{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:HNU24E6HT5XFJFZFITVO2KKIQW","short_pith_number":"pith:HNU24E6H","schema_version":"1.0","canonical_sha256":"3b69ae13c79f6e54972544eaed2948858c9fbc1f983271d460b72c657888a239","source":{"kind":"arxiv","id":"2509.01533","version":1},"attestation_state":"computed","paper":{"title":"Forward-Only Continual Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Fangfang Chen, Jianhua Tang, Jiao Chen, Jiayi He, Zuohong Lv","submitted_at":"2025-09-01T15:10:38Z","abstract_excerpt":"Catastrophic forgetting remains a central challenge in continual learning (CL) with pre-trained models. While existing approaches typically freeze the backbone and fine-tune a small number of parameters to mitigate forgetting, they still rely on iterative error backpropagation and gradient-based optimization, which can be computationally intensive and less suitable for resource-constrained environments. To address this, we propose FoRo, a forward-only, gradient-free continual learning method. FoRo consists of a lightweight prompt tuning strategy and a novel knowledge encoding mechanism, both d"},"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":"2509.01533","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-01T15:10:38Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"d24e56870eb0513a1f59bbc515fd5fc10c20a8d0e47eec24b7c0d35d6eef0085","abstract_canon_sha256":"267e6d7bffffe6636155623eb20d780e37ddaa44613ca855c7efe2d13fd41f99"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:02.699414Z","signature_b64":"gtg3axBl6SdB5vH0rtSEV0Q4f5Q+X71C8W2RZwkGLZA8Gr0mwnmYR2GCl3Xc0GVstRumEzoGJBzcus32x9fQBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b69ae13c79f6e54972544eaed2948858c9fbc1f983271d460b72c657888a239","last_reissued_at":"2026-07-05T12:03:02.698144Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:02.698144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Forward-Only Continual Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Fangfang Chen, Jianhua Tang, Jiao Chen, Jiayi He, Zuohong Lv","submitted_at":"2025-09-01T15:10:38Z","abstract_excerpt":"Catastrophic forgetting remains a central challenge in continual learning (CL) with pre-trained models. While existing approaches typically freeze the backbone and fine-tune a small number of parameters to mitigate forgetting, they still rely on iterative error backpropagation and gradient-based optimization, which can be computationally intensive and less suitable for resource-constrained environments. To address this, we propose FoRo, a forward-only, gradient-free continual learning method. FoRo consists of a lightweight prompt tuning strategy and a novel knowledge encoding mechanism, both d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.01533","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/2509.01533/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":"2509.01533","created_at":"2026-07-05T12:03:02.698202+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.01533v1","created_at":"2026-07-05T12:03:02.698202+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.01533","created_at":"2026-07-05T12:03:02.698202+00:00"},{"alias_kind":"pith_short_12","alias_value":"HNU24E6HT5XF","created_at":"2026-07-05T12:03:02.698202+00:00"},{"alias_kind":"pith_short_16","alias_value":"HNU24E6HT5XFJFZF","created_at":"2026-07-05T12:03:02.698202+00:00"},{"alias_kind":"pith_short_8","alias_value":"HNU24E6H","created_at":"2026-07-05T12:03:02.698202+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/HNU24E6HT5XFJFZFITVO2KKIQW","json":"https://pith.science/pith/HNU24E6HT5XFJFZFITVO2KKIQW.json","graph_json":"https://pith.science/api/pith-number/HNU24E6HT5XFJFZFITVO2KKIQW/graph.json","events_json":"https://pith.science/api/pith-number/HNU24E6HT5XFJFZFITVO2KKIQW/events.json","paper":"https://pith.science/paper/HNU24E6H"},"agent_actions":{"view_html":"https://pith.science/pith/HNU24E6HT5XFJFZFITVO2KKIQW","download_json":"https://pith.science/pith/HNU24E6HT5XFJFZFITVO2KKIQW.json","view_paper":"https://pith.science/paper/HNU24E6H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.01533&json=true","fetch_graph":"https://pith.science/api/pith-number/HNU24E6HT5XFJFZFITVO2KKIQW/graph.json","fetch_events":"https://pith.science/api/pith-number/HNU24E6HT5XFJFZFITVO2KKIQW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HNU24E6HT5XFJFZFITVO2KKIQW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HNU24E6HT5XFJFZFITVO2KKIQW/action/storage_attestation","attest_author":"https://pith.science/pith/HNU24E6HT5XFJFZFITVO2KKIQW/action/author_attestation","sign_citation":"https://pith.science/pith/HNU24E6HT5XFJFZFITVO2KKIQW/action/citation_signature","submit_replication":"https://pith.science/pith/HNU24E6HT5XFJFZFITVO2KKIQW/action/replication_record"}},"created_at":"2026-07-05T12:03:02.698202+00:00","updated_at":"2026-07-05T12:03:02.698202+00:00"}