{"as_of":"2026-08-07T10:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ebcfc33d4a570a10ea6497115b8e2673d57c61c53b61c71c3898459f339d298c","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T10:32:40.855458Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.23674/citation-record","integrity":"/paper/2507.23674/integrity","json":"/paper/2507.23674/citation-record.json","paper":"/paper/2507.23674"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.07201","last_updated":"2023-08-14T15:13:04Z","snapshot_observed_at":"2026-08-02T01:34:38.978920Z","submitted_at":"2023-08-14T15:13:04Z","title":"ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.07201","snapshot_observed_at":"2026-08-06T10:32:40.822292Z","title":"cross encoder","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.822292Z"},"links":{"cited_paper":"/paper/2308.07201","citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:6e202a6eba0bc78f7a8815b4496e2127554905ef21f8dc5c69c5eba552a8dc1d","observation_id":"816698a1-d665-49ae-acf6-4bfc79b4373d","resolution":{"observed_at":"2026-08-06T10:32:40.822292Z","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":"2026-08-06T10:32:41.132125Z","title":"Accessed: 2025-03-27","venue":null,"work_id":"55a05ebd-4fd0-45fa-bcf4-ef59c0d0fa4c","year":2025},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.825326Z"},"links":{"citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:58d489f52a5ab0c697bada472093811c932e8c33887e88a20364b091575b89d5","observation_id":"42f38525-5ee8-42b4-9b16-577104da2f5d","resolution":{"observed_at":"2026-08-06T10:32:41.135041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"small-language-models-for-function-calling-a-comprehensive-guide/4362539","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T10:32:41.088551Z","title":"Ac- cessed: 2025-03-27","venue":null,"work_id":"edeb50dd-8d4f-4181-97d8-ec11796b8575","year":2025},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.828481Z"},"links":{"citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:f2aa11aa0064bcd325f1bf1eb93bd07cf57d1ff90ec626d4f35b28aaa79e165d","observation_id":"479ea014-1d6a-4be4-950c-326d7bc341f1","resolution":{"observed_at":"2026-08-06T10:32:41.094181Z","resolver_source":"raw_fallback","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":"2026-08-06T10:32:41.122368Z","title":"Question pairs dataset","venue":null,"work_id":"51e9e832-c313-4038-9e1f-187019754b96","year":2025},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.836171Z"},"links":{"citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:fc2245a782f3950111efd0df15894f2067ce0ffbe847bad2f77c1d0f08a7a16b","observation_id":"8b49661f-55aa-427b-b1b3-eae04a55b767","resolution":{"observed_at":"2026-08-06T10:32:41.125637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2411.05276","last_updated":"2024-12-09T01:44:10Z","snapshot_observed_at":"2026-08-02T07:37:21.335822Z","submitted_at":"2024-11-08T02:21:19Z","title":"GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic Embedding Caching","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05276","snapshot_observed_at":"2026-08-06T10:32:40.839088Z","title":"Sentence Transformers","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.839088Z"},"links":{"cited_paper":"/paper/2411.05276","citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:ea367060e94e10e91ba222307287a576ba7712a7376705334d2babfd70b40288","observation_id":"dd1fa479-4bea-42f7-a4a2-8a36f4983d45","resolution":{"observed_at":"2026-08-06T10:32:40.839088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.03350","last_updated":"2024-12-28T09:18:36Z","snapshot_observed_at":"2026-08-04T03:34:32.259687Z","submitted_at":"2024-11-04T04:43:01Z","title":"A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.03350","snapshot_observed_at":"2026-08-06T10:32:40.842264Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.842264Z"},"links":{"cited_paper":"/paper/2411.03350","citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:29b2ea631a196af26bdb414ea5bfc761c9c4c00ccaeaeecb3df36327b5269a5a","observation_id":"58b96656-de3d-45ea-bca6-533d3367b13c","resolution":{"observed_at":"2026-08-06T10:32:40.842264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01470","last_updated":"2024-05-02T17:00:02Z","snapshot_observed_at":"2026-07-06T18:08:56.480769Z","submitted_at":"2024-05-02T17:00:02Z","title":"WildChat: 1M ChatGPT Interaction Logs in the Wild","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.01470","snapshot_observed_at":"2026-08-06T10:32:40.848552Z","title":"Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.848552Z"},"links":{"cited_paper":"/paper/2405.01470","citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:49e08cd0e8d3a86e7da03d4dd580cecec756b0a8ae70eccac9036b8c695c7798","observation_id":"3800d17b-8d3c-4391-8279-c3dbbb2e4386","resolution":{"observed_at":"2026-08-06T10:32:40.848552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05685","last_updated":"2023-12-24T02:01:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-09T05:55:52Z","title":"Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.05685","snapshot_observed_at":"2026-08-06T10:32:40.851781Z","title":"Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric P","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.851781Z"},"links":{"cited_paper":"/paper/2306.05685","citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:568b8609901022206641945be194f20189c8ededbe0748b5b0ac95c7584266ce","observation_id":"e71a9c7a-358a-463d-a83b-e7cb7806ce14","resolution":{"observed_at":"2026-08-06T10:32:40.851781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.11998","last_updated":"2024-03-10T19:34:57Z","snapshot_observed_at":"2026-07-06T16:21:45.588487Z","submitted_at":"2023-09-21T12:13:55Z","title":"LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.11998","snapshot_observed_at":"2026-08-06T10:32:40.855458Z","title":"11 A Small LLM’s tweaking prompt Instructions: You are playing a crucial part in a larger caching architecture for serving user queries","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.855458Z"},"links":{"cited_paper":"/paper/2309.11998","citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:04f177469b6ea7e685fe3eefade46510c29c384976335845381b0c617ab4a94b","observation_id":"f8c97b15-ea36-4309-aa4f-247547c64fa7","resolution":{"observed_at":"2026-08-06T10:32:40.855458Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T10:32:40.845333Z","title":"ISBN 9781450383431","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.845333Z"},"links":{"citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:f5d78ee047b801f1a98caec02cfb9fe5b31107cbd14927be92809aa8712506b2","observation_id":"58ec52fe-665d-49bb-8dc0-11659d33f168","resolution":{"observed_at":"2026-08-06T10:32:40.845333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-06T10:32:40.819007Z","title":"doi: 10.18653/v1/2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.819007Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:88c9b4fc51bc2435f0081f75c1bbf04489ff2403043746e3d52159ae60d6ac02","observation_id":"3c1468d1-ffa6-44ef-819c-c0429366c6cd","resolution":{"observed_at":"2026-08-06T10:32:40.819007Z","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":"2026-08-06T10:32:41.142643Z","title":"Accessed: 2025-03-27","venue":null,"work_id":"359e1ca2-f72d-4eb5-bcbc-44c07d3f3518","year":2025},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.815507Z"},"links":{"citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:4748df101c9c9a1ae9db9a7a53d31ec22b6cf91168d54712429e43c336b7ef9d","observation_id":"33b4bf91-d3b4-426d-ba83-81a31e7cc24b","resolution":{"observed_at":"2026-08-06T10:32:41.145987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2406.18665","last_updated":"2025-02-23T08:50:33Z","snapshot_observed_at":"2026-07-30T15:06:18.011623Z","submitted_at":"2024-06-26T18:10:22Z","title":"RouteLLM: Learning to Route LLMs with Preference Data","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.18665","snapshot_observed_at":"2026-08-06T10:32:40.832570Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T10:32:40.832570Z"},"links":{"cited_paper":"/paper/2406.18665","citing_paper":"/paper/2507.23674"},"observation_digest":"sha256:f0236ea90da3921e17222e883aed4c370ee6b22981e5803de0744ef0b897ab72","observation_id":"d3dd630f-f87a-4bb4-9edc-2829f8f409bb","resolution":{"observed_at":"2026-08-06T10:32:40.832570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.23674","last_updated":"2025-09-10T17:59:08Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T10:41:10.135815Z","submitted_at":"2025-07-31T15:50:57Z","title":"TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":1,"verified_fuzzy":3},"total_outbound_references":13},"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 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2507.23674."}