{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:N2ZD7DAFPTRQUV27M74W2BVGGH","short_pith_number":"pith:N2ZD7DAF","schema_version":"1.0","canonical_sha256":"6eb23f8c057ce30a575f67f96d06a631c06754636fb2a64364005200ab49beda","source":{"kind":"arxiv","id":"2607.13612","version":1},"attestation_state":"computed","paper":{"title":"The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alexandra Gomez-Villa, Fabio Arnez","submitted_at":"2026-07-15T08:59:36Z","abstract_excerpt":"Joint-Embedding Predictive Architectures (JEPAs) are the dominant design for latent world models, yet they are usually justified by empirical performance rather than a normative principle. We show that the choice of anti-collapse regulariser determines whether a JEPA's training objective, a prediction loss plus a weighted embedding regulariser, is a valid Active Inference (AIF) variational free energy. We organise four non-contrastive regularisers (VICReg, LogDet, PairDist, and SIGReg) into an entropy-estimator hierarchy indexed by a prior-miscalibration gap, and show that the gap's sign, whet"},"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":"2607.13612","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-15T08:59:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e4ce46a6a571aae378c8c4db7a1709ae286a64a338519311586e1fd94512f297","abstract_canon_sha256":"10fc50665ca4af4a01659af4011ffa6c5a1869ea86e1346398489a79e9b55f26"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-16T01:22:56.908631Z","signature_b64":"/46UvTlPIxenHgKC1lVCNChPS/8TG3qy3dA5LYatOIV+AdM1jXEOFawghAvMzW9w22+srav42G/MQ4q0W+lDBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6eb23f8c057ce30a575f67f96d06a631c06754636fb2a64364005200ab49beda","last_reissued_at":"2026-07-16T01:22:56.907807Z","signature_status":"signed_v1","first_computed_at":"2026-07-16T01:22:56.907807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alexandra Gomez-Villa, Fabio Arnez","submitted_at":"2026-07-15T08:59:36Z","abstract_excerpt":"Joint-Embedding Predictive Architectures (JEPAs) are the dominant design for latent world models, yet they are usually justified by empirical performance rather than a normative principle. We show that the choice of anti-collapse regulariser determines whether a JEPA's training objective, a prediction loss plus a weighted embedding regulariser, is a valid Active Inference (AIF) variational free energy. We organise four non-contrastive regularisers (VICReg, LogDet, PairDist, and SIGReg) into an entropy-estimator hierarchy indexed by a prior-miscalibration gap, and show that the gap's sign, whet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.13612","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/2607.13612/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":"2607.13612","created_at":"2026-07-16T01:22:56.908240+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.13612v1","created_at":"2026-07-16T01:22:56.908240+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.13612","created_at":"2026-07-16T01:22:56.908240+00:00"},{"alias_kind":"pith_short_12","alias_value":"N2ZD7DAFPTRQ","created_at":"2026-07-16T01:22:56.908240+00:00"},{"alias_kind":"pith_short_16","alias_value":"N2ZD7DAFPTRQUV27","created_at":"2026-07-16T01:22:56.908240+00:00"},{"alias_kind":"pith_short_8","alias_value":"N2ZD7DAF","created_at":"2026-07-16T01:22:56.908240+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/N2ZD7DAFPTRQUV27M74W2BVGGH","json":"https://pith.science/pith/N2ZD7DAFPTRQUV27M74W2BVGGH.json","graph_json":"https://pith.science/api/pith-number/N2ZD7DAFPTRQUV27M74W2BVGGH/graph.json","events_json":"https://pith.science/api/pith-number/N2ZD7DAFPTRQUV27M74W2BVGGH/events.json","paper":"https://pith.science/paper/N2ZD7DAF"},"agent_actions":{"view_html":"https://pith.science/pith/N2ZD7DAFPTRQUV27M74W2BVGGH","download_json":"https://pith.science/pith/N2ZD7DAFPTRQUV27M74W2BVGGH.json","view_paper":"https://pith.science/paper/N2ZD7DAF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.13612&json=true","fetch_graph":"https://pith.science/api/pith-number/N2ZD7DAFPTRQUV27M74W2BVGGH/graph.json","fetch_events":"https://pith.science/api/pith-number/N2ZD7DAFPTRQUV27M74W2BVGGH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N2ZD7DAFPTRQUV27M74W2BVGGH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N2ZD7DAFPTRQUV27M74W2BVGGH/action/storage_attestation","attest_author":"https://pith.science/pith/N2ZD7DAFPTRQUV27M74W2BVGGH/action/author_attestation","sign_citation":"https://pith.science/pith/N2ZD7DAFPTRQUV27M74W2BVGGH/action/citation_signature","submit_replication":"https://pith.science/pith/N2ZD7DAFPTRQUV27M74W2BVGGH/action/replication_record"}},"created_at":"2026-07-16T01:22:56.908240+00:00","updated_at":"2026-07-16T01:22:56.908240+00:00"}