{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:J6J525IIBJI6HCODV2ON35YOZ2","short_pith_number":"pith:J6J525II","schema_version":"1.0","canonical_sha256":"4f93dd75080a51e389c3ae9cddf70ece8982498a831847f820b6a1d94e70f3fc","source":{"kind":"arxiv","id":"2606.25232","version":1},"attestation_state":"computed","paper":{"title":"Semantic Allocation in Ordered Bottlenecks: Predictive Residual Inference for Visual Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Erik Ayari, Manuel Traub, Martin V. Butz","submitted_at":"2026-06-23T23:19:12Z","abstract_excerpt":"Ordered bottlenecks aim to provide utility at flexible budgets by assigning coarse information to early tokens and task-relevant detail to later ones. Prior work, including tail dropping (TD), typically enforces ordering by means of a masking-based ordering pressure (MBOP): Late tokens are masked more frequently than early tokens and are therefore encouraged to store less essential fine details. We introduce predictive residual inference for ordered representations (PRIOR), a framework designed to address inherent weaknesses of MBOP. MBOP is prone to weak late-token utility because it lacks an"},"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":"2606.25232","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-23T23:19:12Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"80f228d14ade5338287263e706f925341e21ed126e51a64a51050e01f3f50ff1","abstract_canon_sha256":"a0b27e7058cec4d296ef53eb178e41848e895357212f6577468a7f34aa5cea75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T00:18:21.595417Z","signature_b64":"ExkplNBY//vo84BUhuExoqb2VoGZom6BBmJJIQf7Qabe61jQLVQzfeYMKLBTH17VJRLLzR1b8/qmffGOrB3nCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f93dd75080a51e389c3ae9cddf70ece8982498a831847f820b6a1d94e70f3fc","last_reissued_at":"2026-06-25T00:18:21.594850Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T00:18:21.594850Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic Allocation in Ordered Bottlenecks: Predictive Residual Inference for Visual Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Erik Ayari, Manuel Traub, Martin V. Butz","submitted_at":"2026-06-23T23:19:12Z","abstract_excerpt":"Ordered bottlenecks aim to provide utility at flexible budgets by assigning coarse information to early tokens and task-relevant detail to later ones. Prior work, including tail dropping (TD), typically enforces ordering by means of a masking-based ordering pressure (MBOP): Late tokens are masked more frequently than early tokens and are therefore encouraged to store less essential fine details. We introduce predictive residual inference for ordered representations (PRIOR), a framework designed to address inherent weaknesses of MBOP. MBOP is prone to weak late-token utility because it lacks an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.25232","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/2606.25232/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":"2606.25232","created_at":"2026-06-25T00:18:21.594916+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.25232v1","created_at":"2026-06-25T00:18:21.594916+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.25232","created_at":"2026-06-25T00:18:21.594916+00:00"},{"alias_kind":"pith_short_12","alias_value":"J6J525IIBJI6","created_at":"2026-06-25T00:18:21.594916+00:00"},{"alias_kind":"pith_short_16","alias_value":"J6J525IIBJI6HCOD","created_at":"2026-06-25T00:18:21.594916+00:00"},{"alias_kind":"pith_short_8","alias_value":"J6J525II","created_at":"2026-06-25T00:18:21.594916+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/J6J525IIBJI6HCODV2ON35YOZ2","json":"https://pith.science/pith/J6J525IIBJI6HCODV2ON35YOZ2.json","graph_json":"https://pith.science/api/pith-number/J6J525IIBJI6HCODV2ON35YOZ2/graph.json","events_json":"https://pith.science/api/pith-number/J6J525IIBJI6HCODV2ON35YOZ2/events.json","paper":"https://pith.science/paper/J6J525II"},"agent_actions":{"view_html":"https://pith.science/pith/J6J525IIBJI6HCODV2ON35YOZ2","download_json":"https://pith.science/pith/J6J525IIBJI6HCODV2ON35YOZ2.json","view_paper":"https://pith.science/paper/J6J525II","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.25232&json=true","fetch_graph":"https://pith.science/api/pith-number/J6J525IIBJI6HCODV2ON35YOZ2/graph.json","fetch_events":"https://pith.science/api/pith-number/J6J525IIBJI6HCODV2ON35YOZ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J6J525IIBJI6HCODV2ON35YOZ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J6J525IIBJI6HCODV2ON35YOZ2/action/storage_attestation","attest_author":"https://pith.science/pith/J6J525IIBJI6HCODV2ON35YOZ2/action/author_attestation","sign_citation":"https://pith.science/pith/J6J525IIBJI6HCODV2ON35YOZ2/action/citation_signature","submit_replication":"https://pith.science/pith/J6J525IIBJI6HCODV2ON35YOZ2/action/replication_record"}},"created_at":"2026-06-25T00:18:21.594916+00:00","updated_at":"2026-06-25T00:18:21.594916+00:00"}