{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LNTGKHUI5V3BIOUUGRCI7L3VTD","short_pith_number":"pith:LNTGKHUI","schema_version":"1.0","canonical_sha256":"5b66651e88ed76143a9434448faf7598d8f37ee3e8087ae35ee5f75cc112c3f7","source":{"kind":"arxiv","id":"2505.12392","version":2},"attestation_state":"computed","paper":{"title":"SLOT: Sample-specific Language Model Optimization at Test-time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Guojun Qi, Huatian Zhang, Xiao Wang, Xingyu Zhang, Xueji Fang, Yang Hu, Zhiyang Chen","submitted_at":"2025-05-18T12:37:56Z","abstract_excerpt":"We propose SLOT (Sample-specific Language Model Optimization at Test-time), a novel and parameter-efficient test-time inference approach that enhances a language model's ability to more accurately respond to individual prompts. Existing Large Language Models (LLMs) often struggle with complex instructions, leading to poor performances on those not well represented among general samples. To address this, SLOT conducts few optimization steps at test-time to update a light-weight sample-specific parameter vector. It is added to the final hidden layer before the output head, and enables efficient "},"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":"2505.12392","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-18T12:37:56Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"63da6c3d0d696fb05cf8d2d7fabdd2247da128a6a69e86c7aeebd53bbffd86f1","abstract_canon_sha256":"5b71c37420fe08f47a399f14a2be847d51751b49901054bec6ab18d7eb81594d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:13.819334Z","signature_b64":"TAlvyGHH0PWuhctFC/h8hJU6Z20MK52UjUxiNe4eVJBE5x4WOXKr5obcNau7yua1xRC5w95Tquxz9tdKKkbICQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b66651e88ed76143a9434448faf7598d8f37ee3e8087ae35ee5f75cc112c3f7","last_reissued_at":"2026-07-05T11:09:13.818822Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:13.818822Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SLOT: Sample-specific Language Model Optimization at Test-time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Guojun Qi, Huatian Zhang, Xiao Wang, Xingyu Zhang, Xueji Fang, Yang Hu, Zhiyang Chen","submitted_at":"2025-05-18T12:37:56Z","abstract_excerpt":"We propose SLOT (Sample-specific Language Model Optimization at Test-time), a novel and parameter-efficient test-time inference approach that enhances a language model's ability to more accurately respond to individual prompts. Existing Large Language Models (LLMs) often struggle with complex instructions, leading to poor performances on those not well represented among general samples. To address this, SLOT conducts few optimization steps at test-time to update a light-weight sample-specific parameter vector. It is added to the final hidden layer before the output head, and enables efficient "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.12392","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/2505.12392/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":"2505.12392","created_at":"2026-07-05T11:09:13.818884+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.12392v2","created_at":"2026-07-05T11:09:13.818884+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.12392","created_at":"2026-07-05T11:09:13.818884+00:00"},{"alias_kind":"pith_short_12","alias_value":"LNTGKHUI5V3B","created_at":"2026-07-05T11:09:13.818884+00:00"},{"alias_kind":"pith_short_16","alias_value":"LNTGKHUI5V3BIOUU","created_at":"2026-07-05T11:09:13.818884+00:00"},{"alias_kind":"pith_short_8","alias_value":"LNTGKHUI","created_at":"2026-07-05T11:09:13.818884+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.18089","citing_title":"From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning","ref_index":78,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20189","citing_title":"SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08186","citing_title":"Rethinking Entropy Minimization in Test-Time Adaptation for Autoregressive Models","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.06262","citing_title":"From Exposure to Internalization: Dual-Stream Calibration for In-context Clinical Reasoning","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LNTGKHUI5V3BIOUUGRCI7L3VTD","json":"https://pith.science/pith/LNTGKHUI5V3BIOUUGRCI7L3VTD.json","graph_json":"https://pith.science/api/pith-number/LNTGKHUI5V3BIOUUGRCI7L3VTD/graph.json","events_json":"https://pith.science/api/pith-number/LNTGKHUI5V3BIOUUGRCI7L3VTD/events.json","paper":"https://pith.science/paper/LNTGKHUI"},"agent_actions":{"view_html":"https://pith.science/pith/LNTGKHUI5V3BIOUUGRCI7L3VTD","download_json":"https://pith.science/pith/LNTGKHUI5V3BIOUUGRCI7L3VTD.json","view_paper":"https://pith.science/paper/LNTGKHUI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.12392&json=true","fetch_graph":"https://pith.science/api/pith-number/LNTGKHUI5V3BIOUUGRCI7L3VTD/graph.json","fetch_events":"https://pith.science/api/pith-number/LNTGKHUI5V3BIOUUGRCI7L3VTD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LNTGKHUI5V3BIOUUGRCI7L3VTD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LNTGKHUI5V3BIOUUGRCI7L3VTD/action/storage_attestation","attest_author":"https://pith.science/pith/LNTGKHUI5V3BIOUUGRCI7L3VTD/action/author_attestation","sign_citation":"https://pith.science/pith/LNTGKHUI5V3BIOUUGRCI7L3VTD/action/citation_signature","submit_replication":"https://pith.science/pith/LNTGKHUI5V3BIOUUGRCI7L3VTD/action/replication_record"}},"created_at":"2026-07-05T11:09:13.818884+00:00","updated_at":"2026-07-05T11:09:13.818884+00:00"}