{"as_of":"2026-08-10T03:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1eeeee11d8eb18e0e9e65cc55801042685d283add9d4c37f9c997e0db48368ba","coverage":[{"denominator":149,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T17:58:35.801448Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.00726/citation-record","integrity":"/paper/2502.00726/integrity","json":"/paper/2502.00726/citation-record.json","paper":"/paper/2502.00726"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T17:58:33.758807Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.758807Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:e42bd493f88091f53e8a31ef1271914974e8a826efb5127c9315bba03313153e","observation_id":"65b843eb-6dfd-439f-9eab-6c3c8a0913ce","resolution":{"observed_at":"2026-08-09T17:58:33.758807Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05602","last_updated":"2024-06-10T09:58:55Z","snapshot_observed_at":"2026-08-07T09:40:05.681390Z","submitted_at":"2024-02-08T12:01:24Z","title":"AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05602","snapshot_observed_at":"2026-08-09T17:58:33.763604Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.763604Z"},"links":{"cited_paper":"/paper/2402.05602","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:312f711648b873cb4e0edb04e63ed5859a3e07605dd628a6dc17d1ddcb076811","observation_id":"d558e266-725e-4f94-a38c-89544f374f73","resolution":{"observed_at":"2026-08-09T17:58:33.763604Z","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":"2026-08-09T17:58:33.769250Z","title":"Goodfellow, Moritz Hardt, and Been Kim","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.769250Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:07191e9139908a62219cb34d6c9b15d9343c1c13f43966193b82befad4da2b65","observation_id":"f6439f73-ace9-4191-8d1e-ab307c4c37ea","resolution":{"observed_at":"2026-08-09T17:58:33.769250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1610.01644","last_updated":"2018-11-22T23:40:00Z","snapshot_observed_at":"2026-08-09T22:50:03.459615Z","submitted_at":"2016-10-05T20:59:01Z","title":"Understanding intermediate layers using linear classifier probes","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.01644","snapshot_observed_at":"2026-08-09T17:58:33.774391Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.774391Z"},"links":{"cited_paper":"/paper/1610.01644","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:8c312725834d35666833e4b2d66992865a72f86187e7e2a29b18da7602e32af0","observation_id":"a4a9e301-b854-4202-898d-afdb1cd6bb45","resolution":{"observed_at":"2026-08-09T17:58:33.774391Z","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":"2026-08-09T17:58:33.778968Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.778968Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:034268a43ce5bec62ac7f9937306d68417c11b1c1fe524b95a988f4d9a1bc696","observation_id":"f856586b-d110-4da1-9928-609c1a02592c","resolution":{"observed_at":"2026-08-09T17:58:33.778968Z","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":"2026-08-09T17:58:33.783957Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.783957Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:6e836c6a47d88be2b1ea2cafd8e3064d4a65d0fa0dc6912414b02cd8c7e4c048","observation_id":"c2552bc0-421b-4d77-a446-4c0219ce43e5","resolution":{"observed_at":"2026-08-09T17:58:33.783957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03002","last_updated":"2023-05-04T17:20:26Z","snapshot_observed_at":"2026-08-06T17:14:56.912041Z","submitted_at":"2023-05-04T17:20:26Z","title":"Evaluating Post-hoc Interpretability with Intrinsic Interpretability","version":1},"cited_work":{"arxiv_id":"2305.03002","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.03002","snapshot_observed_at":"2026-08-09T17:58:38.696882Z","title":"Evaluating Post-hoc Interpretability with Intrinsic Interpretability","venue":"cs.CV","work_id":"90a0223f-6762-4164-90d9-7d6e1767fdd1","year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.789373Z"},"links":{"cited_paper":"/paper/2305.03002","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:4c7d91286901ebda9f9de42f869d13a6909e279631561a8ca82f59d163615452","observation_id":"be9022d5-251b-44d4-b3f9-081121d46b18","resolution":{"observed_at":"2026-08-09T17:58:38.701636Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T17:58:33.794501Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.794501Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b280a5e64640021083f24b471caf7cdb21423cb58d33fd8ab0059ec05a35bed2","observation_id":"afb229e1-6b6a-4cb9-8b4c-d4dac0cb8b9a","resolution":{"observed_at":"2026-08-09T17:58:33.794501Z","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":"2026-08-09T17:58:33.799151Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.799151Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:6aed1c7263674485e5496b55b716773b7b9e8e464b18deeacea3b7d54146b96c","observation_id":"4ee9fe13-ca8b-4dbc-9608-8294c33ab145","resolution":{"observed_at":"2026-08-09T17:58:33.799151Z","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":"2026-08-09T17:58:33.803175Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.803175Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b5004688896c168f65fb857f23b75cc675c9d1727296de1dffc0468a0f3ef26f","observation_id":"3f961016-43dc-4059-a534-c27425a252d8","resolution":{"observed_at":"2026-08-09T17:58:33.803175Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08112","last_updated":"2025-11-11T01:13:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-03-14T17:47:09Z","title":"Eliciting Latent Predictions from Transformers with the Tuned Lens","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08112","snapshot_observed_at":"2026-08-09T17:58:33.807792Z","title":"Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.807792Z"},"links":{"cited_paper":"/paper/2303.08112","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:74f3406dd59bb9249b63dfab76d494753450d6c4b32a89534e26b70465a87b53","observation_id":"0bb44a16-8792-49fe-bdbf-0e2c458a5620","resolution":{"observed_at":"2026-08-09T17:58:33.807792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.03819","last_updated":"2025-04-03T01:51:37Z","snapshot_observed_at":"2026-08-04T07:02:56.483682Z","submitted_at":"2023-06-06T16:07:24Z","title":"LEACE: Perfect linear concept erasure in closed form","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.03819","snapshot_observed_at":"2026-08-09T17:58:33.812883Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.812883Z"},"links":{"cited_paper":"/paper/2306.03819","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:69aea06473ec25c2402bc2cd5be7830f46f3487481563a01dce55583147d316c","observation_id":"2ffa5dd3-d5bc-4afd-bcbb-7294553df07c","resolution":{"observed_at":"2026-08-09T17:58:33.812883Z","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":"2026-08-09T17:58:33.818109Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.818109Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:abe9ad8af44a7d028c13c3efc836b90f734eaf6b5d7b76088f6f89f17d134d7d","observation_id":"f5fecd94-5694-424f-9950-789b18097c5b","resolution":{"observed_at":"2026-08-09T17:58:33.818109Z","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":"2026-08-09T17:58:33.822698Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.822698Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:9947642c63869484a5e745e48fc718c4d82326247b848634d06a619e514858d8","observation_id":"e882507a-1a60-4001-9782-020d7eed12db","resolution":{"observed_at":"2026-08-09T17:58:33.822698Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.07143","last_updated":"2021-04-14T22:04:48Z","snapshot_observed_at":"2026-07-06T10:59:45.440838Z","submitted_at":"2021-04-14T22:04:48Z","title":"An Interpretability Illusion for BERT","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.07143","snapshot_observed_at":"2026-08-09T17:58:33.827272Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.827272Z"},"links":{"cited_paper":"/paper/2104.07143","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:2a68e2c9651feab22963ac9338fd6576222dbb0b5130aed7d05df54174bbe5ce","observation_id":"777759fa-b0ae-43f2-a7fd-eb557a7d9d8e","resolution":{"observed_at":"2026-08-09T17:58:33.827272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.15391","last_updated":"2024-02-23T15:47:26Z","snapshot_observed_at":"2026-08-03T22:04:23.465893Z","submitted_at":"2024-02-23T15:47:26Z","title":"Genie: Generative Interactive Environments","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.15391","snapshot_observed_at":"2026-08-09T17:58:33.843023Z","title":"Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.843023Z"},"links":{"cited_paper":"/paper/2402.15391","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:af2e61a83a0e48f8923b30888331bfb7bd1d381498aa0bb402c3d9c70277a66f","observation_id":"75478f8a-0186-45e7-b140-aae29be4f136","resolution":{"observed_at":"2026-08-09T17:58:33.843023Z","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":"2026-08-09T17:58:33.905725Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.905725Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:44479c6e83d99564b08ce4ae270d3303519dfa8071f0e179cbc1b66ab1f0be73","observation_id":"673e2c74-b62b-4795-aa4a-3104b8e84be0","resolution":{"observed_at":"2026-08-09T17:58:33.905725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.04478","last_updated":"2024-09-05T18:00:37Z","snapshot_observed_at":"2026-08-04T17:07:44.327819Z","submitted_at":"2024-09-05T18:00:37Z","title":"Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.04478","snapshot_observed_at":"2026-08-09T17:58:33.959044Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:33.959044Z"},"links":{"cited_paper":"/paper/2409.04478","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:a6a470d47eb9ca0109f2070751e4676fc922bc4570e5d6eaa9ec3f79fc768352","observation_id":"73169e71-9150-45b6-8d69-c444e54fb5ab","resolution":{"observed_at":"2026-08-09T17:58:33.959044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.05402","last_updated":"2021-06-09T21:34:11Z","snapshot_observed_at":"2026-08-09T01:04:46.825805Z","submitted_at":"2021-06-09T21:34:11Z","title":"Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.05402","snapshot_observed_at":"2026-08-09T17:58:34.007315Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.007315Z"},"links":{"cited_paper":"/paper/2106.05402","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:f555954533d96ecf08e3c3010946c2664205be884b69d5e4dbe9485e5df569f8","observation_id":"2807f861-e737-4b6d-9904-4fbb755a3f1a","resolution":{"observed_at":"2026-08-09T17:58:34.007315Z","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":"2026-08-09T17:58:34.084014Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.084014Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:52a7c0712d4ac4311afa245651dbb074072dc8f23318a9f7947722cf9912e59e","observation_id":"c602fef2-78ec-4f1c-af33-fb5e4f5a7cbd","resolution":{"observed_at":"2026-08-09T17:58:34.084014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.07475","last_updated":"2021-06-12T10:41:01Z","snapshot_observed_at":"2026-08-04T05:45:27.387352Z","submitted_at":"2021-02-15T11:33:52Z","title":"Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing","version":2},"cited_work":{"arxiv_id":"2102.07475","doi":null,"metadata_source":"pith","pith_arxiv_id":"2102.07475","snapshot_observed_at":"2026-08-09T17:58:38.573911Z","title":"Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing","venue":"cs.MA","work_id":"7476c47d-c121-4740-ac9f-4315705ea001","year":2021},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.117516Z"},"links":{"cited_paper":"/paper/2102.07475","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:e6f06830e9d3088ec9f52a5e651c1d35257a5888cef05c75d99769c8cfe85c00","observation_id":"bee2e229-2d5e-45ec-889a-2ea6fe28fe0b","resolution":{"observed_at":"2026-08-09T17:58:38.579568Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07169","last_updated":"2021-05-19T11:13:46Z","snapshot_observed_at":"2026-08-09T08:31:53.334105Z","submitted_at":"2020-06-12T13:24:50Z","title":"Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07169","snapshot_observed_at":"2026-08-09T17:58:34.121989Z","title":"Albrecht","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.121989Z"},"links":{"cited_paper":"/paper/2006.07169","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:31e0961f5bd4397dd28037ae6f0ec08ec69a22cb6c0d749cc10257cda2237a20","observation_id":"2e2d92a5-fb28-4829-86f6-555c2d54be8e","resolution":{"observed_at":"2026-08-09T17:58:34.121989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.00336","last_updated":"2017-10-03T00:47:58Z","snapshot_observed_at":"2026-08-09T22:09:51.559236Z","submitted_at":"2017-10-01T11:43:10Z","title":"Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.00336","snapshot_observed_at":"2026-08-09T17:58:34.127500Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.127500Z"},"links":{"cited_paper":"/paper/1710.00336","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:18069c12bc14f44d4ecbfb8287ac9fa8cebbfe2b044f881221c0f95ee81a1de0","observation_id":"08b772ba-a975-4add-94eb-d201e0b5a900","resolution":{"observed_at":"2026-08-09T17:58:34.127500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.22459","last_updated":"2024-10-29T18:48:18Z","snapshot_observed_at":"2026-07-06T19:41:50.722534Z","submitted_at":"2024-10-29T18:48:18Z","title":"Predicting Future Actions of Reinforcement Learning Agents","version":1},"cited_work":{"arxiv_id":"2410.22459","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.22459","snapshot_observed_at":"2026-08-09T17:58:38.519922Z","title":"Predicting Future Actions of Reinforcement Learning Agents","venue":"cs.AI","work_id":"b34792ca-b71c-4ca6-b58c-e5ab971a6baf","year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.132569Z"},"links":{"cited_paper":"/paper/2410.22459","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b00a012844d427c3703a7e3770b4e417fcaebd9971461a20a4411be39d9217e1","observation_id":"74610899-f22c-44fe-b995-c3a96bb4a2dd","resolution":{"observed_at":"2026-08-09T17:58:38.525478Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04332","last_updated":"2024-11-29T18:52:41Z","snapshot_observed_at":"2026-07-06T19:28:30.731752Z","submitted_at":"2024-10-06T02:43:49Z","title":"Gradient Routing: Masking Gradients to Localize Computation in Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04332","snapshot_observed_at":"2026-08-09T17:58:34.140898Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.140898Z"},"links":{"cited_paper":"/paper/2410.04332","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:0d5020ecbf47cda92678ef896f96a7e4499a92580918e6bcd06e3f8f503cfe53","observation_id":"e7a9b6fe-b3b7-464d-8dbb-a23ca77608ba","resolution":{"observed_at":"2026-08-09T17:58:34.140898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.14997","last_updated":"2023-10-28T20:05:52Z","snapshot_observed_at":"2026-07-06T15:21:19.790554Z","submitted_at":"2023-04-28T17:36:53Z","title":"Towards Automated Circuit Discovery for Mechanistic Interpretability","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.14997","snapshot_observed_at":"2026-08-09T17:58:34.145737Z","title":"Mavor-Parker, Aengus Lynch, Stefan Heimer- sheim, and Adrià Garriga-Alonso","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.145737Z"},"links":{"cited_paper":"/paper/2304.14997","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:7537837f5c3482ac1f135007b1df2e32d6fc24d510ceaff0d07a4fbad258985d","observation_id":"c689dd0f-0093-4237-bcae-33c9267541da","resolution":{"observed_at":"2026-08-09T17:58:34.145737Z","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":"2026-08-09T17:58:34.151313Z","title":"Lundberg, and Su-In Lee","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.151313Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:79d02b3c94f56c04b06cdcc1687d4fbab1e61e7e87b2cd0ad496d32cfe4bce51","observation_id":"62405468-213f-4a48-aa08-2b9c2ffaf5bf","resolution":{"observed_at":"2026-08-09T17:58:34.151313Z","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":"2026-08-09T17:58:34.156813Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.156813Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:6577412a65d2f6df02e794bc4f68955131b8191eed4d819a6b466dc79733e575","observation_id":"2b97e4a9-46f1-49a2-a815-5b19898a9e50","resolution":{"observed_at":"2026-08-09T17:58:34.156813Z","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":"2026-08-09T17:58:34.167058Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.167058Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:02a5769b52d582066a2297245fd54281d96070dba30d4d1c64d21d478c048a2f","observation_id":"c721d79a-1d43-4b10-9009-7727cfa477dc","resolution":{"observed_at":"2026-08-09T17:58:34.167058Z","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":"2026-08-09T17:58:34.172162Z","title":"Anders, Wojciech Samek, and Sebastian Lapuschkin","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.172162Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:91b5f5425f9a6e129b818efadf81c057318314bac220759b7421f33d5765212f","observation_id":"a99275f8-077e-40fa-b435-a385232fb5a8","resolution":{"observed_at":"2026-08-09T17:58:34.172162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11944","last_updated":"2024-11-06T22:37:30Z","snapshot_observed_at":"2026-08-04T08:40:10.913790Z","submitted_at":"2024-06-17T17:49:00Z","title":"Transcoders Find Interpretable LLM Feature Circuits","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11944","snapshot_observed_at":"2026-08-09T17:58:34.177121Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.177121Z"},"links":{"cited_paper":"/paper/2406.11944","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:c765ff77cba25263bd039c41caef8e19dc39ca7cccb358f6cd5d906accdb4338","observation_id":"bd06ef27-8348-4db3-9da3-6a3aeef9afdb","resolution":{"observed_at":"2026-08-09T17:58:34.177121Z","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":"2026-08-09T17:58:34.181982Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.181982Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:68c301e70bab6b0a5f81f017f0039a3dea1a801aadcba353f482a61d5fa412fc","observation_id":"ea090d64-0be1-4ae5-9087-8367dbb25916","resolution":{"observed_at":"2026-08-09T17:58:34.181982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.03656","last_updated":"2024-06-05T15:03:37Z","snapshot_observed_at":"2026-08-09T18:18:42.875151Z","submitted_at":"2023-12-06T18:25:53Z","title":"Interpretability Illusions in the Generalization of Simplified Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.03656","snapshot_observed_at":"2026-08-09T17:58:34.186793Z","title":"Lampinen, Lucas Dixon, Danqi Chen, and Asma Ghandeharioun","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.186793Z"},"links":{"cited_paper":"/paper/2312.03656","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:adbb46eed55e71d0a41838e64e1d63177b5483c559cfea75f53f5b14e2845230","observation_id":"f16e372b-3f59-4f61-b756-93891421f381","resolution":{"observed_at":"2026-08-09T17:58:34.186793Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00865","last_updated":"2024-04-23T18:40:33Z","snapshot_observed_at":"2026-07-06T16:41:56.099361Z","submitted_at":"2023-11-01T21:35:32Z","title":"Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00865","snapshot_observed_at":"2026-08-09T17:58:34.191249Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.191249Z"},"links":{"cited_paper":"/paper/2311.00865","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:858df4c76a5340a509fdea2b6aedf5514d15ffb769609e49574e156033437d52","observation_id":"f79d9541-d58c-4083-be5c-4b4ea086d025","resolution":{"observed_at":"2026-08-09T17:58:34.191249Z","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":"2026-08-09T17:58:34.232432Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.232432Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b25fc5b15076dbd1caac3025c712c52a6703a50f5bf9e8ea37ca2db37d300bb2","observation_id":"16607563-7fd7-4f34-b924-a3ffcd294672","resolution":{"observed_at":"2026-08-09T17:58:34.232432Z","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":"2026-08-09T17:58:34.330693Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.330693Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:f6b0f997fddc07c6cc328795fedbd0a9a1d6f3759e18efd2ebed7596ab9bfb8c","observation_id":"178d1e56-2d77-4707-b023-1a6673a87417","resolution":{"observed_at":"2026-08-09T17:58:34.330693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.00138","last_updated":"2018-09-10T18:42:40Z","snapshot_observed_at":"2026-07-31T01:34:33.396969Z","submitted_at":"2017-10-31T23:03:17Z","title":"Visualizing and Understanding Atari Agents","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.00138","snapshot_observed_at":"2026-08-09T17:58:34.346505Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.346505Z"},"links":{"cited_paper":"/paper/1711.00138","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:cc37a6ccc0178ed483d44b23fe3f68677d266d3afde8ce2752a0e988dba4997e","observation_id":"47a099b8-85ce-4690-a3dc-dfffeb589353","resolution":{"observed_at":"2026-08-09T17:58:34.346505Z","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":"2026-08-09T17:58:34.409640Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.409640Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b9496da5dff4f4eea2d534c35d891659b18acc79c32617c2f25f23fe75e77465","observation_id":"9f2ad749-23b2-4a19-8ca3-43871d88c0fc","resolution":{"observed_at":"2026-08-09T17:58:34.409640Z","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":"2026-08-09T17:58:34.414164Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.414164Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:50469d6f433fd51ed9c47f2bfa4256a58238d1c6638f12ef47aef2768e0d7925","observation_id":"c850d4cd-8516-403d-a69f-559708875423","resolution":{"observed_at":"2026-08-09T17:58:34.414164Z","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":"2026-08-09T17:58:34.418509Z","title":"Gupta, Maxim Egorov, and Mykel J","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.418509Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:802507d7966ef30c787af6e3d2ae45c7d8f21219ded7a22be53e3a99644c7ace","observation_id":"596feb83-558a-48ff-a78c-c239219df8f8","resolution":{"observed_at":"2026-08-09T17:58:34.418509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04104","last_updated":"2024-04-17T17:41:20Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-01-10T18:12:16Z","title":"Mastering Diverse Domains through World Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04104","snapshot_observed_at":"2026-08-09T17:58:34.423544Z","title":"Pazukonis, Jimmy Ba, and Timothy P","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.423544Z"},"links":{"cited_paper":"/paper/2301.04104","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:f2c277f7bdc0b403bb00dc47b88bcc4e521812e672da5f09e0d236a10998f272","observation_id":"d2ef5e7d-260d-439d-81dd-06edfa790df0","resolution":{"observed_at":"2026-08-09T17:58:34.423544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.09702","last_updated":"2023-09-18T12:08:14Z","snapshot_observed_at":"2026-08-08T02:41:59.789583Z","submitted_at":"2023-09-18T12:08:14Z","title":"Information based explanation methods for deep learning agents -- with applications on large open-source chess models","version":1},"cited_work":{"arxiv_id":"2309.09702","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.09702","snapshot_observed_at":"2026-08-09T17:58:38.351633Z","title":"Information based explanation methods for deep learning agents -- with applications on large open-source chess models","venue":"cs.LG","work_id":"722ce494-d286-4ea4-a9b0-f70752fecd73","year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.428749Z"},"links":{"cited_paper":"/paper/2309.09702","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:3a02e937eff65ed1cd04934cd625836af5dde9bda490c8502e3eb6b9598813c5","observation_id":"428f95f6-2f0f-43d7-9d80-d9874317bb83","resolution":{"observed_at":"2026-08-09T17:58:38.357079Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03578","last_updated":"2026-01-28T04:55:23Z","snapshot_observed_at":"2026-07-06T17:25:53.950578Z","submitted_at":"2024-02-05T23:06:42Z","title":"LLM Multi-Agent Systems: Challenges and Open Problems","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03578","snapshot_observed_at":"2026-08-09T17:58:34.433749Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.433749Z"},"links":{"cited_paper":"/paper/2402.03578","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:5d856e25625fe6f83eb9e87251e4118b599d5866aeddee1983b15323e1f6b45a","observation_id":"6d7ece02-38f8-414a-91aa-3e06ce11ca94","resolution":{"observed_at":"2026-08-09T17:58:34.433749Z","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":"2026-08-09T17:58:34.438754Z","title":"Zhang, Shaoqing Ren, and Jian Sun","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.438754Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b1ab8ac78839ffd132f5e22086606c8fc15b2909034f251023f4185ef29edda7","observation_id":"f03d42d7-d992-4cdc-b7eb-650b8d7c43ae","resolution":{"observed_at":"2026-08-09T17:58:34.438754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.06861","last_updated":"2023-04-27T08:58:50Z","snapshot_observed_at":"2026-07-06T12:37:36.166956Z","submitted_at":"2022-02-14T16:45:36Z","title":"Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.06861","snapshot_observed_at":"2026-08-09T17:58:34.443745Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.443745Z"},"links":{"cited_paper":"/paper/2202.06861","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:a232317dbf56bb1a305537eb630bc5a6aaf346e45098ed034d2ac5512a7f78ad","observation_id":"474a36d7-298a-4dfe-8884-c6977b97eff0","resolution":{"observed_at":"2026-08-09T17:58:34.443745Z","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":"2026-08-09T17:58:34.448303Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.448303Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:36cea4efec282881df437fa26676b5f773faa07be975d6913b5444d621020ccf","observation_id":"a21500ed-f146-4bb6-a904-8e036df6e20f","resolution":{"observed_at":"2026-08-09T17:58:34.448303Z","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":"2026-08-09T17:58:34.453688Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.453688Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:765dbaec37ff63a5f8b09f87118d3220386631f9e61fa9d3f7d737ca67df74eb","observation_id":"650d48a7-83fa-4957-a906-169cbad4a696","resolution":{"observed_at":"2026-08-09T17:58:34.453688Z","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":"2026-08-09T17:58:34.458689Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.458689Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:9e7b825da31e6b6725bc52d8390cca66848df8b9332a136f45f34a1a99d45718","observation_id":"18527be3-a9cd-4a6e-bcbe-facb0a0100cc","resolution":{"observed_at":"2026-08-09T17:58:34.458689Z","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":"2026-08-09T17:58:34.463218Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.463218Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:40f08b1c5ece5249a94a47863ca38e2691aa7c9109dab8f9050ecacf443ea15b","observation_id":"ad8c16ce-82c6-4461-bfc1-53dda1cf28a0","resolution":{"observed_at":"2026-08-09T17:58:34.463218Z","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":"2026-08-09T17:58:34.467625Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.467625Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:7afd4fd4798cf3fa70ac74d339cd61888e83d6240a996a32982645e4f2d5bddc","observation_id":"675ac4c1-b457-4ce3-86dc-8732021d8bd8","resolution":{"observed_at":"2026-08-09T17:58:34.467625Z","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":"2026-08-09T17:58:34.477098Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.477098Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:8d336256194618e1cabf8b23f6dece7dc92b6ff367a3c0462c43931f9518a8ba","observation_id":"a17e8f16-c1cc-4e4d-bc6d-7ab51408cca9","resolution":{"observed_at":"2026-08-09T17:58:34.477098Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.02566","last_updated":"2023-12-05T08:24:26Z","snapshot_observed_at":"2026-07-06T16:57:01.638187Z","submitted_at":"2023-12-05T08:24:26Z","title":"Structured World Representations in Maze-Solving Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.02566","snapshot_observed_at":"2026-08-09T17:58:34.487265Z","title":"Ivanitskiy, Alex F Spies, Tilman Rauker, Guillaume Corlouer, Chris Mathwin, Lucia Quirke, Can Rager, Rusheb Shah, Dan Valentine, Cecilia G","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.487265Z"},"links":{"cited_paper":"/paper/2312.02566","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:5b1fdae0abcad864867abfbb567e8f643e3c00219a5727ee024f0943bf71025e","observation_id":"14093e62-3412-4abc-a205-9a867b7c139b","resolution":{"observed_at":"2026-08-09T17:58:34.487265Z","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":"2026-08-09T17:58:34.492255Z","title":"Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio García Castañeda, Charlie Beattie, Neil C","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.492255Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:3d866964acdb672ce149015b4ec67b82c78420c56d4878500c9ba5c592e89884","observation_id":"8e3e9039-1dba-47c5-aaa7-c4a2dafd210b","resolution":{"observed_at":"2026-08-09T17:58:34.492255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17700","last_updated":"2024-08-26T19:26:06Z","snapshot_observed_at":"2026-08-09T00:17:09.775764Z","submitted_at":"2024-02-27T17:25:37Z","title":"RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17700","snapshot_observed_at":"2026-08-09T17:58:34.482708Z","title":"ArXiv abs/2402.17700 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.482708Z"},"links":{"cited_paper":"/paper/2402.17700","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:9e40f69d9758706ad8dd6d12193ee883a3e03bcaaaa302d33be8d8ac67e5433a","observation_id":"35972b76-a11f-489e-93a2-65238d76c50a","resolution":{"observed_at":"2026-08-09T17:58:34.482708Z","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":"2026-08-09T17:58:34.508267Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.508267Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:63a6a4c2745aa7bf07911f7a0416790e8631a1ed1eebddab9df35df407e57e42","observation_id":"3c0c336a-ddf1-40b1-852c-7cf04911d8f5","resolution":{"observed_at":"2026-08-09T17:58:34.508267Z","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":"2026-08-09T17:58:34.512799Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.512799Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:7c44860a2235903b52f1e5c2a784118061b75f80f22ee80773e05550ce5b7a5d","observation_id":"cbc7a1b2-70b2-4cfa-8f96-6bc0cd53ab04","resolution":{"observed_at":"2026-08-09T17:58:34.512799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12865","last_updated":"2024-02-20T09:57:08Z","snapshot_observed_at":"2026-07-06T17:32:44.565172Z","submitted_at":"2024-02-20T09:57:08Z","title":"Backward Lens: Projecting Language Model Gradients into the Vocabulary Space","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.12865","snapshot_observed_at":"2026-08-09T17:58:34.663641Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.663641Z"},"links":{"cited_paper":"/paper/2402.12865","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:04e8f399fa634da60242f4959469267f07ffa916690234a5cbb536635038d234","observation_id":"e86bef52-c10e-4ec3-84a9-6796dfc7b738","resolution":{"observed_at":"2026-08-09T17:58:34.663641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00877","last_updated":"2024-06-02T21:57:32Z","snapshot_observed_at":"2026-08-09T21:01:28.567469Z","submitted_at":"2024-06-02T21:57:32Z","title":"Evidence of Learned Look-Ahead in a Chess-Playing Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00877","snapshot_observed_at":"2026-08-09T17:58:34.502210Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.502210Z"},"links":{"cited_paper":"/paper/2406.00877","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:f6e546fda86c04cebed93336ddabc0b96d20f2e7930bf258b258e5f3e6545f5f","observation_id":"7ac49061-4086-47f7-b49b-1b8b6cc12376","resolution":{"observed_at":"2026-08-09T17:58:34.502210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.11279","last_updated":"2018-06-07T04:33:27Z","snapshot_observed_at":"2026-08-04T20:10:18.590341Z","submitted_at":"2017-11-30T09:26:12Z","title":"Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.11279","snapshot_observed_at":"2026-08-09T17:58:34.865388Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.865388Z"},"links":{"cited_paper":"/paper/1711.11279","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:a9c095fb0b91ac11662b39211b4197d5c9c652051393cfe2186b7d34a517d271","observation_id":"077d98ea-bae0-43c9-8e43-dc619218b8c4","resolution":{"observed_at":"2026-08-09T17:58:34.865388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.10906","last_updated":"2024-04-16T20:53:17Z","snapshot_observed_at":"2026-07-06T18:01:14.416754Z","submitted_at":"2024-04-16T20:53:17Z","title":"Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop","version":1},"cited_work":{"arxiv_id":"2404.10906","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.10906","snapshot_observed_at":"2026-08-09T17:58:38.014229Z","title":"Towards a Research Community in Interpretable Reinforcement Learning: the InterpPol Workshop","venue":"cs.AI","work_id":"29c50177-6dc5-4999-b500-01019fa55eda","year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.929780Z"},"links":{"cited_paper":"/paper/2404.10906","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:d879572c17a43feacf73a419c69e80ad3b43e6fd45aa0a8c2ba590178ed98eb1","observation_id":"857431f0-b6fc-4bf1-b2ef-00003c24ce7b","resolution":{"observed_at":"2026-08-09T17:58:38.077506Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.00745","last_updated":"2024-03-01T18:43:51Z","snapshot_observed_at":"2026-08-08T09:40:22.806772Z","submitted_at":"2024-03-01T18:43:51Z","title":"AtP*: An efficient and scalable method for localizing LLM behaviour to components","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.00745","snapshot_observed_at":"2026-08-09T17:58:34.935332Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.935332Z"},"links":{"cited_paper":"/paper/2403.00745","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:db0f887e9a83a6fce485c8f1dc5a1994da81c01185905fcf680777fa37ff54d6","observation_id":"1feee041-aced-46ba-90f2-5eef86974bed","resolution":{"observed_at":"2026-08-09T17:58:34.935332Z","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":"2026-08-09T17:58:34.940900Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.940900Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:ad7106d8079945f522ee87314796db5a36bb56cfb8cb3c536d9d829d8ca927e6","observation_id":"240936e3-953b-4bd6-a87a-1d99f7658706","resolution":{"observed_at":"2026-08-09T17:58:34.940900Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.08468","last_updated":"2023-12-13T19:10:10Z","snapshot_observed_at":"2026-07-06T17:01:18.018700Z","submitted_at":"2023-12-13T19:10:10Z","title":"On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL","version":1},"cited_work":{"arxiv_id":"2312.08468","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.08468","snapshot_observed_at":"2026-08-09T17:58:38.157551Z","title":"On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL","venue":"cs.AI","work_id":"ea921ccc-6909-480a-b18b-7370c7c5f221","year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.717439Z"},"links":{"cited_paper":"/paper/2312.08468","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:2a9fb3e1c2831229b797d1670100f61239b0bd1d25ac6206be2a173777357d18","observation_id":"6da0ed42-df31-4356-b6a5-5aeabb989ca7","resolution":{"observed_at":"2026-08-09T17:58:38.184726Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T17:58:34.956644Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.956644Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:0ed98c378acd9a92eefb15e52185d36763af22638d682c4516282448dffe1596","observation_id":"c5b5ee90-fe90-4f79-ba37-934db3a920bd","resolution":{"observed_at":"2026-08-09T17:58:34.956644Z","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":"2026-08-09T17:58:34.961566Z","title":"Renard, and Marcin Detyniecki","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.961566Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b2f355352f3473fd140e62253d1adf0c54901618c7bd2a71ee5a6e406d34d320","observation_id":"c3b3d285-e882-4def-8a0d-2b33e0dcc46f","resolution":{"observed_at":"2026-08-09T17:58:34.961566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12970","last_updated":"2024-04-28T01:48:57Z","snapshot_observed_at":"2026-08-09T09:52:16.464138Z","submitted_at":"2023-11-21T20:16:02Z","title":"Clustered Policy Decision Ranking","version":2},"cited_work":{"arxiv_id":"2311.12970","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.12970","snapshot_observed_at":"2026-08-09T17:58:37.950429Z","title":"Clustered Policy Decision Ranking","venue":"cs.LG","work_id":"2190e103-3c86-44bf-98f3-f589faced30f","year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.966127Z"},"links":{"cited_paper":"/paper/2311.12970","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:c1c1c844281d22c4d37114e3c677f79a7b907d57a8f9f1f5b94f39c70c20d4c9","observation_id":"05259217-3e98-4bcd-b4cc-5ac23063ed74","resolution":{"observed_at":"2026-08-09T17:58:37.955937Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T17:58:34.970848Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.970848Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:a24769ac67a84936eea4a9fb0d9446ee0c324e6ffb65d4ac941060d01dcbe083","observation_id":"8ff05f0e-cc9a-4861-b5eb-6eebffc535a2","resolution":{"observed_at":"2026-08-09T17:58:34.970848Z","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":"2026-08-09T17:58:34.945729Z","title":"Engelhardt, Wolfgang Konen, and Laurenz Wiskott","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.945729Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:db43f73e43b2d6fd746908915280c041dc615baefd4d4d56ab0e62f6f6af9ad5","observation_id":"30162d0c-3c86-45af-8eae-15120dedfe3e","resolution":{"observed_at":"2026-08-09T17:58:34.945729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.12067","last_updated":"2024-02-19T11:35:01Z","snapshot_observed_at":"2026-07-06T17:32:10.828230Z","submitted_at":"2024-02-19T11:35:01Z","title":"Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks","version":1},"cited_work":{"arxiv_id":"2402.12067","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.12067","snapshot_observed_at":"2026-08-09T17:58:37.973406Z","title":"Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks","venue":"cs.LG","work_id":"bc6c7ea6-0a55-431f-ad01-3d7345849175","year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.951276Z"},"links":{"cited_paper":"/paper/2402.12067","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:ef8a0b818415389c75f5e5d43cf09ff43f58290e537a710eb4ac31798af2971d","observation_id":"8b94741b-f19b-43a7-a01a-8f0d10f3d3f7","resolution":{"observed_at":"2026-08-09T17:58:37.979477Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T17:58:34.985589Z","title":"Lundberg and Su-In Lee","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.985589Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:deb387d4a0caa13b2dc0eb8cf6bc55b198360ce9249a9249c7ac08efc4db60a9","observation_id":"0a8b67ed-bcd5-4c4c-96af-a7acdb603b90","resolution":{"observed_at":"2026-08-09T17:58:34.985589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.05386","last_updated":"2024-11-13T01:40:53Z","snapshot_observed_at":"2026-08-08T17:53:17.777038Z","submitted_at":"2024-05-08T19:31:06Z","title":"Interpretability Needs a New Paradigm","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.05386","snapshot_observed_at":"2026-08-09T17:58:34.990181Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.990181Z"},"links":{"cited_paper":"/paper/2405.05386","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b943c23726fe714802a2009131aaeea059d929ddcfc2ba3ca553ab06372bf850","observation_id":"3328d00a-61f0-4cf4-9a6b-1e2d93d4c71e","resolution":{"observed_at":"2026-08-09T17:58:34.990181Z","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":"2026-08-09T17:58:34.995030Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.995030Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b7383c5e1d61d3b65aebd00aeda8143d0a8fbcfe21f550ce314e274443a9946f","observation_id":"6ab42b82-4dbd-4efe-8c98-d7509854e4fa","resolution":{"observed_at":"2026-08-09T17:58:34.995030Z","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":"2026-08-09T17:58:34.999087Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.999087Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:d9461402c6e10a5866d40b593cb6e822475844671847dda7f4eb9e52dcf8a71b","observation_id":"62acd5db-3215-43cb-b415-4690691683aa","resolution":{"observed_at":"2026-08-09T17:58:34.999087Z","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":"2026-08-09T17:58:34.975548Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.975548Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b1093da40520323a4d331b442d892be40a2829f276a9211d57e010bf4c9fb11e","observation_id":"ab7ce3f4-2f7b-4289-bdff-b2ccf8d32d88","resolution":{"observed_at":"2026-08-09T17:58:34.975548Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02275","last_updated":"2020-03-14T20:33:00Z","snapshot_observed_at":"2026-08-04T19:23:21.346128Z","submitted_at":"2017-06-07T17:35:00Z","title":"Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.02275","snapshot_observed_at":"2026-08-09T17:58:34.980166Z","title":"Abbeel, and Igor Mordatch","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:34.980166Z"},"links":{"cited_paper":"/paper/1706.02275","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:05e58f155c49bdab353b79ce7ec12295047ff72a029bb801ab055372edc9ec0d","observation_id":"f0e58d3f-4f31-4d53-a87e-5ba04550af6b","resolution":{"observed_at":"2026-08-09T17:58:34.980166Z","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":"2026-08-09T17:58:35.075845Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.075845Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:653fbe2afdcf285bd0e6b9f03dd2a58724d966c7b576e0c36c88722fa13137e1","observation_id":"72cb52f5-1785-4b30-9a54-283d888d57b2","resolution":{"observed_at":"2026-08-09T17:58:35.075845Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T17:58:35.125766Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.125766Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:fca3eef9112134ff8c564b1122831e09746ca8bda64541ec26c51f2b411af4a5","observation_id":"b03b03de-fb1a-4007-8e74-f7a58c379210","resolution":{"observed_at":"2026-08-09T17:58:35.125766Z","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":"2026-08-09T17:58:35.188013Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.188013Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:96f8706ff76cd03210fb1c561b2eb7d089fdd8a6b7965ac0b469e9993d43d56e","observation_id":"1f78a458-5c42-442a-98ba-cfaa52fad6d1","resolution":{"observed_at":"2026-08-09T17:58:35.188013Z","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":"2026-08-09T17:58:35.320605Z","title":"Kamhoua, Evangelos E","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.320605Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:59880d9a6db549b55aa233cd85660f7618486acb996c50e7eaf62fd303edc54e","observation_id":"b0c83b20-2e40-456f-b5e7-275922c1e959","resolution":{"observed_at":"2026-08-09T17:58:35.320605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.08466","last_updated":"2024-01-26T13:07:55Z","snapshot_observed_at":"2026-07-06T17:01:18.018700Z","submitted_at":"2023-12-13T19:09:37Z","title":"Efficiently Quantifying Individual Agent Importance in Cooperative MARL","version":2},"cited_work":{"arxiv_id":"2312.08466","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.08466","snapshot_observed_at":"2026-08-09T17:58:37.890225Z","title":"Efficiently Quantifying Individual Agent Importance in Cooperative MARL","venue":"cs.AI","work_id":"f484d154-5ff0-4252-a823-f82ce75c26b7","year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.004225Z"},"links":{"cited_paper":"/paper/2312.08466","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:90b4894019bf5a118d148f570eeef8a3ea8ab43356f4c25375734acb652bc25e","observation_id":"74b2940d-2f34-478e-9ef1-af6b8939bbf9","resolution":{"observed_at":"2026-08-09T17:58:37.895053Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.03526","last_updated":"2023-08-07T12:21:37Z","snapshot_observed_at":"2026-07-06T16:03:22.749870Z","submitted_at":"2023-08-07T12:21:37Z","title":"AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.03526","snapshot_observed_at":"2026-08-09T17:58:35.028666Z","title":"Czarnecki, Nando de Freitas, and Oriol Vinyals","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.028666Z"},"links":{"cited_paper":"/paper/2308.03526","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:10e3031787f64d247e040ca1e8e4bc1b9fc9e106531cea2514a6ce9d92239dbc","observation_id":"8322f049-573f-4109-9dad-38c2c5e5b58b","resolution":{"observed_at":"2026-08-09T17:58:35.028666Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1512.02479","last_updated":"2015-12-08T14:25:29Z","snapshot_observed_at":"2026-07-06T04:39:09.028597Z","submitted_at":"2015-12-08T14:25:29Z","title":"Explaining NonLinear Classification Decisions with Deep Taylor Decomposition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.02479","snapshot_observed_at":"2026-08-09T17:58:35.439276Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.439276Z"},"links":{"cited_paper":"/paper/1512.02479","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:3de006b68b5f4377fa2dc3b0d74644313c8a5659a065a3ad6eca9af52da9a9fa","observation_id":"59cfc6b7-fb22-4761-8c22-e73f57e33e5b","resolution":{"observed_at":"2026-08-09T17:58:35.439276Z","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":"2026-08-09T17:58:35.444499Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.444499Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:d6e2b6b1fb8a1dddd0cd5da89955e7f31032fbecf8ecd3115846cf6b772337c4","observation_id":"950b3852-534a-40a0-b6f0-4500483d7557","resolution":{"observed_at":"2026-08-09T17:58:35.444499Z","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":"2026-08-09T17:58:35.455765Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.455765Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:8ccddebc37a9dbd0edeee459f10e3048a6e4917ab9f98451e654b999754325f0","observation_id":"9c680819-d17a-4b30-894b-4434f96f3ced","resolution":{"observed_at":"2026-08-09T17:58:35.455765Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.11895","last_updated":"2022-09-24T00:43:19Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-09-24T00:43:19Z","title":"In-context Learning and Induction Heads","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.11895","snapshot_observed_at":"2026-08-09T17:58:35.460770Z","title":"Brown, Jack Clark, Jared Kaplan, Sam McCan- dlish, and Christopher Olah","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.460770Z"},"links":{"cited_paper":"/paper/2209.11895","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:96d674132a7bf42082d6fa0795e3319a3a8e75fa006ec752db3d08453e2c81e7","observation_id":"817a4a29-a5d1-4dec-af2b-b74c500c5d19","resolution":{"observed_at":"2026-08-09T17:58:35.460770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08043","last_updated":"2023-10-12T05:33:54Z","snapshot_observed_at":"2026-08-05T23:19:46.123162Z","submitted_at":"2023-10-12T05:33:54Z","title":"Understanding and Controlling a Maze-Solving Policy Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08043","snapshot_observed_at":"2026-08-09T17:58:35.384058Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.384058Z"},"links":{"cited_paper":"/paper/2310.08043","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:59a4ab5e60e3e379fb086d5635817736039bfdfbb8f2488bfe654f67d89fc376","observation_id":"7e94b896-eb04-446a-b460-b1d900990066","resolution":{"observed_at":"2026-08-09T17:58:35.384058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.19223","last_updated":"2023-05-30T17:14:01Z","snapshot_observed_at":"2026-07-06T15:35:33.952097Z","submitted_at":"2023-05-30T17:14:01Z","title":"Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safety","version":1},"cited_work":{"arxiv_id":"2305.19223","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.19223","snapshot_observed_at":"2026-08-09T17:58:37.830175Z","title":"Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safety","venue":"cs.AI","work_id":"c1075bc6-2c5d-429a-814f-40bdb4365e7f","year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.431659Z"},"links":{"cited_paper":"/paper/2305.19223","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:24250fd3adae3ab0227715d0560b0c226500111c274ca5a4d205abcfaa038a56","observation_id":"4a00d4b1-49a3-4d8c-812f-f16c7d7abd85","resolution":{"observed_at":"2026-08-09T17:58:37.834852Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T17:58:35.476436Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.476436Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:ec06349e15ede0441b347cc41c9cc9a9a6d4ef5913b1055b619ad107db3d4ca2","observation_id":"37900b7b-6a65-4a16-a097-3039503ab7d9","resolution":{"observed_at":"2026-08-09T17:58:35.476436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01267","last_updated":"2024-07-24T17:13:55Z","snapshot_observed_at":"2026-08-08T00:49:26.550027Z","submitted_at":"2024-03-02T17:10:44Z","title":"Dissecting Language Models: Machine Unlearning via Selective Pruning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.01267","snapshot_observed_at":"2026-08-09T17:58:35.481241Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.481241Z"},"links":{"cited_paper":"/paper/2403.01267","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:2ae1146b7e942e68607cbf9edf4ba8d899a7c1b8b8a6d9b1b28ac69aa35901ae","observation_id":"920f9ee8-a198-4b0e-9584-e826444ac983","resolution":{"observed_at":"2026-08-09T17:58:35.481241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05217","last_updated":"2023-10-19T21:25:32Z","snapshot_observed_at":"2026-08-02T10:20:00.635719Z","submitted_at":"2023-01-12T18:56:49Z","title":"Progress measures for grokking via mechanistic interpretability","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05217","snapshot_observed_at":"2026-08-09T17:58:35.450766Z","title":"ArXiv abs/2301.05217 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.450766Z"},"links":{"cited_paper":"/paper/2301.05217","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:a2b3fb09375e3ee8c8304be2f5bb71bd6e1ea52381eaca4683869807ddddc2e0","observation_id":"800e2769-5982-4d10-8ef6-e459b01eda81","resolution":{"observed_at":"2026-08-09T17:58:35.450766Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04028","last_updated":"2024-06-06T12:57:31Z","snapshot_observed_at":"2026-07-06T18:26:28.988101Z","submitted_at":"2024-06-06T12:57:31Z","title":"Contrastive Sparse Autoencoders for Interpreting Planning of Chess-Playing Agents","version":1},"cited_work":{"arxiv_id":"2406.04028","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.04028","snapshot_observed_at":"2026-08-09T17:58:37.525584Z","title":"Contrastive Sparse Autoencoders for Interpreting Planning of Chess-Playing Agents","venue":"cs.AI","work_id":"c0879a54-93df-4d5b-a44d-1d83125a0396","year":2024},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.490470Z"},"links":{"cited_paper":"/paper/2406.04028","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:02c81fe3500473f41562f61f7fa929e3ff7246254b30e6fa9f1836454029ebb7","observation_id":"7527b643-3007-44fb-be48-df792df011a4","resolution":{"observed_at":"2026-08-09T17:58:37.623407Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.06665","last_updated":"2025-08-29T04:07:26Z","snapshot_observed_at":"2026-07-06T14:17:30.922581Z","submitted_at":"2022-11-12T13:52:06Z","title":"A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.06665","snapshot_observed_at":"2026-08-09T17:58:35.494855Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.494855Z"},"links":{"cited_paper":"/paper/2211.06665","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:b2be35340a5604eaa094887319b8cdb82ad623146984a0f347b7d8e15e9f9376","observation_id":"75c9485e-04aa-4eee-ab1e-b330b4ecfb1c","resolution":{"observed_at":"2026-08-09T17:58:35.494855Z","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":"2026-08-09T17:58:35.466276Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.466276Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:431507e18c41db13172f7d56cead28a9962b4163b13f31c8a985460b9a1b6f73","observation_id":"2eec571f-bdfe-48bb-822c-76593073558c","resolution":{"observed_at":"2026-08-09T17:58:35.466276Z","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":"2026-08-09T17:58:35.471504Z","title":null,"venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.471504Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:a180b90ab8681101b716265d05e59ffec5538eee41e8c54b3f2d40a971abf946","observation_id":"bc0b50ea-e5f0-42a2-83d7-a6943b644068","resolution":{"observed_at":"2026-08-09T17:58:35.471504Z","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":"2026-08-09T17:58:35.593322Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.593322Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:6c81fc33c36611e278a43382a2d176949e483e2fa003764b6b3c8883c7649eb6","observation_id":"16ea7bb5-ab2b-4af8-9c83-7c8ffccfa159","resolution":{"observed_at":"2026-08-09T17:58:35.593322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.03162","last_updated":"2022-11-06T16:05:39Z","snapshot_observed_at":"2026-07-06T14:15:01.843565Z","submitted_at":"2022-11-06T16:05:39Z","title":"ProtoX: Explaining a Reinforcement Learning Agent via Prototyping","version":1},"cited_work":{"arxiv_id":"2211.03162","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.03162","snapshot_observed_at":"2026-08-09T17:58:37.452756Z","title":"ProtoX: Explaining a Reinforcement Learning Agent via Prototyping","venue":"cs.LG","work_id":"c54bc594-c561-49a7-9fa9-492b4cc14b5f","year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.712783Z"},"links":{"cited_paper":"/paper/2211.03162","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:e857e9173c2a0e23e70663463bd8fed0dbc9c16ab5e402faab9202a22d6fb1e9","observation_id":"dbb6ffb4-c339-4bca-af67-3a2b52153049","resolution":{"observed_at":"2026-08-09T17:58:37.457718Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T17:58:35.486066Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.486066Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:6fb9d2f491f12396eb66fe685fea725c1f8c69c30e9f5f2a820fe612c391c7df","observation_id":"50fa87bf-d07d-4efa-b956-7f45b211f3a6","resolution":{"observed_at":"2026-08-09T17:58:35.486066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.06175","last_updated":"2022-11-11T10:04:29Z","snapshot_observed_at":"2026-08-08T03:18:33.595658Z","submitted_at":"2022-05-12T16:03:26Z","title":"A Generalist Agent","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.06175","snapshot_observed_at":"2026-08-09T17:58:35.796544Z","title":"Ed- wards, Nicolas Manfred Otto Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.796544Z"},"links":{"cited_paper":"/paper/2205.06175","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:759952054e18d8d1612ca71e9d23320e23712d406874c609e5b07a229d10cf49","observation_id":"e109adcc-7216-4b55-931c-a9d21b5d37f8","resolution":{"observed_at":"2026-08-09T17:58:35.796544Z","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":"2026-08-09T17:58:35.801448Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.801448Z"},"links":{"citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:00f980f475915eda7f0b492dea2d9e6ffc466854e4594284b327512f91b1bd26","observation_id":"3b2cc6f9-fed2-463e-9e98-335cba05f1ed","resolution":{"observed_at":"2026-08-09T17:58:35.801448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.13121","last_updated":"2024-04-23T23:28:36Z","snapshot_observed_at":"2026-07-06T16:35:56.467389Z","submitted_at":"2023-10-19T19:34:42Z","title":"Understanding Addition in Transformers","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.13121","snapshot_observed_at":"2026-08-09T17:58:35.499317Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-09T17:58:35.499317Z"},"links":{"cited_paper":"/paper/2310.13121","citing_paper":"/paper/2502.00726"},"observation_digest":"sha256:62e5369c39bf207f8a7d68c552d6f7d7671c936f742efad6f60995ba9e744e0c","observation_id":"54350984-451d-45e3-b014-9bf745f6f1b3","resolution":{"observed_at":"2026-08-09T17:58:35.499317Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.00726","last_updated":"2025-02-02T09:15:27Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-09T22:10:55.662622Z","submitted_at":"2025-02-02T09:15:27Z","title":"Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":1,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":87,"verified_exact":9,"verified_fuzzy":0},"total_outbound_references":149},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 100 of 149 outbound references and 0 inbound Pith citation observations for arXiv:2502.00726."}