{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:BLUT7JGUYUTRQ62V4PYEYP56NQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fd4fd992859e0c2c8ba9e6b332aa5b9abcf9ec830aa2f04abee6922cd85b5da1","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-11T15:15:40Z","title_canon_sha256":"a8736c063dc77bb4996274be525b07afdfd21870cc6d0864230d51a1f13449c9"},"schema_version":"1.0","source":{"id":"2605.18804","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.18804","created_at":"2026-05-20T00:06:23Z"},{"alias_kind":"arxiv_version","alias_value":"2605.18804v1","created_at":"2026-05-20T00:06:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18804","created_at":"2026-05-20T00:06:23Z"},{"alias_kind":"pith_short_12","alias_value":"BLUT7JGUYUTR","created_at":"2026-05-20T00:06:23Z"},{"alias_kind":"pith_short_16","alias_value":"BLUT7JGUYUTRQ62V","created_at":"2026-05-20T00:06:23Z"},{"alias_kind":"pith_short_8","alias_value":"BLUT7JGU","created_at":"2026-05-20T00:06:23Z"}],"graph_snapshots":[{"event_id":"sha256:c9e4d23d754e9c3cebb29a8ccc4250a332c48a1a93e1a1691207b763c9a5a4ef","target":"graph","created_at":"2026-05-20T00:06:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2605.18804/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose Adaptive Multi-Scale Goodness Aggregation (AMSGA), a novel extension of the Forward-Forward (FF) algorithm designed to improve stability, robustness, and generalization in local-learning neural networks. AMSGA addresses several limitations of the original FF framework by introducing multi-scale goodness aggregation across local, intermediate, and global representations; adaptive curriculum-guided hard negative mining; layer-dependent adaptive thresholds; and a warm-up cosine annealing learning-rate schedule for improved optimization stability. Together, these modifications strengthe","authors_text":"Salar Beigzad, Vansh Verma","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-11T15:15:40Z","title":"Adaptive Multi-Scale Goodness Aggregation for Forward-Forward Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18804","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5a5f5398d0472fc3f0194e04ff55cecc3fa4312df810050585449c32d0f8d0d6","target":"record","created_at":"2026-05-20T00:06:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"fd4fd992859e0c2c8ba9e6b332aa5b9abcf9ec830aa2f04abee6922cd85b5da1","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-11T15:15:40Z","title_canon_sha256":"a8736c063dc77bb4996274be525b07afdfd21870cc6d0864230d51a1f13449c9"},"schema_version":"1.0","source":{"id":"2605.18804","kind":"arxiv","version":1}},"canonical_sha256":"0ae93fa4d4c527187b55e3f04c3fbe6c3f86cf0fa2663adc6bdf0ee691752f3b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0ae93fa4d4c527187b55e3f04c3fbe6c3f86cf0fa2663adc6bdf0ee691752f3b","first_computed_at":"2026-05-20T00:06:23.349169Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:06:23.349169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NtN3r9E6fLjC4Crp8Jl/EzCUJ1dPNkclU+tDjO7GGP6rDlYvYMR9WJ5AjH7G+u5lAwdQCpYXnUcgCXUyn2kjDg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:06:23.350025Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.18804","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5a5f5398d0472fc3f0194e04ff55cecc3fa4312df810050585449c32d0f8d0d6","sha256:c9e4d23d754e9c3cebb29a8ccc4250a332c48a1a93e1a1691207b763c9a5a4ef"],"state_sha256":"e6cfbb3da45170280065fc1589833bbe0608fea5a00540bdd98b7a36221a3903"}