{"as_of":"2026-08-09T10:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:222892b9137107eb05d3ded8b8c4880276d19dff4b53f4f3c9abd0bfd435cd50","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T09:25:24.808694Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.20792/citation-record","integrity":"/paper/2607.20792/integrity","json":"/paper/2607.20792/citation-record.json","paper":"/paper/2607.20792"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.07279","last_updated":"2025-03-25T03:36:21Z","snapshot_observed_at":"2026-08-05T02:24:21.044775Z","submitted_at":"2024-11-11T18:59:45Z","title":"The Surprising Effectiveness of Test-Time Training for Few-Shot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.07279","snapshot_observed_at":"2026-08-01T09:25:22.519099Z","title":"The Surprising Effectiveness of Test-Time Training for Few-Shot Learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:22.519099Z"},"links":{"cited_paper":"/paper/2411.07279","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:3fe1a1afcf6ca5df111b39990f830590856ed050cb9866e1900dcef8bc9c201f","observation_id":"bb93ad1b-c975-4ad8-9170-786ee17eae83","resolution":{"observed_at":"2026-08-01T09:25:22.519099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T09:25:23.108005Z","title":"Nested Learning: The Illusion of Deep Learning Architectures","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:23.108005Z"},"links":{"citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:715df306be5c20a0eec5cd245d8b998f7b3ee85ac6e04855730e7d46d2f00675","observation_id":"9d3987ad-d34a-4cb2-8373-9427f0569f11","resolution":{"observed_at":"2026-08-01T09:25:23.108005Z","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":"2026-08-07T09:00:33.558814Z","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":"2026-08-01T09:25:23.313591Z","title":"Titans: Learning to Memorize at Test Time","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:23.313591Z"},"links":{"cited_paper":"/paper/2501.00663","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:6f19ac450c7e8def85992f4b42be74a064a68c47a510378c25c2717e62430682","observation_id":"32d871a7-7a3b-413f-a502-bfd19fa5631a","resolution":{"observed_at":"2026-08-01T09:25:23.313591Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03819","last_updated":"2019-03-05T16:46:19Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-07-10T18:39:15Z","title":"Universal Transformers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03819","snapshot_observed_at":"2026-08-01T09:25:23.477369Z","title":"Universal Transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:23.477369Z"},"links":{"cited_paper":"/paper/1807.03819","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:57f6c17084b82be92ea200542d590ba54c132f32d32538af7ba08a762952fc74","observation_id":"82c55a47-a063-4e65-b3be-635dc9dd2f7f","resolution":{"observed_at":"2026-08-01T09:25:23.477369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.02092","last_updated":"2025-07-02T19:17:29Z","snapshot_observed_at":"2026-08-08T04:36:50.019846Z","submitted_at":"2025-07-02T19:17:29Z","title":"Energy-Based Transformers are Scalable Learners and Thinkers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.02092","snapshot_observed_at":"2026-08-01T09:25:23.671177Z","title":"Energy-Based Transformers Are Scalable Learners and Thinkers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:23.671177Z"},"links":{"cited_paper":"/paper/2507.02092","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:14544293aab032df2e6a3c3cf48a73b80336796f91905c79a48cf23b1c23201c","observation_id":"7bbbeb4b-a6dd-464f-9e4c-f617d887e72d","resolution":{"observed_at":"2026-08-01T09:25:23.671177Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1603.08983","last_updated":"2017-02-21T16:21:21Z","snapshot_observed_at":"2026-08-04T14:24:45.814840Z","submitted_at":"2016-03-29T22:09:00Z","title":"Adaptive Computation Time for Recurrent Neural Networks","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.08983","snapshot_observed_at":"2026-08-01T09:25:23.834390Z","title":"Adaptive Computation Time for Recurrent Neural Networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:23.834390Z"},"links":{"cited_paper":"/paper/1603.08983","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:e8bb1afa15e62e2bf133a925c0c53b25c5cea9da603f3e9c6ef0b9d1373f262a","observation_id":"68f67fee-0891-47ef-bf37-f9b5768e0cfc","resolution":{"observed_at":"2026-08-01T09:25:23.834390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.04664","last_updated":"2025-09-04T21:26:31Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-09-04T21:26:31Z","title":"Why Language Models Hallucinate","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.04664","snapshot_observed_at":"2026-08-01T09:25:24.128504Z","title":"Vempala, and Edwin Zhang","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:24.128504Z"},"links":{"cited_paper":"/paper/2509.04664","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:9715ea5bf8a8cd8017652e017ee12cffabd228ceba35dc0aac273819dd728318","observation_id":"d79fc78e-450d-4318-b6d1-217eedf904e2","resolution":{"observed_at":"2026-08-01T09:25:24.128504Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.00796","last_updated":"2017-01-25T13:01:51Z","snapshot_observed_at":"2026-08-04T14:09:02.234596Z","submitted_at":"2016-12-02T19:18:37Z","title":"Overcoming catastrophic forgetting in neural networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.00796","snapshot_observed_at":"2026-08-01T09:25:24.211976Z","title":"Overcoming Catastrophic Forgetting in Neural Networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:24.211976Z"},"links":{"cited_paper":"/paper/1612.00796","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:4df1639e8b1eed227248f146e5edbe9339dfb47cd951efb328fef5e31478a3cc","observation_id":"8616c42b-de1b-41ed-b16f-b166b0dd805f","resolution":{"observed_at":"2026-08-01T09:25:24.211976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.11174","last_updated":"2021-06-09T14:47:46Z","snapshot_observed_at":"2026-07-06T10:43:36.832191Z","submitted_at":"2021-02-22T16:51:38Z","title":"Linear Transformers Are Secretly Fast Weight Programmers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.11174","snapshot_observed_at":"2026-08-01T09:25:24.287914Z","title":"Linear Transformers Are Secretly Fast Weight Program- mers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:24.287914Z"},"links":{"cited_paper":"/paper/2102.11174","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:10038c9421c60f44c958ae5653168f1138fa027d36a6845d8df044fb7331fcea","observation_id":"b37e3520-e7cf-4ad4-9a83-e0cffc9a5b78","resolution":{"observed_at":"2026-08-01T09:25:24.287914Z","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":"2026-08-01T09:25:24.389981Z","title":"Learning to (Learn at Test Time): RNNs with Expressive Hidden States","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:24.389981Z"},"links":{"cited_paper":"/paper/2407.04620","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:1ffb83bca8804666b286f3cebac761ca5900f69740b257387a3aab40bc6b7d98","observation_id":"e1c7918d-e165-4013-8f91-70f76447e89b","resolution":{"observed_at":"2026-08-01T09:25:24.389981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T09:25:24.520280Z","title":"End-to-End Test-Time Training for Long Context","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:24.520280Z"},"links":{"citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:b06c5c202c7d51ad0f82520bfa7cc250d4202702f70d15b96293d35d3b41ffb9","observation_id":"1c9d5f06-f7c6-40b9-a4f8-60d601b167c8","resolution":{"observed_at":"2026-08-01T09:25:24.520280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06457","last_updated":"2026-06-24T00:07:31Z","snapshot_observed_at":"2026-08-06T19:01:34.447479Z","submitted_at":"2025-07-08T23:54:11Z","title":"A Systematic Analysis of Hybrid Linear Attention","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.06457","snapshot_observed_at":"2026-08-01T09:25:24.630212Z","title":"A Systematic Analysis of Hybrid Linear Attention","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:24.630212Z"},"links":{"cited_paper":"/paper/2507.06457","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:1563eb450c91badf9cc3b23a40d9cfeec366ad91f2fcfbe35c1a01a2b16e30fe","observation_id":"b1977f10-b6d7-443f-925c-2fcd80c4d2fa","resolution":{"observed_at":"2026-08-01T09:25:24.630212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10813","last_updated":"2025-03-04T22:19:41Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-14T17:59:44Z","title":"LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10813","snapshot_observed_at":"2026-08-01T09:25:24.732706Z","title":"LongMemEval: Benchmark- ing Chat Assistants on Long-Term Interactive Memory","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:24.732706Z"},"links":{"cited_paper":"/paper/2410.10813","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:ed81b1816d508fde1c1e7ba1b31c6411871bfff9848d6040fe2abb81892d3509","observation_id":"0886ebec-7179-4d9e-a856-51349bf7defc","resolution":{"observed_at":"2026-08-01T09:25:24.732706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.08913","last_updated":"2022-03-16T19:54:35Z","snapshot_observed_at":"2026-08-05T11:47:40.727016Z","submitted_at":"2022-03-16T19:54:35Z","title":"Memorizing Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.08913","snapshot_observed_at":"2026-08-01T09:25:24.808694Z","title":"Rabe, DeLesley Hutchins, and Christian Szegedy","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:24.808694Z"},"links":{"cited_paper":"/paper/2203.08913","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:7d819094ad88523b0c252040c7d8d0b77491decd26b7793a3b4730d259be83a0","observation_id":"66b9649c-6ff1-45cd-b1fe-220fa1d481c6","resolution":{"observed_at":"2026-08-01T09:25:24.808694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07733","last_updated":"2020-09-10T09:46:02Z","snapshot_observed_at":"2026-07-06T09:28:50.024490Z","submitted_at":"2020-06-13T22:35:21Z","title":"Bootstrap your own latent: A new approach to self-supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07733","snapshot_observed_at":"2026-08-01T09:25:23.940725Z","title":"Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:23.940725Z"},"links":{"cited_paper":"/paper/2006.07733","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:235860c73cc3dbfeae2b3e81cc4d734a8ddae37cc5f163170ffcc211fcd08c1a","observation_id":"c3969594-b647-4ff0-bb03-ab41b500e53d","resolution":{"observed_at":"2026-08-01T09:25:23.940725Z","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":"2026-07-06T02:11:23.670680Z","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":"2026-08-01T09:25:23.570001Z","title":"Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:23.570001Z"},"links":{"cited_paper":"/paper/2502.05171","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:5c271cfb7f17909ebec38343450cb65d5054b1711f72a520d4eb1cf78f173ffc","observation_id":"58af95c1-8e95-436a-851b-161feb419ee8","resolution":{"observed_at":"2026-08-01T09:25:23.570001Z","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":"2026-08-07T06:05:27.895209Z","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":"2026-08-01T09:25:24.052221Z","title":"Training Large Language Models to Reason in a Continuous Latent Space","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:24.052221Z"},"links":{"cited_paper":"/paper/2412.06769","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:5e1d4bb0a9691f26fa02cdd2056996de2619dab5a5eff33ee8c97206c342961e","observation_id":"fff3e90f-e98e-439b-9dbf-928c6f47130f","resolution":{"observed_at":"2026-08-01T09:25:24.052221Z","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":"2026-08-07T22:01:11.686885Z","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":"2026-08-01T09:25:22.804234Z","title":"ATLAS: Learning to Optimally Memorize the Context at Test Time","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:22.804234Z"},"links":{"cited_paper":"/paper/2505.23735","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:73c70d4a07287bbbec2e671350eba604600459015e9d7016a88151bacfd2f5eb","observation_id":"95c165b9-5fa4-4c95-94d3-e8bb32be50bf","resolution":{"observed_at":"2026-08-01T09:25:22.804234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.04906","last_updated":"2022-01-28T12:23:37Z","snapshot_observed_at":"2026-07-06T11:08:17.373935Z","submitted_at":"2021-05-11T09:53:21Z","title":"VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.04906","snapshot_observed_at":"2026-08-01T09:25:22.655581Z","title":"VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:22.655581Z"},"links":{"cited_paper":"/paper/2105.04906","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:5b43160e7d77a36914963ac49a978cc8fe6b98e6aaf9e36e1b1ef19f35ec0fb1","observation_id":"bdbd4626-9a48-4f4b-a310-fce5be848f91","resolution":{"observed_at":"2026-08-01T09:25:22.655581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.13173","last_updated":"2025-04-17T17:59:33Z","snapshot_observed_at":"2026-08-07T16:01:21.030997Z","submitted_at":"2025-04-17T17:59:33Z","title":"It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.13173","snapshot_observed_at":"2026-08-01T09:25:22.981826Z","title":"It Is All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-01T09:25:22.981826Z"},"links":{"cited_paper":"/paper/2504.13173","citing_paper":"/paper/2607.20792"},"observation_digest":"sha256:26c86f8d5b2cf839a915d7f647adc4ac4536779fb67adbecb15284e7ffc7a74a","observation_id":"17e5ef05-45a5-4007-b614-f40000b81685","resolution":{"observed_at":"2026-08-01T09:25:22.981826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.20792","last_updated":"2026-07-22T23:34:56Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T08:26:10.984255Z","submitted_at":"2026-07-22T23:34:56Z","title":"Memoir: Should a Model Write to Its Memory While It Thinks?"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":20},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2607.20792."}