{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2YWEINGPBOESAID5BEHBYKX5KZ","short_pith_number":"pith:2YWEINGP","schema_version":"1.0","canonical_sha256":"d62c4434cf0b8920207d090e1c2afd566b07b5ee4998345ec4dc664dadaea9ad","source":{"kind":"arxiv","id":"2207.06415","version":1},"attestation_state":"computed","paper":{"title":"The Free Energy Principle for Perception and Action: A Deep Learning Perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-bio.NC"],"primary_cat":"cs.LG","authors_text":"Bart Dhoedt, Ozan \\c{C}atal, Pietro Mazzaglia, Tim Verbelen","submitted_at":"2022-07-13T11:07:03Z","abstract_excerpt":"The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in a restricted set of preferred states of the world, i.e., they minimize their free energy. Under this principle, biological agents learn a generative model of the world and plan actions in the future that will maintain the agent in an homeostatic state that satisfies its preferences. This framework lends itself to being realized in silico, as it comprehends important aspects that make it computationally affordable, such as variational inference and amor"},"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":"2207.06415","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-13T11:07:03Z","cross_cats_sorted":["cs.AI","q-bio.NC"],"title_canon_sha256":"02c38bfcd6664f8c7b6ce7e9ce8ee8155cdecc1a0936eb27d5013289a6d84704","abstract_canon_sha256":"6065e8377ad729e7bfb6a050ca6438413d14bfb502041ebff621d71858175034"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:40:12.434672Z","signature_b64":"fUdXrhEd3k5pr37kXoA708WwlplsABRx7VfZjKqSB5B5x4jODehlH44xha8vMGp/D6hNRBOh+kRkVnCRfdzgBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d62c4434cf0b8920207d090e1c2afd566b07b5ee4998345ec4dc664dadaea9ad","last_reissued_at":"2026-07-05T04:40:12.434334Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:40:12.434334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Free Energy Principle for Perception and Action: A Deep Learning Perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-bio.NC"],"primary_cat":"cs.LG","authors_text":"Bart Dhoedt, Ozan \\c{C}atal, Pietro Mazzaglia, Tim Verbelen","submitted_at":"2022-07-13T11:07:03Z","abstract_excerpt":"The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in a restricted set of preferred states of the world, i.e., they minimize their free energy. Under this principle, biological agents learn a generative model of the world and plan actions in the future that will maintain the agent in an homeostatic state that satisfies its preferences. This framework lends itself to being realized in silico, as it comprehends important aspects that make it computationally affordable, such as variational inference and amor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.06415","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/2207.06415/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":"2207.06415","created_at":"2026-07-05T04:40:12.434391+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.06415v1","created_at":"2026-07-05T04:40:12.434391+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.06415","created_at":"2026-07-05T04:40:12.434391+00:00"},{"alias_kind":"pith_short_12","alias_value":"2YWEINGPBOES","created_at":"2026-07-05T04:40:12.434391+00:00"},{"alias_kind":"pith_short_16","alias_value":"2YWEINGPBOESAID5","created_at":"2026-07-05T04:40:12.434391+00:00"},{"alias_kind":"pith_short_8","alias_value":"2YWEINGP","created_at":"2026-07-05T04:40:12.434391+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/2YWEINGPBOESAID5BEHBYKX5KZ","json":"https://pith.science/pith/2YWEINGPBOESAID5BEHBYKX5KZ.json","graph_json":"https://pith.science/api/pith-number/2YWEINGPBOESAID5BEHBYKX5KZ/graph.json","events_json":"https://pith.science/api/pith-number/2YWEINGPBOESAID5BEHBYKX5KZ/events.json","paper":"https://pith.science/paper/2YWEINGP"},"agent_actions":{"view_html":"https://pith.science/pith/2YWEINGPBOESAID5BEHBYKX5KZ","download_json":"https://pith.science/pith/2YWEINGPBOESAID5BEHBYKX5KZ.json","view_paper":"https://pith.science/paper/2YWEINGP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.06415&json=true","fetch_graph":"https://pith.science/api/pith-number/2YWEINGPBOESAID5BEHBYKX5KZ/graph.json","fetch_events":"https://pith.science/api/pith-number/2YWEINGPBOESAID5BEHBYKX5KZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2YWEINGPBOESAID5BEHBYKX5KZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2YWEINGPBOESAID5BEHBYKX5KZ/action/storage_attestation","attest_author":"https://pith.science/pith/2YWEINGPBOESAID5BEHBYKX5KZ/action/author_attestation","sign_citation":"https://pith.science/pith/2YWEINGPBOESAID5BEHBYKX5KZ/action/citation_signature","submit_replication":"https://pith.science/pith/2YWEINGPBOESAID5BEHBYKX5KZ/action/replication_record"}},"created_at":"2026-07-05T04:40:12.434391+00:00","updated_at":"2026-07-05T04:40:12.434391+00:00"}