{"as_of":"2026-08-15T13:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f5e8ba269808107c1b9466a2af472e507675359db4e66322d9120bcc7f4c3940","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-12T02:15:17.079975Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2607.03473/citation-record","integrity":"/paper/2607.03473/integrity","json":"/paper/2607.03473/citation-record.json","paper":"/paper/2607.03473"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-12T02:15:17.079975Z","title":"Multi-agent reinforcement learning for autonomous vehicles: A survey.Autonomous Intelligent Systems, 2 (1):27, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:329a70c118ff471fa74037b3be30ccb21d8bb78a1db176ba56cb542c0184276d","observation_id":"8ef9cac5-cc4d-469d-8d94-ee0a9e5c3b75","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:f902ca4755e2e034347f8afcef2110018daca1d2a165c835b3c282315f9eb0af","observation_id":"849882da-9f36-472b-84c8-047a78591a04","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Multi-agent reinforcement learning as a rehearsal for decentralized planning.Neurocomputing, 190:82–94, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:9dcd44589579bb8482db3126f59611c2d3c46a22687ec86ea89b5a17075bdf31","observation_id":"1c0a4742-745f-4c40-abba-3fe79e4f57b7","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Monotonic value function factorisation for deep multi-agent reinforcement learning.Journal of Machine Learning Research, 21(178):1–51, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:cc416818d7871341152eff14e954e99d459072ca946d3090a230f7da4c35b901","observation_id":"83fb0e7e-0f24-4fb2-a6f3-7e8ccba2259a","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Multi-agent incentive communication via decentralized teammate modeling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:c50420f3a8bbb57a891aca21b0b0212e73f22e1717111a72d87aea7baabedeca","observation_id":"ee7897d9-a37a-45f6-a64f-d0af49b193a6","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"The surprising effectiveness of ppo in cooperative multi-agent games.Advances in neural information processing systems, 35:24611–24624, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:4aa8d6220ccbf2691813d930b5517e3936cf7386b5267cd7c9eb17677a0c0f3d","observation_id":"e7233185-c472-4ee3-8b68-3e4301a7960e","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Multi-agent actor-critic for mixed cooperative-competitive environments","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:a51ef536bf131ce9d9c836024c5d22d6a094a57387bf8cb014c47240432c67ab","observation_id":"bed3de85-08ad-4a64-b280-511fa7f94402","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Learning to communicate with deep multi-agent reinforcement learning.Advances in neural information processing systems, 29, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:77e15e0b50b528e1d745e6d9ab8d27ad21ac3c1a9583bd5fbb75e0f6bdb07235","observation_id":"bac78f20-d2ef-4e13-a0cf-163952b9e686","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Learning multiagent communication with backpropa- gation.Advances in neural information processing systems, 29, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:051e655f5ab89a463ca20e6669a2595f289cb43f0611f77bf844aa6e84e336ad","observation_id":"6397cd9b-869e-4353-a443-a941f92b443d","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Learning when to communicate at scale in multiagent cooperative and competitive tasks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:77524ed5e201a9386a4441facac9a85cb78539bff9e2cd71e790799a84cc6706","observation_id":"7d8e6884-09e2-48ed-96d9-266f034ec5ee","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Efficient multi-agent communication via self-supervised information aggregation.Advances in Neural Information Processing Systems, 35:1020–1033, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:baca339d92b4a73c06d163a0a608b083690e93f8892578fc0ce33d70024e16b9","observation_id":"5011cc1f-05b6-47de-9226-b7bbbbc04f90","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"T2mac: Targeted and trusted multi-agent communication through selective engagement and evidence-driven integration","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:803f074e7ed83357eee96628fdc5960c9aeb053d5648a4e2387e3811966764b9","observation_id":"ec3ff897-3df1-432f-936f-b8fceca01548","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.05366","last_updated":"2020-07-19T01:30:56Z","snapshot_observed_at":"2026-08-03T11:01:01.939812Z","submitted_at":"2019-10-11T18:31:15Z","title":"Learning Nearly Decomposable Value Functions Via Communication Minimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.05366","snapshot_observed_at":"2026-07-12T02:15:17.079975Z","title":"Learning nearly decom- posable value functions via communication minimization.arXiv preprint arXiv:1910.05366, 2019","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"cited_paper":"/paper/1910.05366","citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:4edb87734c7f47d7bbf631024411e1dd10574e237f7accdaebf1de7c9cf07642","observation_id":"1bc60dfe-38a4-439b-bc69-cad298300eb5","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Learning efficient multi-agent communication: An information bottleneck approach","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:42459166daff4880e4ed78a8ce150b9028f417d1bbbd578c1b18a6a0673c2d21","observation_id":"09800af9-affd-4a16-b8a9-704df264eb4a","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.09675","last_updated":"2024-08-19T03:31:20Z","snapshot_observed_at":"2026-08-12T23:01:44.237290Z","submitted_at":"2024-08-19T03:31:20Z","title":"Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.09675","snapshot_observed_at":"2026-07-12T02:15:17.079975Z","title":"Multi-agent reinforcement learning for autonomous driving: A survey.arXiv preprint arXiv:2408.09675, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"cited_paper":"/paper/2408.09675","citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:34597a6d527aef98b699365030df8b9d86b9294906c1aac02f270d13d14e9d4b","observation_id":"3d29be34-18d8-455b-b1f5-fa0021df469b","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Efficient multi-agent communication via shapley message value","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:e2a6b169be9e8c0a70b8ffbdeada6f182ceeae68e8495a75f70ee3f9894b5272","observation_id":"1b82fd81-a5aa-460d-b97a-c86b3ec3c674","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"A value for n-person games","venue":null,"work_id":null,"year":1953},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:8c55d22a7dfdf822d818abd5affbc0cc8543f9d15bad58ca1408468c4ef4c8e0","observation_id":"289b044f-c6a8-45fd-8fd6-0b0ca7d3349d","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Dop: Off- policy multi-agent decomposed policy gradients","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:28b9a38b13e473c9ed38469a7a7211c498ada63f599bf5c759f6d5bc33b0d3cb","observation_id":"accb3c0f-7a18-4f02-93b7-9a2da24ad25f","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Towards making systems forget with machine unlearning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:4149c8a05b525e1fda857c5046666d4732d49220b84afa823c9b28add44b3cbd","observation_id":"5581224f-578d-4dfd-b409-6843865abd1e","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Eternal sunshine of the spotless net: Selective forgetting in deep networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:26c50961609e4da19a7970f67e464c359293c1332c04d92c3157031789f36bb6","observation_id":"b212d9c4-80ba-4af5-8af5-2fedb16c5a8d","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"A survey of machine unlearning.ACM Transactions on Intelligent Systems and Technology, 16(5):1–46, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:8653a6f22c5999c6730279950268b9bbdebe1b19aeba690e3e3bfdd6cef1d3a4","observation_id":"3f048548-be12-4c9d-b03e-56eb8bb59859","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Smacv2: An improved benchmark for cooperative multi-agent reinforcement learning.Advances in Neural Information Processing Systems, 36:37567–37593, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:8e952946c77320ca4f9166133ae908f50bde9f7ba48d10eb2acd79b1c46d7d1a","observation_id":"9fe177db-5ad1-48f7-8817-20fe29f0892f","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Tarmac: Targeted multi-agent communication","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:21ef3a79adbf67a07ae0401ea4b6710df9d332e03ba8bd07dc3e6ef4dff615c7","observation_id":"9cb369d8-820c-4f37-939d-063f19639ed1","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Towards true lossless sparse communication in multi-agent systems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:25685fa5da1756a63737d94fb59acdf7b6cdbc12238e6e87031d30b393f47ba1","observation_id":"c95abbc4-b1ab-4f7b-ac38-71685df78c5c","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Rescom: Reward- shaped curriculum for efficient multi-agent communication learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:aa4052284cd0684a825d26a6a3da4e24138d7b6c910229c9f1698669cf5d964e","observation_id":"7477245e-cfc7-410c-ac1d-3e1ddf8c2443","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Counterfactual multi-agent policy gradients","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:ad52a52970b77236d740bfecf3bd9f3a86c2043f465f6a76d4bcc9da72b65427","observation_id":"8b9ea464-7017-41f7-acb4-309681c64e2a","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Trust region policy optimisation in multi-agent reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:b4dbadbf3cf53081dc23441ec58f3966df13e63d7f6b328711eaa60c72550ab0","observation_id":"71e7966f-1acc-407b-81b4-5085e449c57d","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"Rgmcomm: Return gap minimization via discrete communications in multi-agent reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:c9d62261612f3bea02bde9014c025d128c676b71497620dfa57910f7d29a0ec9","observation_id":"ea45f994-0eeb-4786-97e1-617e52ebf61b","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","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":"2026-07-12T02:15:17.079975Z","title":"surrounded and reflect","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-12T02:15:17.079975Z"},"links":{"citing_paper":"/paper/2607.03473"},"observation_digest":"sha256:9e94ed45aca7c941ed9b3fd9a58319c2479b7a3bff4d7eb1697fc4ec38e1a508","observation_id":"cf92c0e7-4391-40aa-8a3d-442ae9554ec6","resolution":{"observed_at":"2026-07-12T02:15:17.079975Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.03473","last_updated":"2026-07-03T16:34:19Z","latest_version":1,"primary_category":"cs.MA","snapshot_observed_at":"2026-08-13T03:19:03.473216Z","submitted_at":"2026-07-03T16:34:19Z","title":"MUTE: Return-Preserving Communication Unlearning for Efficient Multi-Agent Coordination"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":29,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":29},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.03473."}