{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XVL5SARNBBBJYSWACHBYJ7XAI4","short_pith_number":"pith:XVL5SARN","schema_version":"1.0","canonical_sha256":"bd57d9022d08429c4ac011c384fee0472c0bfa981f17215622b48d844c65f393","source":{"kind":"arxiv","id":"2503.08271","version":2},"attestation_state":"computed","paper":{"title":"LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chao Hao, Man Xu, Wei He, Wenzhe Niu, Yanru Sun, Zongxia Xie","submitted_at":"2025-03-11T10:40:39Z","abstract_excerpt":"Recent research has shown an increasing interest in utilizing pre-trained large language models (LLMs) for a variety of time series applications. However, there are three main challenges when using LLMs as foundational models for time series forecasting: (1) Cross-domain generalization. (2) Cross-modality alignment. (3) Error accumulation in autoregressive frameworks. To address these challenges, we proposed LangTime, a language-guided unified model for time series forecasting that incorporates cross-domain pre-training with reinforcement learning-based fine-tuning. Specifically, LangTime cons"},"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":"2503.08271","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-11T10:40:39Z","cross_cats_sorted":[],"title_canon_sha256":"05ae24873e3cdcc70d14039825d39eb224cd7e8359cb3496ff0465088dbb6b2a","abstract_canon_sha256":"afb14d9cf0ffea507fb9e9890954ac7380f16d25e38ee0b90522fd3fa7ed2c05"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:56.544423Z","signature_b64":"hPgmhS413VZggGHPxPUI2E3DtoeO2dXCrk+pcOGf46ridG2Xx0MEqB8hdiWPAZiw30XiN2i4EAhz94KV9wcmBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd57d9022d08429c4ac011c384fee0472c0bfa981f17215622b48d844c65f393","last_reissued_at":"2026-07-05T11:29:56.543927Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:56.543927Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chao Hao, Man Xu, Wei He, Wenzhe Niu, Yanru Sun, Zongxia Xie","submitted_at":"2025-03-11T10:40:39Z","abstract_excerpt":"Recent research has shown an increasing interest in utilizing pre-trained large language models (LLMs) for a variety of time series applications. However, there are three main challenges when using LLMs as foundational models for time series forecasting: (1) Cross-domain generalization. (2) Cross-modality alignment. (3) Error accumulation in autoregressive frameworks. To address these challenges, we proposed LangTime, a language-guided unified model for time series forecasting that incorporates cross-domain pre-training with reinforcement learning-based fine-tuning. Specifically, LangTime cons"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.08271","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/2503.08271/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":"2503.08271","created_at":"2026-07-05T11:29:56.543987+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.08271v2","created_at":"2026-07-05T11:29:56.543987+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.08271","created_at":"2026-07-05T11:29:56.543987+00:00"},{"alias_kind":"pith_short_12","alias_value":"XVL5SARNBBBJ","created_at":"2026-07-05T11:29:56.543987+00:00"},{"alias_kind":"pith_short_16","alias_value":"XVL5SARNBBBJYSWA","created_at":"2026-07-05T11:29:56.543987+00:00"},{"alias_kind":"pith_short_8","alias_value":"XVL5SARN","created_at":"2026-07-05T11:29:56.543987+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/XVL5SARNBBBJYSWACHBYJ7XAI4","json":"https://pith.science/pith/XVL5SARNBBBJYSWACHBYJ7XAI4.json","graph_json":"https://pith.science/api/pith-number/XVL5SARNBBBJYSWACHBYJ7XAI4/graph.json","events_json":"https://pith.science/api/pith-number/XVL5SARNBBBJYSWACHBYJ7XAI4/events.json","paper":"https://pith.science/paper/XVL5SARN"},"agent_actions":{"view_html":"https://pith.science/pith/XVL5SARNBBBJYSWACHBYJ7XAI4","download_json":"https://pith.science/pith/XVL5SARNBBBJYSWACHBYJ7XAI4.json","view_paper":"https://pith.science/paper/XVL5SARN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.08271&json=true","fetch_graph":"https://pith.science/api/pith-number/XVL5SARNBBBJYSWACHBYJ7XAI4/graph.json","fetch_events":"https://pith.science/api/pith-number/XVL5SARNBBBJYSWACHBYJ7XAI4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XVL5SARNBBBJYSWACHBYJ7XAI4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XVL5SARNBBBJYSWACHBYJ7XAI4/action/storage_attestation","attest_author":"https://pith.science/pith/XVL5SARNBBBJYSWACHBYJ7XAI4/action/author_attestation","sign_citation":"https://pith.science/pith/XVL5SARNBBBJYSWACHBYJ7XAI4/action/citation_signature","submit_replication":"https://pith.science/pith/XVL5SARNBBBJYSWACHBYJ7XAI4/action/replication_record"}},"created_at":"2026-07-05T11:29:56.543987+00:00","updated_at":"2026-07-05T11:29:56.543987+00:00"}