{"as_of":"2026-08-08T16:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fac84b1f5eef10e6a5e7ac1c80c529c72932b341bcdb1104f939e0ed0065b9c6","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:44:47.061248Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2507.21513/citation-record","integrity":"/paper/2507.21513/integrity","json":"/paper/2507.21513/citation-record.json","paper":"/paper/2507.21513"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2109.06129","last_updated":"2021-09-14T07:10:41Z","snapshot_observed_at":"2026-07-06T11:47:13.744987Z","submitted_at":"2021-09-13T17:09:40Z","title":"Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.06129","snapshot_observed_at":"2026-08-06T12:44:44.684595Z","title":"Can language models encode perceptual structure without grounding? a case study in color","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:44.684595Z"},"links":{"cited_paper":"/paper/2109.06129","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:19d96c636f943182c737d7ce00c5ee5c7d511b0c652cce750a4e24da3460cc9f","observation_id":"6286c621-91ba-48e8-9524-86b0e553b85f","resolution":{"observed_at":"2026-08-06T12:44:44.684595Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00752","last_updated":"2024-05-31T17:55:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-01T18:01:34Z","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00752","snapshot_observed_at":"2026-08-06T12:44:45.105857Z","title":"Mamba: Linear-time sequence modeling with selective state spaces","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.105857Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:85de04d9c3cc1ad3ff0241aff2fed0d84488b2749d5f0e991b54b6ede1e36074","observation_id":"3d48ef1e-f33e-48df-a4c5-5b1a930c8b5a","resolution":{"observed_at":"2026-08-06T12:44:45.105857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.10122","last_updated":"2018-05-09T09:06:27Z","snapshot_observed_at":"2026-07-31T21:36:45.596575Z","submitted_at":"2018-03-27T15:08:55Z","title":"World Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.10122","snapshot_observed_at":"2026-08-06T12:44:45.239376Z","title":"World models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.239376Z"},"links":{"cited_paper":"/paper/1803.10122","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:202b4acdf922888dacb781bf86569eac8b3ec215309f1ef12e4764924af7bb68","observation_id":"ea1cd338-5a37-4259-84c5-763c53e6a279","resolution":{"observed_at":"2026-08-06T12:44:45.239376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13382","last_updated":"2024-06-26T14:27:49Z","snapshot_observed_at":"2026-08-08T07:57:05.964427Z","submitted_at":"2022-10-24T16:29:55Z","title":"Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13382","snapshot_observed_at":"2026-08-06T12:44:45.677714Z","title":"Emergent world representations: Exploring a sequence model trained on a synthetic task","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.677714Z"},"links":{"cited_paper":"/paper/2210.13382","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:9549ae87754107b00fb65836eb42cabf1dbc786c6ba76f263caec50ee4f36bfb","observation_id":"591e9dd7-ea97-4d11-bdf5-183f5444bd15","resolution":{"observed_at":"2026-08-06T12:44:45.677714Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.5602","last_updated":"2013-12-19T16:00:08Z","snapshot_observed_at":"2026-07-06T03:31:23.521122Z","submitted_at":"2013-12-19T16:00:08Z","title":"Playing Atari with Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.5602","snapshot_observed_at":"2026-08-06T12:44:45.762383Z","title":"Playing atari with deep reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.762383Z"},"links":{"cited_paper":"/paper/1312.5602","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:6b87e0b2307f4d8a09f7b7ec6088cc42fd51a8509e44eb5317b9e804a359c0ea","observation_id":"b8f51a83-0023-4c75-841b-ae3c95aa4e1c","resolution":{"observed_at":"2026-08-06T12:44:45.762383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:44:48.344330Z","title":"Glove: Global vectors for word representation","venue":null,"work_id":"f5848a21-bb96-4912-a100-6fce6781c404","year":2014},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.985105Z"},"links":{"citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:d532d255d921ea3420016b22ba4c001434bd7a98c462aff49ef5c3a0beb02261","observation_id":"9b485c08-7d18-450f-960c-c0e1db243b34","resolution":{"observed_at":"2026-08-06T12:44:48.418156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.15004","last_updated":"2023-05-22T15:56:25Z","snapshot_observed_at":"2026-08-07T23:56:18.685965Z","submitted_at":"2023-04-28T17:52:11Z","title":"Are Emergent Abilities of Large Language Models a Mirage?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.15004","snapshot_observed_at":"2026-08-06T12:44:46.168916Z","title":"Are emergent abilities of large language models a mirage? arXiv preprint arXiv:2304.15004,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.168916Z"},"links":{"cited_paper":"/paper/2304.15004","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:78c1e7364d50547a715015a0882642ecbe7a833f1b6ea5025671b0972c33d2ec","observation_id":"1b4c3c66-d130-468f-9ffd-784c53fd68b7","resolution":{"observed_at":"2026-08-06T12:44:46.168916Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-06T12:44:46.243710Z","title":"Proximal policy optimization algorithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.243710Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:5105015ca0e8bc41e4fbb1c6eb7771aa1731e19a775853f094d0c33a8a4c4707","observation_id":"7fad64e8-305a-4376-9fbb-416512a7d58d","resolution":{"observed_at":"2026-08-06T12:44:46.243710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.01815","last_updated":"2017-12-05T18:45:38Z","snapshot_observed_at":"2026-08-02T00:39:04.960144Z","submitted_at":"2017-12-05T18:45:38Z","title":"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.01815","snapshot_observed_at":"2026-08-06T12:44:46.361511Z","title":"Mastering chess and shogi by self-play with a general reinforcement learning algorithm","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.361511Z"},"links":{"cited_paper":"/paper/1712.01815","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:36f43a948a49451ab0fb783aaf44db143e2cac0afcf3dd6ae7aae968531036a7","observation_id":"d31759d9-4456-4e96-82a5-6807f12a6c34","resolution":{"observed_at":"2026-08-06T12:44:46.361511Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:44:48.221390Z","title":"Detecting strange attractors in turbulence","venue":null,"work_id":"54dddecd-4858-4946-92f9-def20fceb1b1","year":1980},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.443925Z"},"links":{"citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:48573543e862c1bdf231289eb81f7da80859561fcdfb9f071d8011561a4874be","observation_id":"c145a485-1b06-43fb-aff9-c7ce4f3ad391","resolution":{"observed_at":"2026-08-06T12:44:48.272685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03689","last_updated":"2024-11-10T23:47:33Z","snapshot_observed_at":"2026-07-06T18:26:15.151211Z","submitted_at":"2024-06-06T02:20:31Z","title":"Evaluating the World Model Implicit in a Generative Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.03689","snapshot_observed_at":"2026-08-06T12:44:46.541283Z","title":"Evaluating the world model implicit in a generative model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.541283Z"},"links":{"cited_paper":"/paper/2406.03689","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:00daf868960e3a42f7f4ff759dbec260ee386e6eb258306d5fe4bb65a2abc240","observation_id":"dc4f3591-904d-4298-9185-4194c1b1f435","resolution":{"observed_at":"2026-08-06T12:44:46.541283Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02469","last_updated":"2023-05-04T00:22:49Z","snapshot_observed_at":"2026-08-04T09:46:04.755475Z","submitted_at":"2023-05-04T00:22:49Z","title":"The System Model and the User Model: Exploring AI Dashboard Design","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.02469","snapshot_observed_at":"2026-08-06T12:44:46.618587Z","title":"The system model and the user model: Exploring ai dashboard design","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.618587Z"},"links":{"cited_paper":"/paper/2305.02469","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:cff8583b059e670b8aabfe18d45dd6402e978cb0badc85a6f24c332d2ecc4155","observation_id":"079575da-6efd-4e19-9194-e20e2339800d","resolution":{"observed_at":"2026-08-06T12:44:46.618587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.13261","last_updated":"2023-01-30T20:09:39Z","snapshot_observed_at":"2026-08-07T00:55:37.374272Z","submitted_at":"2023-01-30T20:09:39Z","title":"Emergence of Maps in the Memories of Blind Navigation Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.13261","snapshot_observed_at":"2026-08-06T12:44:46.734328Z","title":"Emergence of maps in the memories of blind navigation agents","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.734328Z"},"links":{"cited_paper":"/paper/2301.13261","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:7da2aa71bfcc15ecabc10999df51190d83f3bd2eda275c039d7a826b393ad4c3","observation_id":"7bcd501f-4f91-44bc-a337-3b28d596c3eb","resolution":{"observed_at":"2026-08-06T12:44:46.734328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.12672","last_updated":"2023-06-23T06:05:31Z","snapshot_observed_at":"2026-07-06T15:45:24.674515Z","submitted_at":"2023-06-22T05:14:00Z","title":"From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.12672","snapshot_observed_at":"2026-08-06T12:44:46.859237Z","title":"From word models to world models: Translating from natural language to the probabilistic language of thought","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.859237Z"},"links":{"cited_paper":"/paper/2306.12672","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:8f40d78f066b46321c7e07fc943d0b2bdb3ab81a123b9339f8f237e4df8bfe97","observation_id":"9093a2ac-a8d0-4bbb-9e40-e099a9ffd600","resolution":{"observed_at":"2026-08-06T12:44:46.859237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12278","last_updated":"2024-10-02T23:37:50Z","snapshot_observed_at":"2026-07-06T19:17:51.711221Z","submitted_at":"2024-09-18T19:28:04Z","title":"Making Large Language Models into World Models with Precondition and Effect Knowledge","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12278","snapshot_observed_at":"2026-08-06T12:44:46.955953Z","title":"Making large language models into world models with precondition and effect knowledge","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.955953Z"},"links":{"cited_paper":"/paper/2409.12278","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:211cdd00bfe3e9dd1d93a551204c29373bdab521d8e0be9a2edac7ee69636989","observation_id":"a364708b-a200-457d-9ef1-484f899cc15c","resolution":{"observed_at":"2026-08-06T12:44:46.955953Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:44:48.098390Z","title":"good approximation","venue":null,"work_id":"08b3ffc1-1b50-458d-acfb-f683fa835a2d","year":2021},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:47.061248Z"},"links":{"citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:1eafd1ec770d04fce0b4932c6117e91c2c49996545981ed619faa559cb615318","observation_id":"919bb990-6d79-40dc-98d8-b61e08409386","resolution":{"observed_at":"2026-08-06T12:44:48.149316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:44:48.657589Z","title":"On interpretability and feature representations: an analysis of the sentiment neuron","venue":null,"work_id":"38545c2e-e483-48bf-bf3a-566ae896a0c5","year":2019},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":1967,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.026404Z"},"links":{"citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:78040aa4ce8808b8216c589f302620f89afe466a2d03a84cd47376a41424fb1b","observation_id":"f775e219-37da-4fe5-b8ed-0c646af56bd1","resolution":{"observed_at":"2026-08-06T12:44:48.740157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.03368","last_updated":"2019-09-08T02:04:32Z","snapshot_observed_at":"2026-08-07T17:17:16.059036Z","submitted_at":"2019-09-08T02:04:32Z","title":"Designing and Interpreting Probes with Control Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.03368","snapshot_observed_at":"2026-08-06T12:44:45.327246Z","title":"Designing and interpreting probes with control tasks","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.327246Z"},"links":{"cited_paper":"/paper/1909.03368","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:f20661d121b6695d2805b21091f9d5181e4e64b66c91fd5ea3482f5a8c56bb46","observation_id":"c8bbf852-0562-422a-b615-80d0610d12ca","resolution":{"observed_at":"2026-08-06T12:44:45.327246Z","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":"2026-08-06T12:44:45.857588Z","title":"Progress measures for grokking via mechanistic interpretability","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.857588Z"},"links":{"cited_paper":"/paper/2301.05217","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:159a6e58d068c3d0d58f7f4e19abdb4aab35185e120818c31bbd18b883091011","observation_id":"c51f6065-9d97-4a80-aaf5-ff084e78088c","resolution":{"observed_at":"2026-08-06T12:44:45.857588Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.01444","last_updated":"2017-04-06T09:48:20Z","snapshot_observed_at":"2026-07-06T05:36:39.612294Z","submitted_at":"2017-04-05T14:20:28Z","title":"Learning to Generate Reviews and Discovering Sentiment","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.01444","snapshot_observed_at":"2026-08-06T12:44:46.073052Z","title":"Learning to generate reviews and discovering sentiment","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:46.073052Z"},"links":{"cited_paper":"/paper/1704.01444","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:e7734b8988730091ed3b6cb96108396275e5a6d9e03ba374743d860ad1e56437","observation_id":"69a5b349-dab4-42c0-b721-548d6573281a","resolution":{"observed_at":"2026-08-06T12:44:46.073052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.01681","last_updated":"2022-12-03T20:18:16Z","snapshot_observed_at":"2026-07-06T14:26:29.296405Z","submitted_at":"2022-12-03T20:18:16Z","title":"Language Models as Agent Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.01681","snapshot_observed_at":"2026-08-06T12:44:44.861954Z","title":"Language models as agent models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:44.861954Z"},"links":{"cited_paper":"/paper/2212.01681","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:211715b8b6f539cbeac186c40fa1c64dc0d35ab683499ee4e859c05fad2faa3d","observation_id":"8b15a018-5b2f-41d8-bd96-1d3fd6c4e905","resolution":{"observed_at":"2026-08-06T12:44:44.861954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:44:48.501256Z","title":"A path towards autonomous machine intelligence version 0.9","venue":null,"work_id":"ec2ec6c9-b9d6-47be-a472-040f74e24e79","year":2022},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.426470Z"},"links":{"citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:61394ce9d89964da8f9a1a2da6bd37f5e74779575a5dc627c5c9fb8f94749eb8","observation_id":"251cec7b-5488-4721-82b9-d31c526dccc8","resolution":{"observed_at":"2026-08-06T12:44:48.570737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:44:48.823016Z","title":"On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp","venue":null,"work_id":"e4fd14d2-cfef-4cf6-addb-542f28fa53d7","year":2021},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:44.946325Z"},"links":{"citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:8afe74623086017d58f7586b356a36afd7f1bf51b0ec49b1468eea374164ccc5","observation_id":"e199c974-158e-41a5-91be-648f04a3b655","resolution":{"observed_at":"2026-08-06T12:44:48.937734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.01644","last_updated":"2018-11-22T23:40:00Z","snapshot_observed_at":"2026-07-06T05:13:30.860932Z","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":"2026-08-06T12:44:44.766290Z","title":"Understanding intermediate layers using linear classifier probes","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:44.766290Z"},"links":{"cited_paper":"/paper/1610.01644","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:3d7a499833af60da3437dad40e9ce1b9ac62295c6344ab351606745a32506187","observation_id":"869c6913-c7a2-4334-bdd0-7d95e39d5e19","resolution":{"observed_at":"2026-08-06T12:44:44.766290Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02207","last_updated":"2024-03-04T18:25:29Z","snapshot_observed_at":"2026-08-06T19:42:39.893943Z","submitted_at":"2023-10-03T17:06:52Z","title":"Language Models Represent Space and Time","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02207","snapshot_observed_at":"2026-08-06T12:44:45.163874Z","title":"Language models represent space and time","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.163874Z"},"links":{"cited_paper":"/paper/2310.02207","citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:636c9b34c1d4d29feba7a0591386ada9f9999097991a705c7e8f308e53e02609","observation_id":"c018aa5b-4895-44e6-bedf-238111973a5a","resolution":{"observed_at":"2026-08-06T12:44:45.163874Z","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":"status/1759933","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:44:47.675527Z","title":"Belinda Z Li, Maxwell Nye, and Jacob Andreas","venue":null,"work_id":"883a7bfa-31ae-4485-aaee-d220d6bc564b","year":null},"citing_paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:45.600333Z"},"links":{"citing_paper":"/paper/2507.21513"},"observation_digest":"sha256:ac87d507ad110a2951e5fbeb241029f2ec9b3670d67bf86de09fdda582350099","observation_id":"84e9f17c-59e1-4c68-b09a-e57589f6e9f8","resolution":{"observed_at":"2026-08-06T12:44:47.749505Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.21513","last_updated":"2025-07-29T05:30:57Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T23:12:58.522240Z","submitted_at":"2025-07-29T05:30:57Z","title":"What Does it Mean for a Neural Network to Learn a \"World Model\"?"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":6},"total_outbound_references":26},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2507.21513."}