{"as_of":"2026-08-08T13:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1d18cbdc9d95284ec198d49e6546f9a5d0eaae618e2e247f74f4076a00b46181","coverage":[{"denominator":128,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:54:50.872920Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T07:46:18.316751Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.01752","snapshot_observed_at":"2026-08-01T07:46:18.316751Z","title":"arXiv preprint arXiv:2507.01752 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21356","last_updated":"2026-07-23T14:19:28Z","snapshot_observed_at":"2026-08-07T19:47:51.895704Z","submitted_at":"2026-07-23T14:19:28Z","title":"Emergent Misalignment Recruits a Pre-existing Persona Subspace","version":1},"reference_index":151,"source":"arxiv_source","source_observed_at":"2026-08-01T07:46:18.316751Z"},"links":{"cited_paper":"/paper/2507.01752","citing_paper":"/paper/2607.21356"},"observation_digest":"sha256:4ca2aaebd8cf2c158727bec76a5b8cb8db7293085c606d68d3eb7add1a3aeb0a","observation_id":"e41f1031-062b-464c-8beb-fa8103d7d353","resolution":{"observed_at":"2026-08-01T07:46:18.316751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.01752/citation-record","integrity":"/paper/2507.01752/integrity","json":"/paper/2507.01752/citation-record.json","paper":"/paper/2507.01752"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-06T20:54:43.651337Z","title":"Scaling laws for neural language models","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:43.651337Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:1a6fe35011e27169fe4bfc36ffb6679557bc351fb3d1b6b5d6a55c33dd4624c2","observation_id":"50228e35-ebc2-46fa-8cab-b0e491ebaa53","resolution":{"observed_at":"2026-08-06T20:54:43.651337Z","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":"2026-08-06T20:54:43.742829Z","title":"Emergent abilities of large language models.Transactions on Machine Learning Research, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:43.742829Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:60b87c960e0b93172a093433eb86e89bed63e331affe52b2f2e0baecdc1648f4","observation_id":"c007f662-ae69-4df3-8ee6-094def7cf30f","resolution":{"observed_at":"2026-08-06T20:54:43.742829Z","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":"2026-08-06T20:54:43.881369Z","title":"Reconstructing training data from trained neural networks.Advances in Neural Information Processing Systems, 35:22911–22924, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:43.881369Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:3f57bb02476200ac51188ace63d657953f54d3fd5dbb4955591e97a317736bdb","observation_id":"569c8339-4ef1-443f-a547-e0b6861bfbee","resolution":{"observed_at":"2026-08-06T20:54:43.881369Z","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":"2026-08-06T20:54:43.947393Z","title":"Extracting training data from large language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:43.947393Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:5a9e0f080b185ab735365845603b11b379d84b137ae97d4cc411b5e0e31a89a3","observation_id":"fbfe35e9-f3a4-4005-a8ce-368bb388ff30","resolution":{"observed_at":"2026-08-06T20:54:43.947393Z","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":"2026-08-06T20:54:44.001088Z","title":"Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, and Florian Tramèr","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.001088Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:ec88b9c1199acb0998745cea91274fa3e204b2ae4c00131bc3fdf5fd14bfaecc","observation_id":"a11edb9e-9862-4011-9208-2a873bd8e9ff","resolution":{"observed_at":"2026-08-06T20:54:44.001088Z","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":"2026-08-06T20:54:44.093491Z","title":"Deep leakage from gradients.Advances in neural information processing systems, 32, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.093491Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:d1ba109d6558d31503a9d4293fa6ab0c4a3287678fe18af455ba0817fe5c84a8","observation_id":"68e4b252-76d8-4f9b-9d29-e3b101df8b5b","resolution":{"observed_at":"2026-08-06T20:54:44.093491Z","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":"2026-08-06T20:54:44.160092Z","title":"Deep models under the gan: Information leakage from collaborative deep learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.160092Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:0812c15d8703d56ed507f4a35e6e2be00f246383ba5b9b539926485a5cfefbf5","observation_id":"99300936-b5c2-4f55-b6c8-a3cd911de0a1","resolution":{"observed_at":"2026-08-06T20:54:44.160092Z","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":"2026-08-06T20:54:44.230589Z","title":"Poisoning language models during instruction tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.230589Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:b8ce12d7cd92460ecbebe9e82788d58e4f1ad108701105cabd95d9168809f4d3","observation_id":"63374ca5-e8a1-48eb-89a9-981bf06e329e","resolution":{"observed_at":"2026-08-06T20:54:44.230589Z","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":"2026-08-06T20:54:44.358349Z","title":"Preserving privacy in large language models: A survey on current threats and solutions.Transactions on Machine Learning Research, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.358349Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:eb21ae23dfd1f577346c797e67e9488ffa69dfbed8ea5a40bfe7cea6875a8880","observation_id":"a7172a76-3a33-4fa6-89d3-d67f5f23df65","resolution":{"observed_at":"2026-08-06T20:54:44.358349Z","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":"2026-08-06T20:54:44.461375Z","title":"Springer, 1st edition, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.461375Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:1930c333771722bfe63d5767767ba290b38e1b43e9c652ac1ca62e5d7f1f1b5e","observation_id":"48579990-4187-4bdb-b112-3d2aa8779802","resolution":{"observed_at":"2026-08-06T20:54:44.461375Z","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":"2026-08-06T20:54:44.559370Z","title":"Hyperparameter optimization","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.559370Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:c2047c21645d3e195a6bdecfacaa24819eb3d466d7efe97270f3c39d5f86738d","observation_id":"8feda394-e9b1-4b1c-9ec9-0d7ac5787cb0","resolution":{"observed_at":"2026-08-06T20:54:44.559370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.08727","last_updated":"2023-01-25T08:01:55Z","snapshot_observed_at":"2026-07-06T14:43:16.371886Z","submitted_at":"2023-01-20T18:47:24Z","title":"Neural Architecture Search: Insights from 1000 Papers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.08727","snapshot_observed_at":"2026-08-06T20:54:44.632449Z","title":"Neural architecture search: Insights from 1000 papers.arXiv:2301.08727, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.632449Z"},"links":{"cited_paper":"/paper/2301.08727","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:97641208002e9253c6d97e801b59a51dbfaf852ce8416c78aa1565a43762dc2e","observation_id":"9f9f85a8-aac7-4791-b206-ae39fb9d02b5","resolution":{"observed_at":"2026-08-06T20:54:44.632449Z","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":"2026-08-06T20:54:44.700765Z","title":"Hansen and A","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.700765Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:46841d20a3fd9cdc100f8417519518e21da12441c8c469d7686009f0165529f7","observation_id":"df8a6f52-0c4c-4d48-83ce-3a8d4a1c4211","resolution":{"observed_at":"2026-08-06T20:54:44.700765Z","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":"2026-08-06T20:54:44.761747Z","title":"Completely derandomized self-adaptation in evolution strategies","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.761747Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:682487e6d94d33898276a1ab9bab263ee00c5aba8a7a474c5160f5f424c1f154","observation_id":"0a96ac76-5935-4e6f-9625-3bcc0e377628","resolution":{"observed_at":"2026-08-06T20:54:44.761747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1604.00772","last_updated":"2023-03-10T09:45:23Z","snapshot_observed_at":"2026-07-06T04:51:38.670553Z","submitted_at":"2016-04-04T08:16:12Z","title":"The CMA Evolution Strategy: A Tutorial","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1604.00772","snapshot_observed_at":"2026-08-06T20:54:44.834297Z","title":"The CMA Evolution Strategy: A Tutorial - V2.arXiv:1604.00772, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.834297Z"},"links":{"cited_paper":"/paper/1604.00772","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:87bc985c6735290cd5efd6e6132f21499999815e4e984378de3484f27f8ebff3","observation_id":"79308102-4c94-4e62-ae5b-3e4bd04aa462","resolution":{"observed_at":"2026-08-06T20:54:44.834297Z","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":"2026-08-06T20:54:44.961436Z","title":"Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces.Journal of Global Optimization, 11(4):341–359, 1997","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:44.961436Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:48504ccf2cd9151cc538841f0142ff3fe4486320a77531d2d1cef6fb0d87c5ad","observation_id":"2f3a76ad-97d5-47b3-9def-5f1a365f5504","resolution":{"observed_at":"2026-08-06T20:54:44.961436Z","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":"2026-08-06T20:54:45.043262Z","title":"Eberhart","venue":null,"work_id":null,"year":1942},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:45.043262Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:a7bb25618447b568e1aef1fa71f0756a96b6e58014a61442e8fdb7e16f1c1a22","observation_id":"5fb62440-f644-4124-9521-9a2ade29ca21","resolution":{"observed_at":"2026-08-06T20:54:45.043262Z","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":"2026-08-06T20:54:45.141784Z","title":null,"venue":null,"work_id":null,"year":1974},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:45.141784Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:5fda48810e25c93ea8c5054a5ed9747e3c3b54d705e9bdccb959ef436280a6d7","observation_id":"04dcb3bc-421c-4dcd-8b9a-82208e7d12e6","resolution":{"observed_at":"2026-08-06T20:54:45.141784Z","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":"2026-08-06T20:54:45.375450Z","title":"The bayesian approach to global optimization","venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:45.375450Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:820f5df552da40b57e5ae008f34758599ded3efe01343c6c4b39b455eae77c1c","observation_id":"f6b51240-dab5-4c0f-87e8-bd35db612380","resolution":{"observed_at":"2026-08-06T20:54:45.375450Z","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":"2026-08-06T20:54:45.454229Z","title":"Cambridge University Press, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:45.454229Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:8c7534a5c6e2be537993af2999b47a6e52f678bb25af4a12bbd6285558c71454","observation_id":"5231d3c1-f66c-4d6b-980f-e958becccaf9","resolution":{"observed_at":"2026-08-06T20:54:45.454229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.03864","last_updated":"2017-09-07T23:28:48Z","snapshot_observed_at":"2026-07-06T05:33:17.404544Z","submitted_at":"2017-03-10T23:02:19Z","title":"Evolution Strategies as a Scalable Alternative to Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.03864","snapshot_observed_at":"2026-08-06T20:54:45.587335Z","title":"Evolution strategies as a scalable alternative to reinforcement learning.arXiv:1703.03864, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:45.587335Z"},"links":{"cited_paper":"/paper/1703.03864","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:6d1380d7e6f9490200aea86c3466a648df8496b55ddf5ea13f4186c159a18081","observation_id":"6f84e4ce-4caa-4098-94a3-75de62c34275","resolution":{"observed_at":"2026-08-06T20:54:45.587335Z","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":"2026-08-06T20:54:45.718364Z","title":"On the exploitability of instruction tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:45.718364Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:6088a40922abfb2e86ebeb657e3ca02fcf23a855119ae40f16c9cdc8b3a5cb7a","observation_id":"6cf82471-f645-452f-9e53-0e94cfc50bba","resolution":{"observed_at":"2026-08-06T20:54:45.718364Z","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":"2026-08-06T20:54:45.825628Z","title":"Catastrophic jailbreak of open- source LLMs via exploiting generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:45.825628Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:b9650fec35602a1fdfc530f7908e278b60364be6f571592519f9cf3bb1d7c0af","observation_id":"74d2331c-7279-4910-9ca2-f8d3925c14f1","resolution":{"observed_at":"2026-08-06T20:54:45.825628Z","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":"2026-08-06T20:54:45.903472Z","title":"Unveiling the generalization power of fine-tuned large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:45.903472Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:859eb55d828ba6a51d280bff66eb2e140c3e9e3828fa6909c2898f2746746ecd","observation_id":"d25fd394-7837-4245-805d-d15d477542ed","resolution":{"observed_at":"2026-08-06T20:54:45.903472Z","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":"2026-08-06T20:54:46.015679Z","title":"RLHFPoison: Reward poisoning attack for reinforcement learning with human feedback in large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:46.015679Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:e1272808b75d028d69e4cf9819eb28b78e0de05f5a24a35716b93bee57ca4b6f","observation_id":"b52c3b72-1d77-4c82-b5a0-5caaaa97d7d5","resolution":{"observed_at":"2026-08-06T20:54:46.015679Z","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":"2026-08-06T20:54:46.091941Z","title":"Best-of-venom: Attacking RLHF by injecting poisoned preference data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:46.091941Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:ddff221e2873d004767bfab3d176cdd6d00b465f235dacd686e69903f93fb49a","observation_id":"dd12839e-d868-41cb-84a7-d99789120e39","resolution":{"observed_at":"2026-08-06T20:54:46.091941Z","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":"2026-08-06T20:54:46.209672Z","title":"Universal jailbreak backdoors from poisoned human feedback","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:46.209672Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:47304bc55f12043f0a8065920c0101c471d6f3ced34d53076a1581a80d6a3f95","observation_id":"aa8cbd95-0a9f-4938-a123-74b662a2e9fa","resolution":{"observed_at":"2026-08-06T20:54:46.209672Z","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":"2026-08-06T20:54:46.315124Z","title":"Is poisoning a real threat to DPO? maybe more so than you think.AAAI Conference on Artificial Intelligence, 39(26):27556–27564, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:46.315124Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:9334894ff9fb50d02bf9fdf11557e9d6c2512195f0c5d79cbff019980f905275","observation_id":"a17e49b9-d9aa-4ce4-887e-1e0f7b400059","resolution":{"observed_at":"2026-08-06T20:54:46.315124Z","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":"2026-08-06T20:54:46.396465Z","title":"Retrofitting word vectors to semantic lexicons","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:46.396465Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:b09ceaa52a8d765403883b462f013cfae27311432b47b03457e47f1147e28015","observation_id":"7ef69642-0336-44af-8e72-a79c055438b6","resolution":{"observed_at":"2026-08-06T20:54:46.396465Z","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":"2410.11330","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:52.903567Z","title":"Evolutionary retrofitting.arXiv:2410.11330, 2024","venue":null,"work_id":"0f72e8ef-78a3-472f-b784-6f4a7c6cf460","year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:46.488669Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:ba05556f56d6006716a9b1f41e7db9f5834867c56a676ad41d3080aad0dbbd29","observation_id":"6019d423-adcd-450a-bc79-1ea8c8ab8782","resolution":{"observed_at":"2026-08-06T20:54:52.948923Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-06T20:54:46.585546Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.arXiv:2402.03300, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:46.585546Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:6d6aaaea90b5f7667b39f14900e9fac83a1664d37e71e84da1265e7d0dc984ed","observation_id":"56855b03-9fe9-495f-b59c-340377ef65bf","resolution":{"observed_at":"2026-08-06T20:54:46.585546Z","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":"2026-08-06T20:54:46.690746Z","title":"Rapin and O","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:46.690746Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:58a7f1cfc4b86b6001a5eec106a052d34845f82aa30e5cbe7e349802ef41f463","observation_id":"2f90574f-09cf-4222-843b-8676ffb03d82","resolution":{"observed_at":"2026-08-06T20:54:46.690746Z","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":"2026-08-06T20:54:46.814269Z","title":"Campi and Simone Garatti","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:46.814269Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:eaa1ceab1053265aeb9b7ed506899ed139f0bb45ac854b686a621a257a62d812","observation_id":"8432ee12-2190-4003-8cd5-5cecf436fc50","resolution":{"observed_at":"2026-08-06T20:54:46.814269Z","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":"2026-08-06T20:54:46.912057Z","title":"Zico Kolter, and Chelsea Finn","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:46.912057Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:a4a45d9707007eef14d158dc1e35114317d105569c049700a5d19ce23921b649","observation_id":"d3a0ce48-2e74-409b-b2ab-d045ba7b3b59","resolution":{"observed_at":"2026-08-06T20:54:46.912057Z","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":"2026-08-06T20:54:47.018460Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.018460Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:0cdf08f85eb0bc3ecd2c445f57a31c4912f1bc41f10494e89d9a7d47f3ab7254","observation_id":"87bee3f6-82e8-48f8-b435-da5624a08ca6","resolution":{"observed_at":"2026-08-06T20:54:47.018460Z","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":"2026-08-06T20:54:47.115636Z","title":"Vapnik and Alexey Y","venue":null,"work_id":null,"year":1971},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.115636Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:aa195d6340c69c31f17e25c89fc4f796c14a2355c421084b32be6e71fe9670a9","observation_id":"c96e5fe1-d592-4385-b00f-3bba964b2204","resolution":{"observed_at":"2026-08-06T20:54:47.115636Z","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":"2026-08-06T20:54:47.207567Z","title":"Bartlett and Shahar Mendelson","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.207567Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:7a6fc51e080b653af98c47bec3d2d6ae6cc6515d040b422485047827c8388fdd","observation_id":"2bc54c03-d2e0-4519-8047-57f859755ed1","resolution":{"observed_at":"2026-08-06T20:54:47.207567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.05862","last_updated":"2019-02-25T02:37:14Z","snapshot_observed_at":"2026-08-03T21:23:40.543104Z","submitted_at":"2018-04-16T18:01:12Z","title":"Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.05862","snapshot_observed_at":"2026-08-06T20:54:47.270581Z","title":"Non-vacuous generalization bounds at the imagenet scale: a pac-bayesian compression approach.arXiv:1804.05862,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.270581Z"},"links":{"cited_paper":"/paper/1804.05862","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:12f3ff02b90a3b701f219d553c37dfd40af262d675454bbd16f1224316900c19","observation_id":"c74089a1-0321-4d6d-8cc9-70ef41400162","resolution":{"observed_at":"2026-08-06T20:54:47.270581Z","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":"2026-08-06T20:54:47.400191Z","title":"Pac-bayes compression bounds so tight that they can explain generalization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.400191Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:258dd51cb931164f4a82dd4b01504f1628cc814e8ea5983443b5ae5865b688e6","observation_id":"7003ab01-d8b7-41d8-9ade-12f146d1f84c","resolution":{"observed_at":"2026-08-06T20:54:47.400191Z","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":"2026-08-06T20:54:47.511505Z","title":"Algorithmic stability and generalization performance","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.511505Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:133dabacdf7775fb5696002764ae305f64cd86ae2c67a678e5e3fd536189d245","observation_id":"d220cdb5-725c-494e-9f3c-22c7defe96f3","resolution":{"observed_at":"2026-08-06T20:54:47.511505Z","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":"2026-08-06T20:54:47.613039Z","title":"Exploiting LLM quantization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.613039Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:5f5d8693b285b19bb568f52b477484ecfb0ff99052f0e0ba43451ad3fa899e83","observation_id":"05fa441d-14ea-4f31-8ed6-4577acc5a1de","resolution":{"observed_at":"2026-08-06T20:54:47.613039Z","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":"2026-08-06T20:54:47.705309Z","title":"Privacy backdoors: stealing data with corrupted pretrained models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.705309Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:3c832e25f70971abefc18d0a30d8c03a625a9d53b2361995562dfcc572129743","observation_id":"c71e0f1b-6880-4f6a-84b1-2cfdef996649","resolution":{"observed_at":"2026-08-06T20:54:47.705309Z","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":"2026-08-06T20:54:47.814940Z","title":"Certified defenses for data poisoning attacks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.814940Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:22cf88497fe74a39d29e69a0fb35fcfd15bf5d6858fa3854dd9257fec7a61025","observation_id":"f55939e2-2fe4-41f9-8600-9b5796809127","resolution":{"observed_at":"2026-08-06T20:54:47.814940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.24372","last_updated":"2026-07-14T15:11:12Z","snapshot_observed_at":"2026-08-08T12:46:47.440877Z","submitted_at":"2025-09-29T07:19:34Z","title":"Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.24372","snapshot_observed_at":"2026-08-06T20:54:47.861802Z","title":"Evolution strategies at scale: Llm fine-tuning beyond reinforcement learning.arXiv preprint arXiv:2509.24372, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.861802Z"},"links":{"cited_paper":"/paper/2509.24372","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:70b61f06483d4cbb924a46d46b3d35eec4a51856626a8de92a68c93951f2490f","observation_id":"08cfb055-de24-4a25-92bc-b230408cc6d1","resolution":{"observed_at":"2026-08-06T20:54:47.861802Z","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":"2026-08-06T20:54:47.961355Z","title":"Evolution strategies at the hyperscale.arXiv preprint arXiv:2511.16652, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:47.961355Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:d54c9d8132a8f2cafdf62f669d5c231d457b0a4a74d9ec59a728291779f8c5eb","observation_id":"d93ecace-3ade-4944-ad8c-7c0125ed432c","resolution":{"observed_at":"2026-08-06T20:54:47.961355Z","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":"2026-08-06T20:54:48.014741Z","title":"Routledge, 2006","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.014741Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:94fcc45502b96b76a2d85fee34d94b1810e953f40f7b8e1c69795d5f6918d6dc","observation_id":"4ea99fb5-c0d0-4878-bb5e-2bf4847e9481","resolution":{"observed_at":"2026-08-06T20:54:48.014741Z","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":"2026-08-06T20:54:48.072354Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.072354Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:743ff3bf77c31f3cf6419286f0c0a683b1e041a30ab3518ded137fc2a6c9ec26","observation_id":"7887e855-1d18-4161-b127-e105d74d88b2","resolution":{"observed_at":"2026-08-06T20:54:48.072354Z","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":"2026-08-06T20:54:48.120318Z","title":"Scaling up: the challenges of urban retrofit.Building research & information, 41(5):499–503, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.120318Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:ac2f1b965ac775394fdd8f8b65e04268c18e6b005ee9cfb637f52ac3c59d842f","observation_id":"e647f807-a38c-45ca-89ff-7b12cd3f5bd8","resolution":{"observed_at":"2026-08-06T20:54:48.120318Z","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":"2026-08-06T20:54:48.190700Z","title":"van der Vaart and J.A","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.190700Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:2e56fb9ba8c3b01ff9920066473e9307bf1279cf7ded1725ebbf50796be3d346","observation_id":"4d826839-4de2-4e6c-ba9d-c6bd7e743ecb","resolution":{"observed_at":"2026-08-06T20:54:48.190700Z","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":"2026-08-06T20:54:48.247069Z","title":"Vapnik.The Nature of Statistical Learning Theory","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.247069Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:743526441517b10806e63c4a0fec8c3d5806268adcd3f4fb3810695c8da60d1c","observation_id":"4fd0d6dc-737b-441f-b538-98a97ffed9d9","resolution":{"observed_at":"2026-08-06T20:54:48.247069Z","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":"2026-08-06T20:54:48.329846Z","title":"Learning in the presence of malicious errors.SIAM Journal on Computing, 22(4):807–837, 1993","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.329846Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:89cab1988a2024770310f17f24096252f13b8bd018831723f30c7d08cda69461","observation_id":"c658dc6c-99b0-4223-ab44-150b3a82fa8d","resolution":{"observed_at":"2026-08-06T20:54:48.329846Z","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":"2026-08-06T20:54:48.412564Z","title":"Lemley, and Percy Liang","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.412564Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:21cfa37ff81dc8f80f5ea14df41bd09013b53e272cf8adc47b26ef5ce93a7edc","observation_id":"1e85c88f-2d70-4310-b6ff-cb60ee09d840","resolution":{"observed_at":"2026-08-06T20:54:48.412564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-07T01:45:38.840969Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-06T20:54:48.472830Z","title":"Training verifiers to solve math word problems","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.472830Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:d2575e749e7bed02df03c12c94a2f3e540ae56beb6b54fb16b740a21e0cab38c","observation_id":"24de9926-3a5b-465b-b244-9e4a729c8127","resolution":{"observed_at":"2026-08-06T20:54:48.472830Z","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":"2026-08-06T20:54:48.532580Z","title":"Measuring mathematical problem solving with the MATH dataset","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.532580Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:dba90efc7064a3827a28ed02d57da30ef6024953cdf568a275df44bd8bc3af35","observation_id":"7acf66ab-60e4-40f5-9d91-2e1a128457a0","resolution":{"observed_at":"2026-08-06T20:54:48.532580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.07830","last_updated":"2019-05-19T23:57:23Z","snapshot_observed_at":"2026-07-31T00:09:56.948833Z","submitted_at":"2019-05-19T23:57:23Z","title":"HellaSwag: Can a Machine Really Finish Your Sentence?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.07830","snapshot_observed_at":"2026-08-06T20:54:48.611631Z","title":"Hellaswag: Can a machine really finish your sentence?arXiv preprint arXiv:1905.07830, 2019","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.611631Z"},"links":{"cited_paper":"/paper/1905.07830","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:504072b8601edb2f8e3af707894a3918de28b0dbe557b7b2e4fdbe29db7cf223","observation_id":"56a8111f-8da7-4b79-8e59-a164c0c77443","resolution":{"observed_at":"2026-08-06T20:54:48.611631Z","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":"2026-08-06T20:54:48.671267Z","title":"GSM-Plus: A comprehensive benchmark for evaluating the robustness of LLMs as mathematical problem solvers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.671267Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:d75dc42df734324fe4d5af1544fa48ee29ac95b9f388d1b5d89f8ee59f598238","observation_id":"ca5d0cef-1652-42e7-92a9-5f83c953ba45","resolution":{"observed_at":"2026-08-06T20:54:48.671267Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.05457","last_updated":"2018-03-14T18:04:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-03-14T18:04:21Z","title":"Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.05457","snapshot_observed_at":"2026-08-06T20:54:48.761949Z","title":"Think you have solved question answering? try arc, the ai2 reasoning challenge","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.761949Z"},"links":{"cited_paper":"/paper/1803.05457","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:0b1103c7bec31205453ecb5d0888268a9830982cf29db89c2cb5453f3649e3e7","observation_id":"40839dbe-df57-473e-a943-4f2d05926255","resolution":{"observed_at":"2026-08-06T20:54:48.761949Z","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":"2026-08-06T20:54:48.838014Z","title":"Random gradient-free minimization of convex functions.Founda- tions of Computational Mathematics, 17(2):527–566, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.838014Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:a0433963a30d09b186d7f6d12552c393e2813c22d07b1d38c586b35e13919746","observation_id":"079d754f-22ce-4b7b-9ffc-da5e9b404969","resolution":{"observed_at":"2026-08-06T20:54:48.838014Z","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":"2026-08-06T20:54:48.888756Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.888756Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:15a22b3271f038a56d475c1d9f16905126b1f13c291f2e9c949130f6b4d9e998","observation_id":"d28ade98-dd4c-4e27-a890-bc9beca46f6f","resolution":{"observed_at":"2026-08-06T20:54:48.888756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.06062","last_updated":"2024-11-26T03:19:15Z","snapshot_observed_at":"2026-07-31T03:44:38.951632Z","submitted_at":"2023-11-10T13:55:05Z","title":"Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.06062","snapshot_observed_at":"2026-08-06T20:54:48.928617Z","title":"Practical membership inference attacks against fine-tuned large language models via self-prompt calibration.arXiv preprint arXiv:2311.06062, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.928617Z"},"links":{"cited_paper":"/paper/2311.06062","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:109ae7d9151358f6e2153b71c645d3b0d75387e584e957161a82bd604533d18f","observation_id":"1a3f3687-8b3c-45a5-9fc2-74a4f06884c8","resolution":{"observed_at":"2026-08-06T20:54:48.928617Z","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":"2026-08-06T20:54:55.566835Z","title":"Membership inference attacks from first principles","venue":null,"work_id":"60e24547-aba4-4f6d-b499-e11d52d88885","year":1914},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:48.975717Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:47b910872ee29f88b704ede57943a26bf05bdfb9f60cee52e00d864e36ef3758","observation_id":"c0bf4c2c-809e-4775-ac23-68d8f128bf4e","resolution":{"observed_at":"2026-08-06T20:54:55.569885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:49.019515Z","title":"Window-based membership inference attacks against fine-tuned large language models.arXiv preprint arXiv:2601.02751, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.019515Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:96102e40a9a0a262e239368c16f99c925949f2c682baacf2ee777085e45ef9cf","observation_id":"a1eb0289-6668-42a3-8051-e79fa5406964","resolution":{"observed_at":"2026-08-06T20:54:49.019515Z","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":"2026-08-06T20:54:55.559306Z","title":"Kearns and R.E","venue":null,"work_id":"121999a1-b6a0-4335-94c3-efffa9383a14","year":1990},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.067572Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:8b8b570be0ffe0b9809b0f65393d724ede0900ccb37034827391221219e3ced6","observation_id":"7091c05d-033d-4ecf-97ee-38f6cf9c7972","resolution":{"observed_at":"2026-08-06T20:54:55.561874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.551670Z","title":"Uniform convergence may be unable to explain generalization in deep learning.Advances in Neural Information Processing Systems, 32, 2019","venue":null,"work_id":"7951b7c6-3788-4f66-9149-ba6c280db0c1","year":2019},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.116542Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:9f26e7f506a88813e9cbe154c1fb430b198ec9e7bacdd915fa283d196b5009f0","observation_id":"a0647d55-fa5a-4a65-a983-462c4a1082f4","resolution":{"observed_at":"2026-08-06T20:54:55.554343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.544482Z","title":"Transformers as algorithms: Generalization and stability in in-context learning","venue":null,"work_id":"3566c8d8-6681-4500-8570-7ffdec25d758","year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.161820Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:7625e13260891e7e283e6c73366bb54470aa0b2936785858d18103482beecdb8","observation_id":"0c286a80-bdb1-4a4f-9423-af9c12a99775","resolution":{"observed_at":"2026-08-06T20:54:55.547009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05526","last_updated":"2017-12-15T04:26:26Z","snapshot_observed_at":"2026-07-06T06:14:30.795326Z","submitted_at":"2017-12-15T04:26:26Z","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.05526","snapshot_observed_at":"2026-08-06T20:54:49.216411Z","title":"Targeted backdoor attacks on deep learning systems using data poisoning.arXiv:1712.05526, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.216411Z"},"links":{"cited_paper":"/paper/1712.05526","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:0c5edd01efe0c0bc036baccbf2a0fb56e3d8028da5871fdcbe8f57dc987ac703","observation_id":"c16f8c9e-3d12-479a-bb32-b4d76473fbab","resolution":{"observed_at":"2026-08-06T20:54:49.216411Z","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":"2026-08-06T20:54:55.537197Z","title":"Medical large language models are vulnerable to data-poisoning attacks.Nature Medicine, 31(2):618–626, 2025","venue":null,"work_id":"2d8e7fdc-1122-4e7e-9937-748cb060ca00","year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.257934Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:1afbe8f2324a46e2730f3f28147f5b7992b31f8ae008ea7119dd8fb6b08bd2f9","observation_id":"55e6d6a3-4cbd-4ef0-948e-c4ee992c288e","resolution":{"observed_at":"2026-08-06T20:54:55.540271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.530340Z","title":"Christiano, Jan Leike, Tom B","venue":null,"work_id":"8bf3909b-2d07-4d0e-9095-044d1589bacb","year":2017},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.301184Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:2dde76418a875ab44760f819919743a8fd8dd0bc66b7c17a99e6d4696819d005","observation_id":"fa640af7-f969-4b49-ae43-342f0bf8ccad","resolution":{"observed_at":"2026-08-06T20:54:55.532824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.522320Z","title":"Direct preference optimization: Your language model is secretly a reward model","venue":null,"work_id":"11299f81-a98c-45d0-92d3-66050afe682f","year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.355934Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:d351d0042719a88a5c7ef302530dbec40fea12421ff8cc6c545a068130e6c2c1","observation_id":"2e9dabd5-3be4-4204-9621-b757eb2d18e5","resolution":{"observed_at":"2026-08-06T20:54:55.525669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.514665Z","title":"Backdooring instruction-tuned large language models with virtual prompt injection","venue":null,"work_id":"83dd9573-9cd2-4d31-ba77-f94802629dc9","year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.401709Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:a2939034779ce79b41b086a9c1edeb0589fcc323420fbaad9f562afddb8eda4f","observation_id":"8d8f334b-8016-4525-ae0e-99c61a7f9d77","resolution":{"observed_at":"2026-08-06T20:54:55.517389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.506432Z","title":"Poisoning retrieval corpora by injecting adversarial passages","venue":null,"work_id":"2f840c6f-e2ce-479d-a44f-f385d0403953","year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.462310Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:ff4ac2ca3445085bc8796265c184b3d8759905bf8e2c341762c46f4eb77570c9","observation_id":"f99c38b7-7a2e-4a96-866e-5e75d2fee624","resolution":{"observed_at":"2026-08-06T20:54:55.509165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.499393Z","title":"How Johnny can persuade LLMs to jailbreak them: Rethinking persuasion to challenge AI safety by humanizing LLMs","venue":null,"work_id":"099dda77-2166-4121-9609-d47eb0debc15","year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.533538Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:6364885f707dd6ba0b4fafccfba01fed756deda7b0ba71fcc016b612a7e08328","observation_id":"f188ee29-50a0-4598-bb0e-e76b26e02eda","resolution":{"observed_at":"2026-08-06T20:54:55.502268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.491725Z","title":"Aligning large language models for faithful integrity against opposing argument.AAAI Conference on Artificial Intelligence, 2025","venue":null,"work_id":"2755a2a1-fb64-4eae-b07f-a37675d01712","year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.581204Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:dc70f3aa8ffbb8b349e7558f0c74fbb87dabe6e44696168e200271f7ec3e44f4","observation_id":"4d3659f8-f26d-476a-9960-7710b6944c7b","resolution":{"observed_at":"2026-08-06T20:54:55.494418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.484198Z","title":"Privacy-preserving instructions for aligning large language models","venue":null,"work_id":"b103b69e-8866-4840-b9d9-99c24962fdf0","year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.620486Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:f98aed027b6d4d3f20e3ed82ab50519bf95904952b69ce65a3efcee8c3c2dff8","observation_id":"88793f8a-aa20-4fa3-9f70-2a3723fdd2be","resolution":{"observed_at":"2026-08-06T20:54:55.486811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-06T20:54:49.655351Z","title":"Llama 2: Open foundation and fine-tuned chat models.arXiv:2307.09288, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.655351Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:7a26591b593cc36817c963cdbc254402c36e0b958755ff00cf03c2842e33e4fb","observation_id":"d2da1b74-2a20-4640-9010-90ce7e5883a1","resolution":{"observed_at":"2026-08-06T20:54:49.655351Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05463","last_updated":"2023-09-11T14:01:45Z","snapshot_observed_at":"2026-08-02T22:47:03.212781Z","submitted_at":"2023-09-11T14:01:45Z","title":"Textbooks Are All You Need II: phi-1.5 technical report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05463","snapshot_observed_at":"2026-08-06T20:54:49.701077Z","title":"Textbooks are all you need II: phi-1.5 technical report.arXiv:2309.05463, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.701077Z"},"links":{"cited_paper":"/paper/2309.05463","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:46472059f4c8b507329e13797eabb96a011d215d976f99d3d5997fc9f0f56e79","observation_id":"4ec7dcdf-407d-4a20-b605-2f466ed3b036","resolution":{"observed_at":"2026-08-06T20:54:49.701077Z","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":"2026-08-06T20:54:55.477695Z","title":"GSM-symbolic: Understanding the limitations of mathematical reasoning in large language models","venue":null,"work_id":"9bf75511-08ce-45c1-b578-d8ffc92546e0","year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.740515Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:f15ce7136088ef90e87d11b4633e3318abe9606be31e25e2f37893f03e9d6083","observation_id":"a9f36943-0659-4b71-b0db-7f8c2089d23e","resolution":{"observed_at":"2026-08-06T20:54:55.480110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.469746Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":"fee67838-e20c-4e0c-b798-76172aa0901e","year":2022},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.782162Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:502d04c1f4a3a4f7b253246c46d182fc0ee74565274d3187cf33f3f5a27b3a1f","observation_id":"f0e597a0-5f3a-4ed3-a489-af25f2b1c409","resolution":{"observed_at":"2026-08-06T20:54:55.472797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.462200Z","title":"Complexity-based prompting for multi-step reasoning.International Conference on Learning Representations, 2022","venue":null,"work_id":"ce469d46-43cc-431b-82f2-73825a67f2ba","year":2022},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.821785Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:e895b688d8e8c037c94fb909d03449c869a381f8bbd12437d9aefd39634a7db8","observation_id":"e35ffec3-2eb9-464e-b3fd-a518e09d78ff","resolution":{"observed_at":"2026-08-06T20:54:55.464910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.455124Z","title":"Smith, and Tao Yu","venue":null,"work_id":"4dd03290-8cb4-4d76-a664-246276fcdee8","year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.863650Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:8355ad6197f658f204a01f7c15bd460c6de891d1079e36bd527b97834368f617","observation_id":"7b326a5a-f48d-4001-a2d4-8ae947a877a7","resolution":{"observed_at":"2026-08-06T20:54:55.457581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.448526Z","title":"Coverage-based example selection for in-context learning","venue":null,"work_id":"fe0fedc7-1f0b-407f-bab1-1fc4f76e24ea","year":2023},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.898719Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:3ebf34f55c352506ec78e91a9ecf0197e1b650ef11d91a81a5a2cb9bb45cc43b","observation_id":"e1e22671-2c40-4efa-aefd-e234c1f3cdeb","resolution":{"observed_at":"2026-08-06T20:54:55.450988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.441767Z","title":"Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions","venue":null,"work_id":"4f452661-e3c4-4446-8968-4aa70f23e461","year":null},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.934892Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:7bac63dc7bc8ff73166192108ed16acdd6eaf0aa4a1a4eb88b666d66774a3165","observation_id":"664d3f99-0878-4ea8-b353-15b63738e0d4","resolution":{"observed_at":"2026-08-06T20:54:55.444562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.435048Z","title":"LoRA: Low-rank adaptation of large language models","venue":null,"work_id":"0c8ea6e7-2bf7-42b7-bdaa-1008e1b1a6d5","year":2022},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:49.987821Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:41a00f62105b9c98aa0c5a611360fb9709dfabb325d0112503acf071610036ab","observation_id":"c388f86e-0c54-45b5-97d5-9786a65d5c05","resolution":{"observed_at":"2026-08-06T20:54:55.437583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.427418Z","title":"LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization","venue":null,"work_id":"6b34fe9a-4b02-4458-a1bb-65386d5908d7","year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.027751Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:873ce44ee34131887064b0ba040eef948600f125e62ac20fee0925014c1cf210","observation_id":"71b67e42-b5ea-4bb7-aaac-7825d165184a","resolution":{"observed_at":"2026-08-06T20:54:55.430591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.420652Z","title":"Sutherland","venue":null,"work_id":"6491dfa3-42d9-4f3b-8cde-8c590217af8f","year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.067288Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:178c090f97ad4498f37317860d35a4b20ce361f723378f948028fc581bd28db4","observation_id":"4083f159-0b47-4063-8ac7-744fb313e390","resolution":{"observed_at":"2026-08-06T20:54:55.423188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.413916Z","title":"Safety alignment should be made more than just a few tokens deep","venue":null,"work_id":"356aae88-369f-47f6-a324-3b749f180381","year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.109727Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:974eeeb2c72317d7fbdd4a1a58825d2fd56302e540101037a91808f242a8d55b","observation_id":"c22cbdd3-5013-4ad5-8efa-5bfe1c24e27b","resolution":{"observed_at":"2026-08-06T20:54:55.416401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.407199Z","title":"What makes large language models reason in (multi-turn) code generation ? InThirteenth International Conference on Learning Representations, 2025","venue":null,"work_id":"8cdb6261-e83a-4778-b369-a98a221b65b7","year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.166691Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:f1f0148bc5b26428d8d5c1de4fc0da4d98a5a2bd3e424be993fa34413a8dbf51","observation_id":"37288cdb-aac3-4a44-82fd-0103f65a90d7","resolution":{"observed_at":"2026-08-06T20:54:55.409787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13441","last_updated":"2025-02-19T05:37:08Z","snapshot_observed_at":"2026-08-07T18:06:40.905031Z","submitted_at":"2025-02-19T05:37:08Z","title":"The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13441","snapshot_observed_at":"2026-08-06T20:54:50.208556Z","title":"The self- improvement paradox: Can language models bootstrap reasoning capabilities without external scaffolding? arXiv:2502.13441, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.208556Z"},"links":{"cited_paper":"/paper/2502.13441","citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:e4222b52f0e2fddc4fce6ba70a22f6dc4b2b539561ad3f72acd392e755c1ec28","observation_id":"0cc759a8-e6ad-4de7-a790-31e039093dbc","resolution":{"observed_at":"2026-08-06T20:54:50.208556Z","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":"2026-08-06T20:54:55.400691Z","title":"Springer, 1996","venue":null,"work_id":"1c9c865d-11f7-4a26-9672-f6be7b586275","year":1996},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.262779Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:2d1d0322ebe02af507d723934173125fac6ad6f474ba247c72f9cc102461d933","observation_id":"c06cdc14-968a-49ed-98ec-51892aabb4c7","resolution":{"observed_at":"2026-08-06T20:54:55.403110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.393497Z","title":"Probability inequalities for sums of bounded random variables.Journal of the American Statistical Association, 58:13–30, 1963","venue":null,"work_id":"cac94e90-c737-4b7a-b627-ce234902d1c8","year":1963},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.318334Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:f78f2b2f9302b8bc5387d65f259ad53ab25668b2e0c5dcf5efb8766bf392ecee","observation_id":"c9ae2c27-3421-4a3f-8756-76df318dc0e0","resolution":{"observed_at":"2026-08-06T20:54:55.395992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.385741Z","title":"Probability inequalities for the sum of independent random variables.Journal of the American Statistical Association, 57(297):33–45, 1962","venue":null,"work_id":"51da70ba-37e3-4281-a2bf-0a2fce30f086","year":1962},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.377950Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:dba1ec76f732ceb1675c1cdc4bb607e139f53e3a80e91d7fc7f565d32e221b20","observation_id":"f260af76-82d0-41c4-b98e-d0c58e739050","resolution":{"observed_at":"2026-08-06T20:54:55.388443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.379137Z","title":"Bonferroni.Teoria statistica delle classi e calcolo delle probabilità","venue":null,"work_id":"f04b9681-103d-4544-8df1-6ff9133df3e7","year":1936},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.431558Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:c58c0144627d720b3ba0d30d162bd6caead494a9b291e38f874a6da864381aa2","observation_id":"4ca98134-9a4d-4233-9e8f-87429bdab396","resolution":{"observed_at":"2026-08-06T20:54:55.381672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.371658Z","title":"Multiple comparisons among means.Journal of the American Statistical Association, 56(293):52–64, 1961","venue":null,"work_id":"ca41330e-f63e-4a19-a7e7-ecbf5c149b2c","year":1961},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.484234Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:664f884902ae55471c803eb2ce18b54a9f7e242870f607144ca84f2de8b58b28","observation_id":"8fb5d143-105e-4da7-9ca5-9741ec3ada39","resolution":{"observed_at":"2026-08-06T20:54:55.374625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2412.04291","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:52.610104Z","title":"Evolutionary pre-prompt optimization for mathematical reasoning.arXiv:2412.04291, 2024","venue":null,"work_id":"43e44fca-f068-4ecc-a831-eb53c0bedb34","year":2024},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.549980Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:e794cb288396be4d2af29e0a4dc8b01ec47948048b005ed74cb243d6d4fb320b","observation_id":"4b8e52f2-7c98-4e31-8b75-647dc4203e95","resolution":{"observed_at":"2026-08-06T20:54:52.645321Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.364343Z","title":"Lower bounds for comparison based evolution strategies using vc-dimension and sign patterns.Algorithmica, 59(3):387–408, 2011","venue":null,"work_id":"0dd02a10-6e1d-4c17-aa9e-40942adf4ec5","year":2011},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.604224Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:f36e2c538f5a21933075bb4dc0e00cbcbf84dcc6eed0cd4944d19dc6cd28e8e7","observation_id":"74f147f0-d914-4ef0-a23e-3b840cf4c2fd","resolution":{"observed_at":"2026-08-06T20:54:55.367150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.356359Z","title":"Springer, 2015","venue":null,"work_id":"2014a915-81d3-4c03-9f8c-0c3000cbc381","year":2015},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.656744Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:f550d1c8a011d58ceeae964bfb5976f9fb7eb9bfef42c69dfe5ab6bca6bc74b6","observation_id":"7823248b-e1a9-45b4-9a43-4e9e5769e93b","resolution":{"observed_at":"2026-08-06T20:54:55.359149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.348432Z","title":"Evolution strategies – a comprehensive introduction.Natural Computing, 1(1):3–52, May 2002","venue":null,"work_id":"ef3ddbbb-e5e0-4e75-bced-5ef764abc74a","year":2002},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.713966Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:98be120b221371d8748c996af768ba789a7cc40571a629a1436daf80f175daa3","observation_id":"762f5bf8-6308-49f7-ae6a-f9e79082ca91","resolution":{"observed_at":"2026-08-06T20:54:55.351147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.341801Z","title":"Fromman-Holzboog Verlag, 1973","venue":null,"work_id":"39ba8aa1-df17-4c44-990e-e1620b8b6c39","year":1973},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.764638Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:a9cecd3298cc5b7d370e6a717fef4e4b9d9e62c0e2d8adcaf9ec72edd186fc38","observation_id":"c6d529d6-a3f7-4b1d-99e2-312d194c8816","resolution":{"observed_at":"2026-08-06T20:54:55.344212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.335307Z","title":"Birkhäuser Basel, 1977","venue":null,"work_id":"86148c1f-2de8-4004-a3cf-8f98412b7fc9","year":1977},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.817935Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:123e774371a31531c43bad699048ebe22d6080802383e962cfd4aea81b036402","observation_id":"a9e126a4-5009-4e38-9917-543a69dc1eab","resolution":{"observed_at":"2026-08-06T20:54:55.337796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:54:55.328667Z","title":"Schumer and K","venue":null,"work_id":"dc35ba35-a7d8-46ae-a8bb-3b1ab8f411cd","year":1968},"citing_paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training","version":4},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-06T20:54:50.872920Z"},"links":{"citing_paper":"/paper/2507.01752"},"observation_digest":"sha256:044a55176544b73fae58a13d967da55a3fef8dacfcb9141c7c787bf6e7aa445f","observation_id":"5d1ce2e3-cc7b-4095-86fc-6fe61457585b","resolution":{"observed_at":"2026-08-06T20:54:55.331095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.01752","last_updated":"2026-06-23T13:09:44Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T20:41:02.833387Z","submitted_at":"2025-07-02T14:29:30Z","title":"Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":64,"verified_exact":2,"verified_fuzzy":34},"total_outbound_references":128},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 100 of 128 outbound references and 1 inbound Pith citation observation for arXiv:2507.01752."}