{"as_of":"2026-08-09T02:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:59a3391920664997c1e6ead1f1f730d16c763853ad203070f334a20fa91f099c","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:18:06.516181Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T11:51:04.058346Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-08-07T14:56:19.901471Z","title":"Reso: A reward-driven self-organizing llm-based multi-agent system for reasoning tasks","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.16997","last_updated":"2025-05-22T17:56:39Z","snapshot_observed_at":"2026-08-07T14:49:30.424513Z","submitted_at":"2025-05-22T17:56:39Z","title":"X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMs","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T14:56:19.901471Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2505.16997"},"observation_digest":"sha256:8475982e23eaeb86901508b4e6ac89b988a03516f1f03d29db27b746316a9b7e","observation_id":"f6c6a078-025c-44de-bc9c-4dac425e4d73","resolution":{"observed_at":"2026-08-07T14:56:19.901471Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-08-07T15:18:06.516181Z","title":"Reso: A reward-driven self- organizing llm-based multi-agent system for reasoning tasks.arXiv preprint arXiv:2503.02390, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.17115","last_updated":"2025-05-30T08:59:59Z","snapshot_observed_at":"2026-08-08T09:16:40.779020Z","submitted_at":"2025-05-21T15:48:13Z","title":"Swarm Intelligence Enhanced Reasoning: A Density-Driven Framework for LLM-Based Multi-Agent Optimization","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T15:18:06.516181Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2505.17115"},"observation_digest":"sha256:47891c7b841fcd1f241e24a63653b99c97be6a36a2218e81f6892618cd18b198","observation_id":"fa354434-7fa9-487d-b74c-d26c1db85583","resolution":{"observed_at":"2026-08-07T15:18:06.516181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-08-07T05:39:59.042322Z","title":"Reso: A reward-driven self- organizing llm-based multi-agent system for reasoning tasks","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07398","last_updated":"2025-06-16T08:45:10Z","snapshot_observed_at":"2026-08-07T05:32:29.009208Z","submitted_at":"2025-06-09T03:43:46Z","title":"G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T05:39:59.042322Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2506.07398"},"observation_digest":"sha256:9a0d6b1d3c483098acbe94000b57a1fcc1b8445dd0d36d39c99838ebc836aa78","observation_id":"ac7fa204-6c4d-44fd-9845-4ee1836164c9","resolution":{"observed_at":"2026-08-07T05:39:59.042322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-08-06T12:52:01.633945Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.21407","last_updated":"2025-08-30T06:01:56Z","snapshot_observed_at":"2026-08-09T01:02:43.312508Z","submitted_at":"2025-07-29T00:27:12Z","title":"Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects","version":2},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-06T12:52:01.633945Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2507.21407"},"observation_digest":"sha256:26625eb7a4a50016b0aea73960f62af782ac55d2ab3ee7b6522f8d416539fa19","observation_id":"fb1703c3-3723-494e-ac79-1ea083567aeb","resolution":{"observed_at":"2026-08-06T12:52:01.633945Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-08-05T16:18:48.779795Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.18763","last_updated":"2025-08-26T07:41:33Z","snapshot_observed_at":"2026-08-08T03:30:31.091934Z","submitted_at":"2025-08-26T07:41:33Z","title":"Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-05T16:18:48.779795Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2508.18763"},"observation_digest":"sha256:24bcee9f97ab02f8d6d77fb844708449aebb679c833e73b051d7508c5a2932c6","observation_id":"e1b881ac-be65-40b1-a94e-d7b13de4415b","resolution":{"observed_at":"2026-08-05T16:18:48.779795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-08-05T12:01:25.779810Z","title":"arXiv preprint arXiv:2503.02390","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.02040","last_updated":"2025-09-02T07:35:20Z","snapshot_observed_at":"2026-08-05T12:01:23.016166Z","submitted_at":"2025-09-02T07:35:20Z","title":"Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-05T12:01:25.779810Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2509.02040"},"observation_digest":"sha256:d0c6308864952329775c0c305e8966bb4eff6cda29ea537b5764d3a793617a02","observation_id":"bcfe041f-c392-4a2d-8914-d8ab87e9e025","resolution":{"observed_at":"2026-08-05T12:01:25.779810Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-08-03T20:17:53.066978Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.20639","last_updated":"2026-08-03T15:58:02Z","snapshot_observed_at":"2026-08-06T23:37:32.107617Z","submitted_at":"2025-11-25T18:56:57Z","title":"Latent Collaboration in Multi-Agent Systems","version":3},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-03T20:17:53.066978Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2511.20639"},"observation_digest":"sha256:fe00978e754e9f147cc527bd03a8fcc2ff98e4270f28bebe0c67401e07da7fa1","observation_id":"a48624ca-37d6-4efd-a8f3-655f2a40d1e1","resolution":{"observed_at":"2026-08-03T20:17:53.066978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-08-04T06:52:33.628375Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.20639","last_updated":"2026-08-03T15:58:02Z","snapshot_observed_at":"2026-08-06T23:37:32.107617Z","submitted_at":"2025-11-25T18:56:57Z","title":"Latent Collaboration in Multi-Agent Systems","version":4},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-04T06:52:33.628375Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2511.20639"},"observation_digest":"sha256:7cd1b7ce02f642bcbba4150ba43cff7355d13cd99d97d91ed75fc43e1300c62c","observation_id":"0d6c2fe0-2d54-47b2-a487-6130f495be57","resolution":{"observed_at":"2026-08-04T06:52:33.628375Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-08-03T09:21:51.197641Z","title":"Reso: A reward-driven self-organizing llm-based multi-agent system for reasoning tasks, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.14192","last_updated":"2026-07-03T11:26:58Z","snapshot_observed_at":"2026-08-07T15:05:40.772054Z","submitted_at":"2026-01-20T17:51:56Z","title":"Toward Efficient Agents: Memory, Tool learning, and Planning","version":2},"reference_index":196,"source":"pdf_text","source_observed_at":"2026-08-03T09:21:51.197641Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2601.14192"},"observation_digest":"sha256:dc39cb18aad02b0192f75cff0ca73141a6511ad9ef13e6dfd9f6138fbc1c0e3d","observation_id":"101452a5-2a87-45a4-bfa7-e4afa39aaf2f","resolution":{"observed_at":"2026-08-03T09:21:51.197641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-08-02T22:58:14.115218Z","title":"Reso: A reward-driven self-organizing llm-based multi-agent system for reasoning tasks","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.15382","last_updated":"2026-05-28T08:46:27Z","snapshot_observed_at":"2026-08-03T23:19:36.572742Z","submitted_at":"2026-02-17T06:31:53Z","title":"The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems","version":2},"reference_index":104,"source":"arxiv_source","source_observed_at":"2026-08-02T22:58:14.115218Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2602.15382"},"observation_digest":"sha256:d31f616bbf2ea45602d167ed7ffd1ea55557be6560074fcbb28af12be99f5882","observation_id":"5cb2fcd0-44a8-43f8-ae8e-926022c89f20","resolution":{"observed_at":"2026-08-02T22:58:14.115218Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":"2503.02390","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.02390","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Reso: A reward-driven self-organizing LLM-based multi-agent system for reasoning tasks","venue":null,"work_id":"cbb58a59-11e9-4265-9c9a-f18cc86b749d","year":2025},"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":84,"source":"pdf_text","source_observed_at":"2026-05-10T04:31:28.242097Z"},"links":{"cited_paper":"/paper/2503.02390","citing_paper":"/paper/2604.18133"},"observation_digest":"sha256:469dabdd1997dd944abca9805c1e602c1577a164567714ef564520ed86f24d18","observation_id":"afd7bc30-0e15-45f5-b9b1-7d3d6e1cfcd7","resolution":{"observed_at":"2026-05-11T11:51:04.061560Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2503.02390/citation-record","integrity":"/paper/2503.02390/integrity","json":"/paper/2503.02390/citation-record.json","paper":"/paper/2503.02390"},"outbound":[],"paper":{"arxiv_id":"2503.02390","last_updated":"2025-05-30T11:40:44Z","latest_version":3,"primary_category":"cs.MA","snapshot_observed_at":"2026-08-07T17:31:11.290779Z","submitted_at":"2025-03-04T08:28:04Z","title":"ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2503.02390."}