{"as_of":"2026-08-10T01:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1254d2608aee14066cd3a20c9099ff35c979d5c5f4bd4b3bc8c65d59753bec8a","coverage":[{"denominator":60,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":60,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-13T19:45:45.674967Z","state":"measured"},{"denominator":64,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":64,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-12T22:19:48.698285Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-06-29T12:23:24.247119Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.23234","snapshot_observed_at":"2026-07-12T22:19:48.698285Z","title":"same track, different prediction for different listeners","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2604.10813","last_updated":"2026-05-24T19:27:06Z","snapshot_observed_at":"2026-07-12T22:19:43.109473Z","submitted_at":"2026-04-12T20:47:58Z","title":"System Identification of Lithium-Ion Battery Equivalent Circuit Models Using Ensemble Kalman Inversion","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-12T22:19:48.698285Z"},"links":{"cited_paper":"/paper/2603.23234","citing_paper":"/paper/2604.10813"},"observation_digest":"sha256:08daade686e291cf497cd67d3f76abb35f693043c10527dc73aa1a1242a8b24a","observation_id":"9e08c8a6-31d4-4268-91a2-633be7bc6341","resolution":{"observed_at":"2026-07-12T22:19:48.698285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"cited_work":{"arxiv_id":"2603.23234","doi":null,"metadata_source":"pith","pith_arxiv_id":"2603.23234","snapshot_observed_at":"2026-06-29T12:23:24.247119Z","title":"Memcollab: Cross-agent memory collaboration via contrastive trajectory distillation.arXiv preprint arXiv:2603.23234","venue":"cs.AI","work_id":"e56d7930-b93c-4208-90d9-2cd0ba43c93b","year":2026},"citing_paper":{"arxiv_id":"2604.10815","last_updated":"2026-04-14T02:07:29Z","snapshot_observed_at":"2026-07-06T22:59:23.126963Z","submitted_at":"2026-04-12T20:56:36Z","title":"MeloTune: On-Device Arousal Learning and Peer-to-Peer Mood Coupling for Proactive Music Curation","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-10T15:02:36.245968Z"},"links":{"cited_paper":"/paper/2603.23234","citing_paper":"/paper/2604.10815"},"observation_digest":"sha256:dc9b2ccc53bac60cadcc0e3ebae5bbdf14eec32e04e247f56e8a474f8d59caa3","observation_id":"140eabc1-fc91-407a-9295-fee7862f50a5","resolution":{"observed_at":"2026-05-29T02:05:04.056600Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"cited_work":{"arxiv_id":"2603.23234","doi":null,"metadata_source":"pith","pith_arxiv_id":"2603.23234","snapshot_observed_at":"2026-06-29T12:23:24.247119Z","title":"Memcollab: Cross-agent memory collaboration via contrastive trajectory distillation.arXiv preprint arXiv:2603.23234","venue":"cs.AI","work_id":"e56d7930-b93c-4208-90d9-2cd0ba43c93b","year":2026},"citing_paper":{"arxiv_id":"2605.08887","last_updated":"2026-05-09T11:12:54Z","snapshot_observed_at":"2026-07-30T17:27:33.373451Z","submitted_at":"2026-05-09T11:12:54Z","title":"Ace-Skill: Bootstrapping Multimodal Agents with Prioritized and Clustered Evolution","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-12T01:26:16.300353Z"},"links":{"cited_paper":"/paper/2603.23234","citing_paper":"/paper/2605.08887"},"observation_digest":"sha256:9b51d48e229c39095288de0d58ccdd6f3ac5177ecccb709fe4b5b5e246833c7e","observation_id":"c387b817-be1a-41e6-90e7-bef9efcf5cb5","resolution":{"observed_at":"2026-05-29T02:05:04.056600Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"cited_work":{"arxiv_id":"2603.23234","doi":null,"metadata_source":"pith","pith_arxiv_id":"2603.23234","snapshot_observed_at":"2026-06-29T12:23:24.247119Z","title":"Memcollab: Cross-agent memory collaboration via contrastive trajectory distillation.arXiv preprint arXiv:2603.23234","venue":"cs.AI","work_id":"e56d7930-b93c-4208-90d9-2cd0ba43c93b","year":2026},"citing_paper":{"arxiv_id":"2605.28224","last_updated":"2026-05-27T09:39:19Z","snapshot_observed_at":"2026-08-06T18:39:44.684179Z","submitted_at":"2026-05-27T09:39:19Z","title":"When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents?","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T12:21:02.155471Z"},"links":{"cited_paper":"/paper/2603.23234","citing_paper":"/paper/2605.28224"},"observation_digest":"sha256:a0befc36254826248302d6b7958b18babd39085a1d389eb95032e2e02f4e1aad","observation_id":"ce5d4d22-76f4-41e9-bb08-624d12b663e4","resolution":{"observed_at":"2026-06-29T12:23:24.248323Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2603.23234/citation-record","integrity":"/paper/2603.23234/integrity","json":"/paper/2603.23234/citation-record.json","paper":"/paper/2603.23234"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Effective context engineering for ai agents, September 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:8658c8a1e46fa077b6a5b736824575231b98c7d6e52d4caa8d3811c91664f3c9","observation_id":"2f2bbfb3-de2e-4f5d-a087-e4b32f99e44d","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07732","last_updated":"2021-08-16T03:57:30Z","snapshot_observed_at":"2026-08-02T19:23:53.535075Z","submitted_at":"2021-08-16T03:57:30Z","title":"Program Synthesis with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07732","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Program synthesis with large language models.arXiv preprint arXiv:2108.07732, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2108.07732","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:0e94851d495f8f8bed7f7600955b8708b156bd9d60e4f04f2d36318b38e79a15","observation_id":"ac7e0abe-07f3-46c6-b4e7-4a75a8b8a287","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Evaluating large language models trained on code.arXiv preprint arXiv:2107.03374, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:38ed4a0ef954ffa4f87d122bf490fa887667fca3410d90491114bc419356eb80","observation_id":"748a5ef3-35d2-43c0-8b0b-371007ecb754","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Dense x retrieval: What retrieval granularity should we use? In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 15159–15177, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:3561824673df0b29faff3be143638111657f42735ec23bf666351a7023295f05","observation_id":"275eed90-37e1-4a97-8bb0-41309878f894","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Lift yourself up: Retrieval-augmented text generation with self-memory.Advances in Neural Information Processing Systems, 36:43780–43799, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:cb5bae188517dacf3971fc3c84021d4146fd487639a7f4355e9f731a1725b8f2","observation_id":"69a788c3-8098-4f61-bbf0-d2523e41df3c","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-07T01:45:38.840969Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:fbc98b9ba5b18f3d47ce8a53c49d76d925aba92111df19b221e1494865f418a2","observation_id":"21952fc4-5b64-402b-be2f-d384af218b4c","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"From local to global: A graph rag approach to query-focused summarization.arXiv preprint arXiv:2404.16130, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2404.16130","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:3341e14e3e2b0e3e1118cc63432d6685c9679f9ea44a94f93023bba9b695b137","observation_id":"750d2c3b-07c9-465f-ab71-56911a864275","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Retrieval-augmented generation for large language models: A survey.arXiv preprint arXiv:2312.10997, 2(1), 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:f0086b609b5cf041c9444b5b62c300a5eff8c9f89c0687aadb23729703706ca8","observation_id":"d74b2453-f1c6-42a1-89db-251c581c7d5f","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Iseeq: Information seeking question generation using dynamic meta-information retrieval and knowledge graphs","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:46e3786bdd34b06a11f304d7c0534fab4fca91c59975ac793949e704b95861b3","observation_id":"4a80a35c-1f31-4b5b-be83-7994c4809c70","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:06e43f16884a696eb2c1a08811615ea5f9ac3d32b9a87522555a4486206ada48","observation_id":"78c428c7-ec9f-4959-9a13-17506cbfdfaf","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"G-retriever: Retrieval-augmented generation for textual graph understanding and question answering.Advances in Neural Information Processing Systems, 37:132876–132907, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:145b2bcf9cf9db7eb1a7c7b8d20c12312f431795e8d4d7451ef754bd49ecb181","observation_id":"76ed8576-585b-4e95-b478-0ea6499e3a45","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.03874","last_updated":"2021-11-08T21:30:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-03-05T18:59:39Z","title":"Measuring Mathematical Problem Solving With the MATH Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.03874","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Measuring mathematical problem solving with the math dataset","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2103.03874","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:db05cb393f9d3cf391e1406d8e8f53aa7386ac1f910900e6c747f262fcb26ee8","observation_id":"82467a78-64fb-4d4f-b246-89603e8f3e77","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.00615","last_updated":"2026-06-01T07:41:52Z","snapshot_observed_at":"2026-08-08T16:59:13.993229Z","submitted_at":"2025-10-01T07:43:49Z","title":"ACON: Optimizing Context Compression for Long-horizon LLM Agents","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.00615","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Acon: Optimizing context compression for long-horizon llm agents.arXiv preprint arXiv:2510.00615, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2510.00615","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:89b98a34123b52548a8321edb1c49113a58bbe257f13494e7dc9b4f9e3c09c72","observation_id":"4e77af6d-75b7-4f33-ab83-d9d2c3e84e83","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Distilling llm agent into small models with retrieval and code tools.arXiv preprint arXiv:2505.17612, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:bfa6f5d43d1cbb7167d7178778ea2ac925709c88d9eb3c2b7a2fbbd90b62cfb1","observation_id":"77ce644a-1c9e-4a13-b49d-e7d254f4e75b","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.06120","last_updated":"2025-05-09T15:21:44Z","snapshot_observed_at":"2026-08-08T19:35:51.229758Z","submitted_at":"2025-05-09T15:21:44Z","title":"LLMs Get Lost In Multi-Turn Conversation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.06120","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Llms get lost in multi-turn conversation.arXiv preprint arXiv:2505.06120, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2505.06120","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:6c3cefbf6e623bda0caef33353b887794cdfafbb5e42fe1b78df23bdea0b7d36","observation_id":"4b567e72-e99d-411c-bd05-a97a06a68542","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in neural information processing systems, 33:9459–9474, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:9af400b37e24823e391414ffd2d40d1670845ec0b9d9f45c9a4a2be929286535","observation_id":"79dacf38-48e8-489e-b224-2821e8d9fa78","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.06595","last_updated":"2023-12-02T17:00:27Z","snapshot_observed_at":"2026-08-06T07:14:23.951745Z","submitted_at":"2023-11-11T15:40:21Z","title":"From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICL","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.06595","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"From classification to generation: Insights into crosslingual retrieval augmented icl.arXiv preprint arXiv:2311.06595, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2311.06595","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:179d7f6263384530509179abf53d5a9cae02bcaabea328d9e62bfa331de069d5","observation_id":"c5199600-cd59-4f1f-a984-9669ff68f926","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Let’s verify step by step","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:d4796c3cd3e3a385acc6625a96d2b60143ed70292162b954f568ed05e97ccbbb","observation_id":"f1ad410b-9438-490f-8286-5d0ae979aeff","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.02893","last_updated":"2018-03-07T22:02:10Z","snapshot_observed_at":"2026-07-06T06:27:10.408557Z","submitted_at":"2018-03-07T22:02:10Z","title":"An efficient framework for learning sentence representations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.02893","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"An efficient framework for learning sentence repre- sentations.arXiv preprint arXiv:1803.02893, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/1803.02893","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:c69cca8f491467e1b6e5a77c6d64ced15323d4583904c0fcfb0da1cf1441a752","observation_id":"bb51c875-0e88-4fab-9232-287f8dfe4060","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.04757","last_updated":"2023-05-18T08:14:08Z","snapshot_observed_at":"2026-07-06T15:24:34.433150Z","submitted_at":"2023-05-08T15:05:16Z","title":"Augmented Large Language Models with Parametric Knowledge Guiding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.04757","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Augmented large language models with parametric knowledge guiding.arXiv preprint arXiv:2305.04757, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2305.04757","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:8dc3f05c0b26422892fe48869f5a60ad8b2d8a4d05715dae839261075953eb2f","observation_id":"5a8c7731-3a42-4cdd-aade-f38f652fd263","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Enhancing intelligent agents with episodic memory","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:80f0cb7625ad6a28fd46b224e1cd993f95a06f2bb419d18ffdb101152afe122b","observation_id":"66b977a4-7bfa-45f7-82c2-d1da47f33722","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Representation learning with contrastive predictive coding.arXiv preprint arXiv:1807.03748, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:4a3f603da2a777b44c5e165c3ea5dc75c455d1af3cfa6ccb224c50b49ce94ed1","observation_id":"14daccfd-bb7b-475a-99fc-3a953f960a20","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.25140","last_updated":"2026-03-16T20:49:28Z","snapshot_observed_at":"2026-08-02T12:08:17.149184Z","submitted_at":"2025-09-29T17:51:03Z","title":"ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.25140","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Reasoningbank: Scaling agent self-evolving with reasoning memory.arXiv preprint arXiv:2509.25140, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2509.25140","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:b08fc5d75ad527d191ef2e2822a8b555d090b6b6741880d4e34756218b5ce30d","observation_id":"04420cf3-35d2-45a4-97b8-8b0457d8655c","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Memgpt: Towards llms as operating systems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:83b4a0ff9f122cf18306c392cb43c10743dad4a0caab6189d4ac38a4c61b3e34","observation_id":"fc886ef0-1db2-4142-a261-8ee8e7ea1117","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06975","last_updated":"2025-02-10T19:14:51Z","snapshot_observed_at":"2026-08-08T23:51:33.892065Z","submitted_at":"2025-02-10T19:14:51Z","title":"Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.06975","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Position: Episodic memory is the missing piece for long-term llm agents.arXiv preprint arXiv:2502.06975, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2502.06975","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:faa7cb8ffc9bf5353968531db41c555097bf8b01e3563e0de8c6fa3d6fb5f44f","observation_id":"7d88d616-6a8a-458d-b465-ed5fbf2efb3f","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"A theoretical analysis of contrastive unsupervised representation learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:f5aec6ed6e62deda30d064914184d78909956eac1cebf2331afa41c2dc360a4b","observation_id":"3febb551-2575-44d6-9c4f-56bf294fdd9f","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Large language models can be easily distracted by irrelevant context","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:9ef8ec5f2f1c54584a494bc145026552b8fde493139a5f7adc947ce8e55e258a","observation_id":"9174d805-5789-424e-a32b-bce3cb741422","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.07952","last_updated":"2025-04-10T17:57:33Z","snapshot_observed_at":"2026-08-07T16:06:45.454822Z","submitted_at":"2025-04-10T17:57:33Z","title":"Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.07952","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Dynamic cheatsheet: Test-time learning with adaptive memory.arXiv preprint arXiv:2504.07952, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2504.07952","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:7ac8f35d6711363d971a63d0df1c54fb363cbce4e6d44e7e2f7b0d0f5b6834f8","observation_id":"48ed72c4-fe32-4e48-902b-fb6809cbff8c","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10671","last_updated":"2024-09-10T13:25:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-15T12:35:42Z","title":"Qwen2 Technical Report","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10671","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Qwen2 technical report.arXiv preprint arXiv:2407.10671, 2(3), 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2407.10671","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:4cce135765c5d6e3a4bae05da16d55ff951faacd987fe36e8ec7d02d983b70f6","observation_id":"75afdc77-c20f-4d55-abac-5c9f6c091863","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Appworld: A controllable world of apps and people for benchmarking interactive coding agents","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:03ccf3843e47b42e4be05d6796c10005a81827e0e57c641e7b2d3746e6ec4f94","observation_id":"10b247c5-11a6-496e-9a05-b15000762200","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Learning to retrieve in-context examples for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:fd8e2e61a65afcd8c2f39bbee958c42892900d0cf35d11d9a060a2773516e43b","observation_id":"688f78a3-b719-4380-afb7-73a75456f0e6","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"A-mem: Agentic memory for llm agents.arXiv preprint arXiv:2502.12110, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2502.12110","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:f6c2aa423d7c89a91fb5420a1d58fe1f82489be8187f3f27b2552f03887b59da","observation_id":"b541e8d0-a1a0-4187-840d-a282deb2bf4d","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Corrective retrieval augmented generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:662619914ad9699625f2b00683755e3e9d5c78e239e4b1c32fe28065bb77f07d","observation_id":"466fc1e4-6e95-47f0-889d-18e02326c2b0","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.19828","last_updated":"2026-01-14T14:21:21Z","snapshot_observed_at":"2026-07-06T22:19:28.487854Z","submitted_at":"2025-08-27T12:26:55Z","title":"Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.19828","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Memory-r1: Enhancing large language model agents to manage and utilize memories via reinforcement learning.arXiv preprint arXiv:2508.19828, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2508.19828","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:0829034df92499cdbb7e99009a6612661cdf98ed9f1a45e91321bed2f9c5a4e9","observation_id":"65ca5fe7-e6f9-465c-8677-eb0d04f7b938","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Buffer of thoughts: Thought-augmented reasoning with large language models.Advances in Neural Information Processing Systems, 37:113519–113544, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:fa89fd054ec4fef274e99194d44362774435f7db391ed4590bfa58db5618e454","observation_id":"02803c80-031d-42ce-94dc-4a831de863ee","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06772","last_updated":"2025-03-11T02:46:19Z","snapshot_observed_at":"2026-08-09T17:57:00.989532Z","submitted_at":"2025-02-10T18:51:47Z","title":"ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.06772","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Reasonflux: Hierarchical llm reasoning via scaling thought templates.arXiv preprint arXiv:2502.06772, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2502.06772","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:917459bab638cc56ed7616d5c2dc3a245391b8e26ab2fb615660757d5c701892","observation_id":"1fe4c723-a5f5-45ce-9755-d51b6a0ec872","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.10063","last_updated":"2023-01-25T18:57:59Z","snapshot_observed_at":"2026-08-09T18:10:53.479735Z","submitted_at":"2022-09-21T01:30:59Z","title":"Generate rather than Retrieve: Large Language Models are Strong Context Generators","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.10063","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Generate rather than retrieve: Large language models are strong context generators.arXiv preprint arXiv:2209.10063, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2209.10063","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:469053fa2dd5f48dab24372f46c7825db3027bafd25e634cd062e1207793129d","observation_id":"f270c882-97cb-4d7c-82d0-a34cfcdf9a3d","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Chain-of-note: Enhancing robustness in retrieval-augmented language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:7f36019ecb44a48be58545c5a99a12785eb6d2a66d251e3c0b0b54d6f1d9e9f8","observation_id":"12770833-bb3e-46cf-8546-3664dea38226","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08674","last_updated":"2023-08-07T12:08:17Z","snapshot_observed_at":"2026-07-06T15:54:58.589775Z","submitted_at":"2023-07-17T17:36:09Z","title":"TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08674","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Tablegpt: Towards unifying tables, nature language and commands into one gpt.arXiv preprint arXiv:2307.08674, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2307.08674","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:ea85bc260afa75a2ebc991ac87b248a13da42dca6535d24fd6c1f3a28c0b2cbc","observation_id":"67472a1a-77e5-435a-944c-bd370f49f0de","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"A survey on the memory mechanism of large language model-based agents","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:586077c356fc66907e3302491392ad71b25d1afc99426e390dd6fa83a004f03e","observation_id":"9d230b5a-6b44-4037-9473-d018ac14e02e","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Memorybank: Enhancing large language models with long-term memory","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:c75baaa46c3eaa67091d829bcf3b6ae55c07aa3a939e66f6aa85d75c235c965c","observation_id":"2619dcee-52dd-47a6-845e-96ff0a0bc814","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15841","last_updated":"2025-07-17T08:53:48Z","snapshot_observed_at":"2026-08-04T10:58:41.179508Z","submitted_at":"2025-06-18T19:44:46Z","title":"MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.15841","snapshot_observed_at":"2026-07-13T19:45:45.674967Z","title":"Mem1: Learning to synergize memory and reasoning for efficient long-horizon agents.arXiv preprint arXiv:2506.15841, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"cited_paper":"/paper/2506.15841","citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:b9c77d5434b9bce7aa9b017fd4809a77530e66a3924fd9dcc885db8d4602c2e8","observation_id":"baad0e77-6921-462e-b2d2-17f564ea1fff","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:7b8a2218c85a4b022bb633860c857f7387c4198127be023ff9f3ce2659858ca1","observation_id":"087d5af1-b20f-42e8-94f4-81655c0a559f","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:a5b4faa6a124c722fc55b3a50ee0d38701c4a0fd3bc64af139f34e98d36372bd","observation_id":"9646226b-a69d-4386-a010-06771ff42950","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"For programming problems, provide base classes or interfaces that can be instantiated to solve specific instances","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:00c506ca0914d401b258348e2eb11aa1bcca90a9a806c3e6ee2681fe6625c24e","observation_id":"a3757f76-ff70-4eb2-bde8-abe605915218","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:937c7c7f5b3056e0e0467d9aab7d1f707af976b47e91f9fdefec5bbd24247e86","observation_id":"9b20e79e-2414-47ea-b26b-3529b0273e20","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:88a2524a480ecd9692f5acf1274eecca640c026b63b9f5091e36d97c8e0dc744","observation_id":"b3592621-a4c0-40a8-ba4b-e0a21fc30ace","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:7b25bbd6e7dbbfee50e5ec81e6c8720698305e28866f558be7d1ab3283eb8175","observation_id":"2cdb6443-5e21-4a33-a1df-1e6166f40640","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:f242cf01f5eb5cc953d8695ee5880539f8b843a84a2769281fec2151af1993f6","observation_id":"1c66b283-498e-46c6-a99b-2b21973c5520","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:32b23db3339418cec0d46e35218eee3598efdd64749757faa99fe5e30199c9e6","observation_id":"873f439d-089e-4bd4-9afe-4fd4069956fe","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Table 17: Prompt template for single-model reasoning extraction","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:1173a2c45d087ee0aaac82fb6272b98c440b6ec2193bb830245b063c5e090695","observation_id":"d40bc341-634a-4a79-9b87-dcfcff7a45c5","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:8360286c0a25769aa84eff51a26149b5f827279b0266988ae535123b3f455a67","observation_id":"0a7bbccd-db06-43cf-bb82-0cc2c7232ba8","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Each extracted strategy must combine: - a trigger (when this strategy should be considered), and - an enforcement rule (what must be enforced or avoided)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:52c565c20ab3baac6f2a500b3d65c408b8e8ada3a61f3226e44f93b04ae7d3e9","observation_id":"276dba8a-b880-400d-b27d-f1c8672ed0a9","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:73268a610ff9ac528f37d40a76a1586752c486d759f11e7046b71415ceeecaaa","observation_id":"41f7be21-d241-4b8c-927d-909041e2de81","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:90c194163badc864ab7c043a8cad921d1d95dbbaf8edada02f39d193aab5a99b","observation_id":"18f42023-14d9-47e3-8833-854811a7bdc2","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"Your output must be exactly one subcategory from the allowed list","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:0d147df227e112e5ad253c2508ca945bd1e6e36be18ea4a8d9370c6abe8f8394","observation_id":"dc367e29-3855-4998-96e2-5b388dd2410f","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"You may NOT output “None”","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:c9922eb2d70f144d7940dad6427c3d889f1e801a5c61ad8725f06834d7e25f04","observation_id":"d32576b1-ce21-433e-ae5a-10997f917d09","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:0e21ff9dea8895bb0d649a89794187ef300618ae1e787c967084774a567cf29b","observation_id":"10e0ebbc-9d95-46c7-84aa-0dfaed8d3d22","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:56d93ad79fccfc8e66416adec1d04e4314e780cd258e379012a803b1b92b06e4","observation_id":"273f6882-6467-461b-adc9-d70e0f1b3a99","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","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":"2026-07-13T19:45:45.674967Z","title":"These rules override all ambiguity","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-07-13T19:45:45.674967Z"},"links":{"citing_paper":"/paper/2603.23234"},"observation_digest":"sha256:7b584c0b9b3db74c67e4ab46cbc59ae5759fd16a34f1678743f1a5ee2e6b5e9d","observation_id":"31d52a82-c866-4689-94ca-c338d3741f3b","resolution":{"observed_at":"2026-07-13T19:45:45.674967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.23234","last_updated":"2026-05-28T08:28:11Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-13T19:45:44.996221Z","submitted_at":"2026-03-24T14:05:47Z","title":"MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation"},"reference_resolution":{"displayed":60,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":58,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":60},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 4 inbound Pith citation observations for arXiv:2603.23234."}