{"as_of":"2026-08-18T04:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:516e5e54da7f2854e5e9ae34766e61630906a5869a83dd34eb021af56a34dca7","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":"2026-08-17T06:30:58.91139+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":"2026-08-09T19:32:34.349962Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.00154","last_updated":"2025-02-03T15:33:59Z","snapshot_observed_at":"2026-08-16T13:03:59.868021Z","submitted_at":"2024-10-31T18:59:46Z","title":"Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.00154","snapshot_observed_at":"2026-08-09T19:32:34.349962Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00306","last_updated":"2025-06-30T16:37:59Z","snapshot_observed_at":"2026-08-17T19:54:13.581996Z","submitted_at":"2025-02-01T04:01:18Z","title":"Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T19:32:34.349962Z"},"links":{"cited_paper":"/paper/2411.00154","citing_paper":"/paper/2502.00306"},"observation_digest":"sha256:f8babde5f98b6b27672a7854985aa535f002a97abc57721fcf36e9ba93dc8bfd","observation_id":"23d861f3-892b-40e2-9866-2882d55ddf9c","resolution":{"observed_at":"2026-08-09T19:32:34.349962Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.00154","last_updated":"2025-02-03T15:33:59Z","snapshot_observed_at":"2026-08-16T13:03:59.868021Z","submitted_at":"2024-10-31T18:59:46Z","title":"Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.00154","snapshot_observed_at":"2026-08-07T10:30:13.959463Z","title":"Available: https://arxiv.org/abs/2411.00154","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05242","last_updated":"2025-06-05T17:01:28Z","snapshot_observed_at":"2026-08-17T09:14:14.617858Z","submitted_at":"2025-06-05T17:01:28Z","title":"SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T10:30:13.959463Z"},"links":{"cited_paper":"/paper/2411.00154","citing_paper":"/paper/2506.05242"},"observation_digest":"sha256:2274702ebfcb3378578958bd024a7d2cc56cca5940853072c23dc84cf61d7f36","observation_id":"ef09c261-98ff-4d95-90e5-f4872f9f1cd2","resolution":{"observed_at":"2026-08-07T10:30:13.959463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.00154","last_updated":"2025-02-03T15:33:59Z","snapshot_observed_at":"2026-08-16T13:03:59.868021Z","submitted_at":"2024-10-31T18:59:46Z","title":"Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.00154","snapshot_observed_at":"2026-08-06T17:21:33.726083Z","title":"arXiv:2411.00154 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.11128","last_updated":"2025-07-15T09:28:44Z","snapshot_observed_at":"2026-08-11T10:44:57.901509Z","submitted_at":"2025-07-15T09:28:44Z","title":"What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T17:21:33.726083Z"},"links":{"cited_paper":"/paper/2411.00154","citing_paper":"/paper/2507.11128"},"observation_digest":"sha256:337ae77023b512e450770d34f126b5fcc1861ba9d00c8f4aaa3e8b4d894e7d69","observation_id":"6b5fe0d9-7c6a-4493-b388-83f1d6223531","resolution":{"observed_at":"2026-08-06T17:21:33.726083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.00154","last_updated":"2025-02-03T15:33:59Z","snapshot_observed_at":"2026-08-16T13:03:59.868021Z","submitted_at":"2024-10-31T18:59:46Z","title":"Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.00154","snapshot_observed_at":"2026-08-05T05:29:26.825478Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.05449","last_updated":"2025-09-05T19:05:49Z","snapshot_observed_at":"2026-08-17T14:11:38.534022Z","submitted_at":"2025-09-05T19:05:49Z","title":"Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern Analysis","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-05T05:29:26.825478Z"},"links":{"cited_paper":"/paper/2411.00154","citing_paper":"/paper/2509.05449"},"observation_digest":"sha256:1e231270adbdac8746b840df87b96d063207b3efbcc003518e7a8d6caeff73da","observation_id":"c8d00c1a-c7da-4ddb-bd2d-d15e6d5c053a","resolution":{"observed_at":"2026-08-05T05:29:26.825478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.00154","last_updated":"2025-02-03T15:33:59Z","snapshot_observed_at":"2026-08-16T13:03:59.868021Z","submitted_at":"2024-10-31T18:59:46Z","title":"Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.00154","snapshot_observed_at":"2026-08-02T02:44:26.331821Z","title":"arXiv preprint arXiv:2411.00154 , doi =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.14242","last_updated":"2026-07-15T18:03:08Z","snapshot_observed_at":"2026-08-14T09:53:27.449814Z","submitted_at":"2026-07-15T18:03:08Z","title":"Implicit Reasoning Steering via Concept Chaining","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-02T02:44:26.331821Z"},"links":{"cited_paper":"/paper/2411.00154","citing_paper":"/paper/2607.14242"},"observation_digest":"sha256:a76d78994c69b76e63e7d126d5428ce13b8f9f788c56c756543ff24c84ab5750","observation_id":"792b38e9-8e3c-47d9-b8d2-9713666ad140","resolution":{"observed_at":"2026-08-02T02:44:26.331821Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.00154","last_updated":"2025-02-03T15:33:59Z","snapshot_observed_at":"2026-08-16T13:03:59.868021Z","submitted_at":"2024-10-31T18:59:46Z","title":"Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models","version":2},"cited_work":{"arxiv_id":"2411.00154","doi":"10.48550/arxiv.2411.00154","metadata_source":"pith","pith_arxiv_id":"2411.00154","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models","venue":"cs.CL","work_id":"80b85e7c-fc1e-498d-95b5-02f6610af03e","year":2024},"citing_paper":{"arxiv_id":"2607.14242","last_updated":"2026-07-15T18:03:08Z","snapshot_observed_at":"2026-08-14T09:53:27.449814Z","submitted_at":"2026-07-15T18:03:08Z","title":"Implicit Reasoning Steering via Concept Chaining","version":1},"reference_index":153,"source":"arxiv_source","source_observed_at":"2026-08-02T02:44:34.803940Z"},"links":{"cited_paper":"/paper/2411.00154","citing_paper":"/paper/2607.14242"},"observation_digest":"sha256:8b7d9cf2dc2b3fb1b827b833ccd334a61e7c6b1d2ede1ddfb5ff2e19e3e95b3b","observation_id":"40f84cde-70f7-4b58-95f7-276ebc2257c0","resolution":{"observed_at":"2026-08-02T02:48:31.840123Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.00154/citation-record","integrity":"/paper/2411.00154/integrity","json":"/paper/2411.00154/citation-record.json","paper":"/paper/2411.00154"},"outbound":[],"paper":{"arxiv_id":"2411.00154","last_updated":"2025-02-03T15:33:59Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T13:03:59.868021Z","submitted_at":"2024-10-31T18:59:46Z","title":"Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.00154."}