{"as_of":"2026-08-10T02:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:de30e2607005f886a97d4671d4cc533b9da45d287cc35da0bb00b76873a6e2dc","coverage":[{"denominator":11,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:50:00.947051Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T06:37:43.829079Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T09:07:47.853577Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"cited_work":{"arxiv_id":"2507.12638","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.12638","snapshot_observed_at":"2026-07-03T09:07:47.853577Z","title":"Reasoning-finetuning repurposes latent representations in base models.arXiv:2507.12638","venue":null,"work_id":"4864f7f2-e0a5-4c23-ae90-5f6fe7cb16ef","year":2025},"citing_paper":{"arxiv_id":"2604.22271","last_updated":"2026-05-01T09:11:21Z","snapshot_observed_at":"2026-07-06T23:08:42.301487Z","submitted_at":"2026-04-24T06:33:32Z","title":"How LLMs Detect and Correct Their Own Errors: The Role of Internal Confidence Signals","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-08T12:30:51.094216Z"},"links":{"cited_paper":"/paper/2507.12638","citing_paper":"/paper/2604.22271"},"observation_digest":"sha256:fa66b6ca16d9bc6dc3bfba1b9af0bdec4c3ca3730b288f0c41da3a4879a9532f","observation_id":"941f5e68-792c-4568-ba71-b24b273bf530","resolution":{"observed_at":"2026-05-11T19:16:06.925057Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"cited_work":{"arxiv_id":"2507.12638","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.12638","snapshot_observed_at":"2026-07-03T09:07:47.853577Z","title":"Reasoning-finetuning repurposes latent representations in base models.arXiv:2507.12638","venue":null,"work_id":"4864f7f2-e0a5-4c23-ae90-5f6fe7cb16ef","year":2025},"citing_paper":{"arxiv_id":"2605.31494","last_updated":"2026-05-29T16:16:13Z","snapshot_observed_at":"2026-07-06T23:40:42.277618Z","submitted_at":"2026-05-29T16:16:13Z","title":"Consolidating Rewarded Perturbations for LLM Post-Training","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T22:37:34.673402Z"},"links":{"cited_paper":"/paper/2507.12638","citing_paper":"/paper/2605.31494"},"observation_digest":"sha256:3898b31acbd093947c3c432767c669381c18cc4c1dcb3e30be821754f5e3d84e","observation_id":"04caf4e6-7821-4b2e-a697-f159d46abb39","resolution":{"observed_at":"2026-06-28T22:42:46.873074Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"cited_work":{"arxiv_id":"2507.12638","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.12638","snapshot_observed_at":"2026-07-03T09:07:47.853577Z","title":"Reasoning-finetuning repurposes latent representations in base models.arXiv:2507.12638","venue":null,"work_id":"4864f7f2-e0a5-4c23-ae90-5f6fe7cb16ef","year":2025},"citing_paper":{"arxiv_id":"2606.00726","last_updated":"2026-07-10T01:15:34Z","snapshot_observed_at":"2026-08-02T05:37:27.284595Z","submitted_at":"2026-05-30T13:38:06Z","title":"Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-06-28T18:49:56.917505Z"},"links":{"cited_paper":"/paper/2507.12638","citing_paper":"/paper/2606.00726"},"observation_digest":"sha256:456e76ae0be429d604d0fc715b766905616a699a02dfd6325ba50e88c04b1aef","observation_id":"cfe84e7f-8438-4786-8b3c-020aa1fbefde","resolution":{"observed_at":"2026-06-28T19:52:35.488796Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"cited_work":{"arxiv_id":"2507.12638","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.12638","snapshot_observed_at":"2026-07-03T09:07:47.853577Z","title":"Reasoning-finetuning repurposes latent representations in base models.arXiv:2507.12638","venue":null,"work_id":"4864f7f2-e0a5-4c23-ae90-5f6fe7cb16ef","year":2025},"citing_paper":{"arxiv_id":"2606.12360","last_updated":"2026-06-10T17:31:16Z","snapshot_observed_at":"2026-07-06T23:51:17.719874Z","submitted_at":"2026-06-10T17:31:16Z","title":"Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-06-27T10:32:57.295159Z"},"links":{"cited_paper":"/paper/2507.12638","citing_paper":"/paper/2606.12360"},"observation_digest":"sha256:2522a13cc402bc03cf75c74e1cb6104ebd7f6bff9890c497aaecbcf038c7b953","observation_id":"f91a972e-3275-4251-bbb7-1c849b4067c3","resolution":{"observed_at":"2026-07-03T09:07:47.855000Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"cited_work":{"arxiv_id":"2507.12638","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.12638","snapshot_observed_at":"2026-07-03T09:07:47.853577Z","title":"Reasoning-finetuning repurposes latent representations in base models.arXiv:2507.12638","venue":null,"work_id":"4864f7f2-e0a5-4c23-ae90-5f6fe7cb16ef","year":2025},"citing_paper":{"arxiv_id":"2606.18284","last_updated":"2026-06-10T02:04:29Z","snapshot_observed_at":"2026-07-06T23:53:45.117607Z","submitted_at":"2026-06-10T02:04:29Z","title":"Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier","version":1},"reference_index":106,"source":"arxiv_source","source_observed_at":"2026-06-27T10:36:09.211639Z"},"links":{"cited_paper":"/paper/2507.12638","citing_paper":"/paper/2606.18284"},"observation_digest":"sha256:89d917f8e6262c86b7261e414995d0e64943b153ba48449ec26124a88b8a7755","observation_id":"d7a7d8ca-45c2-4850-85e2-114e046879b7","resolution":{"observed_at":"2026-07-03T08:57:48.266435Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.12638","snapshot_observed_at":"2026-08-02T06:37:43.829079Z","title":"arXiv:2507.12638 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.12447","last_updated":"2026-07-24T07:10:23Z","snapshot_observed_at":"2026-08-04T12:28:32.750580Z","submitted_at":"2026-07-14T07:24:32Z","title":"The Computational Basis of Confidence in Large Language Models","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-02T06:37:43.829079Z"},"links":{"cited_paper":"/paper/2507.12638","citing_paper":"/paper/2607.12447"},"observation_digest":"sha256:9a5e91265f2af092fdab7b09c494b56b1e800c4da1daece4c9795395682d58e8","observation_id":"2f987154-877f-44e5-a9eb-b6e2fd8e3287","resolution":{"observed_at":"2026-08-02T06:37:43.829079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.12638","snapshot_observed_at":"2026-08-01T15:16:21.171395Z","title":"Reasoning-finetuning repurposes latent representations in base models, 2025 a","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18532","last_updated":"2026-07-20T21:56:22Z","snapshot_observed_at":"2026-08-08T01:52:33.147531Z","submitted_at":"2026-07-20T21:56:22Z","title":"Reasoning Fine-Tuning Induces Persistent Latent Policy States","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-01T15:16:21.171395Z"},"links":{"cited_paper":"/paper/2507.12638","citing_paper":"/paper/2607.18532"},"observation_digest":"sha256:e889316b9984b22d6b94fc603496a3ca691f179a450a0e16058b6fad55c835fe","observation_id":"d1b1a916-9964-45b0-91f3-c4a1b61dcfc8","resolution":{"observed_at":"2026-08-01T15:16:21.171395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.12638/citation-record","integrity":"/paper/2507.12638/integrity","json":"/paper/2507.12638/citation-record.json","paper":"/paper/2507.12638"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:49:59.982410Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T16:49:59.982410Z"},"links":{"citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:a3dfdea5ddbfa80c1fa570d625d498d29e7389d96905d75bae54e1c62247fdd0","observation_id":"c514358f-7107-49ea-a9e3-4f370d7b08c2","resolution":{"observed_at":"2026-08-06T16:49:59.982410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11717","last_updated":"2024-10-30T18:57:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-17T16:36:12Z","title":"Refusal in Language Models Is Mediated by a Single Direction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11717","snapshot_observed_at":"2026-08-06T16:50:00.079537Z","title":"Refusal in language models is mediated by a single direction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T16:50:00.079537Z"},"links":{"cited_paper":"/paper/2406.11717","citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:4d93562341259abe8be4ebecbfb54b0bf3f00e5ef1515ad146f9178c1f31e0f0","observation_id":"0c01c1b5-1cd2-4058-a107-c2f192069f9f","resolution":{"observed_at":"2026-08-06T16:50:00.079537Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.18878","last_updated":"2025-08-05T20:14:47Z","snapshot_observed_at":"2026-08-07T16:40:37.292621Z","submitted_at":"2025-03-24T16:54:26Z","title":"I Have Covered All the Bases Here: Interpreting Reasoning Features in Large Language Models via Sparse Autoencoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.18878","snapshot_observed_at":"2026-08-06T16:50:00.194759Z","title":"Y., Tutubalina, E., and Oseledets, I","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T16:50:00.194759Z"},"links":{"cited_paper":"/paper/2503.18878","citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:9025b627018cba9da0f24519d02625c7c783f6110f31913314c310878d99d435","observation_id":"f8bb119f-f6ac-4136-991c-45324ab7c699","resolution":{"observed_at":"2026-08-06T16:50:00.194759Z","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":"2026-07-06T02:11:23.670680Z","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":"2026-08-06T16:50:00.302715Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T16:50:00.302715Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:caf6633f871c5c628078d3642f70affe51c73440532c9e70a9b5a4fb3fd09f72","observation_id":"8de3171c-a2d8-41c1-bea2-d3c8d33d0061","resolution":{"observed_at":"2026-08-06T16:50:00.302715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.19393","last_updated":"2025-03-01T06:07:39Z","snapshot_observed_at":"2026-07-06T20:29:11.710285Z","submitted_at":"2025-01-31T18:48:08Z","title":"s1: Simple test-time scaling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.19393","snapshot_observed_at":"2026-08-06T16:50:00.387833Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T16:50:00.387833Z"},"links":{"cited_paper":"/paper/2501.19393","citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:c845103cab6e1309c73c5e808d99fd188204f9ff6f8edd2751526db94866edec","observation_id":"29b01fab-4999-49ea-84a9-31297eaec5fc","resolution":{"observed_at":"2026-08-06T16:50:00.387833Z","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":"2026-08-06T16:50:00.517158Z","title":"Steering llama 2 via contrastive activation addition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T16:50:00.517158Z"},"links":{"citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:5c1e7b301725cc0a1acb49a3eeb92e9a9b0b0ce4ab28ff081868c5e730c07018","observation_id":"a92f9e60-ab0d-4f21-89eb-af48e6014b51","resolution":{"observed_at":"2026-08-06T16:50:00.517158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:50:01.107903Z","title":"Understanding reasoning in thinking language models via steering vectors","venue":null,"work_id":"d1b517e1-49d8-40db-bfcb-215eb6db30bd","year":2025},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T16:50:00.618996Z"},"links":{"citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:7a32043af4fe18afa42ae42e551dd48005f4c20de8273afb53dd484fb8b6130f","observation_id":"c5df048c-9ba7-4429-98ee-05c0f3508a8c","resolution":{"observed_at":"2026-08-06T16:50:01.201514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:50:00.733952Z","title":"V., Zhou, D., et al","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T16:50:00.733952Z"},"links":{"citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:da99e916a10e7c16a3b926e22d60ce8db02d30e5ae832db82132e8dde200941b","observation_id":"978acae3-250b-4d22-9da3-b60358b66818","resolution":{"observed_at":"2026-08-06T16:50:00.733952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04404","last_updated":"2025-02-06T08:52:43Z","snapshot_observed_at":"2026-08-09T00:20:19.377517Z","submitted_at":"2025-02-06T08:52:43Z","title":"Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04404","snapshot_observed_at":"2026-08-06T16:50:00.804855Z","title":"Step back to leap forward: Self-backtracking for boosting reasoning of language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T16:50:00.804855Z"},"links":{"cited_paper":"/paper/2502.04404","citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:d1fbb71b9294c698cba0a58630fec6653dd6d164d586a6e88867de0c4fefa04a","observation_id":"9bcb3b60-b291-487b-b18f-1fa649f93c7c","resolution":{"observed_at":"2026-08-06T16:50:00.804855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06773","last_updated":"2025-02-10T18:52:04Z","snapshot_observed_at":"2026-08-08T22:57:17.515334Z","submitted_at":"2025-02-10T18:52:04Z","title":"On the Emergence of Thinking in LLMs I: Searching for the Right Intuition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.06773","snapshot_observed_at":"2026-08-06T16:50:00.873671Z","title":"D., Zhang, X., Gopi, S., Peng, B., Li, B., Kulkarni, J., and Inan, H","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T16:50:00.873671Z"},"links":{"cited_paper":"/paper/2502.06773","citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:3c524ad8c9ab4b65d45ea2de617979e39610495eff5e2ea07a82a2a252ee32cb","observation_id":"0ed703d5-4526-4335-9935-6d8a042fb6da","resolution":{"observed_at":"2026-08-06T16:50:00.873671Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.03373","last_updated":"2025-02-05T17:13:32Z","snapshot_observed_at":"2026-07-06T20:31:41.231839Z","submitted_at":"2025-02-05T17:13:32Z","title":"Demystifying Long Chain-of-Thought Reasoning in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.03373","snapshot_observed_at":"2026-08-06T16:50:00.947051Z","title":"Demystifying long chain-of-thought reasoning in llms","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T16:50:00.947051Z"},"links":{"cited_paper":"/paper/2502.03373","citing_paper":"/paper/2507.12638"},"observation_digest":"sha256:8b6ec900f77f1ee4fa1ca55d18e4014003e137067867a331ea9bcea91356362a","observation_id":"2b13e241-4ce3-4ba6-a4ac-11b01195656a","resolution":{"observed_at":"2026-08-06T16:50:00.947051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.12638","last_updated":"2025-07-16T21:21:03Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T14:46:42.003088Z","submitted_at":"2025-07-16T21:21:03Z","title":"Reasoning-Finetuning Repurposes Latent Representations in Base Models"},"reference_resolution":{"displayed":11,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":1},"total_outbound_references":11},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 7 inbound Pith citation observations for arXiv:2507.12638."}