{"as_of":"2026-08-07T22:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d65dd8d411bde1cc4fd2ecd3cec0c69d957757dbdab972815f5384f9ada4c699","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":31,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T19:49:13.437234Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2604.02522","last_updated":"2026-04-02T21:23:00Z","snapshot_observed_at":"2026-08-03T01:43:37.179078Z","submitted_at":"2026-04-02T21:23:00Z","title":"Opal: Private Memory for Personal AI","version":1},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-05-13T21:09:06.320543Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2604.02522"},"observation_digest":"sha256:2e09c474852d537684431f9238f57c0b76e2b30d8fb662af01c0ba64e29b7947","observation_id":"84f1b10a-cd7c-4cac-a130-d3f2263f3dd2","resolution":{"observed_at":"2026-05-13T21:13:16.731707Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2604.11103","last_updated":"2026-04-24T05:22:49Z","snapshot_observed_at":"2026-07-06T22:59:36.571641Z","submitted_at":"2026-04-13T07:20:20Z","title":"ActorMind: Emulating Human Actor Reasoning for Speech Role-Playing","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-10T16:03:15.572657Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2604.11103"},"observation_digest":"sha256:e3ebde28259d506097ddef7df13fe6a4a430cc907fd98488037595c07f8e3c51","observation_id":"044cad6f-cd16-444a-9898-9021e45a0be5","resolution":{"observed_at":"2026-05-11T09:21:02.401329Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2604.11628","last_updated":"2026-04-13T15:38:43Z","snapshot_observed_at":"2026-07-06T22:59:59.220594Z","submitted_at":"2026-04-13T15:38:43Z","title":"Back to Basics: Let Conversational Agents Remember with Just Retrieval and Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T15:27:00.693277Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2604.11628"},"observation_digest":"sha256:c7ad9f2eb5725c91292f03503e404e4f431cbe2c8e7be181f33f53b6bf8b5ea6","observation_id":"223f5dbd-37e1-46c6-997d-fd5bd61f4670","resolution":{"observed_at":"2026-05-11T10:31:03.680717Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2604.13453","last_updated":"2026-04-15T04:05:10Z","snapshot_observed_at":"2026-08-03T17:25:06.954677Z","submitted_at":"2026-04-15T04:05:10Z","title":"FAST: A Synergistic Framework of Attention and State-space Models for Spatiotemporal Traffic Prediction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T14:20:21.472989Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2604.13453"},"observation_digest":"sha256:c2ad9f5a1b6a59557c4b744bb0eb970ef87da744f4ec5cdcdf1889d7693967bd","observation_id":"b96b758b-4b95-45c5-accd-62a8083728cf","resolution":{"observed_at":"2026-05-10T14:20:29.474708Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2604.15065","last_updated":"2026-04-16T14:28:36Z","snapshot_observed_at":"2026-07-06T23:02:40.364114Z","submitted_at":"2026-04-16T14:28:36Z","title":"Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T12:10:53.363675Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2604.15065"},"observation_digest":"sha256:05bac9bdb1a34121ab3832b8e0e22ab7b5a30a99cf27b9354b5718eb565eb3ad","observation_id":"613d1150-5343-45fd-ae29-ff4c9df7e559","resolution":{"observed_at":"2026-05-10T12:15:22.160832Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2604.15951","last_updated":"2026-04-20T11:32:15Z","snapshot_observed_at":"2026-07-06T23:03:21.422544Z","submitted_at":"2026-04-17T11:12:55Z","title":"Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-05-10T09:08:48.468564Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2604.15951"},"observation_digest":"sha256:3f8efcc6bef2fa4a64add81d0841ac7ec28a75101edf658e1acbe68a08b38c0c","observation_id":"ecb6dd21-69f2-4ea5-b030-5851ce784c50","resolution":{"observed_at":"2026-05-10T09:13:30.259064Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2604.20598","last_updated":"2026-04-22T14:13:50Z","snapshot_observed_at":"2026-08-02T12:14:20.500357Z","submitted_at":"2026-04-22T14:13:50Z","title":"Self-Aware Vector Embeddings for Retrieval-Augmented Generation: A Neuroscience-Inspired Framework for Temporal, Confidence-Weighted, and Relational Knowledge","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-09T23:14:27.058228Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2604.20598"},"observation_digest":"sha256:07efa4788ea2e16f1a508e2ea00b46f2790c786816246f764bde6bc5af86de68","observation_id":"4b714e31-d54a-49c3-94ce-6a076306c870","resolution":{"observed_at":"2026-05-11T14:11:05.749045Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2604.21414","last_updated":"2026-04-23T08:27:43Z","snapshot_observed_at":"2026-07-06T23:08:01.670049Z","submitted_at":"2026-04-23T08:27:43Z","title":"SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-09T22:08:11.410285Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2604.21414"},"observation_digest":"sha256:75ac6c3fa3d7d00a2b312455fe7d7ce920802f7fdae65136d964abbd348bac71","observation_id":"40f7f445-da83-49bd-915f-70ed327f73c6","resolution":{"observed_at":"2026-05-11T14:16:17.515045Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2604.22911","last_updated":"2026-04-24T17:52:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-24T17:52:06Z","title":"RecoverFormer: End-to-End Contact-Aware Recovery for Humanoid Robots","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-08T11:22:24.927334Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2604.22911"},"observation_digest":"sha256:6573c20512da94794ca9db64ba339513dbb2e0fa270008424cbc30a20818e3f4","observation_id":"8cc61e87-fe37-4568-bc01-42486b79c42f","resolution":{"observed_at":"2026-05-11T19:36:15.530339Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.01970","last_updated":"2026-05-15T06:42:15Z","snapshot_observed_at":"2026-07-06T23:15:07.159340Z","submitted_at":"2026-05-03T17:07:20Z","title":"Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-09T17:13:47.722098Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.01970"},"observation_digest":"sha256:c26efb52a334948af3e3d4b7e9dac707f71ae9e180e465d883edbecc6a8e27a3","observation_id":"3fee0e66-622c-4b63-bf91-2c287233c1dc","resolution":{"observed_at":"2026-05-11T16:21:10.204586Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.01970","last_updated":"2026-05-15T06:42:15Z","snapshot_observed_at":"2026-07-06T23:15:07.159340Z","submitted_at":"2026-05-03T17:07:20Z","title":"Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-19T17:30:22.481943Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.01970"},"observation_digest":"sha256:ebba15424279f08f17bd74874bb2a658c41f4dc7b02a0978e1b4e797eb6c0207","observation_id":"b1731610-2550-45ab-9b19-f45b6cfa7ba1","resolution":{"observed_at":"2026-05-19T17:32:41.640574Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.03354","last_updated":"2026-05-07T05:56:54Z","snapshot_observed_at":"2026-07-06T23:16:15.509235Z","submitted_at":"2026-05-05T04:17:22Z","title":"What Happens Inside Agent Memory? Circuit Analysis from Emergence to Diagnosis","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-08T18:33:43.998202Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.03354"},"observation_digest":"sha256:cd8a1c75d14108ddefed67321bf0eff938678ebb5c54f33136e0b34a134e9d1c","observation_id":"edbfada4-f6da-4cfd-b4b1-606e99954193","resolution":{"observed_at":"2026-05-09T06:20:42.091859Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.09253","last_updated":"2026-07-01T02:40:56Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-10T01:41:43Z","title":"Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-30T22:52:01.824412Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.09253"},"observation_digest":"sha256:6fbf20060e63cd0711d7a892f5dad4037396bc3b8fb659390788e9bb34e4c41d","observation_id":"3f1c001e-33e7-476e-9e0d-4fb39b2dc729","resolution":{"observed_at":"2026-07-01T13:45:45.669708Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.09253","last_updated":"2026-07-01T02:40:56Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-10T01:41:43Z","title":"Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-02T23:34:26.508233Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.09253"},"observation_digest":"sha256:81864ebb8152513ea0b03650d7a66352e4780bc9c94475418d8b5dc35419da08","observation_id":"119ffa1f-9de9-4f89-98c6-2d004bd7746e","resolution":{"observed_at":"2026-07-02T23:37:27.003834Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.09942","last_updated":"2026-05-11T03:41:18Z","snapshot_observed_at":"2026-07-06T23:21:59.096464Z","submitted_at":"2026-05-11T03:41:18Z","title":"HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-05-12T04:14:06.843343Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.09942"},"observation_digest":"sha256:c3752f162859b9dccae36a6a9dca7e619f0a9bf2718ee667726bffe0f5657cd8","observation_id":"3a7b1f97-5907-4a57-b15e-e0f6f4d0aea3","resolution":{"observed_at":"2026-05-12T06:31:25.079760Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.12213","last_updated":"2026-06-07T15:52:00Z","snapshot_observed_at":"2026-08-02T04:46:48.769220Z","submitted_at":"2026-05-12T14:51:02Z","title":"Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-13T05:18:15.612521Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.12213"},"observation_digest":"sha256:bba581d39856967d8f418a37f945670351aae9c1dbed675327cda2c2df497a37","observation_id":"02dba5be-f884-4e59-98ee-3bce28183bd9","resolution":{"observed_at":"2026-05-13T05:27:19.102778Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.12213","last_updated":"2026-06-07T15:52:00Z","snapshot_observed_at":"2026-08-02T04:46:48.769220Z","submitted_at":"2026-05-12T14:51:02Z","title":"Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-30T22:21:25.150222Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.12213"},"observation_digest":"sha256:52841dce0feff137b956514656cf13437737a742920b61370d7e4e1fa93257c5","observation_id":"c4874019-53ab-471e-a743-7184cf34ebe8","resolution":{"observed_at":"2026-07-01T14:05:46.291224Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.12260","last_updated":"2026-05-22T14:30:50Z","snapshot_observed_at":"2026-07-06T23:23:59.123377Z","submitted_at":"2026-05-12T15:28:30Z","title":"PRISM: Pareto-Efficient Retrieval over Intent-Aware Structured Memory for Long-Horizon Agents","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-13T05:10:14.521588Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.12260"},"observation_digest":"sha256:04d9a37af2295422b27ae574449c30fba43c84367a7c60d78b0ddad6170b8d3c","observation_id":"c959e3c0-1b1d-4887-b9b8-3dc3189093d8","resolution":{"observed_at":"2026-05-13T05:12:17.792027Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.12260","last_updated":"2026-05-22T14:30:50Z","snapshot_observed_at":"2026-07-06T23:23:59.123377Z","submitted_at":"2026-05-12T15:28:30Z","title":"PRISM: Pareto-Efficient Retrieval over Intent-Aware Structured Memory for Long-Horizon Agents","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-25T06:25:07.387150Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.12260"},"observation_digest":"sha256:ef39f8731d0f698770ff57d87e087c0616065929481f056a6102019c24cbeb77","observation_id":"73196fbd-49fd-49a1-8bde-8865db581949","resolution":{"observed_at":"2026-05-25T06:25:23.601301Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.13438","last_updated":"2026-08-05T04:57:06Z","snapshot_observed_at":"2026-08-07T22:15:14.732329Z","submitted_at":"2026-05-13T12:34:39Z","title":"CogniFold: Always-On Proactive Memory via Cognitive Folding","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-14T19:19:39.974706Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.13438"},"observation_digest":"sha256:2f293df092e3f0a063bff71107b05249dc4538f3c9b936fdeeed2eff50cee55c","observation_id":"00c7a946-82f3-4435-9e55-4cea4344e41d","resolution":{"observed_at":"2026-05-14T19:22:51.368079Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.13438","last_updated":"2026-08-05T04:57:06Z","snapshot_observed_at":"2026-08-07T22:15:14.732329Z","submitted_at":"2026-05-13T12:34:39Z","title":"CogniFold: Always-On Proactive Memory via Cognitive Folding","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-30T21:39:53.793341Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.13438"},"observation_digest":"sha256:e4576dcce240d69bb753a6b464cdbbff54725d33666d99bfb211e6472ad7ba87","observation_id":"6d23993d-0dd6-489c-af59-bda6b24c785b","resolution":{"observed_at":"2026-07-01T14:25:46.115923Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.14498","last_updated":"2026-05-16T21:14:35Z","snapshot_observed_at":"2026-08-07T10:35:34.478545Z","submitted_at":"2026-05-14T07:38:29Z","title":"GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-15T01:54:22.009219Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.14498"},"observation_digest":"sha256:454c74f3fd193b913b35d505369fefe6dd945bd64a22cc3f4e738b94cf76b7c8","observation_id":"1d2c2019-cfe1-4685-8898-47dc1a0a9dc0","resolution":{"observed_at":"2026-05-15T01:58:29.246556Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.14498","last_updated":"2026-05-16T21:14:35Z","snapshot_observed_at":"2026-08-07T10:35:34.478545Z","submitted_at":"2026-05-14T07:38:29Z","title":"GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-20T21:31:45.854294Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.14498"},"observation_digest":"sha256:1ef5e7ad4fea151ce9ca8477ef899755fd4e1b0a3288a141404f57d399e7cfd3","observation_id":"caedd084-8281-407a-922a-03a30d6047a7","resolution":{"observed_at":"2026-05-20T21:33:46.336599Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.23071","last_updated":"2026-06-27T04:19:17Z","snapshot_observed_at":"2026-07-31T17:54:56.408388Z","submitted_at":"2026-05-21T22:03:25Z","title":"The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T05:22:22.228384Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.23071"},"observation_digest":"sha256:c33ea9fb50b6619fd6b9ba6465a52dbc3e9e6b8b6f2b70b1f923d7e8a1630cc7","observation_id":"a2d7401b-c429-435e-9377-34b9d7072b8d","resolution":{"observed_at":"2026-05-25T05:25:23.630242Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.23071","last_updated":"2026-06-27T04:19:17Z","snapshot_observed_at":"2026-07-31T17:54:56.408388Z","submitted_at":"2026-05-21T22:03:25Z","title":"The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-30T16:34:29.680105Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.23071"},"observation_digest":"sha256:32677fa42f0f7049ced4a4c8d895af6f88bf3d9ddf664f5c5775bd3b27cceff1","observation_id":"ea8c2be2-9e55-4bee-ab38-90aecf4992af","resolution":{"observed_at":"2026-06-30T16:35:12.209654Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2605.27864","last_updated":"2026-06-18T14:34:57Z","snapshot_observed_at":"2026-08-07T21:46:02.085316Z","submitted_at":"2026-05-27T02:26:16Z","title":"FundaPod: A Multi-Persona Agent Pod Platform with Knowledge Graph Memory for AI-Assisted Fundamental Investment Research","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T12:40:17.867815Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2605.27864"},"observation_digest":"sha256:c5fa23ec9bbd45d2b71d29b2d08e8f935f5682c408e0b16076d078ee12617d6e","observation_id":"dccb1491-d9da-42cc-8f40-44efa174b1a2","resolution":{"observed_at":"2026-06-29T12:43:25.491504Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2606.00305","last_updated":"2026-06-29T12:51:52Z","snapshot_observed_at":"2026-08-02T00:46:58.308965Z","submitted_at":"2026-05-29T19:32:07Z","title":"Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-06-28T22:10:00.382359Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2606.00305"},"observation_digest":"sha256:ef15461bbaf5369836d8b51d631a2b9ddc2a072bc4aa117f67e3a0c94670c9ec","observation_id":"b84a1694-0bb9-4841-8a31-a737d9b9da86","resolution":{"observed_at":"2026-07-01T19:36:09.539955Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2606.00305","last_updated":"2026-06-29T12:51:52Z","snapshot_observed_at":"2026-08-02T00:46:58.308965Z","submitted_at":"2026-05-29T19:32:07Z","title":"Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance","version":2},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-06-30T10:34:52.474916Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2606.00305"},"observation_digest":"sha256:81a2ebc4d9cddacee05beab147f8a42a6a39db90a946de7ab76ca03c5466aaa9","observation_id":"b39d5d51-c209-4e8f-9df1-443f0e841e10","resolution":{"observed_at":"2026-06-30T10:44:37.293381Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":"2601.03236","doi":"10.48550/arxiv.2601.03236","metadata_source":"pith","pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","venue":"cs.AI","work_id":"9217d063-8ac8-4b58-bc7e-b977719660c2","year":2026},"citing_paper":{"arxiv_id":"2606.08950","last_updated":"2026-06-08T02:51:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-06-08T02:51:40Z","title":"When More Cores Hurts: The Vector Database Scaling Paradox in HPC","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-27T15:19:23.339311Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2606.08950"},"observation_digest":"sha256:9d909d246add6aebe1cdbeb9fe54e1adf0b02783d3debb814b9b0c3da834df38","observation_id":"22d15add-43b3-463e-b3ae-93249989cb5c","resolution":{"observed_at":"2026-06-27T15:20:59.961345Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-01T19:49:13.437234Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16848","last_updated":"2026-07-18T15:09:58Z","snapshot_observed_at":"2026-08-03T22:53:27.118985Z","submitted_at":"2026-07-18T15:09:58Z","title":"Beyond Memory Leaderboards: Evaluating Scientific Memory as Budgeted Context Restoration","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T19:49:13.437234Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2607.16848"},"observation_digest":"sha256:bad04b0af997fc941a3a4cad4e8252718d969525f29048bf558cad5c4ca1e8c8","observation_id":"7180ca2a-3454-44bf-84e3-7a1b0f8b5a4b","resolution":{"observed_at":"2026-08-01T19:49:13.437234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.03236","snapshot_observed_at":"2026-08-01T05:10:30.793660Z","title":"MAGMA: A multi-graph based agentic memory architecture for AI agents,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.22319","last_updated":"2026-07-24T13:58:44Z","snapshot_observed_at":"2026-08-07T08:53:46.139845Z","submitted_at":"2026-07-24T13:58:44Z","title":"Towards Trustworthy and Cost-Efficient Data Integration: From Na\\\"ive RAG to Agentic RAG","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-01T05:10:30.793660Z"},"links":{"cited_paper":"/paper/2601.03236","citing_paper":"/paper/2607.22319"},"observation_digest":"sha256:e8e86d29ff4cf5a26df06a2c54d46b96bcf055b5a737051df4ecc8a4370eef2e","observation_id":"30767dfe-64e5-45ba-bb74-d8cef77d6c0e","resolution":{"observed_at":"2026-08-01T05:10:30.793660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2601.03236/citation-record","integrity":"/paper/2601.03236/integrity","json":"/paper/2601.03236/citation-record.json","paper":"/paper/2601.03236"},"outbound":[],"paper":{"arxiv_id":"2601.03236","last_updated":"2026-04-16T05:56:29Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T22:40:51.136169Z","submitted_at":"2026-01-06T18:29:43Z","title":"MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2601.03236."}