{"as_of":"2026-08-09T21:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:12c281d1bc86c1a0e972629a90e05ef72c8c63f3cbf8e1032fd5ff6c500b2c0f","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-13T01:48:40.840857Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.09603/citation-record","integrity":"/paper/2607.09603/integrity","json":"/paper/2607.09603/citation-record.json","paper":"/paper/2607.09603"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.17246","last_updated":"2025-03-25T23:39:13Z","snapshot_observed_at":"2026-08-02T22:31:10.937767Z","submitted_at":"2024-03-25T22:47:13Z","title":"TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.17246","snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"Claude sonnet 4.5 system card","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"cited_paper":"/paper/2403.17246","citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:cfa3b7fa0a8c0e29ffdefafa81b22380b6256c909191942efec1b4edaa157b44","observation_id":"bc72975c-0e30-458e-b0e0-71c1beb213de","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.05155","last_updated":"2020-04-10T17:57:29Z","snapshot_observed_at":"2026-08-08T14:58:45.190706Z","submitted_at":"2020-04-10T17:57:29Z","title":"Learning to Explore using Active Neural SLAM","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05155","snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"S., Gandhi, D., Gupta, S., Gupta, A., and Salakhutdinov, R","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"cited_paper":"/paper/2004.05155","citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:fcf9e198df513f91aca19a05e786ab3aeead3d618acc52252ac46c06a941b1bd","observation_id":"673144ad-34cc-487b-ac82-b4ca6dde5265","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"com/deepmind-media/Model-Cards/ Gemini-3-Flash-Model-Card.pdf","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:8017037f83003220b11838a46021732c9eb73cb1d69d840a38788f96938b96d2","observation_id":"55632874-4ec6-4b81-ae6d-da26d02f6a5a","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"Optimal task and motion planning and execution for multiagent systems in dynamic envi- ronments.IEEE Transactions on Cybernetics, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:b87a4032de354968448a3be1df52d824d92d00c701820042a8055a2df74374c8","observation_id":"4f7ebd30-0f29-4dcf-b2f9-ca45de5ab11d","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., Hayes, A., Radford, A., et al","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:2f5999f5b9505efece219cc9c216e006a636f9f81d72432d71bede7e24570435","observation_id":"4912d4e6-b040-4e0c-8a14-d745f6dae926","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:efbc8b5dd821537a94b00fd24180c1e7704277dd205903938c460766969433df","observation_id":"317277a7-129e-4feb-a82e-19c0148f072a","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05474","last_updated":"2022-08-26T17:12:17Z","snapshot_observed_at":"2026-07-06T06:14:28.435222Z","submitted_at":"2017-12-14T23:17:24Z","title":"AI2-THOR: An Interactive 3D Environment for Visual AI","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.05474","snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"AI2-THOR: An Interactive 3D Environment for Visual AI.arXiv preprint 1712.05474, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"cited_paper":"/paper/1712.05474","citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:245ce9c7d81afb8497a25e44b9c1041a73334fc4daf7639d3b1606257564cd79","observation_id":"c4ea89e5-5691-4495-9081-e700aae794e3","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"and Montana, G","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:1b105af759e555ddd0b2a5d433f7e2768ff261bce5c8fe288a99227c3d84c786","observation_id":"6189feb0-d221-44cc-a678-2c2ba8a4fb27","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"Scaling large language model-based multi-agent collab- oration","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:4e0d80ba9078b382c6b4a12d9c90bd04cc6c95e8771eaeb17b1d1313293f456b","observation_id":"83d3c816-dee5-4e2c-be3f-5eddbb25a1b3","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.03673","last_updated":"2025-06-05T23:45:32Z","snapshot_observed_at":"2026-08-07T15:49:27.427122Z","submitted_at":"2025-05-06T16:11:49Z","title":"RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.03673","snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"Decentralized monte carlo tree search for partially observable multi-agent pathfinding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"cited_paper":"/paper/2505.03673","citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:3ce93f907a24cbcb07eb9ce2b63af5ea297fc41f49245270982d224547e2cd80","observation_id":"bd5dba63-632e-4a7b-a656-070d8b7b9ac2","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07398","last_updated":"2025-06-16T08:45:10Z","snapshot_observed_at":"2026-08-09T03:15:13.137279Z","submitted_at":"2025-06-09T03:43:46Z","title":"G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07398","snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"just ahead","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"cited_paper":"/paper/2506.07398","citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:c1b509d299cdc91cb8468757e75594852b4cdbc821b4d6c1d2ca825d24b8ce89","observation_id":"d65400a8-1f43-46cc-93eb-21bf779d78b8","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"No agent has PickupObject candidates if they’re already holding something","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:435b4d15a0feb00ea055b5a0bd838549ae3085f49f72a256fd0fddd12c9cc3d9","observation_id":"05c819b0-2b84-4ebc-8102-8ebbb1983a77","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:204164b118373a814c049f842fc69fa18d2a11e7875ed20fc9d3d6e2907ef3c6","observation_id":"9ff6f28e-4efa-47a0-862d-db2c2d566ef0","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:94bbfd5dedeef5481d5ece64e1e983cccd182eb84fc0773c921e45a1ac523624","observation_id":"ba7e8b74-2a7f-4ddc-a933-9287fef08c52","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:cb441fa2a28815b65a5b2232409ca634d33d8f45721c222f463b53551c643348","observation_id":"bd2ec63f-1e97-43ae-be62-8de4fe356c74","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:a05286d9dc210e2d11bff99a52f1821964962fe4d5f57819c27591bda1c682ec","observation_id":"56354075-5eed-459d-8425-e075f5124e48","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:27828d69132498e6d7de2689077e5971b362fe692cee0b063ad31e67f1a56704","observation_id":"9e44c0e3-0c78-4fd5-834c-5d1b51e40012","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T01:48:40.840857Z","title":"Book 1 - Alice: Far left (4 moves), Bob: Just ahead (1 move)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-13T01:48:40.840857Z"},"links":{"citing_paper":"/paper/2607.09603"},"observation_digest":"sha256:dd0ad604118990ece2a9c2451cf6a50709188b9e95fcd1f32f637489e366a562","observation_id":"1e4e2c28-cbc8-4802-a440-4e39e36a27c3","resolution":{"observed_at":"2026-07-13T01:48:40.840857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.09603","last_updated":"2026-07-10T16:57:37Z","latest_version":1,"primary_category":"cs.MA","snapshot_observed_at":"2026-08-07T15:38:14.228591Z","submitted_at":"2026-07-10T16:57:37Z","title":"Mosaic: Runtime-Efficient Multi-Agent Embodied Planning"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":18},"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 9 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2607.09603."}