{"as_of":"2026-08-08T14:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ff073d2a72b06a942a54925f5e23a8cdc54df562bdccbef631ebcd1867cfca7d","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:24:18.802896Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:36:05.728667Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T17:09:59.351425Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"cited_work":{"arxiv_id":"2505.24478","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.24478","snapshot_observed_at":"2026-07-04T17:09:59.351425Z","title":"Optimizing the interface between knowledge graphs and llms for complex reasoning.arXiv preprint arXiv:2505.24478","venue":null,"work_id":"47cb7de1-2690-46a5-bee0-4130ae459406","year":2025},"citing_paper":{"arxiv_id":"2507.05257","last_updated":"2026-06-28T17:25:01Z","snapshot_observed_at":"2026-08-06T19:26:23.035442Z","submitted_at":"2025-07-07T17:59:54Z","title":"Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-16T21:20:22.146417Z"},"links":{"cited_paper":"/paper/2505.24478","citing_paper":"/paper/2507.05257"},"observation_digest":"sha256:2095e5f8730b9df63acf48f959dbf9d8d7e10524f83035740f718c1ba67efd93","observation_id":"a78d5bc6-a3db-4831-9126-6a2abe05f783","resolution":{"observed_at":"2026-05-16T21:20:22.244283Z","resolver_source":"arxiv_id","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":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24478","snapshot_observed_at":"2026-08-06T19:36:05.728667Z","title":"Optimizing the interface between knowledge graphs and llms for complex reasoning.arXiv preprint arXiv:2505.24478,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05257","last_updated":"2026-06-28T17:25:01Z","snapshot_observed_at":"2026-08-06T19:26:23.035442Z","submitted_at":"2025-07-07T17:59:54Z","title":"Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:36:05.728667Z"},"links":{"cited_paper":"/paper/2505.24478","citing_paper":"/paper/2507.05257"},"observation_digest":"sha256:37e29d97a2579b501590e02582166740319791c03cd1729e92c47b3bbb1982c3","observation_id":"9f1c333d-35f2-45ee-b8a8-c5a57b44463a","resolution":{"observed_at":"2026-08-06T19:36:05.728667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"cited_work":{"arxiv_id":"2505.24478","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.24478","snapshot_observed_at":"2026-07-04T17:09:59.351425Z","title":"Optimizing the interface between knowledge graphs and llms for complex reasoning.arXiv preprint arXiv:2505.24478","venue":null,"work_id":"47cb7de1-2690-46a5-bee0-4130ae459406","year":2025},"citing_paper":{"arxiv_id":"2507.07957","last_updated":"2025-07-10T17:40:11Z","snapshot_observed_at":"2026-08-06T11:34:56.054509Z","submitted_at":"2025-07-10T17:40:11Z","title":"MIRIX: Multi-Agent Memory System for LLM-Based Agents","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-15T05:57:52.610922Z"},"links":{"cited_paper":"/paper/2505.24478","citing_paper":"/paper/2507.07957"},"observation_digest":"sha256:2829cf670a547b76b080c035fb79d870cfce606a9089f72ac165a959eb523da3","observation_id":"7fe4392a-810e-4295-8f6d-e1f826bb3582","resolution":{"observed_at":"2026-05-15T05:57:52.792186Z","resolver_source":"arxiv_id","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":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24478","snapshot_observed_at":"2026-08-03T20:21:03.531366Z","title":"Optimizing the interface between knowledge graphs and llms for complex reasoning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.20297","last_updated":"2026-07-10T08:15:53Z","snapshot_observed_at":"2026-08-06T06:57:14.844163Z","submitted_at":"2025-11-25T13:34:54Z","title":"Improving Language Agents through BREW: Bootstrapping expeRientially-learned Environmental knoWledge","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T20:21:03.531366Z"},"links":{"cited_paper":"/paper/2505.24478","citing_paper":"/paper/2511.20297"},"observation_digest":"sha256:02c9a8ebc1a4bb3295979e2c193d4f51d1c905914c311164eb0651502df8f639","observation_id":"0287cc32-91d3-449b-972d-9031ccf8cfcf","resolution":{"observed_at":"2026-08-03T20:21:03.531366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24478","snapshot_observed_at":"2026-08-03T11:01:43.496684Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.07978","last_updated":"2026-07-13T12:55:23Z","snapshot_observed_at":"2026-08-03T11:01:42.993651Z","submitted_at":"2026-01-12T20:20:35Z","title":"Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T11:01:43.496684Z"},"links":{"cited_paper":"/paper/2505.24478","citing_paper":"/paper/2601.07978"},"observation_digest":"sha256:70d79925f1d02bc776217492a2561eca13702406f800929129377567d884eb4c","observation_id":"f4ff70b5-66de-4ac3-aa16-26c4290dbdb2","resolution":{"observed_at":"2026-08-03T11:01:43.496684Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"cited_work":{"arxiv_id":"2505.24478","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.24478","snapshot_observed_at":"2026-07-04T17:09:59.351425Z","title":"Optimizing the interface between knowledge graphs and llms for complex reasoning.arXiv preprint arXiv:2505.24478","venue":null,"work_id":"47cb7de1-2690-46a5-bee0-4130ae459406","year":2025},"citing_paper":{"arxiv_id":"2606.24775","last_updated":"2026-06-23T16:34:55Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-06-23T16:34:55Z","title":"Are We Ready For An Agent-Native Memory System?","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-25T23:52:26.260258Z"},"links":{"cited_paper":"/paper/2505.24478","citing_paper":"/paper/2606.24775"},"observation_digest":"sha256:bffbcac92f86740bfa3247d7e39895d0239a953b3f0158cd61bef55e6d89827f","observation_id":"3476a04a-dfbc-411e-9db3-7ceab32c51b1","resolution":{"observed_at":"2026-07-04T17:09:59.352909Z","resolver_source":"arxiv_id","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":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24478","snapshot_observed_at":"2026-07-30T12:32:19.058233Z","title":"InarXiv:2505.24478","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27056","last_updated":"2026-08-03T12:10:34Z","snapshot_observed_at":"2026-08-06T23:32:35.330918Z","submitted_at":"2026-07-29T15:47:40Z","title":"Setoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-30T12:32:19.058233Z"},"links":{"cited_paper":"/paper/2505.24478","citing_paper":"/paper/2607.27056"},"observation_digest":"sha256:8ac4aadc53ba67970d82929c29984b5869fc843c66edbd643ead8ee8c4fa0cc1","observation_id":"706410d7-bb8d-4148-a1fa-48cee5487d9e","resolution":{"observed_at":"2026-07-30T12:32:19.058233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24478","snapshot_observed_at":"2026-08-04T03:22:55.261407Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.27056","last_updated":"2026-08-03T12:10:34Z","snapshot_observed_at":"2026-08-06T23:32:35.330918Z","submitted_at":"2026-07-29T15:47:40Z","title":"Setoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-04T03:22:55.261407Z"},"links":{"cited_paper":"/paper/2505.24478","citing_paper":"/paper/2607.27056"},"observation_digest":"sha256:04ecb4956bb57724abf328d3d58d35084b396d37982138fb404e25401e52b5bb","observation_id":"d32ee6b9-64e0-43ff-9e27-b027fc2765af","resolution":{"observed_at":"2026-08-04T03:22:55.261407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24478","snapshot_observed_at":"2026-08-03T07:15:57.726447Z","title":"InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13851– 13870, Bangkok, Thailand","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.29433","last_updated":"2026-07-31T13:57:21Z","snapshot_observed_at":"2026-08-08T06:14:07.311938Z","submitted_at":"2026-07-31T13:57:21Z","title":"Know It, Act on It: Investigating Memory Utilization in LLM Personalization","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T07:15:57.726447Z"},"links":{"cited_paper":"/paper/2505.24478","citing_paper":"/paper/2607.29433"},"observation_digest":"sha256:77eba62c67cdb0b4c1957363e237981d6000347a0292798d06702a88554ee087","observation_id":"3fc4bf8d-bcf7-4db9-bbba-ce55c6bdaf41","resolution":{"observed_at":"2026-08-03T07:15:57.726447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.24478/citation-record","integrity":"/paper/2505.24478/integrity","json":"/paper/2505.24478/citation-record.json","paper":"/paper/2505.24478"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2311.07361","last_updated":"2023-12-08T06:30:12Z","snapshot_observed_at":"2026-08-06T14:59:24.918552Z","submitted_at":"2023-11-13T14:26:12Z","title":"The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.07361","snapshot_observed_at":"2026-08-07T12:24:15.383446Z","title":"The im- pact of large language models on scientific discovery: a preliminary study using gpt-4","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:15.383446Z"},"links":{"cited_paper":"/paper/2311.07361","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:1461a265262d3dd91beab8ceace678771c765f0d88fdbf341a33d36c5463cafc","observation_id":"13b970ca-5ec4-4f1d-a599-47212dcd0e20","resolution":{"observed_at":"2026-08-07T12:24:15.383446Z","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":"2026-08-07T12:24:21.396568Z","title":"Self-rag: Learning to retrieve, generate, and critique through self-reflection","venue":null,"work_id":"421360e3-6a5b-4d52-af1c-8af32c6076b2","year":2023},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:15.444844Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:003d5d881ce1842706b7949dc64ba9691af018f89ff24584c251f0d54750c536","observation_id":"7f7c3f8a-c4ea-4443-90ef-9d67223fc2b2","resolution":{"observed_at":"2026-08-07T12:24:21.432992Z","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":"2502.18635","last_updated":"2025-05-08T10:58:09Z","snapshot_observed_at":"2026-08-07T17:48:58.865853Z","submitted_at":"2025-02-25T20:52:06Z","title":"Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.18635","snapshot_observed_at":"2026-08-07T12:24:15.508597Z","title":"Faster, Cheaper, Better: Multi-Objective Hyper- parameter Optimization for LLM and RAG Systems","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:15.508597Z"},"links":{"cited_paper":"/paper/2502.18635","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:8edc5144e88d7be4e08db7037fd91b142a0a557f294d53690bf1878f28c29657","observation_id":"1ddf2c30-4d09-48b0-ac33-6694f1967f6f","resolution":{"observed_at":"2026-08-07T12:24:15.508597Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.02059","last_updated":"2019-06-05T14:56:06Z","snapshot_observed_at":"2026-08-03T01:43:19.305404Z","submitted_at":"2019-06-05T14:56:06Z","title":"Neural Legal Judgment Prediction in English","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.02059","snapshot_observed_at":"2026-08-07T12:24:15.592462Z","title":"Neural le- gal judgment prediction in English","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:15.592462Z"},"links":{"cited_paper":"/paper/1906.02059","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:1bf0f24480379c45ab48a6d6f67b5359bd337acb81bfb515cd2f1b792ca01352","observation_id":"4e8e2b57-6643-4e89-8eef-fafe7868b77f","resolution":{"observed_at":"2026-08-07T12:24:15.592462Z","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":"2026-08-07T12:24:15.657321Z","title":"Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:15.657321Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:ff336d9059da01bd2e052f91fd70f234c4746cbc18e7f76bf31f8e30a073f91e","observation_id":"3468f81f-d668-451e-a5ab-19e42217a5b0","resolution":{"observed_at":"2026-08-07T12:24:15.657321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.01157","last_updated":"2024-03-31T19:56:37Z","snapshot_observed_at":"2026-08-05T05:36:30.902840Z","submitted_at":"2023-05-02T02:21:49Z","title":"Complex Logical Reasoning over Knowledge Graphs using Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.01157","snapshot_observed_at":"2026-08-07T12:24:15.698848Z","title":"Complex logical reasoning over knowledge graphs using large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:15.698848Z"},"links":{"cited_paper":"/paper/2305.01157","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:dc5197ec834052111af9a73dbcf018294892fa9e8b6d78ec39d49173b4ea2381","observation_id":"653020b7-5afe-4380-aaa1-c792bbb8d605","resolution":{"observed_at":"2026-08-07T12:24:15.698848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16130","last_updated":"2025-02-19T10:49:41Z","snapshot_observed_at":"2026-07-06T18:05:11.700127Z","submitted_at":"2024-04-24T18:38:11Z","title":"From Local to Global: A Graph RAG Approach to Query-Focused Summarization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16130","snapshot_observed_at":"2026-08-07T12:24:15.791382Z","title":"From local to global: A graph rag approach to query- focused summarization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:15.791382Z"},"links":{"cited_paper":"/paper/2404.16130","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:8a6d80189cc9a221ad30e8904cfb30b3788b2a158a461e8e04e984d9b6818311","observation_id":"e3eb510d-0c23-470d-afa1-c4f20985f876","resolution":{"observed_at":"2026-08-07T12:24:15.791382Z","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":"2026-08-07T12:24:21.337611Z","title":"A survey on rag meeting llms: Towards retrieval-augmented large language models","venue":null,"work_id":"bbdfc01a-a519-4720-ba45-9439b981f8b7","year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:15.880090Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:d5a2b959b05b55bbfba18da66a07a84ae6a6c43ca9a9fb150235ff6024c7d61d","observation_id":"168a576e-ddf3-469b-b389-29f15ac2727b","resolution":{"observed_at":"2026-08-07T12:24:21.357766Z","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":"2406.19251","last_updated":"2024-06-27T15:18:21Z","snapshot_observed_at":"2026-07-06T18:38:00.379225Z","submitted_at":"2024-06-27T15:18:21Z","title":"AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.19251","snapshot_observed_at":"2026-08-07T12:24:15.987843Z","title":"AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:15.987843Z"},"links":{"cited_paper":"/paper/2406.19251","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:f7ea6e4f9b2ad1390b08a258c2299f713668787694eefa8f051e5b5e82e07a77","observation_id":"14d0529d-6641-49a6-868c-c94eac904311","resolution":{"observed_at":"2026-08-07T12:24:15.987843Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10997","last_updated":"2024-03-27T09:16:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-18T07:47:33Z","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10997","snapshot_observed_at":"2026-08-07T12:24:16.104342Z","title":"Retrieval-augmented generation for large language models: A survey","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:16.104342Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:ee4de8f5db54a32db97486e0feb6fe4f863ecbcdd8d5d6fcfc510c3f9c34df59","observation_id":"a7b54448-36d7-4a6a-8dfc-21757aea9973","resolution":{"observed_at":"2026-08-07T12:24:16.104342Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12837","last_updated":"2024-10-03T22:29:47Z","snapshot_observed_at":"2026-08-07T20:19:18.658172Z","submitted_at":"2024-10-03T22:29:47Z","title":"A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12837","snapshot_observed_at":"2026-08-07T12:24:16.174083Z","title":"A compre- hensive survey of retrieval-augmented generation (rag): Evolution, cur- rent landscape and future directions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:16.174083Z"},"links":{"cited_paper":"/paper/2410.12837","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:65ba7700ec1aaa8b3719410b40e7eceb1d2e0547f8045a8ea78680cbb95cbf0b","observation_id":"d36934c4-3c63-46c1-92c8-ac7bbe1b127a","resolution":{"observed_at":"2026-08-07T12:24:16.174083Z","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":"2026-08-07T12:24:21.269025Z","title":"Hipporag: Neurobiologically inspired long-term memory for large language models","venue":null,"work_id":"1938109e-7b3b-485a-b849-6b909e3b9089","year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:16.257209Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:00026ee743087a6ae552845a06f6bf92512b1ef0b813a5b1c23f95183e4d2a50","observation_id":"2744a68d-46fc-46c5-9b5d-15c04255c481","resolution":{"observed_at":"2026-08-07T12:24:21.295255Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:24:21.192464Z","title":"Retrieval augmented language model pre-training","venue":null,"work_id":"f64fe874-9474-441d-9245-deaafe76d1ba","year":2020},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:16.363935Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:0e1017d723a57b9f69b6e7d1f87741214bb1523d797f91eb928dedb43140e18a","observation_id":"e4c734d6-27ff-4373-b574-f69416052ff6","resolution":{"observed_at":"2026-08-07T12:24:21.232746Z","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":"2501.00309","last_updated":"2025-01-08T05:16:25Z","snapshot_observed_at":"2026-08-07T12:37:34.294206Z","submitted_at":"2024-12-31T06:59:35Z","title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.00309","snapshot_observed_at":"2026-08-07T12:24:16.502163Z","title":"Retrieval-augmented generation with graphs (graphrag)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:16.502163Z"},"links":{"cited_paper":"/paper/2501.00309","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:ec27b3795e47e24bd07d53913937df2143c48a3cd26d4223bf63960f4ae88d30","observation_id":"72c44a39-afad-43ef-88a5-8b5a8dc1ae98","resolution":{"observed_at":"2026-08-07T12:24:16.502163Z","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":"2026-08-07T12:24:21.078368Z","title":"G-retriever: Retrieval-augmented generation for textual graph understanding and question answering","venue":null,"work_id":"5f51eda3-b404-4d02-8144-cb37f19f4059","year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:16.588182Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:9ed07ebfa25e761e36eee873eeae9c39cf45e815f4033172add0c697c3672430","observation_id":"cb2fe11f-47e1-4d7d-a3aa-5788beec09e9","resolution":{"observed_at":"2026-08-07T12:24:21.130753Z","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":"2011.01060","last_updated":"2020-11-12T07:47:48Z","snapshot_observed_at":"2026-07-06T10:10:56.466018Z","submitted_at":"2020-11-02T15:42:40Z","title":"Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01060","snapshot_observed_at":"2026-08-07T12:24:16.677572Z","title":"Constructing a multi-hop qa dataset for comprehen- sive evaluation of reasoning steps","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:16.677572Z"},"links":{"cited_paper":"/paper/2011.01060","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:9afbdf60457e83803844e82687ad393a7cfd42afb5ea2e1043cadeade887b530","observation_id":"b6918372-803c-41a9-981e-114b841b3793","resolution":{"observed_at":"2026-08-07T12:24:16.677572Z","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":"2026-08-07T12:24:21.017814Z","title":"A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions","venue":null,"work_id":"930b8a77-d727-45ac-abb0-5c112aece25f","year":2025},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:16.762761Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:6545af9ec2fa97dd1fcfe4e7d73867f1f68756c0c5cf22de61cb2a8a81f9a211","observation_id":"29d3fffd-0d56-4536-bd52-9d9718053f70","resolution":{"observed_at":"2026-08-07T12:24:21.040162Z","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":"2407.19794","last_updated":"2024-08-17T11:31:56Z","snapshot_observed_at":"2026-08-07T14:30:14.274747Z","submitted_at":"2024-07-29T08:38:14Z","title":"Introducing a new hyper-parameter for RAG: Context Window Utilization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.19794","snapshot_observed_at":"2026-08-07T12:24:16.852973Z","title":"Introducing a new hyper-parameter for RAG: Context Window Utilization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:16.852973Z"},"links":{"cited_paper":"/paper/2407.19794","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:1a3a9e48d4b063d751d1304dc2995d15077f2757050e9ca7e8a27e1d24b89f97","observation_id":"09d65a32-0caf-4344-b9a8-8c67e77a417a","resolution":{"observed_at":"2026-08-07T12:24:16.852973Z","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":"2026-08-07T12:24:20.950337Z","title":"Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective","venue":null,"work_id":"9dae05bd-f7da-4a06-8e8e-b3ed47e269a6","year":2025},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:16.962661Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:a8c31d2bb3e1b03217a4b830c3fe3a123aacde37fa2fb604183e5dba3dfe67a1","observation_id":"976ca581-a21d-4f6a-bf93-24468deb30ab","resolution":{"observed_at":"2026-08-07T12:24:20.991065Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:24:17.057049Z","title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.057049Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:93c644899afccfb6b7bf728e1dc86d807eafd8596af6fb2b1917ddd3fb61e07c","observation_id":"9dd9e6d9-48f7-425d-a75a-729674dfebe5","resolution":{"observed_at":"2026-08-07T12:24:17.057049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.03205","last_updated":"2024-01-06T12:40:45Z","snapshot_observed_at":"2026-07-06T17:12:18.575364Z","submitted_at":"2024-01-06T12:40:45Z","title":"The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.03205","snapshot_observed_at":"2026-08-07T12:24:17.146987Z","title":"The dawn after the dark: An empirical study on factuality hallucination in large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.146987Z"},"links":{"cited_paper":"/paper/2401.03205","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:ed45b524d5713cf5b1b3392d11b8b4a027d53640f2880f846d852a054052719c","observation_id":"b7df3a95-87d7-46a9-93f2-564b19cae85e","resolution":{"observed_at":"2026-08-07T12:24:17.146987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01061","last_updated":"2024-02-24T03:03:12Z","snapshot_observed_at":"2026-07-06T16:26:26.426279Z","submitted_at":"2023-10-02T10:14:43Z","title":"Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01061","snapshot_observed_at":"2026-08-07T12:24:17.191085Z","title":"Reasoning on graphs: Faithful and interpretable large language model reasoning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.191085Z"},"links":{"cited_paper":"/paper/2310.01061","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:4f6c70bfc9ce0bf82d807907c4ea24f04ea09dc513e2349175aa1d43b0f2c8da","observation_id":"65861c8d-207e-4a18-a465-dc6613b5c3a8","resolution":{"observed_at":"2026-08-07T12:24:17.191085Z","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":"2026-08-07T12:24:20.877909Z","title":"When Not to Trust Language Models: Investigating Ef- fectiveness of Parametric and Non-Parametric Memories","venue":null,"work_id":"b9087ad5-c1ee-4eaf-831d-21cad93adf44","year":2023},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.216905Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:759185f306e638702eeb24ce004b627ff920e50a2c3e54f50e84e8905f67866c","observation_id":"00f9c8f2-110f-469a-b766-744277affe30","resolution":{"observed_at":"2026-08-07T12:24:20.899670Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:24:20.806986Z","title":"Unifying large language models and knowledge graphs: A roadmap","venue":null,"work_id":"af4854e8-ee02-45e0-808f-03a70629414e","year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.258393Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:71ff6247583502b5526f9d8a0825d7b29d66034f6a14beb598c27faf02e0fc24","observation_id":"fe7a1125-1416-423b-ba73-b89292065dbd","resolution":{"observed_at":"2026-08-07T12:24:20.828056Z","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":"2408.08921","last_updated":"2024-09-10T15:38:56Z","snapshot_observed_at":"2026-07-06T19:01:41.274385Z","submitted_at":"2024-08-15T12:20:24Z","title":"Graph Retrieval-Augmented Generation: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.08921","snapshot_observed_at":"2026-08-07T12:24:17.285998Z","title":"Graph retrieval-augmented generation: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.285998Z"},"links":{"cited_paper":"/paper/2408.08921","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:9e2dd290dbbd008f401f3f4bc683dec6150fb2ce9673922583a1c1cf7804f2d9","observation_id":"b57a5a8f-ebe4-41f8-97c3-5a8b9727a356","resolution":{"observed_at":"2026-08-07T12:24:17.285998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.01066","last_updated":"2019-09-04T09:33:20Z","snapshot_observed_at":"2026-07-06T08:18:37.267833Z","submitted_at":"2019-09-03T11:11:08Z","title":"Language Models as Knowledge Bases?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.01066","snapshot_observed_at":"2026-08-07T12:24:17.337158Z","title":"Language models as knowledge bases?","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.337158Z"},"links":{"cited_paper":"/paper/1909.01066","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:0805acb9fa665f5c39a66d4133379631d4f87be3901af8b7a3462d934a5798c8","observation_id":"d53a9a2a-4f87-45e8-b897-943741a11f4c","resolution":{"observed_at":"2026-08-07T12:24:17.337158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08910","last_updated":"2020-10-05T21:26:45Z","snapshot_observed_at":"2026-08-03T03:30:56.636820Z","submitted_at":"2020-02-10T18:55:58Z","title":"How Much Knowledge Can You Pack Into the Parameters of a Language Model?","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08910","snapshot_observed_at":"2026-08-07T12:24:17.428871Z","title":"How much knowledge can you pack into the parameters of a language model?","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.428871Z"},"links":{"cited_paper":"/paper/2002.08910","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:c408d9e87615d8b27150398e2e65d0c344c2fc81b915bbfb80b120fe914998fc","observation_id":"17ffbb01-9f3a-43d5-be5d-dc0c5a91fd26","resolution":{"observed_at":"2026-08-07T12:24:17.428871Z","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":"2026-08-07T12:24:20.738846Z","title":"Toward expert-level medical question answering with large language models","venue":null,"work_id":"dff300c9-fec7-45ee-ad59-3d01d6b55fc1","year":2025},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.492765Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:1a06fc903b8f58388cc05196a4112724e726d400c352ea142c8094037ba61dc7","observation_id":"f6918fe1-2120-4680-af76-75ee7be95807","resolution":{"observed_at":"2026-08-07T12:24:20.775177Z","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":"1803.06643","last_updated":"2018-03-18T11:28:12Z","snapshot_observed_at":"2026-07-06T06:28:52.591595Z","submitted_at":"2018-03-18T11:28:12Z","title":"The Web as a Knowledge-base for Answering Complex Questions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.06643","snapshot_observed_at":"2026-08-07T12:24:17.571739Z","title":"The web as a knowledge-base for an- swering complex questions","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.571739Z"},"links":{"cited_paper":"/paper/1803.06643","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:50d679d7588401bbd9c36fae192ed17a1fa0545c5adcf09124831668e1acb67b","observation_id":"b75b0161-a6a3-493b-a1f5-dc42df55badf","resolution":{"observed_at":"2026-08-07T12:24:17.571739Z","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":"2026-08-07T12:24:20.671504Z","title":"From Lou- vain to Leiden: guaranteeing well-connected communities","venue":null,"work_id":"44e6700d-ce98-4c67-b125-cd08c8edc765","year":2019},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.649318Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:765b9249ef7cad54007a5ea26ca2e2a626ffbc1a037ad4130c3a916b51ba458f","observation_id":"d60689bb-0d9b-49cb-a55a-bbfb3403cc81","resolution":{"observed_at":"2026-08-07T12:24:20.697947Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:24:20.604792Z","title":"MuSiQue: Multihop Questions via Single-hop Ques- tion Composition","venue":null,"work_id":"0e3f72c3-f62a-42a6-86e5-6881480d963a","year":2022},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.714193Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:d255f36a931a4d09a76dbf411cff767ca9cb1d306242b592611b158eae8e2eb3","observation_id":"22b16d43-7936-4bd3-885e-1df58a8bd070","resolution":{"observed_at":"2026-08-07T12:24:20.629069Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:24:20.537238Z","title":"Attention is all you need","venue":null,"work_id":"bf703426-eb82-4c2f-96e4-fa97c858fc5b","year":2017},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.797974Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:6ef48aaf2f83acf10361456e90200e25ad12e9f2a33ba76505bf09ad23aedf54","observation_id":"949562c3-cef3-4cc3-aa9b-1e29b9573dc3","resolution":{"observed_at":"2026-08-07T12:24:20.567554Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:24:20.475543Z","title":"Cost-effective hyperparameter optimization for large language model generation infer- ence","venue":null,"work_id":"7ceb6e6e-f4c3-4b7f-a45f-361ff4b8107a","year":2023},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:17.896702Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:fd3925370b21ec3066f236bb7cc7c493ca4b03b9037032553319e80b00e153d8","observation_id":"46123b5f-ed33-4cb4-9ce2-a0fdbb7876d3","resolution":{"observed_at":"2026-08-07T12:24:20.499373Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:24:20.400351Z","title":"Knowledge graph prompting for multi-document question answering","venue":null,"work_id":"21b19a21-6566-4b72-ae28-9a70b8606c37","year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:18.005319Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:43c281a1802fb90d0f35a640132ec9ccba0f8c939f274960a4fdb6d5617390d0","observation_id":"6935cda2-fb46-4dd2-ba85-21845a939417","resolution":{"observed_at":"2026-08-07T12:24:20.440262Z","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":"2303.17564","last_updated":"2023-12-21T06:21:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-03-30T17:30:36Z","title":"BloombergGPT: A Large Language Model for Finance","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.17564","snapshot_observed_at":"2026-08-07T12:24:18.105079Z","title":"Bloomberggpt: A large language model for finance","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:18.105079Z"},"links":{"cited_paper":"/paper/2303.17564","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:4f774d026a7f0e241ed5d4a18c6f7b4b2fa5227fd9fdf6228a71a70c440a4cbb","observation_id":"d72ae51a-1b6f-41ba-a68c-72cf4ba3c543","resolution":{"observed_at":"2026-08-07T12:24:18.105079Z","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":"2026-08-07T12:24:20.279228Z","title":"Corrective retrieval augmented generation","venue":null,"work_id":"501a5792-47fa-4331-b84d-eee07f8faca1","year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:18.196309Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:cc95b555816b3278618ab0789b7d025e5a249d19b3c307ffad2f2a5b3a66d7b6","observation_id":"7c4c4ca7-5098-4e5a-8126-77db7d6b5885","resolution":{"observed_at":"2026-08-07T12:24:20.375628Z","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":"1809.09600","last_updated":"2018-09-25T17:28:20Z","snapshot_observed_at":"2026-07-31T11:19:06.421362Z","submitted_at":"2018-09-25T17:28:20Z","title":"HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.09600","snapshot_observed_at":"2026-08-07T12:24:18.261666Z","title":"HotpotQA: A dataset for diverse, explainable multi- hop question answering","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:18.261666Z"},"links":{"cited_paper":"/paper/1809.09600","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:b95884390952286c6467bfe2022b2b6bc277c7833627297b946c419ade364e59","observation_id":"d1df7672-1a54-4408-80bc-b4fb07a2f919","resolution":{"observed_at":"2026-08-07T12:24:18.261666Z","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":"2026-08-07T12:24:20.078023Z","title":"The value of semantic parse labeling for knowledge base question answering","venue":null,"work_id":"dc00463e-badb-4dc9-bba1-ec87d1b687a2","year":2016},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:18.376728Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:f69f068bb14ba9ac6fd7cf367393963faf25ccd27e1c3849259a80cd75dce2b0","observation_id":"2af00f6f-54a5-4490-a1f3-ddcc4df41b99","resolution":{"observed_at":"2026-08-07T12:24:20.180382Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:24:19.855062Z","title":"Neural, symbolic and neural-symbolic reasoning on knowledge graphs","venue":null,"work_id":"c2c0d449-8ce5-4b6a-8eea-5c119b2aebd5","year":2021},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:18.469088Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:2d766f536695b912ae31104480b72892c2a40da2f4ebc54358db492d4a71570f","observation_id":"11d6f940-e636-4097-a90e-87c56c88b3c3","resolution":{"observed_at":"2026-08-07T12:24:19.944000Z","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":"2202.13296","last_updated":"2022-07-27T15:23:37Z","snapshot_observed_at":"2026-07-06T12:42:05.358877Z","submitted_at":"2022-02-27T05:25:20Z","title":"Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.13296","snapshot_observed_at":"2026-08-07T12:24:18.586254Z","title":"Subgraph retrieval enhanced model for multi-hop knowl- edge base question answering","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:18.586254Z"},"links":{"cited_paper":"/paper/2202.13296","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:64ed6332e9a4b943f493d620da055cc01f5d1d006cc219feda5555b6c460b828","observation_id":"4e82ef37-86fd-45c0-a6e3-a5e100cda4e7","resolution":{"observed_at":"2026-08-07T12:24:18.586254Z","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":"2026-08-07T12:24:19.685384Z","title":"Variational reasoning for question answering with knowledge graph","venue":null,"work_id":"86e0c0d7-3d04-4c23-bdcc-8b4da06f707b","year":2018},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:18.703372Z"},"links":{"citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:738bd3ff28b40bc068ca88609162bb395228348b40fcccfa65d247b65062065f","observation_id":"47e9bedb-7279-4a58-8b51-d5879210ac96","resolution":{"observed_at":"2026-08-07T12:24:19.773386Z","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":"2402.19473","last_updated":"2024-06-21T08:26:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-29T18:59:01Z","title":"Retrieval-Augmented Generation for AI-Generated Content: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19473","snapshot_observed_at":"2026-08-07T12:24:18.802896Z","title":"Retrieval-augmented generation for ai-generated content: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:18.802896Z"},"links":{"cited_paper":"/paper/2402.19473","citing_paper":"/paper/2505.24478"},"observation_digest":"sha256:f7783cd375fbaa696d46f14b114ad3ae03b768c5aac6c34b1355152a08623efb","observation_id":"927811b4-bd0b-4a9b-a46d-5cd5d03ea687","resolution":{"observed_at":"2026-08-07T12:24:18.802896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.24478","last_updated":"2025-05-30T11:27:59Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T20:20:57.223527Z","submitted_at":"2025-05-30T11:27:59Z","title":"Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":0,"verified_fuzzy":19},"total_outbound_references":42},"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 42 of 42 outbound references and 9 inbound Pith citation observations for arXiv:2505.24478."}