{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:2HPWVFZDPUHUFC76ETA7BW74U4","short_pith_number":"pith:2HPWVFZD","schema_version":"1.0","canonical_sha256":"d1df6a97237d0f428bfe24c1f0dbfca70b4bc47284b0d270139d4552d95d6c47","source":{"kind":"arxiv","id":"1709.08568","version":2},"attestation_state":"computed","paper":{"title":"The Consciousness Prior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Yoshua Bengio","submitted_at":"2017-09-25T15:59:11Z","abstract_excerpt":"A new prior is proposed for learning representations of high-level concepts of the kind we manipulate with language. This prior can be combined with other priors in order to help disentangling abstract factors from each other. It is inspired by cognitive neuroscience theories of consciousness, seen as a bottleneck through which just a few elements, after having been selected by attention from a broader pool, are then broadcast and condition further processing, both in perception and decision-making. The set of recently selected elements one becomes aware of is seen as forming a low-dimensional"},"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":"1709.08568","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-25T15:59:11Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"6a422aac23e8f855a90ba5dea927cc1e99143f26a33a87acd01f83532a848507","abstract_canon_sha256":"306c07ae3b0de96a377e8cbdd2d97016f50bcdd7ef56af0a430072220b324bb4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:23:19.392174Z","signature_b64":"AXYDNJB3NIhyYeSz0nDnm5z6TecPV0gRaVl0I9dnaTSvNrGqqZs3BqMBp1o51YvrY0D1MzQweDc9ol7UNcGnCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1df6a97237d0f428bfe24c1f0dbfca70b4bc47284b0d270139d4552d95d6c47","last_reissued_at":"2026-07-05T00:23:19.391786Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:23:19.391786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Consciousness Prior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Yoshua Bengio","submitted_at":"2017-09-25T15:59:11Z","abstract_excerpt":"A new prior is proposed for learning representations of high-level concepts of the kind we manipulate with language. This prior can be combined with other priors in order to help disentangling abstract factors from each other. It is inspired by cognitive neuroscience theories of consciousness, seen as a bottleneck through which just a few elements, after having been selected by attention from a broader pool, are then broadcast and condition further processing, both in perception and decision-making. The set of recently selected elements one becomes aware of is seen as forming a low-dimensional"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.08568","kind":"arxiv","version":2},"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/1709.08568/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":"1709.08568","created_at":"2026-07-05T00:23:19.391842+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.08568v2","created_at":"2026-07-05T00:23:19.391842+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.08568","created_at":"2026-07-05T00:23:19.391842+00:00"},{"alias_kind":"pith_short_12","alias_value":"2HPWVFZDPUHU","created_at":"2026-07-05T00:23:19.391842+00:00"},{"alias_kind":"pith_short_16","alias_value":"2HPWVFZDPUHUFC76","created_at":"2026-07-05T00:23:19.391842+00:00"},{"alias_kind":"pith_short_8","alias_value":"2HPWVFZD","created_at":"2026-07-05T00:23:19.391842+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00034","citing_title":"Bayesian updates from coalgebraic determinisation","ref_index":293,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06380","citing_title":"Emergent Language as an Approach to Conscious AI","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2511.19115","citing_title":"AI Consciousness and Existential Risk","ref_index":78,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19376","citing_title":"Generative Recursive Reasoning","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19376","citing_title":"Generative Recursive Reasoning","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2303.08128","citing_title":"ViperGPT: Visual Inference via Python Execution for Reasoning","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2512.15891","citing_title":"Dynamical Mechanisms for Coordinating Long-term Working Memory Based on the Precision of Spike-timing in Cortical Neurons","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2602.13215","citing_title":"When to Think Fast and Slow? AMOR: Adaptive Entropy Gate for Hybrid Models","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2HPWVFZDPUHUFC76ETA7BW74U4","json":"https://pith.science/pith/2HPWVFZDPUHUFC76ETA7BW74U4.json","graph_json":"https://pith.science/api/pith-number/2HPWVFZDPUHUFC76ETA7BW74U4/graph.json","events_json":"https://pith.science/api/pith-number/2HPWVFZDPUHUFC76ETA7BW74U4/events.json","paper":"https://pith.science/paper/2HPWVFZD"},"agent_actions":{"view_html":"https://pith.science/pith/2HPWVFZDPUHUFC76ETA7BW74U4","download_json":"https://pith.science/pith/2HPWVFZDPUHUFC76ETA7BW74U4.json","view_paper":"https://pith.science/paper/2HPWVFZD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.08568&json=true","fetch_graph":"https://pith.science/api/pith-number/2HPWVFZDPUHUFC76ETA7BW74U4/graph.json","fetch_events":"https://pith.science/api/pith-number/2HPWVFZDPUHUFC76ETA7BW74U4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2HPWVFZDPUHUFC76ETA7BW74U4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2HPWVFZDPUHUFC76ETA7BW74U4/action/storage_attestation","attest_author":"https://pith.science/pith/2HPWVFZDPUHUFC76ETA7BW74U4/action/author_attestation","sign_citation":"https://pith.science/pith/2HPWVFZDPUHUFC76ETA7BW74U4/action/citation_signature","submit_replication":"https://pith.science/pith/2HPWVFZDPUHUFC76ETA7BW74U4/action/replication_record"}},"created_at":"2026-07-05T00:23:19.391842+00:00","updated_at":"2026-07-05T00:23:19.391842+00:00"}