{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FYBTKGGPUNVBJRRERYRFZUM3SC","short_pith_number":"pith:FYBTKGGP","schema_version":"1.0","canonical_sha256":"2e033518cfa36a14c6248e225cd19b9088ffa2d1e0f9889b86de37350166b78c","source":{"kind":"arxiv","id":"2405.17425","version":1},"attestation_state":"computed","paper":{"title":"From Neurons to Neutrons: A Case Study in Interpretability","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["nucl-th"],"primary_cat":"cs.LG","authors_text":"Mike Williams, Niklas Nolte, Ouail Kitouni, Sokratis Trifinopoulos, V\\'ictor Samuel P\\'erez-D\\'iaz","submitted_at":"2024-05-27T17:59:35Z","abstract_excerpt":"Mechanistic Interpretability (MI) promises a path toward fully understanding how neural networks make their predictions. Prior work demonstrates that even when trained to perform simple arithmetic, models can implement a variety of algorithms (sometimes concurrently) depending on initialization and hyperparameters. Does this mean neuron-level interpretability techniques have limited applicability? We argue that high-dimensional neural networks can learn low-dimensional representations of their training data that are useful beyond simply making good predictions. Such representations can be unde"},"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":"2405.17425","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-27T17:59:35Z","cross_cats_sorted":["nucl-th"],"title_canon_sha256":"5f809c42c19b02b0cb353010bf65b6c2f5107a4a7bb1c2c83e4234881380205f","abstract_canon_sha256":"69b5c5657ab01fb3a304144027b31213481b1ad956b54aa40c797428b485a0b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:44.238565Z","signature_b64":"9qWsQsDR43eV5dYkP2iPWmoeMqRGJYdY7rs++mi4GJ/0GUsT+O7f0WzyO15sVrZNq+VLpshdUkgT745WVhc4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e033518cfa36a14c6248e225cd19b9088ffa2d1e0f9889b86de37350166b78c","last_reissued_at":"2026-07-05T08:23:44.238114Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:44.238114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Neurons to Neutrons: A Case Study in Interpretability","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["nucl-th"],"primary_cat":"cs.LG","authors_text":"Mike Williams, Niklas Nolte, Ouail Kitouni, Sokratis Trifinopoulos, V\\'ictor Samuel P\\'erez-D\\'iaz","submitted_at":"2024-05-27T17:59:35Z","abstract_excerpt":"Mechanistic Interpretability (MI) promises a path toward fully understanding how neural networks make their predictions. Prior work demonstrates that even when trained to perform simple arithmetic, models can implement a variety of algorithms (sometimes concurrently) depending on initialization and hyperparameters. Does this mean neuron-level interpretability techniques have limited applicability? We argue that high-dimensional neural networks can learn low-dimensional representations of their training data that are useful beyond simply making good predictions. Such representations can be unde"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17425","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/2405.17425/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":"2405.17425","created_at":"2026-07-05T08:23:44.238171+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.17425v1","created_at":"2026-07-05T08:23:44.238171+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17425","created_at":"2026-07-05T08:23:44.238171+00:00"},{"alias_kind":"pith_short_12","alias_value":"FYBTKGGPUNVB","created_at":"2026-07-05T08:23:44.238171+00:00"},{"alias_kind":"pith_short_16","alias_value":"FYBTKGGPUNVBJRRE","created_at":"2026-07-05T08:23:44.238171+00:00"},{"alias_kind":"pith_short_8","alias_value":"FYBTKGGP","created_at":"2026-07-05T08:23:44.238171+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/FYBTKGGPUNVBJRRERYRFZUM3SC","json":"https://pith.science/pith/FYBTKGGPUNVBJRRERYRFZUM3SC.json","graph_json":"https://pith.science/api/pith-number/FYBTKGGPUNVBJRRERYRFZUM3SC/graph.json","events_json":"https://pith.science/api/pith-number/FYBTKGGPUNVBJRRERYRFZUM3SC/events.json","paper":"https://pith.science/paper/FYBTKGGP"},"agent_actions":{"view_html":"https://pith.science/pith/FYBTKGGPUNVBJRRERYRFZUM3SC","download_json":"https://pith.science/pith/FYBTKGGPUNVBJRRERYRFZUM3SC.json","view_paper":"https://pith.science/paper/FYBTKGGP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.17425&json=true","fetch_graph":"https://pith.science/api/pith-number/FYBTKGGPUNVBJRRERYRFZUM3SC/graph.json","fetch_events":"https://pith.science/api/pith-number/FYBTKGGPUNVBJRRERYRFZUM3SC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FYBTKGGPUNVBJRRERYRFZUM3SC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FYBTKGGPUNVBJRRERYRFZUM3SC/action/storage_attestation","attest_author":"https://pith.science/pith/FYBTKGGPUNVBJRRERYRFZUM3SC/action/author_attestation","sign_citation":"https://pith.science/pith/FYBTKGGPUNVBJRRERYRFZUM3SC/action/citation_signature","submit_replication":"https://pith.science/pith/FYBTKGGPUNVBJRRERYRFZUM3SC/action/replication_record"}},"created_at":"2026-07-05T08:23:44.238171+00:00","updated_at":"2026-07-05T08:23:44.238171+00:00"}