{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LHEZFEMKKSWDQVSYCZJZ6DVS7M","short_pith_number":"pith:LHEZFEMK","schema_version":"1.0","canonical_sha256":"59c992918a54ac38565816539f0eb2fb18738d20f0f1cfc149e9ce8bb5f8e105","source":{"kind":"arxiv","id":"2607.13047","version":1},"attestation_state":"computed","paper":{"title":"Targeted Recovery of Weight-Space Mechanisms From Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Antoine Vigouroux, Lee Sharkey","submitted_at":"2026-06-19T19:07:38Z","abstract_excerpt":"Parameter decomposition (PD) decomposes neural networks into interpretable computational components that faithfully reflect the original network's operations. However, scaling PD to large models requires vast compute, making it a costly and risky endeavor. Here we propose targeted PD (tPD), which identifies only the components that process specific inputs of interest -- from isolated prompts to large subtasks -- by introducing a high-rank catch-all component that handles all non-target data. We validate tPD on toy models and on transformer language models trained on The Pile, where it recovers"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.13047","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-19T19:07:38Z","cross_cats_sorted":[],"title_canon_sha256":"a413db97d61f55020fb021b291712f95f1627d1439a2f10f459cb5e1d7e5250a","abstract_canon_sha256":"1ff4f58962c4191d8d7acfaaebbd26d1a013e29016747aa9265e579155c3ee9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-16T00:21:53.450627Z","signature_b64":"H+V/XJjYdBEYl6ZgJtNUPN1V2xWL3MpkPMqtnHZ40iYVzF9fj5UQX/m62z/eK6W8JwhDyhXtTmTbvZID4xh0Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59c992918a54ac38565816539f0eb2fb18738d20f0f1cfc149e9ce8bb5f8e105","last_reissued_at":"2026-07-16T00:21:53.449797Z","signature_status":"signed_v1","first_computed_at":"2026-07-16T00:21:53.449797Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Targeted Recovery of Weight-Space Mechanisms From Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Antoine Vigouroux, Lee Sharkey","submitted_at":"2026-06-19T19:07:38Z","abstract_excerpt":"Parameter decomposition (PD) decomposes neural networks into interpretable computational components that faithfully reflect the original network's operations. However, scaling PD to large models requires vast compute, making it a costly and risky endeavor. Here we propose targeted PD (tPD), which identifies only the components that process specific inputs of interest -- from isolated prompts to large subtasks -- by introducing a high-rank catch-all component that handles all non-target data. We validate tPD on toy models and on transformer language models trained on The Pile, where it recovers"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13047","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/2607.13047/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.13047","created_at":"2026-07-16T00:21:53.450229+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.13047v1","created_at":"2026-07-16T00:21:53.450229+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13047","created_at":"2026-07-16T00:21:53.450229+00:00"},{"alias_kind":"pith_short_12","alias_value":"LHEZFEMKKSWD","created_at":"2026-07-16T00:21:53.450229+00:00"},{"alias_kind":"pith_short_16","alias_value":"LHEZFEMKKSWDQVSY","created_at":"2026-07-16T00:21:53.450229+00:00"},{"alias_kind":"pith_short_8","alias_value":"LHEZFEMK","created_at":"2026-07-16T00:21:53.450229+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LHEZFEMKKSWDQVSYCZJZ6DVS7M","json":"https://pith.science/pith/LHEZFEMKKSWDQVSYCZJZ6DVS7M.json","graph_json":"https://pith.science/api/pith-number/LHEZFEMKKSWDQVSYCZJZ6DVS7M/graph.json","events_json":"https://pith.science/api/pith-number/LHEZFEMKKSWDQVSYCZJZ6DVS7M/events.json","paper":"https://pith.science/paper/LHEZFEMK"},"agent_actions":{"view_html":"https://pith.science/pith/LHEZFEMKKSWDQVSYCZJZ6DVS7M","download_json":"https://pith.science/pith/LHEZFEMKKSWDQVSYCZJZ6DVS7M.json","view_paper":"https://pith.science/paper/LHEZFEMK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.13047&json=true","fetch_graph":"https://pith.science/api/pith-number/LHEZFEMKKSWDQVSYCZJZ6DVS7M/graph.json","fetch_events":"https://pith.science/api/pith-number/LHEZFEMKKSWDQVSYCZJZ6DVS7M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LHEZFEMKKSWDQVSYCZJZ6DVS7M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LHEZFEMKKSWDQVSYCZJZ6DVS7M/action/storage_attestation","attest_author":"https://pith.science/pith/LHEZFEMKKSWDQVSYCZJZ6DVS7M/action/author_attestation","sign_citation":"https://pith.science/pith/LHEZFEMKKSWDQVSYCZJZ6DVS7M/action/citation_signature","submit_replication":"https://pith.science/pith/LHEZFEMKKSWDQVSYCZJZ6DVS7M/action/replication_record"}},"created_at":"2026-07-16T00:21:53.450229+00:00","updated_at":"2026-07-16T00:21:53.450229+00:00"}