{"as_of":"2026-08-22T03:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:21fdd1d5cdd5513affbd90f74faa2809159fc93a7f2c25f525533f07d1dab5c9","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-15T12:09:16.468055Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2603.09221/citation-record","integrity":"/paper/2603.09221/integrity","json":"/paper/2603.09221/citation-record.json","paper":"/paper/2603.09221"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.04517","last_updated":"2024-12-06T15:42:07Z","snapshot_observed_at":"2026-08-16T13:54:34.627474Z","submitted_at":"2024-05-07T17:50:21Z","title":"xLSTM: Extended Long Short-Term Memory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04517","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"xlstm: Extended long short-term memory.arXiv preprint arXiv:2405.04517,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2405.04517","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:5dddb1a4a9686bad007ec4a23ad9df2bc639e3fd6f7e11de4b59d07438818f67","observation_id":"8e4324de-148f-4028-aa05-ea8189e15752","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.00663","last_updated":"2024-12-31T22:32:03Z","snapshot_observed_at":"2026-08-16T10:45:05.594811Z","submitted_at":"2024-12-31T22:32:03Z","title":"Titans: Learning to Memorize at Test Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.00663","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Titans: Learning to memorize at test time.arXiv preprint arXiv:2501.00663,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2501.00663","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:d9f59fb078dfed1940d055340667bfa7bd9bef7d848503403495ca46ce4852d7","observation_id":"b212fa0f-809e-417f-9151-aaeb59022787","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.23735","last_updated":"2025-05-29T17:57:16Z","snapshot_observed_at":"2026-08-14T11:18:31.036773Z","submitted_at":"2025-05-29T17:57:16Z","title":"ATLAS: Learning to Optimally Memorize the Context at Test Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.23735","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Atlas: Learning to optimally memorize the context at test time.URL https://arxiv","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2505.23735","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:d1117031136cda91476c903a1d79ae7e6ad119eca480502cc4889e962dd4c91d","observation_id":"1c34e7f2-9bc9-4425-a69e-3ee15492f910","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"The riccati equation(book).Berlin and New York, Springer-Verlag, 1991, 347,","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:a265a00c5e8c34a117430091810a12e91d3561bb00df7ab9f5fe7177dd7f24d3","observation_id":"dbe85ece-f7d8-4c00-aa56-6ce08f17feaa","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1406.1078","last_updated":"2014-09-03T00:25:02Z","snapshot_observed_at":"2026-08-15T13:36:27.756066Z","submitted_at":"2014-06-03T17:47:08Z","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1406.1078","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Learning phrase representations using rnn encoder-decoder for statistical machine translation.arXiv preprint arXiv:1406.1078,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/1406.1078","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:17b19a494744c2c0720d32e10adeb8881672debe57eba77e15a252d45073a54c","observation_id":"04952d78-9fe0-4662-a288-842eb0d9893f","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.21060","last_updated":"2024-05-31T17:50:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-31T17:50:01Z","title":"Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.21060","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"and Gu, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2405.21060","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:fa96aa3d83a6bf5e82476aef21d90521252da71c3e311edfc46af5d6ccfec11d","observation_id":"358b04d7-f3e2-43c2-b2d7-2eae303134e9","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01107","last_updated":"2024-10-01T20:30:37Z","snapshot_observed_at":"2026-08-18T17:22:56.092367Z","submitted_at":"2024-02-02T02:48:03Z","title":"Simulation of Graph Algorithms with Looped Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01107","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2402.01107","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:55d598ca3dfd9ee3d69021736683d458447b888ac2bf47fd531581673ac8169c","observation_id":"751c406e-0ba2-4d13-9622-f63b155ca20c","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.08007","last_updated":"2025-06-09T17:59:53Z","snapshot_observed_at":"2026-08-19T19:12:34.221057Z","submitted_at":"2025-06-09T17:59:53Z","title":"Reinforcement Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.08007","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Reinforcement pre-training.arXiv preprint arXiv:2506.08007,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2506.08007","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:b1aed5b7ff549a7617792e3ab9948f1d57489943bc45971b06e302b92749baa4","observation_id":"19deb135-22eb-4e42-a387-9f0ed445e441","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.13676","last_updated":"2024-11-20T19:51:25Z","snapshot_observed_at":"2026-08-14T20:26:44.533985Z","submitted_at":"2024-11-20T19:51:25Z","title":"Hymba: A Hybrid-head Architecture for Small Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.13676","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"S., Liu, S.-Y., Van Keirsbilck, M., Chen, M.-H., Suhara, Y., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2411.13676","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:e337238d04a8f6a155842b71f61f8c6113d50de96984ec12e9ad3de5dcd32bbc","observation_id":"00fb7d85-b6dd-487d-9948-49c2878c645e","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Mom: Linear sequence modeling with mixture-of-memories.arXiv preprint arXiv:2502.13685,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:4e0eabe0256d6d5754b366da38cab0278c0c07a46725a8081270943fc75ba1ae","observation_id":"7207a865-88dc-43a1-95a8-1e04cb37f2d8","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05171","last_updated":"2025-02-17T17:14:04Z","snapshot_observed_at":"2026-08-15T00:53:48.099058Z","submitted_at":"2025-02-07T18:55:02Z","title":"Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05171","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"R., Kailkhura, B., Bhatele, A., and Goldstein, T","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2502.05171","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:5c33467ec034645987b5d06daba148a8dd62cefe54f65f3cd595fc17c5f16f93","observation_id":"fc6b477b-fcd2-409b-850d-b8bb70353cfe","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","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":"2026-08-17T20:47:46.242385Z","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":"2026-07-15T12:09:16.468055Z","title":"and Dao, T","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2312.00752","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:95f94f643290d4b247cd05a853652a8df1b1180360790f2fc48c05f808b72fdb","observation_id":"9f28ca49-85c9-44ed-9ee6-a1bb4747e12f","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00396","last_updated":"2022-08-05T17:54:38Z","snapshot_observed_at":"2026-08-14T01:02:41.198730Z","submitted_at":"2021-10-31T03:32:18Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00396","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Efficiently modeling long sequences with structured state spaces.arXiv preprint arXiv:2111.00396, 2021a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2111.00396","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:256e986b3a68dea85cb07d1d82ed8524c345346b6ec1bf7392c1e3227241ee1b","observation_id":"9afe5823-cf32-4513-a03a-6fa30ae30dcd","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.04178","last_updated":"2025-06-05T02:21:52Z","snapshot_observed_at":"2026-08-17T02:17:22.510859Z","submitted_at":"2025-06-04T17:25:39Z","title":"OpenThoughts: Data Recipes for Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.04178","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Openthoughts: Data recipes for reasoning models.URL https://arxiv","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2506.04178","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:db223e98103756416a928f8ec686305c7374963bac6c1a7e82ea12e7be387d08","observation_id":"faa9a1f6-7fd8-45d5-a18f-9df796efc293","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:b966e70d742fc74e0fd134330b31eadcdbf9386cb82204edf7ccdb8375d643f1","observation_id":"d1baa6d2-0c18-4035-8eeb-6cba0953d19a","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","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":"2026-08-18T07:04:58.690133Z","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":"2026-07-15T12:09:16.468055Z","title":"and Schmidhuber, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/1803.10122","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:50382cf4561ece5dac64317d22fa01d193a1bb3bcff0834cd6c7272550011ca2","observation_id":"c627677a-69f1-4e92-981c-4c332157d653","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01603","last_updated":"2020-03-17T17:10:58Z","snapshot_observed_at":"2026-08-20T13:32:55.916408Z","submitted_at":"2019-12-03T18:57:16Z","title":"Dream to Control: Learning Behaviors by Latent Imagination","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01603","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Dream to control: Learning behaviors by latent imagination.arXiv preprint arXiv:1912.01603,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/1912.01603","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:bb91cef67b274e15dd2052061b384ccccc123414f45c5d57cc6e1149ce25183f","observation_id":"bbfac5d4-0d98-49e7-8428-c5e9af73f459","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04955","last_updated":"2022-07-19T18:14:36Z","snapshot_observed_at":"2026-08-21T19:31:44.251317Z","submitted_at":"2022-03-09T18:58:28Z","title":"Temporal Difference Learning for Model Predictive Control","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04955","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Temporal difference learning for model predictive control.arXiv preprint arXiv:2203.04955,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2203.04955","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:1df979fcfda95c893c79a70f362e89cf29216798b6663be45c2eb13553377a73","observation_id":"eafb0b50-f16f-42ca-8105-9781c824625a","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.16828","last_updated":"2024-03-21T17:56:19Z","snapshot_observed_at":"2026-07-31T05:32:29.431480Z","submitted_at":"2023-10-25T17:57:07Z","title":"TD-MPC2: Scalable, Robust World Models for Continuous Control","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.16828","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Td-mpc2: Scalable, robust world models for continuous control.arXiv preprint arXiv:2310.16828,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2310.16828","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:ff3dddd756fc8be23aeb1f16e5e70fd9605b4d1c0a5c6548ff372b4ab4d1bf1b","observation_id":"1b0a595f-bb9c-445f-b29c-d4334d1bfea6","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14992","last_updated":"2023-10-23T07:24:28Z","snapshot_observed_at":"2026-08-15T11:39:07.998070Z","submitted_at":"2023-05-24T10:28:28Z","title":"Reasoning with Language Model is Planning with World Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14992","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"J., Wang, Z., Wang, D","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2305.14992","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:aca717f34590aac937fcc44cc23de4cd476d1cae927f635a4dbd7d8d336b6660","observation_id":"94914fa7-8ee6-445b-924a-b01d0229cb85","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06769","last_updated":"2025-11-03T00:53:34Z","snapshot_observed_at":"2026-08-17T06:12:37.525438Z","submitted_at":"2024-12-09T18:55:56Z","title":"Training Large Language Models to Reason in a Continuous Latent Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06769","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Training large language models to reason in a continuous latent space.arXiv preprint arXiv:2412.06769,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2412.06769","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:b51e27db7384076bece2c1b0ff3325dba5e871adf368fe0fa58e4bfc2f62e95f","observation_id":"5d24e057-23f1-4c70-9b46-9ea51e271968","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"N., Prabhumoye, S., Kautz, J., Patwary, M., Shoeybi, M., Catanzaro, B., and Choi, Y","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:82c43cef414f06fd3af09d34004a79ecf4603e89ee308cb30f021f289f496509","observation_id":"00c30892-adf2-4c53-abdb-936f5002d76a","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.03874","last_updated":"2021-11-08T21:30:18Z","snapshot_observed_at":"2026-08-21T04:25:41.592030Z","submitted_at":"2021-03-05T18:59:39Z","title":"Measuring Mathematical Problem Solving With the MATH Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.03874","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Measuring mathematical problem solving with the math dataset.arXiv preprint arXiv:2103.03874,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2103.03874","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:723b44779fd350c35082daab9ce0502f336cfe3aff8e84077399dc53ab398624","observation_id":"50de6cee-1b9a-4b70-8905-73db18cbd01f","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"A., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:811fcb81bb7794e5fd3bb3e377915391c428d772dbea272c8cff8686c2deda9f","observation_id":"87456be6-6f4a-4214-80fa-5ce1baea5fcd","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14207","last_updated":"2024-10-02T14:32:59Z","snapshot_observed_at":"2026-08-17T07:18:08.615152Z","submitted_at":"2024-07-19T11:12:08Z","title":"Longhorn: State Space Models are Amortized Online Learners","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14207","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Longhorn: State space models are amortized online learners.arXiv preprint arXiv:2407.14207,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2407.14207","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:3bc3caf4998029446e60786a6619f11cc8f8dd217099b56dc1f703f6b87558e4","observation_id":"d5aff8de-b336-4d22-96cf-19c30755bb05","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.08291","last_updated":"2023-05-15T01:18:23Z","snapshot_observed_at":"2026-08-20T11:15:33.226264Z","submitted_at":"2023-05-15T01:18:23Z","title":"Large Language Model Guided Tree-of-Thought","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.08291","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Large language model guided tree-of-thought.arXiv preprint arXiv:2305.08291,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2305.08291","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:350f77aeb9d1f1c2b2c6f94028fce63acf589b55b9968a164ab7c8d46532b985","observation_id":"d698be93-ffc9-42dc-a6dc-575cd643e950","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14456","last_updated":"2025-03-30T13:46:44Z","snapshot_observed_at":"2026-08-19T20:24:52.256383Z","submitted_at":"2025-03-18T17:31:05Z","title":"RWKV-7 \"Goose\" with Expressive Dynamic State Evolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14456","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Rwkv-7\" goose\" with expressive dynamic state evolution.arXiv preprint arXiv:2503.14456, 2025a","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2503.14456","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:12803dcd2d37bdf663d610ff8aeb5b94a0afba3043149350d0ee93d277f9e2c3","observation_id":"742dde78-69a3-492c-b41d-7d8a374bd9df","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07522","last_updated":"2025-02-28T02:20:49Z","snapshot_observed_at":"2026-08-16T13:44:03.977685Z","submitted_at":"2024-06-11T17:50:51Z","title":"Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07522","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Samba: Simple hybrid state space models for efficient unlimited context language modeling.arXiv preprint arXiv:2406.07522,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2406.07522","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:796417d118e167551cd5f650026cbe163610b0cbd625a0bd3e556cd3e824b19d","observation_id":"6dbd1192-92f5-4530-b7c5-08b399574af6","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.17416","last_updated":"2025-02-24T18:49:05Z","snapshot_observed_at":"2026-08-20T10:44:15.085375Z","submitted_at":"2025-02-24T18:49:05Z","title":"Reasoning with Latent Thoughts: On the Power of Looped Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.17416","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2502.17416","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:e45bba5c15e77034b0870886f756beeef8de14fbd1638083f7e6cf3369906d61","observation_id":"ef2b899b-92e5-4a83-8c7b-793a18f7a1d5","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language models.arXiv preprint arXiv:2402.03300,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:f9ea5e33e568524df958366e43d4be995f5b603617811ee9e8f8c5ceec26d96a","observation_id":"6245b61a-0462-4dee-a1a9-b3ec27dccd05","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.06941","last_updated":"2025-11-20T00:19:24Z","snapshot_observed_at":"2026-08-09T21:19:24.140229Z","submitted_at":"2025-06-07T22:42:29Z","title":"The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.06941","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"The illusion of thinking: Understanding the strengths and limitations of reasoning models via the lens of problem complexity","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2506.06941","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:0f6ff6763f7a164142f004df796baa43975ff4c2faaab9b341fe21eaf3bf6942","observation_id":"943f47e7-105d-4da0-9618-0f3943d6f598","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03314","last_updated":"2024-08-06T17:35:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:35:05Z","title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03314","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Scaling llm test-time compute optimally can be more effective than scaling model parameters.arXiv preprint arXiv:2408.03314,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2408.03314","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:770ab07e5e241f8b5f4b9771426b4918bb885300d761441c2196d10418ad7ab9","observation_id":"9d934d02-be7a-4042-aad9-da871b7efabc","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08621","last_updated":"2023-08-09T08:53:08Z","snapshot_observed_at":"2026-08-18T21:18:20.077927Z","submitted_at":"2023-07-17T16:40:01Z","title":"Retentive Network: A Successor to Transformer for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08621","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Retentive network: A successor to transformer for large language models.arXiv preprint arXiv:2307.08621,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2307.08621","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:5cc296e448633256821d10cc0591faa81ea398bc6194b8e70e1f3a510c0085d8","observation_id":"84c34f5d-6aa9-4958-9be5-7adfe7b31779","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04620","last_updated":"2025-08-31T18:32:59Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-05T16:23:20Z","title":"Learning to (Learn at Test Time): RNNs with Expressive Hidden States","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.04620","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Learning to (learn at test time): Rnns with expressive hidden states.arXiv preprint arXiv:2407.04620,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2407.04620","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:a4b0514ebfec50b9ad954a24e82b04bdde85178824dbc90dd8d44ea38e784704","observation_id":"cc9994e7-763a-49ae-adef-958d476c6513","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"End-to-end test-time training for long context.arXiv preprint arXiv:2512.23675,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:e1ff0498db301859b957b651916194d48b98ce5f1d1691c751a9360de769d652","observation_id":"24028d9a-c31c-4211-8aef-291fa9ff5eff","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05858","last_updated":"2024-10-15T13:43:50Z","snapshot_observed_at":"2026-08-19T23:44:27.389926Z","submitted_at":"2023-09-11T22:42:50Z","title":"Uncovering mesa-optimization algorithms in Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05858","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Uncovering mesa-optimization algorithms in transformers.arXiv preprint arXiv:2309.05858,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2309.05858","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:1b12f9dd16781377e2fe94415eb53721ce89269ecb701db865da8c423e7bcb6f","observation_id":"197bb3a4-9682-4dd5-bc3c-dd00706898e0","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.05233","last_updated":"2026-06-03T16:16:54Z","snapshot_observed_at":"2026-08-16T14:30:34.288202Z","submitted_at":"2025-06-05T16:50:23Z","title":"MesaNet: Sequence Modeling by Locally Optimal Test-Time Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.05233","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Mesanet: Sequence modeling by locally optimal test-time training.arXiv preprint arXiv:2506.05233,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2506.05233","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:92a72a3c3dc721959e8d9d761b4b560f1f9e3e30f4be6f1e4872dabb292d3d57","observation_id":"4204e8bc-b8dd-4d18-b7b7-d1592ec0ad47","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.21734","last_updated":"2025-08-04T08:45:08Z","snapshot_observed_at":"2026-08-21T00:19:04.881415Z","submitted_at":"2025-06-26T19:39:54Z","title":"Hierarchical Reasoning Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.21734","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2506.21734","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:10e9252b7374e4dd011b4eae0e0727f693a0fd87b966679e1e2e1bd687ccb624","observation_id":"082420cc-d84d-4390-8bf1-6ee580290e00","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.11171","last_updated":"2023-03-07T17:57:37Z","snapshot_observed_at":"2026-08-16T01:49:22.176843Z","submitted_at":"2022-03-21T17:48:52Z","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.11171","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Self-consistency improves chain of thought reasoning in language models.arXiv preprint arXiv:2203.11171,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2203.11171","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:951ad97040fd1cb983a1d5fcdf3dd87a0c3cceb0f8f2678b93faf6d61b5fd3b0","observation_id":"91f52f97-81b6-4e7c-a2da-0596f92752a3","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12424","last_updated":"2024-03-16T21:10:38Z","snapshot_observed_at":"2026-08-16T14:41:35.235110Z","submitted_at":"2023-11-21T08:32:38Z","title":"Looped Transformers are Better at Learning Learning Algorithms","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.12424","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Looped transformers are better at learning learning algorithms.arXiv preprint arXiv:2311.12424, 2023a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2311.12424","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:42782d9a4dbc3433191ded3af07f67160959b46184fb3cd0b18302e7f7572ddb","observation_id":"52f26761-85b8-4931-b7c4-f7990128c0c8","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.13837","last_updated":"2025-11-24T06:11:04Z","snapshot_observed_at":"2026-08-14T14:03:15.178702Z","submitted_at":"2025-04-18T17:59:56Z","title":"Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.13837","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?URL https://arxiv","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2504.13837","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:7d86836352307cd7570c05454f47723153ebd6132116c59f382b3e5a99347c49","observation_id":"d6c5eceb-afb5-409d-9933-064bcbef1c3a","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.16175","last_updated":"2026-02-05T18:03:03Z","snapshot_observed_at":"2026-08-01T10:41:29.420510Z","submitted_at":"2026-01-22T18:24:00Z","title":"Learning to Discover at Test Time","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.16175","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Learning to discover at test time.arXiv preprint arXiv:2601.16175,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2601.16175","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:6b25090ff015a549b9158d2f2fc78c2b6ddfce360817e4e2112427e0b8828705","observation_id":"559408aa-c6e0-4bd3-9eb9-1ae0a39d89bd","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Pretraining language models to ponder in continuous space.arXiv preprint arXiv:2505.20674,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:2564cda996ceb2a3b7b939c731dcf613937c39046bdd45883f6bbd21a7aaf91c","observation_id":"d97c9438-8049-4dc2-bcb2-df68ae252969","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.23884","last_updated":"2025-05-29T17:50:34Z","snapshot_observed_at":"2026-08-08T17:47:51.120243Z","submitted_at":"2025-05-29T17:50:34Z","title":"Test-Time Training Done Right","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.23884","snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"T., and Tan, H","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"cited_paper":"/paper/2505.23884","citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:0a1195836ef370b0dfba604de87f2499a1073818d3a5d914bcd1cb03f4d9a0c1","observation_id":"ca9f0afb-e7c0-40f6-a8e6-fee7cbe2acde","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Emergence of superposition: Unveiling the training dynamics of chain of continuous thought.arXiv preprint arXiv:2509.23365, 2025a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:654fa6ec640372d80586ec4fffc4f2e109b224a8bd5a2421f620ab83c1bac52e","observation_id":"f57d357d-fa63-4595-b8b3-3c85d7fd2e20","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"In the main text, we use𝒉 0 in place of𝒉 𝑖𝑛𝑖𝑡 without separate notations when no confusion arises","venue":null,"work_id":null,"year":1960},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:869358944ead14765dd7e697dde9e8863d4759448432104d87017bf9895602ce","observation_id":"115557d2-d4d5-4dce-a765-2dcbd8cdffa6","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"𝜕 𝜕𝜃 𝒙 𝜕 𝜕𝜃 𝝃 # =","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:45ac68ae65b6723c23abb2c07515f4ac9f6243d8827ade13453b05a8533ed5d2","observation_id":"15fac4d8-9830-4f8e-8652-9b7a391031fc","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:66e661374c0a26eb49f0219c7611d41f9930db4542c9c07aea105b26dac924d7","observation_id":"6672788d-cf77-4ed1-be99-8c4e4851847f","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Although [𝒀 1,𝒀 2] are partitioned row-wise and distributed across CUDA blocks, this normalization can be performed independently within each kernel without synchronization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:119196e6c8343672265f3fa55b56411dc381aecba73b1d654b5eefc1caeb30a1","observation_id":"f6526f8e-1cab-4587-8d71-d42b84f83463","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"To the best of our knowledge, it is the only other approach that explores online RL–based adaptation at test time","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:b4ac89f4a6c11270d9421d7c037463dc8ccb38fec262cf5b2dfaa314dab49ea7","observation_id":"f1946c24-7def-4b14-b619-765a55c17872","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"world”, upon which it performs planning and decision-making. All parameters of this “world","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:84cc470ed9fc46befc2ad2b98c5c2670b7e84d0a38839439a0bb7442aef583d6","observation_id":"8c36b370-ca67-4b7e-b825-f80a0f053e03","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"thinking","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:fe46e57b99d0c9a58c981b77801970e2e9c877c3949609e51886625c23c0dfc1","observation_id":"dc6b4faa-d194-4989-b81d-f07f4323d6c7","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-15T12:09:16.468055Z","title":"Memory footprint is evaluated separately by measuring peak GPU memory usage during execution, reported in GB","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-15T12:09:16.468055Z"},"links":{"citing_paper":"/paper/2603.09221"},"observation_digest":"sha256:75685e16811476c1f1070938d86698eafdbaa26b82e1f06561b142460bcf4546","observation_id":"01d2426b-e098-4846-bf7c-98b54ec604b6","resolution":{"observed_at":"2026-07-15T12:09:16.468055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.09221","last_updated":"2026-05-29T05:28:18Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T17:42:16.302165Z","submitted_at":"2026-03-10T05:42:13Z","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":53,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":53},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2603.09221."}