{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3ZXTLYDOTIS5FB4HC2Y3EICUA4","short_pith_number":"pith:3ZXTLYDO","schema_version":"1.0","canonical_sha256":"de6f35e06e9a25d2878716b1b22054072c2c5d6d84a466780abe091348f276d7","source":{"kind":"arxiv","id":"2511.12723","version":2},"attestation_state":"computed","paper":{"title":"LAYA: Layer-wise Attention Aggregation for Interpretable Depth-Aware Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gennaro Vessio","submitted_at":"2025-11-16T18:22:02Z","abstract_excerpt":"Deep neural networks typically rely on the representation produced by their final hidden layer to make predictions, implicitly assuming that this single vector fully captures the semantics encoded across all preceding transformations. However, intermediate layers contain rich and complementary information---ranging from low-level patterns to high-level abstractions---that is often discarded when the decision head depends solely on the last representation. This paper revisits the role of the output layer and introduces LAYA (Layer-wise Attention Aggregator), a novel output head that dynamically"},"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":"2511.12723","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-11-16T18:22:02Z","cross_cats_sorted":[],"title_canon_sha256":"da4514a63be07e702c3c889a1deea270fbf1710015b21f1905e9e0b319ebb741","abstract_canon_sha256":"fb1c722f24458be6763d01391763689ca0e8bdb5a24aaf1a02a713314d2d800d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T00:35:59.860163Z","signature_b64":"KRlRbeyi5CQuBCpqzImZ6c980JqU9R2a6W7b6oOGFKHOQC+1HSkMKbmyBt7NqW5At/zvg3ieYGbPgNzZ+6KCBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de6f35e06e9a25d2878716b1b22054072c2c5d6d84a466780abe091348f276d7","last_reissued_at":"2026-08-04T00:35:59.858531Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T00:35:59.858531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LAYA: Layer-wise Attention Aggregation for Interpretable Depth-Aware Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gennaro Vessio","submitted_at":"2025-11-16T18:22:02Z","abstract_excerpt":"Deep neural networks typically rely on the representation produced by their final hidden layer to make predictions, implicitly assuming that this single vector fully captures the semantics encoded across all preceding transformations. However, intermediate layers contain rich and complementary information---ranging from low-level patterns to high-level abstractions---that is often discarded when the decision head depends solely on the last representation. This paper revisits the role of the output layer and introduces LAYA (Layer-wise Attention Aggregator), a novel output head that dynamically"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.12723","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/2511.12723/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":"2511.12723","created_at":"2026-08-04T00:35:59.859743+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.12723v2","created_at":"2026-08-04T00:35:59.859743+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.12723","created_at":"2026-08-04T00:35:59.859743+00:00"},{"alias_kind":"pith_short_12","alias_value":"3ZXTLYDOTIS5","created_at":"2026-08-04T00:35:59.859743+00:00"},{"alias_kind":"pith_short_16","alias_value":"3ZXTLYDOTIS5FB4H","created_at":"2026-08-04T00:35:59.859743+00:00"},{"alias_kind":"pith_short_8","alias_value":"3ZXTLYDO","created_at":"2026-08-04T00:35:59.859743+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/3ZXTLYDOTIS5FB4HC2Y3EICUA4","json":"https://pith.science/pith/3ZXTLYDOTIS5FB4HC2Y3EICUA4.json","graph_json":"https://pith.science/api/pith-number/3ZXTLYDOTIS5FB4HC2Y3EICUA4/graph.json","events_json":"https://pith.science/api/pith-number/3ZXTLYDOTIS5FB4HC2Y3EICUA4/events.json","paper":"https://pith.science/paper/3ZXTLYDO"},"agent_actions":{"view_html":"https://pith.science/pith/3ZXTLYDOTIS5FB4HC2Y3EICUA4","download_json":"https://pith.science/pith/3ZXTLYDOTIS5FB4HC2Y3EICUA4.json","view_paper":"https://pith.science/paper/3ZXTLYDO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.12723&json=true","fetch_graph":"https://pith.science/api/pith-number/3ZXTLYDOTIS5FB4HC2Y3EICUA4/graph.json","fetch_events":"https://pith.science/api/pith-number/3ZXTLYDOTIS5FB4HC2Y3EICUA4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3ZXTLYDOTIS5FB4HC2Y3EICUA4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3ZXTLYDOTIS5FB4HC2Y3EICUA4/action/storage_attestation","attest_author":"https://pith.science/pith/3ZXTLYDOTIS5FB4HC2Y3EICUA4/action/author_attestation","sign_citation":"https://pith.science/pith/3ZXTLYDOTIS5FB4HC2Y3EICUA4/action/citation_signature","submit_replication":"https://pith.science/pith/3ZXTLYDOTIS5FB4HC2Y3EICUA4/action/replication_record"}},"created_at":"2026-08-04T00:35:59.859743+00:00","updated_at":"2026-08-04T00:35:59.859743+00:00"}