{"as_of":"2026-08-18T10:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6f7d17f0bd2483dfb352c9430bf650ba0fc5158671b8ddcd93b1909eea10569d","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T00:37:59.974546Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2608.04095/citation-record","integrity":"/paper/2608.04095/integrity","json":"/paper/2608.04095/citation-record.json","paper":"/paper/2608.04095"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T00:37:59.838413Z","title":"Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.838413Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:c2c848a3bf61f1a4dde7b02f9f63f6a8ae4a6448bf4f37d7262c34ecd24bcf1b","observation_id":"97ff1583-63a7-4491-835b-1020c8790feb","resolution":{"observed_at":"2026-08-08T00:37:59.838413Z","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":"2026-08-08T00:37:59.843724Z","title":"Classification Problem Solving","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.843724Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:8b8527c8e07e62587a34e4363c353383ea54f64d404e9f1b3c8eb4067639da01","observation_id":"9b88af69-865c-4701-a040-100695004869","resolution":{"observed_at":"2026-08-08T00:37:59.843724Z","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":"2026-08-08T00:37:59.846985Z","title":", title =","venue":null,"work_id":null,"year":1980},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.846985Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:d391a27d4e88a6039ebb12e1d2b122fd12806ac58327565e6705617c2da481db","observation_id":"f3f18c57-06f0-4eb0-96f1-1c60e7ca2c7d","resolution":{"observed_at":"2026-08-08T00:37:59.846985Z","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":"2026-08-08T00:37:59.850185Z","title":"New Ways to Make Microcircuits Smaller---Duplicate Entry","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.850185Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:5f28a297b5e1db235a759bf31c60dc8796e74347ffbd48450cb6191acacdf38a","observation_id":"d84ef02c-2a10-48b7-b0bd-1857daf3ea86","resolution":{"observed_at":"2026-08-08T00:37:59.850185Z","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":"2026-08-08T00:37:59.853139Z","title":"Clancey and Glenn Rennels , abstract =","venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.853139Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:8fd3741cfaa2530fc7cd4beb8d83434d6d5e55a851730c77a78054a4c40b1d1e","observation_id":"dcbf3cdb-e0cd-43f4-b526-1ec099bb3ee4","resolution":{"observed_at":"2026-08-08T00:37:59.853139Z","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":"2026-08-08T00:37:59.856415Z","title":"and Rennels, Glenn R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.856415Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:162654d687535606ba699d83536a9766d2b6a8f34f42ca79a1b4af1fe673e150","observation_id":"cd95bd7d-42f5-4220-8af8-437d089a2738","resolution":{"observed_at":"2026-08-08T00:37:59.856415Z","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":"2026-08-08T00:37:59.859575Z","title":"Poligon: A System for Parallel Problem Solving","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.859575Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:07bca5813d0101cf9179a971847083418d3d175407fed113c139c93c61e2c41b","observation_id":"73f9ee5f-7441-4f8f-b2d2-afb843069b82","resolution":{"observed_at":"2026-08-08T00:37:59.859575Z","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":"2026-08-08T00:37:59.862856Z","title":"Transfer of Rule-Based Expertise through a Tutorial Dialogue","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.862856Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:cb06f6b1d259b748260856c45424cb3795a800cf936d54d25df5bd78e84263da","observation_id":"ee9f9046-95ed-4090-baa7-7d49f38271c0","resolution":{"observed_at":"2026-08-08T00:37:59.862856Z","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":"2026-08-08T00:37:59.866028Z","title":"The Engineering of Qualitative Models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.866028Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:1b14c775fd28ba84314465e3f277579ccff70e9cb34224355014350b94918069","observation_id":"8ba79a15-f8d5-451c-928b-82670544fbdf","resolution":{"observed_at":"2026-08-08T00:37:59.866028Z","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":"2026-08-08T00:37:59.869872Z","title":"2023 , eprint=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.869872Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:f4eeb0debfe18c710ce961d42b0b42af8036932c68e66009a1d518dc08fcdd58","observation_id":"85da1a32-623a-42ae-868d-5f9f9bb96902","resolution":{"observed_at":"2026-08-08T00:37:59.869872Z","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":"2026-08-08T00:37:59.872716Z","title":"Pluto: The 'Other' Red Planet","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.872716Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:1d9cd1830eac60dcd6c8c2822c5817b75529d423aaa8f8ae3bbac0588066f721","observation_id":"ffa28658-fe0a-42ed-99c0-a1f886abccfe","resolution":{"observed_at":"2026-08-08T00:37:59.872716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-08T00:37:59.875326Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.875326Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:eb72f2ffac72f605f2fab7d8088c2020a268ae30d6270867dbcc78aa13da1e09","observation_id":"61b8d33e-2fb9-4e98-ae89-864c90c8c120","resolution":{"observed_at":"2026-08-08T00:37:59.875326Z","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":"2026-08-08T00:37:59.878758Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.878758Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:16d38ed38a233b81b35df8bf6675205b94b5e8b6fdf7177349ebe9e8672f3e91","observation_id":"9442148b-2433-4b2e-a2f0-872047b303ed","resolution":{"observed_at":"2026-08-08T00:37:59.878758Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T00:38:00.802354Z","title":"and Cai, Carrie J","venue":null,"work_id":"74046978-a96b-47e7-bbbd-4916951370bb","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.881390Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:e3387ea4cbe49085b03a82a69a821a77b807702ee3989d1dcaf5f42d402d18f2","observation_id":"b80c2ae7-9fde-426a-920e-b23bc1c1c96b","resolution":{"observed_at":"2026-08-08T00:38:00.806871Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.11401","last_updated":"2021-04-12T15:42:18Z","snapshot_observed_at":"2026-08-07T05:44:30.677502Z","submitted_at":"2020-05-22T21:34:34Z","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.11401","snapshot_observed_at":"2026-08-08T00:37:59.884104Z","title":"Retrieval-Augmented Generation for Knowledge-Intensive","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.884104Z"},"links":{"cited_paper":"/paper/2005.11401","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:c0499198f93219ca782a5bda1186716ab02e51425471e1555ccedd367f5f6b04","observation_id":"c9b7efb7-495a-4cac-8081-1bf46d31148e","resolution":{"observed_at":"2026-08-08T00:37:59.884104Z","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":"2026-08-08T00:37:59.887205Z","title":"and Zhang, Hao and Gonzalez, Joseph E","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.887205Z"},"links":{"cited_paper":"/paper/2306.05685","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:d19d1d8a197e9efea1bad75ff5b57469c0a7b42f13a32c449d38c47eafb25ba4","observation_id":"006008b7-a3b2-4bc5-9691-1a5466d5c130","resolution":{"observed_at":"2026-08-08T00:37:59.887205Z","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":"2026-08-08T00:37:59.890715Z","title":", title =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.890715Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:f962586d3a97bc9a202a09f8b43b03e1c66fa581362eab88e58d4b7b891449d5","observation_id":"7cf1b921-64b9-43c1-94f9-d9870853d7ef","resolution":{"observed_at":"2026-08-08T00:37:59.890715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17753","last_updated":"2024-02-27T18:42:31Z","snapshot_observed_at":"2026-08-17T18:26:41.317652Z","submitted_at":"2024-02-27T18:42:31Z","title":"Evaluating Very Long-Term Conversational Memory of LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17753","snapshot_observed_at":"2026-08-08T00:37:59.893626Z","title":"Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) , year =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.893626Z"},"links":{"cited_paper":"/paper/2402.17753","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:511b521e54c7697b3650152686b0161392fc44dcff779cccfd97a37dc926e9d3","observation_id":"444623f4-d8cd-499c-b50b-8a2c754d9086","resolution":{"observed_at":"2026-08-08T00:37:59.893626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10813","last_updated":"2025-03-04T22:19:41Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-14T17:59:44Z","title":"LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10813","snapshot_observed_at":"2026-08-08T00:37:59.896710Z","title":"The Thirteenth International Conference on Learning Representations (ICLR 2025) , year =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.896710Z"},"links":{"cited_paper":"/paper/2410.10813","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:b4f2285e85a1dd74f59321589780a41ff2156a1e33bad512c2363e465f7e86cb","observation_id":"b0820898-1ca1-49b5-8664-cfb9e9a79377","resolution":{"observed_at":"2026-08-08T00:37:59.896710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.07243","last_updated":"2018-09-25T18:55:07Z","snapshot_observed_at":"2026-08-14T19:53:03.553178Z","submitted_at":"2018-01-22T18:58:18Z","title":"Personalizing Dialogue Agents: I have a dog, do you have pets too?","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.07243","snapshot_observed_at":"2026-08-08T00:37:59.899899Z","title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018) , year =","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.899899Z"},"links":{"cited_paper":"/paper/1801.07243","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:4aea298f24629bcc23e0ddedf43d50f5b5aec455468264636175be0070fd07ad","observation_id":"d680c403-dbaf-4710-af6a-e0ea2efc51f0","resolution":{"observed_at":"2026-08-08T00:37:59.899899Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1502.05698","last_updated":"2015-12-31T13:08:14Z","snapshot_observed_at":"2026-08-14T22:59:21.551079Z","submitted_at":"2015-02-19T20:46:10Z","title":"Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks","version":10},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.05698","snapshot_observed_at":"2026-08-08T00:37:59.902888Z","title":"and van Merri","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.902888Z"},"links":{"cited_paper":"/paper/1502.05698","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:cddcee13b882705c351c5b616c8395d5c38213afa8b136b2dc862105fbbb9a15","observation_id":"14639bbf-1329-480d-8ef0-2eda9b4259e2","resolution":{"observed_at":"2026-08-08T00:37:59.902888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08560","last_updated":"2024-02-12T18:59:46Z","snapshot_observed_at":"2026-08-15T11:39:49.280087Z","submitted_at":"2023-10-12T17:51:32Z","title":"MemGPT: Towards LLMs as Operating Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08560","snapshot_observed_at":"2026-08-08T00:37:59.905857Z","title":"and Stoica, Ion and Gonzalez, Joseph E","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.905857Z"},"links":{"cited_paper":"/paper/2310.08560","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:bf932d68851f98543949f53603ffa57f51f4c8be7dd3b9f058afe2156e98e004","observation_id":"5587e67e-08ca-4ade-a764-d3ee729721b1","resolution":{"observed_at":"2026-08-08T00:37:59.905857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10250","last_updated":"2023-05-21T06:20:28Z","snapshot_observed_at":"2026-08-17T12:28:12.995477Z","submitted_at":"2023-05-17T14:40:29Z","title":"MemoryBank: Enhancing Large Language Models with Long-Term Memory","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10250","snapshot_observed_at":"2026-08-08T00:37:59.909092Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2024) , year =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.909092Z"},"links":{"cited_paper":"/paper/2305.10250","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:df682cb3b106a3482b40b1a37206c62004182b17f6e53f777aa0ee39ea3c73dd","observation_id":"05702d8a-9e37-413c-9e3b-9b81c23b2fea","resolution":{"observed_at":"2026-08-08T00:37:59.909092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.19413","last_updated":"2025-04-28T01:46:35Z","snapshot_observed_at":"2026-08-14T23:23:31.683130Z","submitted_at":"2025-04-28T01:46:35Z","title":"Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.19413","snapshot_observed_at":"2026-08-08T00:37:59.912057Z","title":"arXiv preprint , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.912057Z"},"links":{"cited_paper":"/paper/2504.19413","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:b8f18167517699150973de9a0e1c423a5808a0bb37e9259768196d932d65662f","observation_id":"995184a7-fe03-4fed-a199-1bf14c0dd984","resolution":{"observed_at":"2026-08-08T00:37:59.912057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.03724","last_updated":"2025-12-03T03:19:27Z","snapshot_observed_at":"2026-08-16T06:38:04.806216Z","submitted_at":"2025-07-04T17:21:46Z","title":"MemOS: A Memory OS for AI System","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.03724","snapshot_observed_at":"2026-08-08T00:37:59.914998Z","title":"arXiv preprint , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.914998Z"},"links":{"cited_paper":"/paper/2507.03724","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:5c012755ffe113bbdaa5792aa3cc0ea3a5226f00a9badad1386bb85c30326155","observation_id":"8bffb9db-da89-423b-ad97-e757067458fa","resolution":{"observed_at":"2026-08-08T00:37:59.914998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12110","last_updated":"2025-10-08T01:46:37Z","snapshot_observed_at":"2026-08-03T02:27:06.991396Z","submitted_at":"2025-02-17T18:36:14Z","title":"A-MEM: Agentic Memory for LLM Agents","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.12110","snapshot_observed_at":"2026-08-08T00:37:59.918402Z","title":"Advances in Neural Information Processing Systems 38 (NeurIPS 2025) , year =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.918402Z"},"links":{"cited_paper":"/paper/2502.12110","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:59ef4666bb10ed81ff47b7541d3c13e2a2f9b10a462c8e8c47987ef57879b75f","observation_id":"1fb03448-2c13-4fd7-be0d-a10c463e6957","resolution":{"observed_at":"2026-08-08T00:37:59.918402Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.13743","last_updated":"2023-12-03T16:18:55Z","snapshot_observed_at":"2026-08-16T14:41:01.139935Z","submitted_at":"2023-11-23T00:24:40Z","title":"FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.13743","snapshot_observed_at":"2026-08-08T00:37:59.921665Z","title":"and Khashanah, Khaldoun , title =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.921665Z"},"links":{"cited_paper":"/paper/2311.13743","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:44312b0108e689f508e3161c270b7ddfc8af0f2874ae022712a9777ae2b2e36c","observation_id":"057d7015-a75e-4f64-9d26-0be8bed2763a","resolution":{"observed_at":"2026-08-08T00:37:59.921665Z","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":"2026-08-08T00:37:59.924531Z","title":", title =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.924531Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:b74c830f10b0cf3d4a1e7ef1633592afd579aaaf5b1e5213408e7c3d9c10662e","observation_id":"5ee80260-0124-47cb-8628-3ba01c17aa99","resolution":{"observed_at":"2026-08-08T00:37:59.924531Z","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":"2026-08-08T00:37:59.927225Z","title":"and Busby, Ethan C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.927225Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:452fdafdf0ea0298c3e7665adbee1b22e02c512bda9ad4ec2597dcf4f40507d1","observation_id":"21840af2-415a-42e2-b397-7db1b4c5a9ee","resolution":{"observed_at":"2026-08-08T00:37:59.927225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.16805","last_updated":"2023-08-31T15:25:51Z","snapshot_observed_at":"2026-08-17T10:01:47.401953Z","submitted_at":"2023-08-31T15:25:51Z","title":"Emergent phenomena in living systems: a statistical mechanical perspective","version":1},"cited_work":{"arxiv_id":"2308.16805","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.16805","snapshot_observed_at":"2026-08-08T00:38:00.278676Z","title":"Emergent phenomena in living systems: a statistical mechanical perspective","venue":"cond-mat.stat-mech","work_id":"a7313ae7-473d-4c32-90e9-e77987c442ac","year":2023},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.929956Z"},"links":{"cited_paper":"/paper/2308.16805","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:9709559deb9540232738e6a522cf29df08b783f8055980f7860b05b31241bc32","observation_id":"62a03a22-f1e4-44d1-bbf6-523f055f748a","resolution":{"observed_at":"2026-08-08T00:38:00.284299Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:37:59.932716Z","title":"arXiv preprint , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.932716Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:f2b537dfb30051b9d12b03017754bcf629f95c7ca7562822bd7a5793dd37781a","observation_id":"1cbdc1e8-9af1-4897-9f02-4e567858052c","resolution":{"observed_at":"2026-08-08T00:37:59.932716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12659","last_updated":"2024-06-19T03:38:56Z","snapshot_observed_at":"2026-08-16T14:17:06.781974Z","submitted_at":"2024-02-20T02:16:16Z","title":"FinBen: A Holistic Financial Benchmark for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12659","snapshot_observed_at":"2026-08-08T00:37:59.935357Z","title":"Advances in Neural Information Processing Systems 37, Datasets and Benchmarks Track (NeurIPS 2024) , year =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.935357Z"},"links":{"cited_paper":"/paper/2402.12659","citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:d4638dcdb86b2bbcbc8cc92fd64678ef60c5e22fa5429361efe23f807d011d01","observation_id":"9b92c8c7-e81f-4b70-8da2-1c33eb40d220","resolution":{"observed_at":"2026-08-08T00:37:59.935357Z","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":"2026-08-08T00:38:00.952235Z","title":"Journal of Risk and Uncertainty , year =","venue":null,"work_id":"abbc3313-d40c-413b-a5b5-758884116545","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.938240Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:75a3cec0c4a3cae3fea2bec9b76b66300f5ca10f0ce87723612632ac6b3fda8c","observation_id":"ce7720b4-5c5b-4784-8bb0-42ccee866576","resolution":{"observed_at":"2026-08-08T00:38:00.955433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.943309Z","title":"Quarterly Journal of Economics , year =","venue":null,"work_id":"0ddb6faf-4cb7-4a58-93ed-e33cfe6b8e33","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.941086Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:1863215e4600aa30e8f022315cecb44023af89c740c5d7173e7295508f62d946","observation_id":"a20fee12-cdad-49f6-a436-2acaf17dab39","resolution":{"observed_at":"2026-08-08T00:38:00.946403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.934162Z","title":"Quarterly Journal of Economics , year =","venue":null,"work_id":"b76b92d6-eb1e-445c-9170-2923551a0d4e","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.943676Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:b987133d6cbb1296e8ee6e50b72d37bc00b295150924030fb24cbeb422956a3a","observation_id":"89d888f2-c55c-4823-b97e-c03bcadb2813","resolution":{"observed_at":"2026-08-08T00:38:00.937327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.925019Z","title":"Quarterly Journal of Economics , year =","venue":null,"work_id":"8fd370d0-e0f2-43e5-bf29-2be46ff14c20","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.946329Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:78fb2c24d6d4b53bbc9111f1cc2f8321656aef2f42414c67ae42c396817dde6d","observation_id":"19a35fc8-5c26-472b-872d-bba8e6dbc857","resolution":{"observed_at":"2026-08-08T00:38:00.928187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.916371Z","title":"Journal of Finance , year =","venue":null,"work_id":"fda27154-9fc0-4ee0-9c55-c71de5fac266","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.948907Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:08a26b000c54ef533ae57442e21031f8cbf4fdba3282f576a70d0c7e409eb5d6","observation_id":"5dc4457f-bfaa-4646-8670-a9b04d053f00","resolution":{"observed_at":"2026-08-08T00:38:00.919402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.907774Z","title":"Journal of Financial Economics , year =","venue":null,"work_id":"96c5195a-7722-4961-be3d-c9bacab55e82","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.951365Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:51f146930c9494f3346d7960c44a01843713245cd38d7b0c5c53aac217d69dab","observation_id":"3b6f067e-5ffc-43fb-a881-09c4abbdedfa","resolution":{"observed_at":"2026-08-08T00:38:00.910678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.898730Z","title":"Evidence for Countercyclical Risk Aversion: An Experiment with Financial Professionals , journal =","venue":null,"work_id":"8fde0b20-ed6e-4bba-afe6-1af7c4d4e0a7","year":2015},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.953796Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:9ef4b804ef7cac57a852ef4d51fc7d5fab79ff8802ff22c05165d180fe88fae1","observation_id":"2745ae64-aaf5-42c0-9381-41f9dbde45e6","resolution":{"observed_at":"2026-08-08T00:38:00.901689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.888890Z","title":"Quarterly Journal of Economics , year =","venue":null,"work_id":"5b2c3147-9ace-4cd9-8c3f-5cdebe8d9cb3","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.956330Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:3f2aaf13b50421e193227473664e390fd2b689e606acda9b823b7a5f8fa529f4","observation_id":"fd1bce7c-42ee-4cbd-9695-519d36a504e6","resolution":{"observed_at":"2026-08-08T00:38:00.892514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.879259Z","title":"A Model of Reference-Dependent Preferences , journal =","venue":null,"work_id":"ef86369d-674d-40cb-ab0e-0c52e0f16783","year":2006},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.958756Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:a23a10da878d965715b4c1e526b902fc4921864e090fc99916465e6e2bac433a","observation_id":"4018b4e8-48e7-4e6c-9595-646536f6ff35","resolution":{"observed_at":"2026-08-08T00:38:00.882689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.870016Z","title":"and Cochrane, John H","venue":null,"work_id":"80567715-c8d6-45c7-828f-76fe2d16abff","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.961502Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:4acc40c46cddc1a8178c14a89b1a4ee70e40839af7663254779d033e57960a23","observation_id":"20f53d85-ca89-48c1-928c-5bf6f63d263d","resolution":{"observed_at":"2026-08-08T00:38:00.873114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.860495Z","title":"and Hirshleifer, David and Jiang, Danling and Lim, Sonya S","venue":null,"work_id":"fb4c1eb2-1399-4776-bee2-4904fa15e062","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.964140Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:4cddca066b291747962d039ccb0fa2da98b3b78675465ff189a478f6cd51166f","observation_id":"9cb77dd8-57a3-4b53-91e1-b44508c8b513","resolution":{"observed_at":"2026-08-08T00:38:00.863482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.850763Z","title":", title =","venue":null,"work_id":"12e5da98-0db5-4064-9781-7d13bfc153b1","year":2012},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.966764Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:8ef247ccb69f8774aae771d4921f63102aadb55b2fbd8bfdc15344b0e132d659","observation_id":"1f08cd39-a72f-41dd-b236-3e99e6ff386c","resolution":{"observed_at":"2026-08-08T00:38:00.853793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.841463Z","title":"and Newby, Rick and Sanghani, Jay , title =","venue":null,"work_id":"0fe59e8f-7f3f-4f06-90ed-e830ff5c4baf","year":null},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.969261Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:f7fcc4eacba3290b01d6ccef212022f06a8edc54df4c710bc708d1932032b773","observation_id":"217b4456-2902-401f-b20a-eb3892b9b6e3","resolution":{"observed_at":"2026-08-08T00:38:00.844688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.832375Z","title":null,"venue":null,"work_id":"25cbf685-e06e-4ed8-87cc-722905688292","year":2020},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.971893Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:52da8d0abf43f5ab6a388d171d0c83ab6fcfc91193d2619997ff65345f9021b7","observation_id":"c59df1ba-90ca-457a-9ed9-46c7d10ae1ec","resolution":{"observed_at":"2026-08-08T00:38:00.835332Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-08T00:38:00.822950Z","title":"2021 , month = nov, note =","venue":null,"work_id":"f1924a25-7672-45a4-9a08-48eab52686e1","year":2020},"citing_paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-08T00:37:59.974546Z"},"links":{"citing_paper":"/paper/2608.04095"},"observation_digest":"sha256:ea48d1313113f8a7616581bb52353f1c55f58483f1bc36af27aa825c119f272c","observation_id":"580f66b8-977a-46c0-92c7-020d32736ae0","resolution":{"observed_at":"2026-08-08T00:38:00.825964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.04095","last_updated":"2026-08-04T18:00:04Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-16T09:52:32.451303Z","submitted_at":"2026-08-04T18:00:04Z","title":"FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":2,"verified_fuzzy":14},"total_outbound_references":47},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2608.04095."}