{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:D5TXA47EXMHU6V3MQKUP4UIQ4L","short_pith_number":"pith:D5TXA47E","canonical_record":{"source":{"id":"2605.21726","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-20T20:36:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"94a2ea0214e835b5654d7041a64ff83bce081f0b466f8137fc91e88a2644a24f","abstract_canon_sha256":"233713411dc12e9e912877c80a36d6ae51f8dc8067a58050741a2206afc716ed"},"schema_version":"1.0"},"canonical_sha256":"1f677073e4bb0f4f576c82a8fe5110e2de1275be0add38ece20bcc8266210633","source":{"kind":"arxiv","id":"2605.21726","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.21726","created_at":"2026-05-22T01:03:29Z"},{"alias_kind":"arxiv_version","alias_value":"2605.21726v1","created_at":"2026-05-22T01:03:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21726","created_at":"2026-05-22T01:03:29Z"},{"alias_kind":"pith_short_12","alias_value":"D5TXA47EXMHU","created_at":"2026-05-22T01:03:29Z"},{"alias_kind":"pith_short_16","alias_value":"D5TXA47EXMHU6V3M","created_at":"2026-05-22T01:03:29Z"},{"alias_kind":"pith_short_8","alias_value":"D5TXA47E","created_at":"2026-05-22T01:03:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:D5TXA47EXMHU6V3MQKUP4UIQ4L","target":"record","payload":{"canonical_record":{"source":{"id":"2605.21726","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-20T20:36:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"94a2ea0214e835b5654d7041a64ff83bce081f0b466f8137fc91e88a2644a24f","abstract_canon_sha256":"233713411dc12e9e912877c80a36d6ae51f8dc8067a58050741a2206afc716ed"},"schema_version":"1.0"},"canonical_sha256":"1f677073e4bb0f4f576c82a8fe5110e2de1275be0add38ece20bcc8266210633","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:03:29.550602Z","signature_b64":"pMfryuWVTk6L0LAZnOk/vAjhJ62tHn9RnoaGXpGvXUtSjTmSckN9Yn4MJuNZTLEGi7fxWkYTt568tR0I0NVNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f677073e4bb0f4f576c82a8fe5110e2de1275be0add38ece20bcc8266210633","last_reissued_at":"2026-05-22T01:03:29.549977Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:03:29.549977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.21726","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-22T01:03:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kD0NzqDftxHJ2Vhec+xuzSnKvaRF5EbotKVFodemdh1VVizUrh9bhXnavym6O1tm4UlNPPUXHBPKchAcH/FFBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T11:11:48.657388Z"},"content_sha256":"f3c9313998aff5fc27d93951318156c69a86099b0132b657c66618f0ed13028c","schema_version":"1.0","event_id":"sha256:f3c9313998aff5fc27d93951318156c69a86099b0132b657c66618f0ed13028c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:D5TXA47EXMHU6V3MQKUP4UIQ4L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Probabilistic Attribution For Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Bethany Lusch, Carlo Graziani, Michael E. Papka, Shilpika Shilpika, Venkatram Vishwanath","submitted_at":"2026-05-20T20:36:26Z","abstract_excerpt":"The generative nature of Large Language Models (LLMs) is reflected in the conditional probabilities they compute to sample each response token given the previous tokens. These probabilities encode the distributional structure that the model learns in training and exploits in inference. In this work, we use these probabilities to situate LLMs within the mathematical theory of stochastic processes. We use this framework to design a model-agnostic probabilistic token attribution measure, using Bayes rule to invert the next-token log-probabilities so as to capture the models internal representatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21726","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.21726/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-22T01:03:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NyvWzewGP9ozN6S3zCW9kfGlMzGwlFe7oHYGmqqv1Bm2I818FMLMr/E0HX52qy3ZT8/LX2joGDzKKPCmuYpvBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T11:11:48.657788Z"},"content_sha256":"3acc32d67e3a670fb957ce81af86c4e6e0226fcbb0b08621c66b9322810aa832","schema_version":"1.0","event_id":"sha256:3acc32d67e3a670fb957ce81af86c4e6e0226fcbb0b08621c66b9322810aa832"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D5TXA47EXMHU6V3MQKUP4UIQ4L/bundle.json","state_url":"https://pith.science/pith/D5TXA47EXMHU6V3MQKUP4UIQ4L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D5TXA47EXMHU6V3MQKUP4UIQ4L/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T11:11:48Z","links":{"resolver":"https://pith.science/pith/D5TXA47EXMHU6V3MQKUP4UIQ4L","bundle":"https://pith.science/pith/D5TXA47EXMHU6V3MQKUP4UIQ4L/bundle.json","state":"https://pith.science/pith/D5TXA47EXMHU6V3MQKUP4UIQ4L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D5TXA47EXMHU6V3MQKUP4UIQ4L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:D5TXA47EXMHU6V3MQKUP4UIQ4L","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":"233713411dc12e9e912877c80a36d6ae51f8dc8067a58050741a2206afc716ed","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-20T20:36:26Z","title_canon_sha256":"94a2ea0214e835b5654d7041a64ff83bce081f0b466f8137fc91e88a2644a24f"},"schema_version":"1.0","source":{"id":"2605.21726","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.21726","created_at":"2026-05-22T01:03:29Z"},{"alias_kind":"arxiv_version","alias_value":"2605.21726v1","created_at":"2026-05-22T01:03:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21726","created_at":"2026-05-22T01:03:29Z"},{"alias_kind":"pith_short_12","alias_value":"D5TXA47EXMHU","created_at":"2026-05-22T01:03:29Z"},{"alias_kind":"pith_short_16","alias_value":"D5TXA47EXMHU6V3M","created_at":"2026-05-22T01:03:29Z"},{"alias_kind":"pith_short_8","alias_value":"D5TXA47E","created_at":"2026-05-22T01:03:29Z"}],"graph_snapshots":[{"event_id":"sha256:3acc32d67e3a670fb957ce81af86c4e6e0226fcbb0b08621c66b9322810aa832","target":"graph","created_at":"2026-05-22T01:03:29Z","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/2605.21726/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The generative nature of Large Language Models (LLMs) is reflected in the conditional probabilities they compute to sample each response token given the previous tokens. These probabilities encode the distributional structure that the model learns in training and exploits in inference. In this work, we use these probabilities to situate LLMs within the mathematical theory of stochastic processes. We use this framework to design a model-agnostic probabilistic token attribution measure, using Bayes rule to invert the next-token log-probabilities so as to capture the models internal representatio","authors_text":"Bethany Lusch, Carlo Graziani, Michael E. Papka, Shilpika Shilpika, Venkatram Vishwanath","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-20T20:36:26Z","title":"Probabilistic Attribution For Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21726","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:f3c9313998aff5fc27d93951318156c69a86099b0132b657c66618f0ed13028c","target":"record","created_at":"2026-05-22T01:03:29Z","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":"233713411dc12e9e912877c80a36d6ae51f8dc8067a58050741a2206afc716ed","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-20T20:36:26Z","title_canon_sha256":"94a2ea0214e835b5654d7041a64ff83bce081f0b466f8137fc91e88a2644a24f"},"schema_version":"1.0","source":{"id":"2605.21726","kind":"arxiv","version":1}},"canonical_sha256":"1f677073e4bb0f4f576c82a8fe5110e2de1275be0add38ece20bcc8266210633","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1f677073e4bb0f4f576c82a8fe5110e2de1275be0add38ece20bcc8266210633","first_computed_at":"2026-05-22T01:03:29.549977Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-22T01:03:29.549977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pMfryuWVTk6L0LAZnOk/vAjhJ62tHn9RnoaGXpGvXUtSjTmSckN9Yn4MJuNZTLEGi7fxWkYTt568tR0I0NVNBw==","signature_status":"signed_v1","signed_at":"2026-05-22T01:03:29.550602Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.21726","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3c9313998aff5fc27d93951318156c69a86099b0132b657c66618f0ed13028c","sha256:3acc32d67e3a670fb957ce81af86c4e6e0226fcbb0b08621c66b9322810aa832"],"state_sha256":"c04eaea89d3e363d64b7df2af51d1c1654030eff7ebc3e07973899befc0ebaf2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FWWmmiilbzjspol900umlNObxEpWQ962wJKQT4rr/8tR5Ry8a7bvkSN9QzY3xVdfBslLIXcbujhdk67bJ5lqDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T11:11:48.660429Z","bundle_sha256":"f8348da3aa3529117223d910a286400a1278f90f823e5c2783ae279fb6583b4d"}}