{"as_of":"2026-08-06T17:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1329c12bec3344780908c911f6106282bd6b39175ac2c516408a9b4b21af5998","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":14,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T13:56:09.628721Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T19:16:00.644972Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":"2111.15141","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-07-01T19:16:00.644972Z","title":"Path integral sampler: a stochastic control approach for sam- pling.arXiv preprint arXiv:2111.15141","venue":null,"work_id":"323f76c4-798a-404e-8867-24c711b8a957","year":2022},"citing_paper":{"arxiv_id":"2507.10797","last_updated":"2026-05-12T19:14:11Z","snapshot_observed_at":"2026-08-02T15:12:57.871811Z","submitted_at":"2025-07-14T20:50:51Z","title":"Multi-Armed Sampling Problem and the End of Exploration","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-19T04:14:52.554045Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2507.10797"},"observation_digest":"sha256:e1842ccb4370abe1904cbb216160204cbc78231c51c6676b7afc21681113f4b2","observation_id":"9fb6f889-582e-45d1-987a-254da79af841","resolution":{"observed_at":"2026-05-19T04:17:02.772459Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-08-05T13:56:09.628721Z","title":"Adrianne Zhong, Ben Kuznets-Speck, and Michael R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.00316","last_updated":"2025-08-30T02:24:52Z","snapshot_observed_at":"2026-08-05T13:56:06.397474Z","submitted_at":"2025-08-30T02:24:52Z","title":"Continuously Tempered Diffusion Samplers","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T13:56:09.628721Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2509.00316"},"observation_digest":"sha256:ff825307e31c5bca591ef46e6f76b6aea67263a4ae4d5848ca6c7fb6ed730f0b","observation_id":"195441c1-ee8d-4423-a52e-6f7e256574da","resolution":{"observed_at":"2026-08-05T13:56:09.628721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-08-05T12:35:07.035210Z","title":"Path Integral Sampler: a stochastic control approach for sampling,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.01543","last_updated":"2025-09-01T15:18:03Z","snapshot_observed_at":"2026-08-05T12:35:06.335100Z","submitted_at":"2025-09-01T15:18:03Z","title":"Feynman-Kac-Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T12:35:07.035210Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2509.01543"},"observation_digest":"sha256:cbc8f92f94866c1bc4320c3f494d9f262481376e7783baf3e51e92921117c633","observation_id":"fee7c493-9617-4a7a-81d5-9f45d2fe3cbb","resolution":{"observed_at":"2026-08-05T12:35:07.035210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":"2111.15141","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-07-01T19:16:00.644972Z","title":"Path integral sampler: a stochastic control approach for sam- pling.arXiv preprint arXiv:2111.15141","venue":null,"work_id":"323f76c4-798a-404e-8867-24c711b8a957","year":2022},"citing_paper":{"arxiv_id":"2509.20599","last_updated":"2026-05-09T13:53:14Z","snapshot_observed_at":"2026-07-06T22:30:41.141182Z","submitted_at":"2025-09-24T22:43:19Z","title":"Explicit and Effectively Symmetric Schemes for Neural SDEs on Lie Groups","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-05-18T13:40:33.651916Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2509.20599"},"observation_digest":"sha256:88aaa615500d32dd923564fe809c7c0810b35225f9166d4e26090c1e97c03cdf","observation_id":"dbeaf6e7-dd87-40be-bb5e-5f06841dfdfc","resolution":{"observed_at":"2026-05-18T13:41:25.438899Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-08-03T06:26:59.071208Z","title":"and Chen, Y","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.23231","last_updated":"2026-06-08T14:26:38Z","snapshot_observed_at":"2026-08-03T06:15:48.448754Z","submitted_at":"2026-01-30T17:59:09Z","title":"Solving Inverse Problems with Flow-based Models via Model Predictive Control","version":2},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-03T06:26:59.071208Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2601.23231"},"observation_digest":"sha256:844dd5e093c39f0ba58e443046dd9556154b47631c5f5aa58a098b764519defe","observation_id":"7b0ecfc9-2f27-4289-a1ac-d4236108901c","resolution":{"observed_at":"2026-08-03T06:26:59.071208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":"2111.15141","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-07-01T19:16:00.644972Z","title":"Path integral sampler: a stochastic control approach for sam- pling.arXiv preprint arXiv:2111.15141","venue":null,"work_id":"323f76c4-798a-404e-8867-24c711b8a957","year":2022},"citing_paper":{"arxiv_id":"2602.10933","last_updated":"2026-05-19T13:50:37Z","snapshot_observed_at":"2026-07-06T22:45:29.609382Z","submitted_at":"2026-02-11T15:12:43Z","title":"CMAD: Cooperative Multi-Agent Diffusion via Stochastic Optimal Control","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-21T13:40:14.354702Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2602.10933"},"observation_digest":"sha256:56b4b82a7065b740b4c8f2b3292d478dea5da3f332f8441dd91bbb9fb92d0017","observation_id":"78830614-32cd-4934-afc2-96acf097805c","resolution":{"observed_at":"2026-05-21T13:44:11.593452Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":"2111.15141","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-07-01T19:16:00.644972Z","title":"Path integral sampler: a stochastic control approach for sam- pling.arXiv preprint arXiv:2111.15141","venue":null,"work_id":"323f76c4-798a-404e-8867-24c711b8a957","year":2022},"citing_paper":{"arxiv_id":"2604.21456","last_updated":"2026-05-09T11:13:02Z","snapshot_observed_at":"2026-07-06T23:08:01.670049Z","submitted_at":"2026-04-23T09:13:59Z","title":"Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamics","version":1},"reference_index":112,"source":"pdf_text","source_observed_at":"2026-05-09T22:15:38.911842Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2604.21456"},"observation_digest":"sha256:3d3add238726acdd67a26933df8e60247ee9f08fddd19ab09a39af0eb20a0a07","observation_id":"9303b3cd-f41d-426f-8d61-96dacf52f485","resolution":{"observed_at":"2026-05-09T22:49:15.970310Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":"2111.15141","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-07-01T19:16:00.644972Z","title":"Path integral sampler: a stochastic control approach for sam- pling.arXiv preprint arXiv:2111.15141","venue":null,"work_id":"323f76c4-798a-404e-8867-24c711b8a957","year":2022},"citing_paper":{"arxiv_id":"2604.21456","last_updated":"2026-05-09T11:13:02Z","snapshot_observed_at":"2026-07-06T23:08:01.670049Z","submitted_at":"2026-04-23T09:13:59Z","title":"Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamics","version":2},"reference_index":112,"source":"pdf_text","source_observed_at":"2026-05-12T00:59:20.790604Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2604.21456"},"observation_digest":"sha256:7209d6e14d8f0ea42ea8fa993bcbe85a7b86f5433ce0d81b24179acdaf2841e5","observation_id":"da5e9ac0-9ace-4455-a22c-a9f290f19e1f","resolution":{"observed_at":"2026-05-12T08:31:26.728459Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":"2111.15141","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-07-01T19:16:00.644972Z","title":"Path integral sampler: a stochastic control approach for sam- pling.arXiv preprint arXiv:2111.15141","venue":null,"work_id":"323f76c4-798a-404e-8867-24c711b8a957","year":2022},"citing_paper":{"arxiv_id":"2605.07959","last_updated":"2026-05-08T16:22:08Z","snapshot_observed_at":"2026-07-06T23:20:15.696237Z","submitted_at":"2026-05-08T16:22:08Z","title":"Convergent Stochastic Training of Attention and Understanding LoRA","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-11T02:48:35.907351Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2605.07959"},"observation_digest":"sha256:221d2cfc7815114fd99bc41660aa335e20a98aa106b1400fa9d1a8b07ec20320","observation_id":"d8cbe3d3-e04d-4c6f-853b-f139b3b06c55","resolution":{"observed_at":"2026-05-11T03:05:55.082746Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":"2111.15141","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-07-01T19:16:00.644972Z","title":"Path integral sampler: a stochastic control approach for sam- pling.arXiv preprint arXiv:2111.15141","venue":null,"work_id":"323f76c4-798a-404e-8867-24c711b8a957","year":2022},"citing_paper":{"arxiv_id":"2605.08928","last_updated":"2026-05-09T13:00:37Z","snapshot_observed_at":"2026-07-06T23:21:06.773761Z","submitted_at":"2026-05-09T13:00:37Z","title":"Learning Generative Dynamics with Soft Law Constraints: A McKean-Vlasov FBSDE Approach","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-12T01:55:25.100938Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2605.08928"},"observation_digest":"sha256:e43469553b760023432dc5281a7476f0c46db9ab9adaeae42b75dcbf9669a403","observation_id":"862da54b-5f1f-4446-bf10-0b0b656f685c","resolution":{"observed_at":"2026-05-12T01:56:14.712940Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":"2111.15141","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-07-01T19:16:00.644972Z","title":"Path integral sampler: a stochastic control approach for sam- pling.arXiv preprint arXiv:2111.15141","venue":null,"work_id":"323f76c4-798a-404e-8867-24c711b8a957","year":2022},"citing_paper":{"arxiv_id":"2605.16399","last_updated":"2026-06-30T14:10:18Z","snapshot_observed_at":"2026-07-06T23:27:34.514541Z","submitted_at":"2026-05-12T18:34:14Z","title":"Stable and Near-Reversible Diffusion ODE Solvers for Image Editing","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-20T22:05:12.770906Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2605.16399"},"observation_digest":"sha256:7f57e79f3cb7132ae4c71f85e0dcfe2a1ac2691f891c189b4aaf4d190451b1b4","observation_id":"c457cce8-a366-46de-a25e-6e962002b087","resolution":{"observed_at":"2026-05-20T22:09:07.532767Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":"2111.15141","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-07-01T19:16:00.644972Z","title":"Path integral sampler: a stochastic control approach for sam- pling.arXiv preprint arXiv:2111.15141","venue":null,"work_id":"323f76c4-798a-404e-8867-24c711b8a957","year":2022},"citing_paper":{"arxiv_id":"2605.31498","last_updated":"2026-06-08T17:55:28Z","snapshot_observed_at":"2026-07-06T23:40:42.277618Z","submitted_at":"2026-05-29T16:20:59Z","title":"Scalable Inference-Time Annealing with Surrogate Likelihood Estimators","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-06-28T22:54:55.415927Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2605.31498"},"observation_digest":"sha256:e0f8c408ba7278383e64d5ee755376847397f8284598c30a76a3379513e49cb3","observation_id":"22c391ab-90c9-4bd3-a904-0caa0444e6ab","resolution":{"observed_at":"2026-07-01T19:16:00.646920Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-08-01T22:43:04.968559Z","title":"International Conference on Learning Representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15682","last_updated":"2026-07-17T06:50:34Z","snapshot_observed_at":"2026-08-06T15:39:14.605143Z","submitted_at":"2026-07-17T06:50:34Z","title":"Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-01T22:43:04.968559Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2607.15682"},"observation_digest":"sha256:7f15b882a6b66d56440a142e59facbc974e005cd7698f0fa0089f272bdfada5e","observation_id":"6f13b9af-ee16-4db8-a830-d5dc076adcf2","resolution":{"observed_at":"2026-08-01T22:43:04.968559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.15141","snapshot_observed_at":"2026-08-01T07:35:29.170741Z","title":"Zhang and Y","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21436","last_updated":"2026-07-23T15:39:27Z","snapshot_observed_at":"2026-08-05T12:36:34.456966Z","submitted_at":"2026-07-23T15:39:27Z","title":"Stochastic Quantization as Optimal Control","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-01T07:35:29.170741Z"},"links":{"cited_paper":"/paper/2111.15141","citing_paper":"/paper/2607.21436"},"observation_digest":"sha256:1c980226deb880344317f3f8488597e7749c2be28aa4013e29e86df49679b93a","observation_id":"22b723de-c7be-4104-88d8-64f92265cba9","resolution":{"observed_at":"2026-08-01T07:35:29.170741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2111.15141/citation-record","integrity":"/paper/2111.15141/integrity","json":"/paper/2111.15141/citation-record.json","paper":"/paper/2111.15141"},"outbound":[],"paper":{"arxiv_id":"2111.15141","last_updated":"2022-03-10T01:42:08Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T12:13:27.826267Z","submitted_at":"2021-11-30T05:50:12Z","title":"Path Integral Sampler: a stochastic control approach for sampling"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2111.15141."}