{"as_of":"2026-08-16T17:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0c0b0d0a1466cc2714195913f8be90ea6b8bff910e475ecad18dd47a389c155c","coverage":[{"denominator":60,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":60,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T15:55:16.505120Z","state":"measured"},{"denominator":61,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":61,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T20:44:11.206380Z","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":"2026-06-29T21:03:58.927008Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"cited_work":{"arxiv_id":"2509.14431","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.14431","snapshot_observed_at":"2026-08-04T01:49:12.424451Z","title":"maintenance mode","venue":null,"work_id":"f897cdcf-2db0-4429-adee-8f343a6bcf31","year":1999},"citing_paper":{"arxiv_id":"2605.25867","last_updated":"2026-05-25T13:56:02Z","snapshot_observed_at":"2026-08-15T21:58:16.201656Z","submitted_at":"2026-05-25T13:56:02Z","title":"CINOC: Cardinality-Invariant Neural Operator Policies for Scalable PDE Control","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T20:44:11.206380Z"},"links":{"cited_paper":"/paper/2509.14431","citing_paper":"/paper/2605.25867"},"observation_digest":"sha256:d16e8d3826257911ca5e13ae96917925331ba0eab9eb43153cfbbf7565e782a8","observation_id":"3a854840-a2d4-447b-9c3f-1d0fa03063f7","resolution":{"observed_at":"2026-08-04T01:49:12.424451Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.14431/citation-record","integrity":"/paper/2509.14431/integrity","json":"/paper/2509.14431/citation-record.json","paper":"/paper/2509.14431"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2209.03859","last_updated":"2022-09-08T14:58:50Z","snapshot_observed_at":"2026-08-16T16:33:51.295754Z","submitted_at":"2022-09-08T14:58:50Z","title":"A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.03859","snapshot_observed_at":"2026-08-15T15:55:16.269383Z","title":"A survey on large-population systems and scalable multi- agent reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.269383Z"},"links":{"cited_paper":"/paper/2209.03859","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:4c63dc3137276fcfaaaf099b6a36a0e93671665aebe6ad87d913bfa79800d542","observation_id":"238c2908-c23b-4f7b-9dc3-de323b312ad8","resolution":{"observed_at":"2026-08-15T15:55:16.269383Z","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":"2026-08-15T15:55:16.281673Z","title":"A comprehensive survey of multiagent reinforcement learning,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.281673Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:34186c05589287df691b1d73776ac51a908c0a7dd4b825d3e7013a5861f45cb9","observation_id":"2027b7af-3f5a-48e1-83d5-3a18dcc44bfa","resolution":{"observed_at":"2026-08-15T15:55:16.281673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10256","last_updated":"2024-07-03T00:27:14Z","snapshot_observed_at":"2026-08-16T14:34:14.993553Z","submitted_at":"2023-12-15T23:16:54Z","title":"Multi-agent Reinforcement Learning: A Comprehensive Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10256","snapshot_observed_at":"2026-08-15T15:55:16.286481Z","title":"Multi-agent reinforcement learning: A comprehensive survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.286481Z"},"links":{"cited_paper":"/paper/2312.10256","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:2c5f490a4a9019b2908830151867ba252598ae1b10b09c2e8aa16dc25eb5ed95","observation_id":"74d06281-526d-426e-b831-5b9113213324","resolution":{"observed_at":"2026-08-15T15:55:16.286481Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:17.022845Z","title":"Multi-agent reinforcement learning: A review of challenges and applications,","venue":null,"work_id":"43375ebe-fb7e-4dc6-aea8-e2cfc8e5f431","year":2021},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.290570Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:b25adfd9e1a12da89536ac809db8495004738405632ccc37737ebdfa2c2cf508","observation_id":"17e87db0-46a6-40fa-984a-7223c76a49de","resolution":{"observed_at":"2026-08-15T15:55:17.026297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:17.013410Z","title":"Coach-player multi-agent reinforcement learning for dynamic team composition,","venue":null,"work_id":"7fc8b706-fcdb-4bb9-ae0b-acd08c77ff3a","year":2021},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.294343Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:08893dff010480d275513c2d65ac5c6edd90472f937d27852a887a44a94f3573","observation_id":"02e7054e-7f78-47ac-a880-5c750d080ce2","resolution":{"observed_at":"2026-08-15T15:55:17.016954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.01202","last_updated":"2019-06-04T05:36:43Z","snapshot_observed_at":"2026-08-14T16:21:25.000929Z","submitted_at":"2019-06-04T05:36:43Z","title":"Learning Transferable Cooperative Behavior in Multi-Agent Teams","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.01202","snapshot_observed_at":"2026-08-15T15:55:16.297722Z","title":"Learning transferable coopera- tive behavior in multi-agent teams,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.297722Z"},"links":{"cited_paper":"/paper/1906.01202","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:8932ac8795bbdeb23562d479b31f33a34861e9a2c4df9cbaea6af2cb38443055","observation_id":"6a5616ef-6bf1-478c-9d45-b8ef068d45e9","resolution":{"observed_at":"2026-08-15T15:55:16.297722Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:17.003847Z","title":"A multitask- based transfer framework for cooperative multi-agent reinforcement learning,","venue":null,"work_id":"94be9abd-f5fe-43c7-b403-53401c208349","year":2025},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.301517Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:e4563adf270ddb9e508d18c9ab92a2b8280dfee2ee6763e1ed6837f577f1e937","observation_id":"a526216f-fb24-4195-9ef4-467ddd0575ae","resolution":{"observed_at":"2026-08-15T15:55:17.007100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.304827Z","title":"Mdp homomorphic networks: Group symmetries in reinforcement learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.304827Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:92ab2b4ed6710674433059733580d862057a62090fde4b5a10235cfbdbcd54a7","observation_id":"e01ce536-7d21-4157-8767-a1bfa4081581","resolution":{"observed_at":"2026-08-15T15:55:16.304827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04439","last_updated":"2022-03-08T23:09:25Z","snapshot_observed_at":"2026-08-16T17:16:01.312656Z","submitted_at":"2022-03-08T23:09:25Z","title":"$\\mathrm{SO}(2)$-Equivariant Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04439","snapshot_observed_at":"2026-08-15T15:55:16.307904Z","title":"so(2)-equivariant reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.307904Z"},"links":{"cited_paper":"/paper/2203.04439","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:8afce36a5a7d1198c1c29d359b7142b7a73d243abe06426bfe5a4fa232ad5464","observation_id":"0c077ab3-371a-4a59-9d12-05325d456fc2","resolution":{"observed_at":"2026-08-15T15:55:16.307904Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.989266Z","title":"A survey of scalable re- inforcement learning,","venue":null,"work_id":"b049cab0-8d1b-4d77-8d3e-6199b5e7fac1","year":2022},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.311538Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:91bd43171733b9142a6681ea683f2e0368c54475fc9514bef59be32f217426c4","observation_id":"82488764-a8c3-4a01-a17e-026704104561","resolution":{"observed_at":"2026-08-15T15:55:16.992829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.979856Z","title":"Equivariant reinforcement learning under partial observability,","venue":null,"work_id":"6cff919f-795b-4eb4-8945-bcdb31b9f71e","year":2023},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.314871Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:5675fde1b69d4db619aaab7f7d91efe6b10968ffdd93f3e48a9d4b55a413afae","observation_id":"22590380-15cd-417c-8a70-c1898ca56638","resolution":{"observed_at":"2026-08-15T15:55:16.983556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.08039","last_updated":"2020-07-04T08:37:46Z","snapshot_observed_at":"2026-08-10T03:56:00.709199Z","submitted_at":"2020-03-18T04:29:42Z","title":"ROMA: Multi-Agent Reinforcement Learning with Emergent Roles","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.08039","snapshot_observed_at":"2026-08-15T15:55:16.318318Z","title":"Roma: Multi- agent reinforcement learning with emergent roles,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.318318Z"},"links":{"cited_paper":"/paper/2003.08039","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:91c3499cd261e998955b42e06452e360579ccc20aca9da797e87d14be6c5ac50","observation_id":"98d5209e-6d33-4520-9617-225115ec0286","resolution":{"observed_at":"2026-08-15T15:55:16.318318Z","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":"2026-08-15T15:55:16.321910Z","title":"Heterogeneous-agent reinforcement learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.321910Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:ee0aaa6dd5c7863b2abe25a5f92beae07f3cb42da9ef8081eeeb98549b78f479","observation_id":"e14d38ef-d923-418d-b3ff-ff0a175f8e4f","resolution":{"observed_at":"2026-08-15T15:55:16.321910Z","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":"2026-08-15T15:55:16.325634Z","title":"The surprising effectiveness of ppo in cooperative multi-agent games,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.325634Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:35b96a9aebc6cdf0310d73dc483aeea0a022953fcea448ddbd044075a73263a0","observation_id":"0ea76bfe-20b3-4e6d-b469-468b7f9e2cc6","resolution":{"observed_at":"2026-08-15T15:55:16.325634Z","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":"2026-08-15T15:55:16.329059Z","title":"A comprehensive survey on graph neural networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.329059Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:ba4eadc0462bbdce1c4a7782943177b148ad88dd1f9ac3dc26862eae63bb9006","observation_id":"ac77d581-b22b-42f9-8396-c2eaa2f6926e","resolution":{"observed_at":"2026-08-15T15:55:16.329059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.04181","last_updated":"2024-03-28T19:35:01Z","snapshot_observed_at":"2026-08-16T15:56:46.382341Z","submitted_at":"2023-02-08T16:40:11Z","title":"Attending to Graph Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04181","snapshot_observed_at":"2026-08-15T15:55:16.332302Z","title":"Attending to graph transformers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.332302Z"},"links":{"cited_paper":"/paper/2302.04181","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:d38e745e96b9a1ff4ed8e89fa169b597487b45c6a8a71d4e022de0999843c06f","observation_id":"474e495e-ad7c-446d-9834-dbdd28eb65d7","resolution":{"observed_at":"2026-08-15T15:55:16.332302Z","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":"2026-08-15T15:55:16.336365Z","title":"Markov games as a framework for multi-agent rein- forcement learning,","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.336365Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:c7a4a44abf14153bd4f70ff4ed6ac809512ae8fc71dae62746043be619a7eea5","observation_id":"f9d00e69-49ec-429f-a2ab-b1d4370c9283","resolution":{"observed_at":"2026-08-15T15:55:16.336365Z","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":"2026-08-15T15:55:16.339847Z","title":"Multi-agent reinforcement learning: Independent vs. cooper- ative agents,","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.339847Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:bde8d940934b9b7b97bfffd3eba585a61e5fb9dd8ad408e16f99d2f19a61426a","observation_id":"0c3b6427-8ed9-4c52-8724-2c7fa9fdc9d4","resolution":{"observed_at":"2026-08-15T15:55:16.339847Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.945400Z","title":"Deep decentralized multi-task multi-agent reinforcement learning under par- tial observability,","venue":null,"work_id":"e7fb338e-0262-44b7-a681-35c3de8a03d3","year":2017},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.343658Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:b059cb9808fd683d47ca29e8938d5f72abe59879b6e6849ff66e89790f45a09e","observation_id":"3fedb593-adcc-4997-bef3-2a967b696977","resolution":{"observed_at":"2026-08-15T15:55:16.948938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.936539Z","title":null,"venue":null,"work_id":"4645183f-2daa-4d0e-98de-5c5984c8f558","year":2016},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.347139Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:2dbbb73e2dbf290634e12bc6ecc1165ea508637f150f9d2eebb78b1fb3de2510","observation_id":"1be68fee-1b62-4979-a500-99f20bf1695f","resolution":{"observed_at":"2026-08-15T15:55:16.939898Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05296","last_updated":"2017-06-16T14:47:21Z","snapshot_observed_at":"2026-08-14T20:53:35.783079Z","submitted_at":"2017-06-16T14:47:21Z","title":"Value-Decomposition Networks For Cooperative Multi-Agent Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05296","snapshot_observed_at":"2026-08-15T15:55:16.351098Z","title":"Value-decomposition networks for cooperative multi-agent learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.351098Z"},"links":{"cited_paper":"/paper/1706.05296","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:ab993a251840b1c744ade2ef849910b2f34ad875cb570fcb152b6f5975ea50b5","observation_id":"0aeadd67-b321-4643-8129-1cf604a21f30","resolution":{"observed_at":"2026-08-15T15:55:16.351098Z","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":"2026-08-15T15:55:16.355084Z","title":"Monotonic value function factorisation for deep multi- agent reinforcement learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.355084Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:90693ce199fe6734d5fb0c7a1ac3669bbc245435cdd17fdb0cb65971f89e188f","observation_id":"8d4b751b-be76-4b7f-add5-b4730ebbe13a","resolution":{"observed_at":"2026-08-15T15:55:16.355084Z","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":"2026-08-15T15:55:16.358546Z","title":"Multi-agent actor-critic for mixed cooperative-competitive environments,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.358546Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:18f20872dc27c95afbfce85a6166e50bb82098ce3299e2d9dca21dc336936bda","observation_id":"f30ef178-7e23-45ca-926d-64bae4147221","resolution":{"observed_at":"2026-08-15T15:55:16.358546Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.917320Z","title":"Transfer learning in multi-agent reinforcement learning domains,","venue":null,"work_id":"e2a91ae9-5302-4f4d-90e9-f59ede170fab","year":2011},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.362196Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:9375678d19c27e690c15dba7e9262991c152e2cd63c6c8fd36e7456a0a04087e","observation_id":"fc4027ae-ddcf-4a66-9ff5-1f296cd86e0e","resolution":{"observed_at":"2026-08-15T15:55:16.920626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.908467Z","title":"Graph convolutional neural networks for web-scale recommender systems,","venue":null,"work_id":"da3d930b-5f81-423e-9c63-25653cc3cbfa","year":2018},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.365923Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:413fb6868152a4dad5430a3390bcb072abaacd70d1bf86367d2b743500cc43a7","observation_id":"31fdcecc-709f-4843-88db-68f012ddf1cc","resolution":{"observed_at":"2026-08-15T15:55:16.911682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.899988Z","title":"Molecular contrastive learning of representations via graph neural networks,","venue":null,"work_id":"a84d5afe-63db-4692-81a9-f6b1a9e5d344","year":2022},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.369592Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:efbe4d0914f553272e42e691881ea7404469cdc4bb01615eee819466a6b52a0d","observation_id":"a38f3d42-3257-42d2-8c93-162c68d25491","resolution":{"observed_at":"2026-08-15T15:55:16.903061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.891206Z","title":"Prediction of protein–protein interac- tion using graph neural networks,","venue":null,"work_id":"36728df3-d282-4a90-94ec-421f9005b143","year":2022},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.373509Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:03adbd89ba64ed64eeb5618b86f7ed12c1ac6f1603e04311453d43c5635bab36","observation_id":"8ab55dbd-134d-4d3d-9618-609d3d37fde6","resolution":{"observed_at":"2026-08-15T15:55:16.894471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.882644Z","title":"Deeprank-gnn: a graph neural network framework to learn patterns in protein–protein interfaces,","venue":null,"work_id":"0e8f8ec0-e19f-4d0c-b151-d054863455af","year":2023},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.377448Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:0b1165f722d9b912c0ddde96a851e61e10adf26c59675cba917999ff99a7a509","observation_id":"300b74fd-af12-42fe-986a-3220409f6bb9","resolution":{"observed_at":"2026-08-15T15:55:16.885845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14746","last_updated":"2025-03-11T05:00:19Z","snapshot_observed_at":"2026-08-16T13:40:55.993684Z","submitted_at":"2024-06-20T21:36:54Z","title":"Behavior-Inspired Neural Networks for Relational Inference","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.14746","snapshot_observed_at":"2026-08-15T15:55:16.381268Z","title":"Behavior-inspired neural networks for relational inference,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.381268Z"},"links":{"cited_paper":"/paper/2406.14746","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:a50aafff42c621c4dc505ebfcf19d6fbc1c90981307969aaf94cb5d6a14e30ac","observation_id":"87af17af-c274-482a-826d-b58eab8a0f48","resolution":{"observed_at":"2026-08-15T15:55:16.381268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.19089","last_updated":"2025-05-16T21:51:27Z","snapshot_observed_at":"2026-08-14T22:56:36.023680Z","submitted_at":"2025-01-31T12:34:09Z","title":"Resolving Oversmoothing with Opinion Dissensus","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.19089","snapshot_observed_at":"2026-08-15T15:55:16.385192Z","title":"Resolving over- smoothing with opinion dissensus,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.385192Z"},"links":{"cited_paper":"/paper/2501.19089","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:60c6eeeca2546f4dab415e580149b3e83e7b98556b8df01ebdf7d7aa777d62b3","observation_id":"7a92e378-0c1e-445e-a0bb-023ac29c55fc","resolution":{"observed_at":"2026-08-15T15:55:16.385192Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.872224Z","title":"Qmix-gnn: A graph neural network- based heterogeneous multi-agent reinforcement learning model for im- proved collaboration and decision-making,","venue":null,"work_id":"9e300b47-92e3-4302-8f90-da09948bf415","year":2025},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.389196Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:05f556ed2b1e49ba8d1692aec226131cd6f1bee57a937ec9eba70cf433bc3a6d","observation_id":"5e6ab510-233a-4fbf-ab45-982f36365fa1","resolution":{"observed_at":"2026-08-15T15:55:16.876841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.863194Z","title":"Graph neural network-based multi-agent reinforcement learning for resilient distributed coordination of multi-robot systems,","venue":null,"work_id":"bad75f0e-410c-42e5-ba42-68ad37fed503","year":2024},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.392810Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:e5b3f1081cde78f3f0007938fb1f1f93c89827e7506f76d86ed260f052e7da1a","observation_id":"7b1a011f-c77f-4018-90ff-0ab0581ec6c1","resolution":{"observed_at":"2026-08-15T15:55:16.866421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.09202","last_updated":"2020-02-11T13:46:23Z","snapshot_observed_at":"2026-08-14T18:11:18.087694Z","submitted_at":"2018-10-22T12:17:40Z","title":"Graph Convolutional Reinforcement Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.09202","snapshot_observed_at":"2026-08-15T15:55:16.396528Z","title":"Graph convolutional rein- forcement learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.396528Z"},"links":{"cited_paper":"/paper/1810.09202","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:ce4739c963a81581832eb71269f32d4f75aa947f756bf92fd08bab69b903a622","observation_id":"8f38ef46-b623-4e35-b807-845c904a3260","resolution":{"observed_at":"2026-08-15T15:55:16.396528Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.853231Z","title":"Multi-agent graph- attention communication and teaming","venue":null,"work_id":"12990861-9620-4618-a45b-90e123975904","year":2021},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.400473Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:94db024c6e46e5bd6573b673c7ce0b6df4803b32a1444cdbaf73b91123466beb","observation_id":"216278b6-5439-4c60-84c7-199dca765a72","resolution":{"observed_at":"2026-08-15T15:55:16.857116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.842978Z","title":"Leveraging graph neural networks and multi-agent reinforcement learning for inventory control in supply chains,","venue":null,"work_id":"66b536cc-82e0-4f9d-a05e-c6489b6b1790","year":2025},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.404528Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:cb3b79c9b0e396ba4bcdddc98f7a7dec77b9e1cd74b5b65d5bea9e091d93edc4","observation_id":"9e8b92dd-d900-4072-9290-d6b90394974b","resolution":{"observed_at":"2026-08-15T15:55:16.846876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.13478","last_updated":"2021-05-02T16:16:03Z","snapshot_observed_at":"2026-08-12T23:06:05.148534Z","submitted_at":"2021-04-27T21:09:51Z","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.13478","snapshot_observed_at":"2026-08-15T15:55:16.408224Z","title":"Geometric deep learning: Grids, groups, graphs, geodesics, and gauges,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.408224Z"},"links":{"cited_paper":"/paper/2104.13478","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:26aad8688d84091b84035d298736a0adb2b812f810a13320c41130d5410f9bb9","observation_id":"673d3bc0-b19c-44dc-a9b7-dfbc20e90b00","resolution":{"observed_at":"2026-08-15T15:55:16.408224Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09588","last_updated":"2026-04-12T19:24:18Z","snapshot_observed_at":"2026-08-15T09:26:37.270786Z","submitted_at":"2024-06-13T21:02:03Z","title":"Learning Color Equivariant Representations","version":7},"cited_work":{"arxiv_id":"2406.09588","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.09588","snapshot_observed_at":"2026-08-15T15:55:16.628931Z","title":"Learning Color Equivariant Representations","venue":"cs.CV","work_id":"2f628b53-277a-4480-8305-db8ce5793695","year":2024},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.411809Z"},"links":{"cited_paper":"/paper/2406.09588","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:8c945b7fd39db8c5319c6f65bb284d72a178880bdf7e50fed904c0702fc61175","observation_id":"1c5f2a55-8897-4ef7-b23e-ced29e1cf56d","resolution":{"observed_at":"2026-08-15T15:55:16.636356Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.04123","last_updated":"2025-03-06T06:00:55Z","snapshot_observed_at":"2026-08-16T12:52:38.643884Z","submitted_at":"2025-03-06T06:00:55Z","title":"GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.04123","snapshot_observed_at":"2026-08-15T15:55:16.415541Z","title":"Gagrasp: Geometric algebra diffusion for dexterous grasping,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.415541Z"},"links":{"cited_paper":"/paper/2503.04123","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:ad814f844e5fb1f6aeceb36ba21e3fa5955a87520498e0aed3a4e47ee8e96874","observation_id":"d84f610a-23ca-469f-8e6c-741427c5f48d","resolution":{"observed_at":"2026-08-15T15:55:16.415541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.11842","last_updated":"2024-05-25T20:31:15Z","snapshot_observed_at":"2026-08-16T15:06:31.603779Z","submitted_at":"2023-08-23T00:18:17Z","title":"${\\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.11842","snapshot_observed_at":"2026-08-15T15:55:16.419525Z","title":"E(3)-equivariant actor-critic methods for cooperative multi-agent reinforcement learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.419525Z"},"links":{"cited_paper":"/paper/2308.11842","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:1c1964b25a6ec53864a067e704f635ba955a7c8955b2c5502e3bc6757aff31a7","observation_id":"ca39b313-e6f9-4691-91d6-b3332ece0fbd","resolution":{"observed_at":"2026-08-15T15:55:16.419525Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.832465Z","title":"Boosting multiagent reinforcement learning via permutation invariant and permutation equivariant networks,","venue":null,"work_id":"7e9b55a0-4b4a-4232-a13a-d1a8a541e5f9","year":2022},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.423434Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:2e94ebfc0e63700c4a3f82fce6c375c3640c39585747535531fc70a3ea426110","observation_id":"384b80ef-5a04-40ee-9b59-01f6640c0c84","resolution":{"observed_at":"2026-08-15T15:55:16.836396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.821990Z","title":"Se (3)-equivariant robot learning and con- trol: A tutorial survey,","venue":null,"work_id":"36149f08-d53c-4e40-939a-dd546ee77026","year":2025},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.427118Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:4250daf709574e118d8610d24677fb6706acbf5a600e6a3046e8bf7d6d90a733","observation_id":"0ada074f-8a94-4b88-9db9-2947d1bfaa2c","resolution":{"observed_at":"2026-08-15T15:55:16.826060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.15615","last_updated":"2025-03-19T18:01:14Z","snapshot_observed_at":"2026-08-16T12:48:29.538464Z","submitted_at":"2025-03-19T18:01:14Z","title":"PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.15615","snapshot_observed_at":"2026-08-15T15:55:16.430666Z","title":"Penguin: Partially equivariant graph neural networks for sample efficient marl,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.430666Z"},"links":{"cited_paper":"/paper/2503.15615","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:ef051eb6c2c5d4a1903e182080ebefa7066e3dd8a63b909cc3bdcb63cf2dcba9","observation_id":"18987b13-0f9b-4b39-900e-c516d2c08abf","resolution":{"observed_at":"2026-08-15T15:55:16.430666Z","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":"2026-08-15T15:55:16.435437Z","title":"Group equivariant convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.435437Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:1357c37a2f9bb210a825636dcc7589be86936d13f58e54a993e810084951f1d5","observation_id":"722f6297-ab27-41c7-ad37-5b3fe1fd2c30","resolution":{"observed_at":"2026-08-15T15:55:16.435437Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.10130","last_updated":"2018-02-25T13:43:49Z","snapshot_observed_at":"2026-08-14T19:50:35.166135Z","submitted_at":"2018-01-30T18:28:30Z","title":"Spherical CNNs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.10130","snapshot_observed_at":"2026-08-15T15:55:16.439546Z","title":"Spherical cnns,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.439546Z"},"links":{"cited_paper":"/paper/1801.10130","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:ae69749925c1016e2899f1196ae3f417398082a247cb182b0912c26d7c512ba8","observation_id":"1b0d26ef-a8b1-45e1-9812-f1b20f10d65a","resolution":{"observed_at":"2026-08-15T15:55:16.439546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.08498","last_updated":"2016-12-27T04:38:28Z","snapshot_observed_at":"2026-08-14T21:23:38.943981Z","submitted_at":"2016-12-27T04:38:28Z","title":"Steerable CNNs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.08498","snapshot_observed_at":"2026-08-15T15:55:16.443647Z","title":"Steerable cnns,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.443647Z"},"links":{"cited_paper":"/paper/1612.08498","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:c54727754d2972e49cfa82ee7e10f2552771f0ce9f34855473414390cb6c83b8","observation_id":"9f9d8b13-b6db-4d19-a987-96e32df10e44","resolution":{"observed_at":"2026-08-15T15:55:16.443647Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.805926Z","title":"Learning so (3) equivariant representations with spherical cnns,","venue":null,"work_id":"7e024df9-45bf-4db4-815e-14db186932d1","year":2018},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.448082Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:e26c36b91198e45f144035e375162c8e3832d20e022aa35841f14b154bd9a4ef","observation_id":"d4a21a72-b4e5-48e5-a8de-5f6dfa560d10","resolution":{"observed_at":"2026-08-15T15:55:16.809792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.795424Z","title":"Equivariant multi-view networks,","venue":null,"work_id":"1a3d44d8-fbf1-4126-a690-ca2cfe2e3436","year":2019},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.451917Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:db48875743d5a13bfbd65d0fdd9658427672e74eff289c9bd4f7a5982524723f","observation_id":"225ed64f-43ab-4fbb-a23f-422efe12075a","resolution":{"observed_at":"2026-08-15T15:55:16.799438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.785314Z","title":"Sensing flow gradients is necessary for learning autonomous underwater navigation,","venue":null,"work_id":"e11f8dcf-d45b-4dd0-9bb4-299ce1ff9b24","year":2025},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.455507Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:a93b4afcf88b7e47dac50e13160152c75d33982f40f9474dbea27bfc285cbb8c","observation_id":"e2590fd9-c148-4e80-808b-d8ee4b9f3e5e","resolution":{"observed_at":"2026-08-15T15:55:16.788832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.459433Z","title":"Do transformers really perform badly for graph representation?","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.459433Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:07caa7bee53bfc987c15eb4d77d04ab983cbd36c6d4b0f61c0164c456444a455","observation_id":"12a87930-ba35-429c-9333-53f1adbb2f9d","resolution":{"observed_at":"2026-08-15T15:55:16.459433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04810","last_updated":"2023-01-07T15:17:55Z","snapshot_observed_at":"2026-08-16T17:15:50.816102Z","submitted_at":"2022-03-09T15:40:10Z","title":"Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04810","snapshot_observed_at":"2026-08-15T15:55:16.463799Z","title":"Benchmarking graphormer on large-scale molecular modeling datasets,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.463799Z"},"links":{"cited_paper":"/paper/2203.04810","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:1b13d6efc502aed5a1b77267080591a98ad409f6a9d8dc7972d28d2c467e2fb1","observation_id":"4cb337d6-4cda-4e10-aab0-b20d2930a3a1","resolution":{"observed_at":"2026-08-15T15:55:16.463799Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.770689Z","title":"Boost- ing sample efficiency and generalization in multi-agent reinforcement learning via equivariance,","venue":null,"work_id":"db22595a-c219-4342-a0e3-400b43aca963","year":2024},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.468358Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:4f7f219a3b4c8462b9c662629c3ac34e6856754672f9dcc39a6cb2dc9630984f","observation_id":"8df4cb57-85e0-4bbd-be66-3fdeecdf10a8","resolution":{"observed_at":"2026-08-15T15:55:16.774344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-13T11:38:10.906031Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-15T15:55:16.472230Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.472230Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:d241d11e2d6a718fa5b383b5009a329f6fb321001139c27cd2d5309740d16bb3","observation_id":"c70ea9b7-c7d1-4940-a896-3d3a288b4a88","resolution":{"observed_at":"2026-08-15T15:55:16.472230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-08-13T22:35:40.714745Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-15T15:55:16.476405Z","title":"Graph attention networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.476405Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:868b34cb4bd92f530c68efb7f48213a7b3a4cc05f5f958180f13fa7ab230d538","observation_id":"f501bd5a-4b63-4d8b-b59a-1f801dc72004","resolution":{"observed_at":"2026-08-15T15:55:16.476405Z","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":"2026-08-15T15:55:16.480789Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.480789Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:eefa04cc6bc6f2fe8fc64998fc00d3608d5bfbae773b089a399265c3df74c0e5","observation_id":"5fd432e2-593d-4e01-8947-a8d0d1d4a7a0","resolution":{"observed_at":"2026-08-15T15:55:16.480789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.04908","last_updated":"2018-07-24T04:13:05Z","snapshot_observed_at":"2026-08-14T21:11:53.639117Z","submitted_at":"2017-03-15T03:30:13Z","title":"Emergence of Grounded Compositional Language in Multi-Agent Populations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.04908","snapshot_observed_at":"2026-08-15T15:55:16.484863Z","title":"Emergence of grounded com- positional language in multi-agent populations,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.484863Z"},"links":{"cited_paper":"/paper/1703.04908","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:240c9366937324ca40a5084789e5cf8950d55b4cb8fcf3ed5685a0e8a44d0c95","observation_id":"3833da55-f7f1-454f-88c5-71f01979d641","resolution":{"observed_at":"2026-08-15T15:55:16.484863Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.756094Z","title":"Pettingzoo: Gym for multi-agent reinforcement learning,","venue":null,"work_id":"c5ddb863-3390-44f9-8e98-d66e1bb5e61e","year":2021},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.489615Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:65f7b14814cd29b09b0d2b60a54b03f4c3ba463327926673e3304cacfcfac3b2","observation_id":"2d73e198-0772-4b9b-87b6-33639a09d3cf","resolution":{"observed_at":"2026-08-15T15:55:16.759943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.493681Z","title":"Curriculum learning,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.493681Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:c59a41c6b4db9144e3aa1bc3aeeeee084d0781ae4ee8e267a90f264db654a8d7","observation_id":"f262c014-5651-40be-a15b-de5968b01cd6","resolution":{"observed_at":"2026-08-15T15:55:16.493681Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.735900Z","title":"Teacher–student curriculum learning,","venue":null,"work_id":"a8e4675d-0350-4c22-a052-4c11cc5f50dd","year":2019},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.497861Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:4b6f27e184e4257ca8c49c69e793f248ab4a31f330bfec1b7eb940a45f8bb8d0","observation_id":"42a5ee9e-8293-40d2-b98e-6f74eaf854c6","resolution":{"observed_at":"2026-08-15T15:55:16.743180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:55:16.501485Z","title":"An introduction to the kalman filter,","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.501485Z"},"links":{"citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:9c3c862187159f78d9576ca51e189a548f957c8c4d96dfff729687fa16d3e904","observation_id":"30282eb0-0d4c-45dc-91aa-3c0c4040684d","resolution":{"observed_at":"2026-08-15T15:55:16.501485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.15389","last_updated":"2025-03-05T15:35:35Z","snapshot_observed_at":"2026-08-16T13:49:49.206335Z","submitted_at":"2024-05-24T09:41:06Z","title":"Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.15389","snapshot_observed_at":"2026-08-15T15:55:16.505120Z","title":"Be- yond canonicalization: How tensorial messages improve equivariant message passing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T15:55:16.505120Z"},"links":{"cited_paper":"/paper/2405.15389","citing_paper":"/paper/2509.14431"},"observation_digest":"sha256:13e3afdbda3b432e8bfe2f562769e9ece4389b6937b7d1856b88642f942ffc73","observation_id":"5579bd01-37be-4a31-ac4b-65fec44d2b72","resolution":{"observed_at":"2026-08-15T15:55:16.505120Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.14431","last_updated":"2026-07-31T18:12:08Z","latest_version":2,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-16T14:03:20.469384Z","submitted_at":"2025-09-17T21:11:05Z","title":"Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control"},"reference_resolution":{"displayed":60,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":36,"verified_exact":1,"verified_fuzzy":23},"total_outbound_references":60},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2509.14431."}