{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HEVFZ4KAU4BT3Q65GQPDUQFRKE","short_pith_number":"pith:HEVFZ4KA","schema_version":"1.0","canonical_sha256":"392a5cf140a7033dc3dd341e3a40b1510899552444e5365e76f3f1f085862523","source":{"kind":"arxiv","id":"2501.15740","version":1},"attestation_state":"computed","paper":{"title":"Propositional Interpretability in Artificial Intelligence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"David J. Chalmers","submitted_at":"2025-01-27T03:06:06Z","abstract_excerpt":"Mechanistic interpretability is the program of explaining what AI systems are doing in terms of their internal mechanisms. I analyze some aspects of the program, along with setting out some concrete challenges and assessing progress to date. I argue for the importance of propositional interpretability, which involves interpreting a system's mechanisms and behavior in terms of propositional attitudes: attitudes (such as belief, desire, or subjective probability) to propositions (e.g. the proposition that it is hot outside). Propositional attitudes are the central way that we interpret and expla"},"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":"2501.15740","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-01-27T03:06:06Z","cross_cats_sorted":[],"title_canon_sha256":"bb1630b223f4001d13b24699608c6a424b69532f6f4de3f1cc2e175c1e83d595","abstract_canon_sha256":"17ce220b4f61da850c4a8bf328d8979350e0a73375d4b43fe118961c1f748aa6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:49.873474Z","signature_b64":"9mkh5dEhxflEEjUZGiEGMVap6xjieSCIZFKU720Y8RmStak/ocQioTfW4Mwl8a1wlC/gwe+JaOnV8ltUYEyfDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"392a5cf140a7033dc3dd341e3a40b1510899552444e5365e76f3f1f085862523","last_reissued_at":"2026-07-05T10:05:49.872939Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:49.872939Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Propositional Interpretability in Artificial Intelligence","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"David J. Chalmers","submitted_at":"2025-01-27T03:06:06Z","abstract_excerpt":"Mechanistic interpretability is the program of explaining what AI systems are doing in terms of their internal mechanisms. I analyze some aspects of the program, along with setting out some concrete challenges and assessing progress to date. I argue for the importance of propositional interpretability, which involves interpreting a system's mechanisms and behavior in terms of propositional attitudes: attitudes (such as belief, desire, or subjective probability) to propositions (e.g. the proposition that it is hot outside). Propositional attitudes are the central way that we interpret and expla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15740","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/2501.15740/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":"2501.15740","created_at":"2026-07-05T10:05:49.872996+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.15740v1","created_at":"2026-07-05T10:05:49.872996+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15740","created_at":"2026-07-05T10:05:49.872996+00:00"},{"alias_kind":"pith_short_12","alias_value":"HEVFZ4KAU4BT","created_at":"2026-07-05T10:05:49.872996+00:00"},{"alias_kind":"pith_short_16","alias_value":"HEVFZ4KAU4BT3Q65","created_at":"2026-07-05T10:05:49.872996+00:00"},{"alias_kind":"pith_short_8","alias_value":"HEVFZ4KA","created_at":"2026-07-05T10:05:49.872996+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26523","citing_title":"Radical AI Interpretability","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12268","citing_title":"The Impossibility of Eliciting Latent Knowledge","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20600","citing_title":"The New Associationism: Lessons from Deep Learning","ref_index":67,"is_internal_anchor":false},{"citing_arxiv_id":"2506.18852","citing_title":"Mechanistic Interpretability Needs Philosophy","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09391","citing_title":"Do Linear Probes Generalize Better in Persona Coordinates?","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2511.17408","citing_title":"The Impact of Off-Policy Training Data on Probe Generalisation","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09391","citing_title":"Do Linear Probes Generalize Better in Persona Coordinates?","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HEVFZ4KAU4BT3Q65GQPDUQFRKE","json":"https://pith.science/pith/HEVFZ4KAU4BT3Q65GQPDUQFRKE.json","graph_json":"https://pith.science/api/pith-number/HEVFZ4KAU4BT3Q65GQPDUQFRKE/graph.json","events_json":"https://pith.science/api/pith-number/HEVFZ4KAU4BT3Q65GQPDUQFRKE/events.json","paper":"https://pith.science/paper/HEVFZ4KA"},"agent_actions":{"view_html":"https://pith.science/pith/HEVFZ4KAU4BT3Q65GQPDUQFRKE","download_json":"https://pith.science/pith/HEVFZ4KAU4BT3Q65GQPDUQFRKE.json","view_paper":"https://pith.science/paper/HEVFZ4KA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.15740&json=true","fetch_graph":"https://pith.science/api/pith-number/HEVFZ4KAU4BT3Q65GQPDUQFRKE/graph.json","fetch_events":"https://pith.science/api/pith-number/HEVFZ4KAU4BT3Q65GQPDUQFRKE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HEVFZ4KAU4BT3Q65GQPDUQFRKE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HEVFZ4KAU4BT3Q65GQPDUQFRKE/action/storage_attestation","attest_author":"https://pith.science/pith/HEVFZ4KAU4BT3Q65GQPDUQFRKE/action/author_attestation","sign_citation":"https://pith.science/pith/HEVFZ4KAU4BT3Q65GQPDUQFRKE/action/citation_signature","submit_replication":"https://pith.science/pith/HEVFZ4KAU4BT3Q65GQPDUQFRKE/action/replication_record"}},"created_at":"2026-07-05T10:05:49.872996+00:00","updated_at":"2026-07-05T10:05:49.872996+00:00"}