011f70515655f56e23772bd0a7e8b32460b71d4","observation_id":"16030a24-627d-42f6-9656-a4b7149b6626","resolution":{"observed_at":"2026-08-11T11:57:17.905006Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-10T23:09:24.993164Z","title":"Lazyllm: Dynamic token pruning for efficient long context llm inference","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.01986","last_updated":"2025-07-24T18:44:26Z","snapshot_observed_at":"2026-08-17T22:17:18.822354Z","submitted_at":"2024-12-30T17:31:37Z","title":"FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T23:09:24.993164Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2501.01986"},"observation_digest":"sha256:9cd2869fc50903a67f5bf208528b30462b689715b63f0a9ed01ca7b66ce1caa7","observation_id":"a127aadd-1e90-49a8-ac08-bc2d54c11073","resolution":{"observed_at":"2026-08-10T23:09:24.993164Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-10T20:00:12.593109Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.09532","last_updated":"2025-02-01T06:13:27Z","snapshot_observed_at":"2026-08-14T16:36:01.545683Z","submitted_at":"2025-01-16T13:34:33Z","title":"AdaFV: Rethinking of Visual-Language alignment for VLM acceleration","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T20:00:12.593109Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2501.09532"},"observation_digest":"sha256:972decd4eea6dbaeb7313537deb6fb5cbc63d1c6a047780551e1f711d825ed23","observation_id":"906151df-18d8-44de-a0b1-6f5b768d23fe","resolution":{"observed_at":"2026-08-10T20:00:12.593109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-10T16:40:36.625437Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12959","last_updated":"2025-02-05T09:35:38Z","snapshot_observed_at":"2026-08-17T11:23:14.840856Z","submitted_at":"2025-01-22T15:33:17Z","title":"Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T16:40:36.625437Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2501.12959"},"observation_digest":"sha256:7a1d9e82ea87274f5fdc1ab31e3c38f5b227ba8695eef87956585cec766cf51f","observation_id":"6ddf8048-7b81-4ad7-b994-1d2c9de8826e","resolution":{"observed_at":"2026-08-10T16:40:36.625437Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-10T15:21:23.439544Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.16376","last_updated":"2025-05-19T00:48:23Z","snapshot_observed_at":"2026-08-14T17:19:57.580276Z","submitted_at":"2025-01-24T02:50:13Z","title":"SwiftPrune: Hessian-Free Weight Pruning for Large Language Models","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T15:21:23.439544Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2501.16376"},"observation_digest":"sha256:dda86e170ec60b44229c9bd99de763fbd84f3c9bd7ad0a3786f75a3f73a30731","observation_id":"26e91560-350c-408d-9815-e258f90218e4","resolution":{"observed_at":"2026-08-10T15:21:23.439544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-08T11:48:01.188051Z","title":"Lazyllm: Dynamic token pruning for efficient long context llm inference","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07864","last_updated":"2025-06-12T11:45:57Z","snapshot_observed_at":"2026-08-10T11:58:20.177769Z","submitted_at":"2025-02-11T18:20:18Z","title":"TransMLA: Multi-Head Latent Attention Is All You Need","version":5},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T11:48:01.188051Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2502.07864"},"observation_digest":"sha256:d45ab9a489bd1ae999330c2d3cbf0db5ce233ac25930a5bf11896fa40866322f","observation_id":"62bc1830-6ac5-4227-ab28-144eedf6fe5e","resolution":{"observed_at":"2026-08-08T11:48:01.188051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-07T23:19:37.822448Z","title":"Lazyllm: Dynamic token pruning for efficient long context llm inference, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.08910","last_updated":"2025-02-13T02:52:01Z","snapshot_observed_at":"2026-08-17T18:25:45.737890Z","submitted_at":"2025-02-13T02:52:01Z","title":"InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T23:19:37.822448Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2502.08910"},"observation_digest":"sha256:0a3381cc5beea08779b7ee3fdcffe6e13f3906b96e2cebd79e84af22a2411a28","observation_id":"f1e4bc0d-81d0-4354-bc7d-ea55a0ebc2c7","resolution":{"observed_at":"2026-08-07T23:19:37.822448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-09T04:28:03.925945Z","title":"Lazyllm: Dynamic token pruning for efficient long context llm inference","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.10424","last_updated":"2025-02-05T20:43:48Z","snapshot_observed_at":"2026-08-20T23:39:16.252274Z","submitted_at":"2025-02-05T20:43:48Z","title":"QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T04:28:03.925945Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2502.10424"},"observation_digest":"sha256:94493abce00575822d46b54e87a63bc7e998a14744c33afe04638fd4bc7e0c94","observation_id":"7a42e937-f4c5-4818-95b8-56574eddf90d","resolution":{"observed_at":"2026-08-09T04:28:03.925945Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-07T14:43:24.234222Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17787","last_updated":"2025-05-23T12:00:09Z","snapshot_observed_at":"2026-08-17T12:17:50.468576Z","submitted_at":"2025-05-23T12:00:09Z","title":"Titanus: Enabling KV Cache Pruning and Quantization On-the-Fly for LLM Acceleration","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:43:24.234222Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2505.17787"},"observation_digest":"sha256:2dd0dbb267efc5b0c17a004ba6ac1705a74842f4631d69cc44675096ef18d50d","observation_id":"241ba12b-38b3-44e0-975b-b543ce66e8a2","resolution":{"observed_at":"2026-08-07T14:43:24.234222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-05T23:32:55.004748Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.05282","last_updated":"2026-07-21T08:08:53Z","snapshot_observed_at":"2026-08-20T13:50:20.706573Z","submitted_at":"2025-08-07T11:26:40Z","title":"Not All Errors Are Created Equal: ASCoT Addresses Late-Stage Fragility in Efficient LLM Reasoning","version":6},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T23:32:55.004748Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2508.05282"},"observation_digest":"sha256:76b06eb93fe3cf40c541d0f361fb178c7af69423b06af8006ea4ac450d7e2cd4","observation_id":"d6750712-6789-43a0-8b92-61b36e8e0c86","resolution":{"observed_at":"2026-08-05T23:32:55.004748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-05T17:55:08.556414Z","title":"Lazyllm: Dynamic token pruning for efficient long context llm inference","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.15881","last_updated":"2025-08-25T02:24:20Z","snapshot_observed_at":"2026-08-19T17:54:54.763717Z","submitted_at":"2025-08-21T15:25:40Z","title":"TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill and Decode Inference","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T17:55:08.556414Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2508.15881"},"observation_digest":"sha256:0c9d73c939743f7af976cb35c9fd38a321cc21c1447694ca1442a363f7b6db26","observation_id":"d7ec6af8-00c3-42e1-aba7-a1a71f7b5545","resolution":{"observed_at":"2026-08-05T17:55:08.556414Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-05T12:52:44.609841Z","title":"Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.01190","last_updated":"2025-09-06T17:46:00Z","snapshot_observed_at":"2026-08-20T05:21:20.824530Z","submitted_at":"2025-09-01T07:15:25Z","title":"Efficient Large Language Models with Zero-Shot Adjustable Acceleration","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T12:52:44.609841Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2509.01190"},"observation_digest":"sha256:41f79c190cb30b1bc2450588a5cd956d8747f00cf266784383526ba2897b30a7","observation_id":"bfa35c9f-fdef-4003-9f08-d30a3be4ddf8","resolution":{"observed_at":"2026-08-05T12:52:44.609841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":"2407.14057","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-07-10T00:56:40.961590Z","title":"Lazyllm: Dynamic token pruning for efficient long context LLM inference.arXiv preprint arXiv:2407.14057","venue":"cs.CL","work_id":"490f102f-1700-4b06-8f29-69e79a840bdb","year":2024},"citing_paper":{"arxiv_id":"2603.10726","last_updated":"2026-05-20T10:27:28Z","snapshot_observed_at":"2026-08-16T00:06:10.598472Z","submitted_at":"2026-03-11T12:59:12Z","title":"PrefixWall: Mitigating Prefix Caching Side Channels in Shared LLM Systems","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-21T12:14:09.509302Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2603.10726"},"observation_digest":"sha256:0b80aa9005bbafd4e19c72ed2bc6b05ade6c2e39ebff0890006620872fb2843a","observation_id":"095b1f06-f40b-4e43-a394-3b7c995d4bf2","resolution":{"observed_at":"2026-05-21T12:15:06.802670Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-07-13T16:19:49.419805Z","title":"Lazyllm: Dynamic token pruning for efficient long context LLM inference.arXiv preprint arXiv:2407.14057,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.28457","last_updated":"2026-06-24T09:39:21Z","snapshot_observed_at":"2026-08-16T15:57:44.899177Z","submitted_at":"2026-03-30T13:59:38Z","title":"Three non-Hermitian random matrix universality classes of complex edge statistics: Spacing ratios and distributions","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-13T16:19:49.419805Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2603.28457"},"observation_digest":"sha256:a8ba843d36cb34a6399cae3adb13b1a7fa0365633f32d553cb5fff39d2887e74","observation_id":"b2c48ef6-0535-4acd-81ab-175191fe5533","resolution":{"observed_at":"2026-07-13T16:19:49.419805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":"2407.14057","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-07-10T00:56:40.961590Z","title":"Lazyllm: Dynamic token pruning for efficient long context LLM inference.arXiv preprint arXiv:2407.14057","venue":"cs.CL","work_id":"490f102f-1700-4b06-8f29-69e79a840bdb","year":2024},"citing_paper":{"arxiv_id":"2603.28458","last_updated":"2026-04-06T09:47:34Z","snapshot_observed_at":"2026-08-17T16:54:14.554754Z","submitted_at":"2026-03-30T13:59:51Z","title":"HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-14T21:40:28.854082Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2603.28458"},"observation_digest":"sha256:b6f3b689b2011e5186bb4c12946d2fd2bfc3e26dc95a44a100f69e4432f8b057","observation_id":"b450e870-4055-41ef-b915-f0328dd08015","resolution":{"observed_at":"2026-05-14T21:43:00.779043Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":"2407.14057","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-07-10T00:56:40.961590Z","title":"Lazyllm: Dynamic token pruning for efficient long context LLM inference.arXiv preprint arXiv:2407.14057","venue":"cs.CL","work_id":"490f102f-1700-4b06-8f29-69e79a840bdb","year":2024},"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":9,"source":"pdf_text","source_observed_at":"2026-05-08T10:43:01.724760Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2605.06221"},"observation_digest":"sha256:d33894c160afebe13d81f7ae770b7002577312ef5af86b694db1eb76a1c73feb","observation_id":"61ba891d-7883-404b-b8ab-d92c297cd844","resolution":{"observed_at":"2026-05-11T19:56:08.366548Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":"2407.14057","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-07-10T00:56:40.961590Z","title":"Lazyllm: Dynamic token pruning for efficient long context LLM inference.arXiv preprint arXiv:2407.14057","venue":"cs.CL","work_id":"490f102f-1700-4b06-8f29-69e79a840bdb","year":2024},"citing_paper":{"arxiv_id":"2605.06554","last_updated":"2026-05-07T16:49:28Z","snapshot_observed_at":"2026-08-15T09:50:04.863916Z","submitted_at":"2026-05-07T16:49:28Z","title":"Long Context Pre-Training with Lighthouse Attention","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-08T10:10:38.610613Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2605.06554"},"observation_digest":"sha256:be3cf468067cc6463daf1864c8760343a1ec02047d35072dd1780fbfe96f0fcb","observation_id":"432516dd-b0d1-43bd-a336-0787e7da1fb6","resolution":{"observed_at":"2026-05-11T20:11:09.463896Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":"2407.14057","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-07-10T00:56:40.961590Z","title":"Lazyllm: Dynamic token pruning for efficient long context LLM inference.arXiv preprint arXiv:2407.14057","venue":"cs.CL","work_id":"490f102f-1700-4b06-8f29-69e79a840bdb","year":2024},"citing_paper":{"arxiv_id":"2606.00523","last_updated":"2026-05-30T04:31:29Z","snapshot_observed_at":"2026-08-15T04:06:40.971012Z","submitted_at":"2026-05-30T04:31:29Z","title":"ProactiveLLM: Learning Active Interaction for Streaming Large Language Models","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-06-28T19:07:48.425181Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2606.00523"},"observation_digest":"sha256:8b82c7f3b8c424e78a5b646dd50d174f5d4e89cd6da24e1db5fbee707857b8e0","observation_id":"13087462-aed8-413f-922b-2539c831d469","resolution":{"observed_at":"2026-06-28T19:12:34.874551Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":"2407.14057","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-07-10T00:56:40.961590Z","title":"Lazyllm: Dynamic token pruning for efficient long context LLM inference.arXiv preprint arXiv:2407.14057","venue":"cs.CL","work_id":"490f102f-1700-4b06-8f29-69e79a840bdb","year":2024},"citing_paper":{"arxiv_id":"2607.08057","last_updated":"2026-07-09T02:11:18Z","snapshot_observed_at":"2026-08-15T13:49:22.533438Z","submitted_at":"2026-07-09T02:11:18Z","title":"Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-10T00:55:52.215700Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2607.08057"},"observation_digest":"sha256:82338c70eb0e722134eb76664f30db24b90f8847caca12f00643075d59bc2c88","observation_id":"037ea3a9-2cf1-49e0-a4c6-45d98885b7a1","resolution":{"observed_at":"2026-07-10T00:56:40.962769Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-07-14T12:08:05.502310Z","title":"arXiv preprint arXiv:2407.14057 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10386","last_updated":"2026-07-11T16:29:51Z","snapshot_observed_at":"2026-08-14T18:11:23.754748Z","submitted_at":"2026-07-11T16:29:51Z","title":"Structured Thoughts For Improved Reasoning And Context Pruning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-07-14T12:08:05.502310Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2607.10386"},"observation_digest":"sha256:8e2431b544f05dd2488b9a073f10752abed1c6dda22146c60f59699a85a96a96","observation_id":"2518dda7-f2aa-429c-ac6a-066eadd27f77","resolution":{"observed_at":"2026-07-14T12:08:05.502310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-02T09:51:03.373817Z","title":"arXiv preprint arXiv:2407.14057 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16252","last_updated":"2026-06-27T03:25:36Z","snapshot_observed_at":"2026-08-16T23:14:00.224337Z","submitted_at":"2026-06-27T03:25:36Z","title":"SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-02T09:51:03.373817Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2607.16252"},"observation_digest":"sha256:00288d6110bff7ebf04d896108495c1a9ebc1664c57d08695111f8b9e31bad0d","observation_id":"421cfbb6-0894-4c99-9f39-701ab756934d","resolution":{"observed_at":"2026-08-02T09:51:03.373817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-01T20:54:08.630899Z","title":"arXiv preprint arXiv:2407.14057 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16528","last_updated":"2026-07-17T22:09:01Z","snapshot_observed_at":"2026-08-18T01:38:13.939880Z","submitted_at":"2026-07-17T22:09:01Z","title":"Hierarchical Domain Generalization","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-01T20:54:08.630899Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2607.16528"},"observation_digest":"sha256:3c89ccf053bc289324756bf54712e44ba8e06fd27382bc6e2b65183eaac69501","observation_id":"8c003081-64d6-4499-944b-ac596e9bfbf5","resolution":{"observed_at":"2026-08-01T20:54:08.630899Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-07-31T11:04:04.538729Z","title":"arXiv preprint arXiv:2407.14057 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.24593","last_updated":"2026-07-27T15:58:07Z","snapshot_observed_at":"2026-08-14T10:40:42.044988Z","submitted_at":"2026-07-27T15:58:07Z","title":"PIVOT: Efficient Query-Group Indexing for Token-Level Sparse Attention","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-07-31T11:04:04.538729Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2607.24593"},"observation_digest":"sha256:bb829a3d50fcabad0793b1bb8e7d14d2103e1f96512626b64470103c39d8d452","observation_id":"ec0f7d09-cb78-4a40-a4ec-8285c62a8ff6","resolution":{"observed_at":"2026-07-31T11:04:04.538729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14057","snapshot_observed_at":"2026-08-05T04:16:08.394782Z","title":"arXiv preprint arXiv:2407.14057 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04010","last_updated":"2026-08-04T17:59:58Z","snapshot_observed_at":"2026-08-15T09:45:02.472901Z","submitted_at":"2026-08-04T17:59:58Z","title":"ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-05T04:16:08.394782Z"},"links":{"cited_paper":"/paper/2407.14057","citing_paper":"/paper/2608.04010"},"observation_digest":"sha256:258f79060d5550f461a38c4198c639c22c2d6c1eb55edf249e4e95fef99e88ec","observation_id":"7abf6c4e-4cbc-41bc-97a8-00d91866cd5b","resolution":{"observed_at":"2026-08-05T04:16:08.394782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2407.14057/citation-record","integrity":"/paper/2407.14057/integrity","json":"/paper/2407.14057/citation-record.json","paper":"/paper/2407.14057"},"outbound":[],"paper":{"arxiv_id":"2407.14057","last_updated":"2024-07-19T06:34:45Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-19T22:54:34.890320Z","submitted_at":"2024-07-19T06:34:45Z","title":"LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:2407.14057."}