{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:JRBUWS5AGJXTP4RHNZOTMAMEQP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a3a8b73954f0e77b0a18fd2aeb8d1c071d1a87f21bc1a9e1f200f9d4f2fda0a7","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T14:21:53Z","title_canon_sha256":"6f45ae0a1aafece67619f89cc5876ce33cb1d4342804481955b41a3cbb24a5cb"},"schema_version":"1.0","source":{"id":"2606.04928","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.04928","created_at":"2026-06-04T01:09:56Z"},{"alias_kind":"arxiv_version","alias_value":"2606.04928v1","created_at":"2026-06-04T01:09:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04928","created_at":"2026-06-04T01:09:56Z"},{"alias_kind":"pith_short_12","alias_value":"JRBUWS5AGJXT","created_at":"2026-06-04T01:09:56Z"},{"alias_kind":"pith_short_16","alias_value":"JRBUWS5AGJXTP4RH","created_at":"2026-06-04T01:09:56Z"},{"alias_kind":"pith_short_8","alias_value":"JRBUWS5A","created_at":"2026-06-04T01:09:56Z"}],"graph_snapshots":[{"event_id":"sha256:4c937893b896ce6b425d55bc64f318bf8bc07c39adf323f16192c2d75d2247a0","target":"graph","created_at":"2026-06-04T01:09:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.04928/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and data provenance. Understanding which training data most influenced a model's output remains a fundamental open problem. We address this challenge through training data attribution (TDA) for auto-regressive LLMs by expanding upon the inverse formulation: How would training data be affected if the model had seen the generated output during training? Our method perturbs the base model using bidirectional gradient optimization (gradient ascent and desce","authors_text":"Fr\\'ed\\'eric Berdoz, Kaan Bayraktar, Luca A. Lanzend\\\"orfer, Roger Wattenhofer","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T14:21:53Z","title":"Data Attribution in Large Language Models via Bidirectional Gradient Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04928","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b2615868a3df1ed5fa408ae7b6e416260a1f43db83991a3fc2d383adf55c1a90","target":"record","created_at":"2026-06-04T01:09:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a3a8b73954f0e77b0a18fd2aeb8d1c071d1a87f21bc1a9e1f200f9d4f2fda0a7","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T14:21:53Z","title_canon_sha256":"6f45ae0a1aafece67619f89cc5876ce33cb1d4342804481955b41a3cbb24a5cb"},"schema_version":"1.0","source":{"id":"2606.04928","kind":"arxiv","version":1}},"canonical_sha256":"4c434b4ba0326f37f2276e5d36018483c474b22b7b0930a930c567252cbfc643","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4c434b4ba0326f37f2276e5d36018483c474b22b7b0930a930c567252cbfc643","first_computed_at":"2026-06-04T01:09:56.162971Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:09:56.162971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PQRs1taKxiYR9zRPs4u1DdjbvED2ApBAO+RlHsZ3CP1hDwryFHboleFIUh/VeFE7H70cE+Lo15Cn/8x8L2NbDA==","signature_status":"signed_v1","signed_at":"2026-06-04T01:09:56.163802Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.04928","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b2615868a3df1ed5fa408ae7b6e416260a1f43db83991a3fc2d383adf55c1a90","sha256:4c937893b896ce6b425d55bc64f318bf8bc07c39adf323f16192c2d75d2247a0"],"state_sha256":"779eb3a93d4e6056fb5a3801168868212748c0dc9c88f635d2d432dafa7d12a1"}