{"as_of":"2026-08-08T12:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:83a13d6353641844a9f7aa92c8b5e87b8ccfb8749f5cc88980f9b47e03195388","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:17:11.627462Z","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":"2026-07-02T20:57:23.836145Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.04004","last_updated":"2024-02-09T01:56:38Z","snapshot_observed_at":"2026-07-06T17:26:14.973632Z","submitted_at":"2024-02-06T13:59:56Z","title":"Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04004","snapshot_observed_at":"2026-08-07T14:17:11.627462Z","title":"Under- standing the effect of noise in llm training data with algo- rithmic chains of thought.arXiv preprint arXiv:2402.04004,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19529","last_updated":"2025-05-29T16:57:36Z","snapshot_observed_at":"2026-08-07T22:25:26.276499Z","submitted_at":"2025-05-26T05:29:47Z","title":"Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-07T14:17:11.627462Z"},"links":{"cited_paper":"/paper/2402.04004","citing_paper":"/paper/2505.19529"},"observation_digest":"sha256:5ebb8ec560ea1d1d5cdd62bf432894ee0ab5db89058d593a32e11a8b38c2cb13","observation_id":"18420bcd-1426-47cd-8f14-8638b35a56f6","resolution":{"observed_at":"2026-08-07T14:17:11.627462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04004","last_updated":"2024-02-09T01:56:38Z","snapshot_observed_at":"2026-07-06T17:26:14.973632Z","submitted_at":"2024-02-06T13:59:56Z","title":"Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04004","snapshot_observed_at":"2026-08-07T00:55:54.725250Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.12468","last_updated":"2025-06-17T03:17:11Z","snapshot_observed_at":"2026-08-07T00:46:40.868394Z","submitted_at":"2025-06-14T12:14:15Z","title":"Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:54.725250Z"},"links":{"cited_paper":"/paper/2402.04004","citing_paper":"/paper/2506.12468"},"observation_digest":"sha256:e96d8b12719ad604db06dfc2b56ffedb527d61719b05a7e3bdf206ea0029bf5d","observation_id":"7dbed18f-9106-4ec9-82ec-f9b81019f051","resolution":{"observed_at":"2026-08-07T00:55:54.725250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04004","last_updated":"2024-02-09T01:56:38Z","snapshot_observed_at":"2026-07-06T17:26:14.973632Z","submitted_at":"2024-02-06T13:59:56Z","title":"Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought","version":2},"cited_work":{"arxiv_id":"2402.04004","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.04004","snapshot_observed_at":"2026-07-02T20:57:23.836145Z","title":"Understanding the effect of noise in llm training data with algorithmic chains of thought","venue":null,"work_id":"83d7e95a-5790-4e0e-89a0-c18fe9ce89c2","year":2024},"citing_paper":{"arxiv_id":"2512.03847","last_updated":"2026-05-06T14:15:19Z","snapshot_observed_at":"2026-08-03T04:49:11.537109Z","submitted_at":"2025-12-03T14:48:38Z","title":"DVPO: Distributional Value Modeling-based Policy Optimization for LLM Post-Training","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-17T01:46:21.744857Z"},"links":{"cited_paper":"/paper/2402.04004","citing_paper":"/paper/2512.03847"},"observation_digest":"sha256:2391c315b533a24c8f1979f336ceaf10abd8364ae3a508dadb258dd5a2be8ada","observation_id":"aa38baef-3eb8-441d-932b-be699de9694e","resolution":{"observed_at":"2026-05-17T01:48:50.962607Z","resolver_source":"arxiv_id","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":"2402.04004","last_updated":"2024-02-09T01:56:38Z","snapshot_observed_at":"2026-07-06T17:26:14.973632Z","submitted_at":"2024-02-06T13:59:56Z","title":"Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought","version":2},"cited_work":{"arxiv_id":"2402.04004","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.04004","snapshot_observed_at":"2026-07-02T20:57:23.836145Z","title":"Understanding the effect of noise in llm training data with algorithmic chains of thought","venue":null,"work_id":"83d7e95a-5790-4e0e-89a0-c18fe9ce89c2","year":2024},"citing_paper":{"arxiv_id":"2604.21637","last_updated":"2026-07-06T17:24:47Z","snapshot_observed_at":"2026-07-12T18:37:44.672856Z","submitted_at":"2026-04-23T12:53:16Z","title":"Multilinguality at the Edge: Developing Language Models for the Global South","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-09T21:32:37.544226Z"},"links":{"cited_paper":"/paper/2402.04004","citing_paper":"/paper/2604.21637"},"observation_digest":"sha256:3cdb48e6d9103e467c3a1225ace77de4d85e65ab868afbc6a08ce2869cd76c67","observation_id":"f22c3d0f-e544-4dab-be37-d5c324526e6e","resolution":{"observed_at":"2026-05-11T14:36:04.806352Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2402.04004","last_updated":"2024-02-09T01:56:38Z","snapshot_observed_at":"2026-07-06T17:26:14.973632Z","submitted_at":"2024-02-06T13:59:56Z","title":"Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought","version":2},"cited_work":{"arxiv_id":"2402.04004","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.04004","snapshot_observed_at":"2026-07-02T20:57:23.836145Z","title":"Understanding the effect of noise in llm training data with algorithmic chains of thought","venue":null,"work_id":"83d7e95a-5790-4e0e-89a0-c18fe9ce89c2","year":2024},"citing_paper":{"arxiv_id":"2605.17610","last_updated":"2026-05-17T19:10:36Z","snapshot_observed_at":"2026-07-06T23:28:36.134614Z","submitted_at":"2026-05-17T19:10:36Z","title":"SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-20T14:03:56.859481Z"},"links":{"cited_paper":"/paper/2402.04004","citing_paper":"/paper/2605.17610"},"observation_digest":"sha256:7d40dcd4b23764bae036c5753717c0559e106edb112196d237b3eacb70cbcfad","observation_id":"c11d1893-217a-46dc-bd17-1c6e4b6fcfb0","resolution":{"observed_at":"2026-05-20T14:08:21.174093Z","resolver_source":"arxiv_id","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":"2402.04004","last_updated":"2024-02-09T01:56:38Z","snapshot_observed_at":"2026-07-06T17:26:14.973632Z","submitted_at":"2024-02-06T13:59:56Z","title":"Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought","version":2},"cited_work":{"arxiv_id":"2402.04004","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.04004","snapshot_observed_at":"2026-07-02T20:57:23.836145Z","title":"Understanding the effect of noise in llm training data with algorithmic chains of thought","venue":null,"work_id":"83d7e95a-5790-4e0e-89a0-c18fe9ce89c2","year":2024},"citing_paper":{"arxiv_id":"2606.08068","last_updated":"2026-07-08T15:21:39Z","snapshot_observed_at":"2026-07-12T14:47:31.355938Z","submitted_at":"2026-06-06T09:33:32Z","title":"DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination","version":1},"reference_index":146,"source":"arxiv_source","source_observed_at":"2026-06-27T19:58:32.016341Z"},"links":{"cited_paper":"/paper/2402.04004","citing_paper":"/paper/2606.08068"},"observation_digest":"sha256:947f221194f8b5ca731283edd027e4ed7f5a4ce62bcb9077c39101a1942258de","observation_id":"36850f32-7461-4373-b85b-ac793698843d","resolution":{"observed_at":"2026-07-02T20:57:23.837752Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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"}}],"links":{"evidence":"/evidence","html":"/paper/2402.04004/citation-record","integrity":"/paper/2402.04004/integrity","json":"/paper/2402.04004/citation-record.json","paper":"/paper/2402.04004"},"outbound":[],"paper":{"arxiv_id":"2402.04004","last_updated":"2024-02-09T01:56:38Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T17:26:14.973632Z","submitted_at":"2024-02-06T13:59:56Z","title":"Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought"},"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":"2026-08-08T06:32:00.761636+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 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.04004."}