{"as_of":"2026-08-08T15:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b7c652029519283cde30fd4514dcb08979aa971a836b7cf79e207d698c7eb997","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:11:17.508971Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T05:12:30.358427Z","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":"2026-08-06T05:12:32.763864Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"cited_work":{"arxiv_id":"2505.16221","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.16221","snapshot_observed_at":"2026-08-06T05:12:32.763864Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","venue":"cs.AI","work_id":"dfafaf8d-e8b5-4c54-99be-96f47ebeef90","year":2025},"citing_paper":{"arxiv_id":"2508.02209","last_updated":"2025-08-04T09:00:01Z","snapshot_observed_at":"2026-08-08T01:35:49.570669Z","submitted_at":"2025-08-04T09:00:01Z","title":"Balancing Information Accuracy and Response Timeliness in Networked LLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T05:12:30.358427Z"},"links":{"cited_paper":"/paper/2505.16221","citing_paper":"/paper/2508.02209"},"observation_digest":"sha256:21d755001b253f5afc724d6df9a721476468f01fb6198f4d48c50e91a7dccc22","observation_id":"00d5258b-8b42-4b4d-a9e6-27e880bd964e","resolution":{"observed_at":"2026-08-06T05:12:32.860659Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.16221/citation-record","integrity":"/paper/2505.16221/integrity","json":"/paper/2505.16221/citation-record.json","paper":"/paper/2505.16221"},"outbound":[{"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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-07T15:11:17.377349Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.377349Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:29a03c6ec8c6d6c8d844f03a2919e7a617e0ee156bf99c57a6d9c26a0d31b67e","observation_id":"98d5218d-0356-439e-b79e-c698bec99aa0","resolution":{"observed_at":"2026-08-07T15:11:17.377349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.16421","last_updated":"2024-04-19T04:57:37Z","snapshot_observed_at":"2026-08-08T15:42:55.537967Z","submitted_at":"2023-03-29T03:05:43Z","title":"ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models","version":3},"cited_work":{"arxiv_id":"2303.16421","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.16421","snapshot_observed_at":"2026-08-07T15:11:17.799418Z","title":"ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models","venue":"cs.CL","work_id":"2b2e5320-62b8-4c33-82b0-1bb2055600d5","year":2023},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.387110Z"},"links":{"cited_paper":"/paper/2303.16421","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:832795eba15a1fa757e3978e113cc70e728f2efba372252a040d802919da7530","observation_id":"f1b8e9c3-1adb-4d38-8746-3c9f7c720aae","resolution":{"observed_at":"2026-08-07T15:11:17.804529Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:11:17.391752Z","title":"Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.391752Z"},"links":{"citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:33ee3736f0423a82b88157797c564095f5e2944ca8646875c620fed980169a29","observation_id":"2c2becf4-31d5-4ad3-b125-5f8a59840f88","resolution":{"observed_at":"2026-08-07T15:11:17.391752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.06857","last_updated":"2026-08-03T10:27:28Z","snapshot_observed_at":"2026-08-06T23:27:27.553671Z","submitted_at":"2024-09-10T20:45:43Z","title":"What is the Role of Small Models in the LLM Era: A Survey","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.06857","snapshot_observed_at":"2026-08-07T15:11:17.400739Z","title":"What is the role of small models in the llm era: A survey.arXiv preprint arXiv:2409.06857,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.400739Z"},"links":{"cited_paper":"/paper/2409.06857","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:a4298fe97e5a6911609077d585cc662e0afbaa4709cb884f8c150d143866fe1f","observation_id":"83ff80c2-26b6-4d4a-9080-b6a4b08d7654","resolution":{"observed_at":"2026-08-07T15:11:17.400739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-07T15:11:17.410189Z","title":"Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.410189Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:8c13dd9fc5717780862f205dd870d92b9545351fc8d8db03d3f1c32c9304a9e4","observation_id":"5dfa909c-c980-461c-b32d-7a1654252f13","resolution":{"observed_at":"2026-08-07T15:11:17.410189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.03834","last_updated":"2025-03-17T15:08:47Z","snapshot_observed_at":"2026-08-05T19:53:12.719014Z","submitted_at":"2024-10-04T18:02:48Z","title":"GraphRouter: A Graph-based Router for LLM Selections","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.03834","snapshot_observed_at":"2026-08-07T15:11:17.414033Z","title":"Graphrouter: A graph-based router for llm selections","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.414033Z"},"links":{"cited_paper":"/paper/2410.03834","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:adea723cea225cfbd877a945f00d4c0164da1feae31669f6b7f2ff61481ed905","observation_id":"c95e968f-97f7-4df0-abe0-5eec708e0034","resolution":{"observed_at":"2026-08-07T15:11:17.414033Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T15:11:17.418633Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.418633Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:08ac1952fab6084b40f31d610cb01a77d135aee0c52602b54202727789f48abb","observation_id":"069df2ac-0260-43a1-91ca-6f94f046e6f7","resolution":{"observed_at":"2026-08-07T15:11:17.418633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.03874","last_updated":"2021-11-08T21:30:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-03-05T18:59:39Z","title":"Measuring Mathematical Problem Solving With the MATH Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.03874","snapshot_observed_at":"2026-08-07T15:11:17.426762Z","title":"Measuring mathematical problem solving with the math dataset.arXiv preprint arXiv:2103.03874,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.426762Z"},"links":{"cited_paper":"/paper/2103.03874","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:79833413b2749f937c862ec172ea9275c8bdb1607a4602f1f383e21b9c435287","observation_id":"c52a99ea-ff7a-4cd4-9e8a-801a660d0dca","resolution":{"observed_at":"2026-08-07T15:11:17.426762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.10657","last_updated":"2025-05-20T14:59:21Z","snapshot_observed_at":"2026-08-08T08:35:41.016025Z","submitted_at":"2025-03-08T04:07:07Z","title":"RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.10657","snapshot_observed_at":"2026-08-07T15:11:17.431359Z","title":"Routereval: A comprehensive benchmark for routing llms to explore model-level scaling up in llms.arXiv preprint arXiv:2503.10657,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.431359Z"},"links":{"cited_paper":"/paper/2503.10657","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:116c88f9cf25253907df9d637f60e6c172c74e0b9ccce6cc2a17c5dc3b623be4","observation_id":"f9b76be3-78ac-49d5-82ed-63d56d03b83a","resolution":{"observed_at":"2026-08-07T15:11:17.431359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.02561","last_updated":"2023-06-30T21:39:54Z","snapshot_observed_at":"2026-08-05T15:27:19.717152Z","submitted_at":"2023-06-05T03:32:26Z","title":"LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.02561","snapshot_observed_at":"2026-08-07T15:11:17.439648Z","title":"Llm-blender: Ensembling large language models with pairwise ranking and generative fusion.arXiv preprint arXiv:2306.02561,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.439648Z"},"links":{"cited_paper":"/paper/2306.02561","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:8380c432c709e46a49855cfd329dfa86bde1161ee6f07e96579f8afaaf01792f","observation_id":"49450460-5ef7-4d2f-872b-e5b496c3bb6f","resolution":{"observed_at":"2026-08-07T15:11:17.439648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-07T15:11:17.444316Z","title":"Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.444316Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:7521b5d8d7534cbd44fbab16719bf9221224289f2d7e9a805abd894ea6dc7c58","observation_id":"018e734a-9868-475c-bd3d-65cc79230b4b","resolution":{"observed_at":"2026-08-07T15:11:17.444316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01890","last_updated":"2021-06-03T14:34:17Z","snapshot_observed_at":"2026-07-06T11:15:40.152443Z","submitted_at":"2021-06-03T14:34:17Z","title":"SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01890","snapshot_observed_at":"2026-08-07T15:11:17.448589Z","title":"Simcls: A simple framework for contrastive learning of abstractive summarization.arXiv preprint arXiv:2106.01890,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.448589Z"},"links":{"cited_paper":"/paper/2106.01890","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:a88a1c53cba464824502fdef647ce1eee450b4bbe294adcfff5cb1b8c1d85374","observation_id":"a165b283-98a8-40eb-a0f3-1a1b91fb7a56","resolution":{"observed_at":"2026-08-07T15:11:17.448589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.06569","last_updated":"2023-05-26T05:48:29Z","snapshot_observed_at":"2026-08-07T17:12:33.580104Z","submitted_at":"2022-03-13T05:05:10Z","title":"SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization","version":2},"cited_work":{"arxiv_id":"2203.06569","doi":null,"metadata_source":"pith","pith_arxiv_id":"2203.06569","snapshot_observed_at":"2026-08-07T15:11:17.642471Z","title":"SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization","venue":"cs.CL","work_id":"450158dd-9825-4b1e-bc57-f2eb0df7d780","year":2022},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.457684Z"},"links":{"cited_paper":"/paper/2203.06569","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:0b7adee4e5a1b815dcc8ee256e8c43af0fb3d7a29fa7e46612638775c8ec3e9f","observation_id":"f16862dc-de39-41ba-92ec-bf35c1834da9","resolution":{"observed_at":"2026-08-07T15:11:17.648488Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21801","last_updated":"2025-07-18T08:20:23Z","snapshot_observed_at":"2026-08-02T11:46:58.603316Z","submitted_at":"2025-04-30T16:57:48Z","title":"DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21801","snapshot_observed_at":"2026-08-07T15:11:17.462875Z","title":"ZZ Ren, Zhihong Shao, Junxiao Song, Huajian Xin, Haocheng Wang, Wanjia Zhao, Liyue Zhang, Zhe Fu, Qihao Zhu, Dejian Yang, et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.462875Z"},"links":{"cited_paper":"/paper/2504.21801","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:516c4ad797d0b0a2a599d1e45c66a77ee49b5b83b8b2afdc6d0b66b360f9ca06","observation_id":"16a59abc-42f5-4549-9af3-7f1087c77506","resolution":{"observed_at":"2026-08-07T15:11:17.462875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.06538","last_updated":"2017-01-23T18:10:00Z","snapshot_observed_at":"2026-07-06T05:27:13.416519Z","submitted_at":"2017-01-23T18:10:00Z","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.06538","snapshot_observed_at":"2026-08-07T15:11:17.466646Z","title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.arXiv preprint arXiv:1701.06538,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.466646Z"},"links":{"cited_paper":"/paper/1701.06538","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:9f06db99dee3df5a3cbf682f65b3bf69986fa9f420b1efb66f4d278e2a8067d9","observation_id":"c3164259-a084-45f9-b105-2c42059b315a","resolution":{"observed_at":"2026-08-07T15:11:17.466646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03314","last_updated":"2024-08-06T17:35:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:35:05Z","title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03314","snapshot_observed_at":"2026-08-07T15:11:17.475653Z","title":"Scaling llm test-time compute optimally can be more effective than scaling model parameters.arXiv preprint arXiv:2408.03314,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.475653Z"},"links":{"cited_paper":"/paper/2408.03314","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:9a02c76775ca707895a2227f0b6d9637437ed05096716814bcf40bcba002c905","observation_id":"58137cba-4e09-4b2b-82f6-1e80f68e5450","resolution":{"observed_at":"2026-08-07T15:11:17.475653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-07T15:11:17.480116Z","title":"Qwen2.5 technical report.arXiv preprint arXiv:2412.15115,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.480116Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:58a5bd043d85642120f1084606aa43c1209403d638ebd534d5abf6bace1bc266","observation_id":"56670945-d38a-4755-b634-c265dccd252f","resolution":{"observed_at":"2026-08-07T15:11:17.480116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-07T15:11:17.483284Z","title":"Llama 2: Open foundation and fine-tuned chat models.arXiv preprint arXiv:2307.09288,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.483284Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:66e3427374657b8c016dd5ea1ccdd9749382337d5dd06293966baa642059d881","observation_id":"cf3c5653-c41b-463d-9d1b-cc8aa5df1408","resolution":{"observed_at":"2026-08-07T15:11:17.483284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10491","last_updated":"2024-01-22T17:16:37Z","snapshot_observed_at":"2026-08-04T23:37:08.155969Z","submitted_at":"2024-01-19T05:02:46Z","title":"Knowledge Fusion of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10491","snapshot_observed_at":"2026-08-07T15:11:17.487698Z","title":"Knowledge fusion of large language models.arXiv preprint arXiv:2401.10491,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.487698Z"},"links":{"cited_paper":"/paper/2401.10491","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:4b5bae7afac466e61159c32432fb5b8420235bd3953dee2436d937573d4409ce","observation_id":"3f381e3c-4203-48f6-8c7c-7a3a057e09ff","resolution":{"observed_at":"2026-08-07T15:11:17.487698Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01542","last_updated":"2024-05-09T16:04:20Z","snapshot_observed_at":"2026-07-06T16:26:46.131908Z","submitted_at":"2023-10-02T18:31:35Z","title":"Fusing Models with Complementary Expertise","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01542","snapshot_observed_at":"2026-08-07T15:11:17.491867Z","title":"Fusing models with complementary expertise.arXiv preprint arXiv:2310.01542,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.491867Z"},"links":{"cited_paper":"/paper/2310.01542","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:7ba1ad20ed884119f775ee0722b30b6ebf3ec6861c4c88c921fcf66398a3468f","observation_id":"6e111868-9f4f-468c-bda8-3467697e0de3","resolution":{"observed_at":"2026-08-07T15:11:17.491867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04692","last_updated":"2024-06-07T07:04:10Z","snapshot_observed_at":"2026-08-07T21:51:22.136369Z","submitted_at":"2024-06-07T07:04:10Z","title":"Mixture-of-Agents Enhances Large Language Model Capabilities","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04692","snapshot_observed_at":"2026-08-07T15:11:17.496688Z","title":"Mixture-of-agents enhances large language model capabilities.arXiv preprint arXiv:2406.04692,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.496688Z"},"links":{"cited_paper":"/paper/2406.04692","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:a104d474937eaa2d8ba968f1e98a0c27de80680546cd51d9eb9cf4a13623a9f8","observation_id":"aeeafed2-02ca-4a45-801d-431efa80e8b6","resolution":{"observed_at":"2026-08-07T15:11:17.496688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:11:17.836583Z","title":"Which llm to play? convergence-aware online model selection with time-increasing bandits","venue":null,"work_id":"047bb74a-849b-41d2-a3ad-b6fbfed653af","year":2024},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.500344Z"},"links":{"citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:8be3a016a6aafe4bd780b7968d4aee6f79f835c4ee265a06042da6550c99e431","observation_id":"61955ac3-42b2-407e-9d19-fc9172cc3b35","resolution":{"observed_at":"2026-08-07T15:11:17.841325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01068","last_updated":"2022-06-21T17:04:40Z","snapshot_observed_at":"2026-08-06T03:13:37.403059Z","submitted_at":"2022-05-02T17:49:50Z","title":"OPT: Open Pre-trained Transformer Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.01068","snapshot_observed_at":"2026-08-07T15:11:17.505087Z","title":"Opt: Open pre-trained transformer language models.arXiv preprint arXiv:2205.01068,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.505087Z"},"links":{"cited_paper":"/paper/2205.01068","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:ee844785718c4311aa8892c2388e15ca4a7f597cd720a408057774847ae6254c","observation_id":"ab3414fc-0653-48c1-9560-8176bec39071","resolution":{"observed_at":"2026-08-07T15:11:17.505087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11931","last_updated":"2024-06-17T13:51:35Z","snapshot_observed_at":"2026-08-07T06:30:25.302107Z","submitted_at":"2024-06-17T13:51:35Z","title":"DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11931","snapshot_observed_at":"2026-08-07T15:11:17.508971Z","title":"Deepseek-coder-v2: Breaking the barrier of closed-source models in code intelligence.arXiv preprint arXiv:2406.11931,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.508971Z"},"links":{"cited_paper":"/paper/2406.11931","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:14bc6aa0810ab31813a0172d3db474fd3392364920f2f7138a0922ae769f1bd7","observation_id":"583335e7-1336-47f1-ada2-8c3354229c46","resolution":{"observed_at":"2026-08-07T15:11:17.508971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04088","last_updated":"2024-01-08T18:47:34Z","snapshot_observed_at":"2026-08-08T06:16:25.839566Z","submitted_at":"2024-01-08T18:47:34Z","title":"Mixtral of Experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04088","snapshot_observed_at":"2026-08-07T15:11:17.435510Z","title":"Mixtral of experts.arXiv preprint arXiv:2401.04088,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":1991,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.435510Z"},"links":{"cited_paper":"/paper/2401.04088","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:a432bf3daac53672232f691cef8ef709652410fa8a08f565c7c77fc271001119","observation_id":"ca070952-f65b-4791-9cff-77ba8786d23c","resolution":{"observed_at":"2026-08-07T15:11:17.435510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15789","last_updated":"2023-09-27T17:08:40Z","snapshot_observed_at":"2026-08-06T08:57:12.331150Z","submitted_at":"2023-09-27T17:08:40Z","title":"Large Language Model Routing with Benchmark Datasets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.15789","snapshot_observed_at":"2026-08-07T15:11:17.471621Z","title":"Large language model routing with benchmark datasets.arXiv preprint arXiv:2309.15789,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.471621Z"},"links":{"cited_paper":"/paper/2309.15789","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:b8e613531ef862e89ed8be51c0a1aefd1ff7fed88539d5fbf8da6f4988a4df01","observation_id":"af962790-1ffa-4c39-adfc-edd5e37800f7","resolution":{"observed_at":"2026-08-07T15:11:17.471621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.13007","last_updated":"2024-06-21T19:34:27Z","snapshot_observed_at":"2026-08-05T18:07:14.796740Z","submitted_at":"2023-09-22T17:12:45Z","title":"ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.13007","snapshot_observed_at":"2026-08-07T15:11:17.395603Z","title":"Reconcile: Round-table conference improves reasoning via consensus among diverse llms.arXiv preprint arXiv:2309.13007,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.395603Z"},"links":{"cited_paper":"/paper/2309.13007","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:d8f06127cf7f862c73fe1daff048b434638f915077f6d3546bdc0743e97864a4","observation_id":"b672965b-05d9-48b8-b89f-240cdbe535d8","resolution":{"observed_at":"2026-08-07T15:11:17.395603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.08692","last_updated":"2023-11-15T04:40:43Z","snapshot_observed_at":"2026-07-06T16:47:45.504109Z","submitted_at":"2023-11-15T04:40:43Z","title":"Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.08692","snapshot_observed_at":"2026-08-07T15:11:17.453059Z","title":"Routing to the expert: Efficient reward-guided ensemble of large language models.arXiv preprint arXiv:2311.08692,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.453059Z"},"links":{"cited_paper":"/paper/2311.08692","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:17255c5abfcab576a83f00daf84667916f35ddd58ce977bf8f69cf182fe721f1","observation_id":"b2f5239e-f702-4185-8ac9-c9ea9028a953","resolution":{"observed_at":"2026-08-07T15:11:17.453059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02031","last_updated":"2024-09-03T10:19:52Z","snapshot_observed_at":"2026-07-06T16:27:07.155310Z","submitted_at":"2023-10-03T13:17:35Z","title":"OceanGPT: A Large Language Model for Ocean Science Tasks","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02031","snapshot_observed_at":"2026-08-07T15:11:17.383015Z","title":"Oceangpt: A large language model for ocean science tasks.arXiv preprint arXiv:2310.02031,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.383015Z"},"links":{"cited_paper":"/paper/2310.02031","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:dfce11fe65810d98f333e223dca52096ea2da560c46cb9332e8949c4735cfc3f","observation_id":"2c3c1228-c1b4-4c39-9413-110048decd5f","resolution":{"observed_at":"2026-08-07T15:11:17.383015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-07T15:11:17.405752Z","title":"Evaluating large language models trained on code.arXiv preprint arXiv:2107.03374,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.405752Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:3e8e1b778af077f39fc32a093c134c6c4594e9e77346f4e6f41d5ecdd9b8311a","observation_id":"1b4b8f71-c265-46a3-bb6a-5323fc749095","resolution":{"observed_at":"2026-08-07T15:11:17.405752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.03300","last_updated":"2021-01-12T18:57:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-09-07T17:59:25Z","title":"Measuring Massive Multitask Language Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.03300","snapshot_observed_at":"2026-08-07T15:11:17.422882Z","title":"Measuring massive multitask language understanding.arXiv preprint arXiv:2009.03300,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T15:11:17.422882Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2505.16221"},"observation_digest":"sha256:ad48b9e52a58927d76e831bed5ac302b706e19c4c64c172110af036b7ab84902","observation_id":"a7c682ba-fbeb-4e37-9a09-359652378c18","resolution":{"observed_at":"2026-08-07T15:11:17.422882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.16221","last_updated":"2025-05-22T04:46:04Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-08T05:31:07.480386Z","submitted_at":"2025-05-22T04:46:04Z","title":"LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":28,"verified_exact":2,"verified_fuzzy":1},"total_outbound_references":31},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2505.16221."}