{"as_of":"2026-08-10T01:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f4144b7b94a9728cc3c738bb1293bab882c96f9fdef4cd926e165e843d8486b2","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":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":22,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T19:10:00.146972Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":11,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2411.18279","last_updated":"2025-05-06T15:08:00Z","snapshot_observed_at":"2026-07-06T19:57:55.925634Z","submitted_at":"2024-11-27T12:13:39Z","title":"Large Language Model-Brained GUI Agents: A Survey","version":12},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-19T11:08:27.472508Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2411.18279"},"observation_digest":"sha256:5169eddddd54693568f7d62c006493d20acc9716b2b98cb5928797de646d56ea","observation_id":"ffd589cf-8436-4a2b-a609-4c1ba1740507","resolution":{"observed_at":"2026-05-19T11:08:28.017293Z","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":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2501.06322","last_updated":"2025-01-10T19:56:50Z","snapshot_observed_at":"2026-07-30T22:07:13.869232Z","submitted_at":"2025-01-10T19:56:50Z","title":"Multi-Agent Collaboration Mechanisms: A Survey of LLMs","version":1},"reference_index":118,"source":"pdf_text","source_observed_at":"2026-05-13T15:54:54.146003Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2501.06322"},"observation_digest":"sha256:dd16c3a0abd4aac53af94957a016dd9d1f28bfee96afbb1f0614d570d28dfd38","observation_id":"61f93453-9e4e-4792-a08a-e298c07da7a6","resolution":{"observed_at":"2026-05-13T15:54:54.272152Z","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":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-07T19:10:00.146972Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.10177","last_updated":"2025-03-02T08:14:27Z","snapshot_observed_at":"2026-08-07T19:03:58.124857Z","submitted_at":"2025-02-14T14:12:09Z","title":"STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T19:10:00.146972Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2502.10177"},"observation_digest":"sha256:d6aeee430ab3ea2881e4cf658e2080647c173dd75a14c2672703d3c613c6a5b8","observation_id":"33fe289c-27b1-48f6-bff1-57e016643d53","resolution":{"observed_at":"2026-08-07T19:10:00.146972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-07T11:05:22.527201Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.03546","last_updated":"2025-06-04T04:05:38Z","snapshot_observed_at":"2026-08-09T16:00:31.083802Z","submitted_at":"2025-06-04T04:05:38Z","title":"From Virtual Agents to Robot Teams: A Multi-Robot Framework Evaluation in High-Stakes Healthcare Context","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T11:05:22.527201Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2506.03546"},"observation_digest":"sha256:c9ac5e1f6d22d833f79cde8cb536492261c0477754ce05f72e0833e37dbdc304","observation_id":"8ebe0ff7-0c27-46a3-8e56-958debc7e8f8","resolution":{"observed_at":"2026-08-07T11:05:22.527201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-07T05:15:46.313451Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions.arXiv preprint arXiv:2405.11106, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08507","last_updated":"2025-06-12T07:40:49Z","snapshot_observed_at":"2026-08-09T04:59:40.606657Z","submitted_at":"2025-06-10T07:04:25Z","title":"MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:15:46.313451Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2506.08507"},"observation_digest":"sha256:9d2b15960bde0ff1c54add2dd371555ac1d45ed8009849f103817cf2afefaeda","observation_id":"6dfa02b4-64d2-4034-907e-24d1ff83c84a","resolution":{"observed_at":"2026-08-07T05:15:46.313451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-06T21:02:33.207487Z","title":"LLM-based multi-agent rein- forcement learning: Current and future directions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01378","last_updated":"2025-09-01T13:53:03Z","snapshot_observed_at":"2026-08-09T23:29:28.996896Z","submitted_at":"2025-07-02T05:44:17Z","title":"RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T21:02:33.207487Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2507.01378"},"observation_digest":"sha256:7d1c935f12d761fe3c09f67f5073ed4a92648197f376f0f60133d976b8507169","observation_id":"a1f76433-9b2a-45fa-8d74-ed3c8261d4c1","resolution":{"observed_at":"2026-08-06T21:02:33.207487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2507.02592","last_updated":"2025-07-03T12:59:07Z","snapshot_observed_at":"2026-08-09T01:03:55.193390Z","submitted_at":"2025-07-03T12:59:07Z","title":"WebSailor: Navigating Super-human Reasoning for Web Agent","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-17T15:37:09.572241Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2507.02592"},"observation_digest":"sha256:e577f5d71366229ff7f68225691d1a7e243c7be90ed7d7617aa0e20667405d15","observation_id":"003568f4-bc90-4abd-ba59-6c8b11e9ba29","resolution":{"observed_at":"2026-05-17T15:37:09.712100Z","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":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2507.21046","last_updated":"2026-01-16T20:59:08Z","snapshot_observed_at":"2026-08-01T06:32:44.461162Z","submitted_at":"2025-07-28T17:59:05Z","title":"A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence","version":4},"reference_index":125,"source":"arxiv_source","source_observed_at":"2026-05-14T22:23:14.621091Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2507.21046"},"observation_digest":"sha256:eb66f5fe6454a49387ae3252af89b3cb8ddd868f693d67a85a9badef703b34d6","observation_id":"85482870-44a4-4f09-822f-316d2cf3f20c","resolution":{"observed_at":"2026-05-14T22:23:15.856569Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T13:46:02.788254Z","title":"Llm-based multi-agent reinforcement learn- ing: Current and future directions","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.00347","last_updated":"2025-08-30T04:02:33Z","snapshot_observed_at":"2026-08-09T19:57:10.685641Z","submitted_at":"2025-08-30T04:02:33Z","title":"LLM-Driven Policy Diffusion: Enhancing Generalization in Offline Reinforcement Learning","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-05T13:46:02.788254Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2509.00347"},"observation_digest":"sha256:8cd83cd98efc53d610e0f0d0bdb82f9e75c44ce4d36d3e83d7412728ba070ed5","observation_id":"fabc8d4a-808d-47a5-a9f6-17af5f205a5f","resolution":{"observed_at":"2026-08-05T13:46:02.788254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T05:11:23.150380Z","title":"https://arxiv.org/abs/2405.11106","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.05716","last_updated":"2025-09-06T13:38:03Z","snapshot_observed_at":"2026-08-09T04:19:25.694084Z","submitted_at":"2025-09-06T13:38:03Z","title":"A Survey of the State-of-the-Art in Conversational Question Answering Systems","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-05T05:11:23.150380Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2509.05716"},"observation_digest":"sha256:1febfbeb9d8c45c1c6328a848e4404292be4608e313b966e6d6e7d262388bb53","observation_id":"b6f36d5c-56ce-427c-b255-cd2b859afd21","resolution":{"observed_at":"2026-08-05T05:11:23.150380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2510.14063","last_updated":"2026-04-07T19:28:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-10-15T20:04:40Z","title":"Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-18T06:38:24.695002Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2510.14063"},"observation_digest":"sha256:54e8cf9f81ef44d083b4d70d431989e8f53d9f5816269329b6deb007ae58139a","observation_id":"372fa8f8-d5dd-4ae9-9150-6eac6627c569","resolution":{"observed_at":"2026-05-18T06:41:00.563951Z","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":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2601.01885","last_updated":"2026-07-23T06:23:36Z","snapshot_observed_at":"2026-08-03T14:57:06.604444Z","submitted_at":"2026-01-05T08:24:16Z","title":"Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-16T18:32:01.569665Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2601.01885"},"observation_digest":"sha256:d387a138b16506c773872c32a8552c49128afe50dec1b88c2bf88f66560fd6ee","observation_id":"a79c6109-ad03-40be-85dd-c06562f1f2e7","resolution":{"observed_at":"2026-05-16T18:33:15.280434Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-03T12:45:05.696276Z","title":"all”: Summarize all non-system messages. •“N","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.01885","last_updated":"2026-07-23T06:23:36Z","snapshot_observed_at":"2026-08-03T14:57:06.604444Z","submitted_at":"2026-01-05T08:24:16Z","title":"Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents","version":3},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T12:45:05.696276Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2601.01885"},"observation_digest":"sha256:ac225330e44f20dfa8ca317d98d32af2e55f8e82309d1d58392c2fc556c723f9","observation_id":"bb1d87e6-2825-4d45-9ca8-e2197dcb73b7","resolution":{"observed_at":"2026-08-03T12:45:05.696276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2603.12631","last_updated":"2026-04-27T03:20:35Z","snapshot_observed_at":"2026-08-01T19:17:30.395711Z","submitted_at":"2026-03-13T04:04:17Z","title":"Joint Optimization of Multi-agent Memory System","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-15T12:12:35.056095Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2603.12631"},"observation_digest":"sha256:077e64135bf2b3dd6dd71584d22886af39a20937345c973adcf6d34d81b5bff3","observation_id":"45508f44-d710-45e9-bd5d-5a2d02df45e5","resolution":{"observed_at":"2026-05-15T12:15:34.604330Z","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":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2604.15840","last_updated":"2026-04-17T08:41:26Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-17T08:41:26Z","title":"CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T08:06:29.467987Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2604.15840"},"observation_digest":"sha256:8cac90c112b3ebea91545dd72e8bcf16c6e561c8c25a85a2d9e4001343420d15","observation_id":"397440b8-dd14-462a-a9ab-5249f6538543","resolution":{"observed_at":"2026-05-10T09:08:27.138711Z","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":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2604.17191","last_updated":"2026-04-19T01:40:39Z","snapshot_observed_at":"2026-07-06T23:04:19.063534Z","submitted_at":"2026-04-19T01:40:39Z","title":"Do LLM-derived graph priors improve multi-agent coordination?","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T06:09:08.100187Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2604.17191"},"observation_digest":"sha256:68bc6961c0bf7196c5ac4011f584ec17485a61420d2d9b3a5da7912554bf7786","observation_id":"b6176c7e-de33-4803-9b7a-c001c63ab2b5","resolution":{"observed_at":"2026-05-10T06:11:20.213591Z","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":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2604.18133","last_updated":"2026-04-20T12:00:31Z","snapshot_observed_at":"2026-08-03T09:21:05.928583Z","submitted_at":"2026-04-20T12:00:31Z","title":"Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures","version":1},"reference_index":131,"source":"pdf_text","source_observed_at":"2026-05-10T04:31:28.242097Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2604.18133"},"observation_digest":"sha256:d7f0384db41d54a3fdb22e4787dac1493a29f0257a1f9476cee7c75f7875e24c","observation_id":"1a223834-cc8a-4e71-b107-2c9564581554","resolution":{"observed_at":"2026-05-11T11:51:03.815274Z","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":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2605.12655","last_updated":"2026-06-10T15:03:44Z","snapshot_observed_at":"2026-08-08T21:43:49.970250Z","submitted_at":"2026-05-12T19:01:16Z","title":"Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-06-30T22:11:35.277901Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2605.12655"},"observation_digest":"sha256:7d902f45e4e99317993eb0626212df5c3b8d4892adae89c6e5c4d09ebb294fc3","observation_id":"74ac8ecf-caab-4893-8287-bfa7872dd406","resolution":{"observed_at":"2026-06-30T22:15:05.737340Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2605.18799","last_updated":"2026-05-11T09:22:39Z","snapshot_observed_at":"2026-08-03T03:54:24.879979Z","submitted_at":"2026-05-11T09:22:39Z","title":"ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-20T22:51:56.666980Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2605.18799"},"observation_digest":"sha256:58e635c4470f721640a6cf0de9862ad8d405c66769df3171875947d5e5fcd3fa","observation_id":"153ca3c2-f869-454f-ba54-cb13aa871ed0","resolution":{"observed_at":"2026-05-20T22:53:49.364775Z","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":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2605.25746","last_updated":"2026-05-25T11:59:58Z","snapshot_observed_at":"2026-07-06T23:35:41.527169Z","submitted_at":"2026-05-25T11:59:58Z","title":"Multi-Agent Coordination Adaptation via Structure-Guided Orchestration","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T19:36:42.234848Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2605.25746"},"observation_digest":"sha256:1480a9e288c80475ba52e23c8084257c937f584a1bddd961f5b068904993403a","observation_id":"71033bfd-bc23-4bd3-b91e-9ef02af06b6d","resolution":{"observed_at":"2026-06-29T19:43:54.922885Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":"2405.11106","doi":"10.48550/arxiv.2405.11106","metadata_source":"arxiv_reference","pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Llm-based multi-agent reinforcement learning: Current and future directions","venue":"arXiv (Cornell University)","work_id":"e7511e2d-a711-4d59-8e5e-0a287d51e08d","year":2024},"citing_paper":{"arxiv_id":"2606.19920","last_updated":"2026-06-18T08:14:43Z","snapshot_observed_at":"2026-08-08T09:55:57.888883Z","submitted_at":"2026-06-18T08:14:43Z","title":"Deep-Unfolded Coordination","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T17:05:44.372648Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2606.19920"},"observation_digest":"sha256:f9874877fd34d87148bcb5abfd13e5a99fecc183a0dfeeb931c7b54e50d59098","observation_id":"aae5e59c-699b-44fc-801a-cdd5a662ea25","resolution":{"observed_at":"2026-07-04T04:19:34.375856Z","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":"2405.11106","last_updated":"2024-05-17T22:10:23Z","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.11106","snapshot_observed_at":"2026-08-02T10:20:56.162142Z","title":"arXiv preprint arXiv:2405.11106 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22643","last_updated":"2026-06-24T02:40:49Z","snapshot_observed_at":"2026-08-08T03:20:50.943223Z","submitted_at":"2026-06-24T02:40:49Z","title":"Reason Before You Retrieve: Agentic Planning for Multi-modal RAG","version":1},"reference_index":131,"source":"arxiv_source","source_observed_at":"2026-08-02T10:20:56.162142Z"},"links":{"cited_paper":"/paper/2405.11106","citing_paper":"/paper/2607.22643"},"observation_digest":"sha256:aec61722f5e072db0a0f7b01bde8fe99571f94eff5c146e5165862ec6574d65a","observation_id":"8f191f63-7402-4817-8025-0b843c6c6b89","resolution":{"observed_at":"2026-08-02T10:20:56.162142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2405.11106/citation-record","integrity":"/paper/2405.11106/integrity","json":"/paper/2405.11106/citation-record.json","paper":"/paper/2405.11106"},"outbound":[],"paper":{"arxiv_id":"2405.11106","last_updated":"2024-05-17T22:10:23Z","latest_version":1,"primary_category":"cs.MA","snapshot_observed_at":"2026-08-05T03:50:46.269415Z","submitted_at":"2024-05-17T22:10:23Z","title":"LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions"},"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":"2026-08-09T06:31:02.800959+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 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2405.11106."}