{"as_of":"2026-08-08T18:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7d485220ec4a62e42568e565aec4c097bfe473adb7ce6edadff7fae770ffe117","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:56:23.700909Z","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":"2026-07-04T00:59:21.189215Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.07809","last_updated":"2024-03-12T16:46:54Z","snapshot_observed_at":"2026-07-06T17:43:27.034067Z","submitted_at":"2024-03-12T16:46:54Z","title":"pyvene: A Library for Understanding and Improving PyTorch Models via Interventions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07809","snapshot_observed_at":"2026-08-07T10:56:23.700909Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.03978","last_updated":"2025-06-09T04:15:05Z","snapshot_observed_at":"2026-08-08T01:31:04.404758Z","submitted_at":"2025-06-04T14:08:44Z","title":"Structured Pruning for Diverse Best-of-N Reasoning Optimization","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T10:56:23.700909Z"},"links":{"cited_paper":"/paper/2403.07809","citing_paper":"/paper/2506.03978"},"observation_digest":"sha256:58a9034ec415aa72038070c1aeaf0561e45b120ca0028f56f2f3f1c7c0aece1b","observation_id":"5cd69a75-741c-4ee8-8b26-70958fa4e55a","resolution":{"observed_at":"2026-08-07T10:56:23.700909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07809","last_updated":"2024-03-12T16:46:54Z","snapshot_observed_at":"2026-07-06T17:43:27.034067Z","submitted_at":"2024-03-12T16:46:54Z","title":"pyvene: A Library for Understanding and Improving PyTorch Models via Interventions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07809","snapshot_observed_at":"2026-08-07T10:40:16.892693Z","title":"D.; Manning, C","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.04774","last_updated":"2025-06-05T09:06:59Z","snapshot_observed_at":"2026-08-08T13:42:49.606307Z","submitted_at":"2025-06-05T09:06:59Z","title":"Fine-Grained Interpretation of Political Opinions in Large Language Models","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T10:40:16.892693Z"},"links":{"cited_paper":"/paper/2403.07809","citing_paper":"/paper/2506.04774"},"observation_digest":"sha256:6e99184b08d67e6e87988e5a347c3c2a9bec355855cc48b67989e01aac558730","observation_id":"979e272c-86d7-4a6a-a427-b389965464dd","resolution":{"observed_at":"2026-08-07T10:40:16.892693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07809","last_updated":"2024-03-12T16:46:54Z","snapshot_observed_at":"2026-07-06T17:43:27.034067Z","submitted_at":"2024-03-12T16:46:54Z","title":"pyvene: A Library for Understanding and Improving PyTorch Models via Interventions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07809","snapshot_observed_at":"2026-08-05T20:24:34.903518Z","title":"pyvene: A library for understand- ing and improving PyTorch models via interventions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.10553","last_updated":"2025-08-14T11:45:34Z","snapshot_observed_at":"2026-08-07T14:27:34.599800Z","submitted_at":"2025-08-14T11:45:34Z","title":"eDIF: A European Deep Inference Fabric for Remote Interpretability of LLM","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T20:24:34.903518Z"},"links":{"cited_paper":"/paper/2403.07809","citing_paper":"/paper/2508.10553"},"observation_digest":"sha256:9afdc6752393133dbbcba0332c5d5ccc0af3fdd15e872ea9662de5c77b10823f","observation_id":"59350acb-0aec-4aa8-ab73-0e3ec09872d3","resolution":{"observed_at":"2026-08-05T20:24:34.903518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07809","last_updated":"2024-03-12T16:46:54Z","snapshot_observed_at":"2026-07-06T17:43:27.034067Z","submitted_at":"2024-03-12T16:46:54Z","title":"pyvene: A Library for Understanding and Improving PyTorch Models via Interventions","version":1},"cited_work":{"arxiv_id":"2403.07809","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.07809","snapshot_observed_at":"2026-07-04T00:59:21.189215Z","title":"interpretability illusion","venue":null,"work_id":"2d05416d-0181-483b-9e96-80cdf9c4acbc","year":2023},"citing_paper":{"arxiv_id":"2605.02234","last_updated":"2026-05-04T05:09:21Z","snapshot_observed_at":"2026-07-06T23:15:23.301944Z","submitted_at":"2026-05-04T05:09:21Z","title":"Bucketing the Good Apples: A Method for Diagnosing and Improving Causal Abstraction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-08T19:12:30.627633Z"},"links":{"cited_paper":"/paper/2403.07809","citing_paper":"/paper/2605.02234"},"observation_digest":"sha256:c6703238674a3714bb4d7f32493da1b4e64852a0c6876e1af1dbbf525b57ff4a","observation_id":"aada4440-e477-4574-8b59-fc870fd15e66","resolution":{"observed_at":"2026-05-09T06:00:36.277602Z","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":"2403.07809","last_updated":"2024-03-12T16:46:54Z","snapshot_observed_at":"2026-07-06T17:43:27.034067Z","submitted_at":"2024-03-12T16:46:54Z","title":"pyvene: A Library for Understanding and Improving PyTorch Models via Interventions","version":1},"cited_work":{"arxiv_id":"2403.07809","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.07809","snapshot_observed_at":"2026-07-04T00:59:21.189215Z","title":"interpretability illusion","venue":null,"work_id":"2d05416d-0181-483b-9e96-80cdf9c4acbc","year":2023},"citing_paper":{"arxiv_id":"2606.19594","last_updated":"2026-06-17T20:56:44Z","snapshot_observed_at":"2026-08-02T12:17:21.328593Z","submitted_at":"2026-06-17T20:56:44Z","title":"Unsupervised Causal Abstractions Discovery","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-06-26T20:45:15.226889Z"},"links":{"cited_paper":"/paper/2403.07809","citing_paper":"/paper/2606.19594"},"observation_digest":"sha256:261923da3d9fe0a36970defb8039f5e160d495a72272ac1b852d91774227a041","observation_id":"b3653f2a-cc7a-4d5b-a724-45933d6da4cf","resolution":{"observed_at":"2026-07-04T00:59:21.191741Z","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/2403.07809/citation-record","integrity":"/paper/2403.07809/integrity","json":"/paper/2403.07809/citation-record.json","paper":"/paper/2403.07809"},"outbound":[],"paper":{"arxiv_id":"2403.07809","last_updated":"2024-03-12T16:46:54Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T17:43:27.034067Z","submitted_at":"2024-03-12T16:46:54Z","title":"pyvene: A Library for Understanding and Improving PyTorch Models via Interventions"},"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 5 inbound Pith citation observations for arXiv:2403.07809."}