{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FPSK2ACZ54LFDAJ6AVNS244XSA","short_pith_number":"pith:FPSK2ACZ","schema_version":"1.0","canonical_sha256":"2be4ad0059ef1651813e055b2d739790373aacede0197ae3221fe916cb82b8d1","source":{"kind":"arxiv","id":"2604.12406","version":2},"attestation_state":"computed","paper":{"title":"LightTune: Lightweight Forward-Only Online Fine-Tuning with Applications to Link Adaptation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"LightTune provides a backpropagation-free online fine-tuning method that adapts ML models on mobile devices to new conditions by triggering forward-only updates only when performance drops below a threshold.","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Federico Penna, Ramy E. Ali","submitted_at":"2026-04-14T07:43:07Z","abstract_excerpt":"Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training conditions. While continual learning and adaptation are essential to mitigate this distributional shift, conventional online learning methods are often computationally prohibitive for resource-constrained devices. In this paper, we propose LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees. LightTune opportunistically refines ML models using live test-time 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":"2604.12406","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2026-04-14T07:43:07Z","cross_cats_sorted":[],"title_canon_sha256":"b0aa21225563cedd15e2f6e86f9ef472543083f9eae3950688be91399a9ebf54","abstract_canon_sha256":"136137b5b0dbd39546a7458dc4cd6baf9bd6b5df343119acedaa09f594c4bb35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:04:02.235327Z","signature_b64":"Wb1Mt5pJf6GUaJNWNyOdqnALLZkgrUbyb79UZp7y6Wr1UQ08BMcK4rhCN6vyqeTNc8F8n6/EQhevaPI3RtfpCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2be4ad0059ef1651813e055b2d739790373aacede0197ae3221fe916cb82b8d1","last_reissued_at":"2026-05-22T01:04:02.234604Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:04:02.234604Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LightTune: Lightweight Forward-Only Online Fine-Tuning with Applications to Link Adaptation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"LightTune provides a backpropagation-free online fine-tuning method that adapts ML models on mobile devices to new conditions by triggering forward-only updates only when performance drops below a threshold.","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Federico Penna, Ramy E. Ali","submitted_at":"2026-04-14T07:43:07Z","abstract_excerpt":"Deploying machine learning (ML) algorithms on mobile phones is bottlenecked by performance degradation under dynamic, real-world conditions that differ from the offline training conditions. While continual learning and adaptation are essential to mitigate this distributional shift, conventional online learning methods are often computationally prohibitive for resource-constrained devices. In this paper, we propose LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees. LightTune opportunistically refines ML models using live test-time d"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees, enables a substantial reduction in the average BLER prediction error of up to 48.8% and average throughput improvements of up to 15.5% compared to conventional OLLA.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That opportunistically triggering fine-tuning only when performance falls below a predefined threshold, combined with forward-only updates, is sufficient to achieve reliable adaptation and convergence under real-world distributional shifts without introducing instability or excessive overhead.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"LightTune introduces a lightweight forward-only online fine-tuning framework that reduces BLER prediction error by up to 48.8% and boosts throughput by 15.5% in 6G link adaptation.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"LightTune provides a backpropagation-free online fine-tuning method that adapts ML models on mobile devices to new conditions by triggering forward-only updates only when performance drops below a threshold.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c7bc6fe6bdcdecf5413aea576cd7c2204a94f515a86723f2b7108aa99e36290a"},"source":{"id":"2604.12406","kind":"arxiv","version":2},"verdict":{"id":"092116ee-184c-4b01-8b96-539d899326ce","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T14:33:04.617389Z","strongest_claim":"LightTune, a lightweight, backpropagation-free online fine-tuning framework with provable convergence guarantees, enables a substantial reduction in the average BLER prediction error of up to 48.8% and average throughput improvements of up to 15.5% compared to conventional OLLA.","one_line_summary":"LightTune introduces a lightweight forward-only online fine-tuning framework that reduces BLER prediction error by up to 48.8% and boosts throughput by 15.5% in 6G link adaptation.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That opportunistically triggering fine-tuning only when performance falls below a predefined threshold, combined with forward-only updates, is sufficient to achieve reliable adaptation and convergence under real-world distributional shifts without introducing instability or excessive overhead.","pith_extraction_headline":"LightTune provides a backpropagation-free online fine-tuning method that adapts ML models on mobile devices to new conditions by triggering forward-only updates only when performance drops below a threshold."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.12406/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":"2604.12406","created_at":"2026-05-22T01:04:02.234725+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.12406v2","created_at":"2026-05-22T01:04:02.234725+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.12406","created_at":"2026-05-22T01:04:02.234725+00:00"},{"alias_kind":"pith_short_12","alias_value":"FPSK2ACZ54LF","created_at":"2026-05-22T01:04:02.234725+00:00"},{"alias_kind":"pith_short_16","alias_value":"FPSK2ACZ54LFDAJ6","created_at":"2026-05-22T01:04:02.234725+00:00"},{"alias_kind":"pith_short_8","alias_value":"FPSK2ACZ","created_at":"2026-05-22T01:04:02.234725+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/FPSK2ACZ54LFDAJ6AVNS244XSA","json":"https://pith.science/pith/FPSK2ACZ54LFDAJ6AVNS244XSA.json","graph_json":"https://pith.science/api/pith-number/FPSK2ACZ54LFDAJ6AVNS244XSA/graph.json","events_json":"https://pith.science/api/pith-number/FPSK2ACZ54LFDAJ6AVNS244XSA/events.json","paper":"https://pith.science/paper/FPSK2ACZ"},"agent_actions":{"view_html":"https://pith.science/pith/FPSK2ACZ54LFDAJ6AVNS244XSA","download_json":"https://pith.science/pith/FPSK2ACZ54LFDAJ6AVNS244XSA.json","view_paper":"https://pith.science/paper/FPSK2ACZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.12406&json=true","fetch_graph":"https://pith.science/api/pith-number/FPSK2ACZ54LFDAJ6AVNS244XSA/graph.json","fetch_events":"https://pith.science/api/pith-number/FPSK2ACZ54LFDAJ6AVNS244XSA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FPSK2ACZ54LFDAJ6AVNS244XSA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FPSK2ACZ54LFDAJ6AVNS244XSA/action/storage_attestation","attest_author":"https://pith.science/pith/FPSK2ACZ54LFDAJ6AVNS244XSA/action/author_attestation","sign_citation":"https://pith.science/pith/FPSK2ACZ54LFDAJ6AVNS244XSA/action/citation_signature","submit_replication":"https://pith.science/pith/FPSK2ACZ54LFDAJ6AVNS244XSA/action/replication_record"}},"created_at":"2026-05-22T01:04:02.234725+00:00","updated_at":"2026-05-22T01:04:02.234725+00:00"}