{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4ZNVFY6HY3VOLAJVGEZAREVWNY","short_pith_number":"pith:4ZNVFY6H","canonical_record":{"source":{"id":"2505.20633","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-27T02:18:59Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"457e67f21d1efeafdd9a8d6b1690c235c415a3110cabab701ae9eeead74bf424","abstract_canon_sha256":"08ac86c7733cba862e3b24dcb74f4a922c2c5402fcd8b71c75760160c5c7db9a"},"schema_version":"1.0"},"canonical_sha256":"e65b52e3c7c6eae5813531320892b66e12fb9b126e56f84c6bf27cad61fc74b6","source":{"kind":"arxiv","id":"2505.20633","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.20633","created_at":"2026-07-05T11:10:15Z"},{"alias_kind":"arxiv_version","alias_value":"2505.20633v1","created_at":"2026-07-05T11:10:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20633","created_at":"2026-07-05T11:10:15Z"},{"alias_kind":"pith_short_12","alias_value":"4ZNVFY6HY3VO","created_at":"2026-07-05T11:10:15Z"},{"alias_kind":"pith_short_16","alias_value":"4ZNVFY6HY3VOLAJV","created_at":"2026-07-05T11:10:15Z"},{"alias_kind":"pith_short_8","alias_value":"4ZNVFY6H","created_at":"2026-07-05T11:10:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4ZNVFY6HY3VOLAJVGEZAREVWNY","target":"record","payload":{"canonical_record":{"source":{"id":"2505.20633","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-27T02:18:59Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"457e67f21d1efeafdd9a8d6b1690c235c415a3110cabab701ae9eeead74bf424","abstract_canon_sha256":"08ac86c7733cba862e3b24dcb74f4a922c2c5402fcd8b71c75760160c5c7db9a"},"schema_version":"1.0"},"canonical_sha256":"e65b52e3c7c6eae5813531320892b66e12fb9b126e56f84c6bf27cad61fc74b6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:15.740167Z","signature_b64":"rVLxVffBidArHn538e+55MZlrSQ3/t8c3Vr2FXC3OzbBOJfweuOwKJIV88MV8ycQz60/WBSq1y0YOAl4iQ9ABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e65b52e3c7c6eae5813531320892b66e12fb9b126e56f84c6bf27cad61fc74b6","last_reissued_at":"2026-07-05T11:10:15.739203Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:15.739203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.20633","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:10:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nvlzESehF9G0YpNpi4zFdK4YF/cdiSdZKmrMPoUfQbxlq7u4brEaIXivk/S5tEY349T4iECrvdJMtEmkY0mkDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T14:49:22.451001Z"},"content_sha256":"ab7253b9378ae0c48d3b9a24716383f5f4c34b6b88d65d858bd7ebf1d87aa2a4","schema_version":"1.0","event_id":"sha256:ab7253b9378ae0c48d3b9a24716383f5f4c34b6b88d65d858bd7ebf1d87aa2a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4ZNVFY6HY3VOLAJVGEZAREVWNY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Test-Time Learning for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Bin Xiao, Chao Shuai, Guohao Chen, Jinwu Hu, Mingkui Tan, Wei Luo, Xutao Wen, Yuanqing Li, Zhitian Zhang","submitted_at":"2025-05-27T02:18:59Z","abstract_excerpt":"While Large Language Models (LLMs) have exhibited remarkable emergent capabilities through extensive pre-training, they still face critical limitations in generalizing to specialized domains and handling diverse linguistic variations, known as distribution shifts. In this paper, we propose a Test-Time Learning (TTL) paradigm for LLMs, namely TLM, which dynamically adapts LLMs to target domains using only unlabeled test data during testing. Specifically, we first provide empirical evidence and theoretical insights to reveal that more accurate predictions from LLMs can be achieved by minimizing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20633","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/2505.20633/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:10:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vY/GzEAAsg4c/7EHuNRq+dpJQ22pk65l34AlQC1hpZPBYjaHpYcbHSi9zmdVEogn0i+fzlWZxJ2XrPThY6tTAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T14:49:22.451510Z"},"content_sha256":"858ab1266928d3965cc0113ed45cbfa41163f39df1718da60a72cc808f7c3745","schema_version":"1.0","event_id":"sha256:858ab1266928d3965cc0113ed45cbfa41163f39df1718da60a72cc808f7c3745"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4ZNVFY6HY3VOLAJVGEZAREVWNY/bundle.json","state_url":"https://pith.science/pith/4ZNVFY6HY3VOLAJVGEZAREVWNY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4ZNVFY6HY3VOLAJVGEZAREVWNY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-23T14:49:22Z","links":{"resolver":"https://pith.science/pith/4ZNVFY6HY3VOLAJVGEZAREVWNY","bundle":"https://pith.science/pith/4ZNVFY6HY3VOLAJVGEZAREVWNY/bundle.json","state":"https://pith.science/pith/4ZNVFY6HY3VOLAJVGEZAREVWNY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4ZNVFY6HY3VOLAJVGEZAREVWNY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4ZNVFY6HY3VOLAJVGEZAREVWNY","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":"08ac86c7733cba862e3b24dcb74f4a922c2c5402fcd8b71c75760160c5c7db9a","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-27T02:18:59Z","title_canon_sha256":"457e67f21d1efeafdd9a8d6b1690c235c415a3110cabab701ae9eeead74bf424"},"schema_version":"1.0","source":{"id":"2505.20633","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.20633","created_at":"2026-07-05T11:10:15Z"},{"alias_kind":"arxiv_version","alias_value":"2505.20633v1","created_at":"2026-07-05T11:10:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20633","created_at":"2026-07-05T11:10:15Z"},{"alias_kind":"pith_short_12","alias_value":"4ZNVFY6HY3VO","created_at":"2026-07-05T11:10:15Z"},{"alias_kind":"pith_short_16","alias_value":"4ZNVFY6HY3VOLAJV","created_at":"2026-07-05T11:10:15Z"},{"alias_kind":"pith_short_8","alias_value":"4ZNVFY6H","created_at":"2026-07-05T11:10:15Z"}],"graph_snapshots":[{"event_id":"sha256:858ab1266928d3965cc0113ed45cbfa41163f39df1718da60a72cc808f7c3745","target":"graph","created_at":"2026-07-05T11:10:15Z","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/2505.20633/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While Large Language Models (LLMs) have exhibited remarkable emergent capabilities through extensive pre-training, they still face critical limitations in generalizing to specialized domains and handling diverse linguistic variations, known as distribution shifts. In this paper, we propose a Test-Time Learning (TTL) paradigm for LLMs, namely TLM, which dynamically adapts LLMs to target domains using only unlabeled test data during testing. Specifically, we first provide empirical evidence and theoretical insights to reveal that more accurate predictions from LLMs can be achieved by minimizing ","authors_text":"Bin Xiao, Chao Shuai, Guohao Chen, Jinwu Hu, Mingkui Tan, Wei Luo, Xutao Wen, Yuanqing Li, Zhitian Zhang","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-27T02:18:59Z","title":"Test-Time Learning for Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20633","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:ab7253b9378ae0c48d3b9a24716383f5f4c34b6b88d65d858bd7ebf1d87aa2a4","target":"record","created_at":"2026-07-05T11:10:15Z","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":"08ac86c7733cba862e3b24dcb74f4a922c2c5402fcd8b71c75760160c5c7db9a","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-27T02:18:59Z","title_canon_sha256":"457e67f21d1efeafdd9a8d6b1690c235c415a3110cabab701ae9eeead74bf424"},"schema_version":"1.0","source":{"id":"2505.20633","kind":"arxiv","version":1}},"canonical_sha256":"e65b52e3c7c6eae5813531320892b66e12fb9b126e56f84c6bf27cad61fc74b6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e65b52e3c7c6eae5813531320892b66e12fb9b126e56f84c6bf27cad61fc74b6","first_computed_at":"2026-07-05T11:10:15.739203Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:10:15.739203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rVLxVffBidArHn538e+55MZlrSQ3/t8c3Vr2FXC3OzbBOJfweuOwKJIV88MV8ycQz60/WBSq1y0YOAl4iQ9ABw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:10:15.740167Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.20633","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ab7253b9378ae0c48d3b9a24716383f5f4c34b6b88d65d858bd7ebf1d87aa2a4","sha256:858ab1266928d3965cc0113ed45cbfa41163f39df1718da60a72cc808f7c3745"],"state_sha256":"f126ac91209b1c38e70f1c331fc9d86047b1ecface001dbd9aa7ab77f167c6fa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"05UmfyjzahZaTnY6DaRH/OFFZJ1E7A7WVi2au3ocM0q79ATE+03wLR5ai92Hopn/WyL+H0TFBYT3go6+ozmhDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T14:49:22.455252Z","bundle_sha256":"3eea74a5bdb460cbd67fc307e80bf66281b3d959efdd61df1ebb151144048d51"}}