{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:Q4F3D4ANFJHQFTCJ3X7CQNVIG2","short_pith_number":"pith:Q4F3D4AN","schema_version":"1.0","canonical_sha256":"870bb1f00d2a4f02cc49ddfe2836a8368ed62bcb76343aa773886bafef7e2223","source":{"kind":"arxiv","id":"2606.09861","version":1},"attestation_state":"computed","paper":{"title":"Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiale Zheng, Jianfeng Zhang, Junchi Yan, Lujia Pan, Ruiying Qi, Yunhao Zhang","submitted_at":"2026-05-31T16:04:11Z","abstract_excerpt":"While Next-Token Prediction (NTP) has unified LLM pretraining, its adaptation to unbounded, continuous time series (TS) remains open. To bridge the gap, we introduce UniTok, a universal tokenizer that transforms TS into discrete tokens, and UniTok-FM, a foundation model pretrained via NTP on these tokens. UniTok-FM is a general-purpose foundation model that supports zero-shot and prompt-boosted forecasting, as well as few-shot generation and classification via training-free in-context inference--a capability not achieved by prior works. Technically, UniTok is a vector-quantized autoencoder inc"},"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":"2606.09861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-31T16:04:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"06e47daf7341365d82264418dc7048f034a0a6caada8b21198343dc7fcd672f4","abstract_canon_sha256":"b40fa442197904752c28e3e9245bbe7108eaf25844a64bfecf365261c7859afb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T00:08:30.040248Z","signature_b64":"/jqvzfyUB6m2dvy62b/1BiW/vJbhXFKBvCf9uE8pgwQ9AxwegUV1ys85mcasfaq0SQYoJn3YgGKKtJs/nUMtDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"870bb1f00d2a4f02cc49ddfe2836a8368ed62bcb76343aa773886bafef7e2223","last_reissued_at":"2026-06-10T00:08:30.039203Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T00:08:30.039203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiale Zheng, Jianfeng Zhang, Junchi Yan, Lujia Pan, Ruiying Qi, Yunhao Zhang","submitted_at":"2026-05-31T16:04:11Z","abstract_excerpt":"While Next-Token Prediction (NTP) has unified LLM pretraining, its adaptation to unbounded, continuous time series (TS) remains open. To bridge the gap, we introduce UniTok, a universal tokenizer that transforms TS into discrete tokens, and UniTok-FM, a foundation model pretrained via NTP on these tokens. UniTok-FM is a general-purpose foundation model that supports zero-shot and prompt-boosted forecasting, as well as few-shot generation and classification via training-free in-context inference--a capability not achieved by prior works. Technically, UniTok is a vector-quantized autoencoder inc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.09861","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/2606.09861/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":"2606.09861","created_at":"2026-06-10T00:08:30.039406+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.09861v1","created_at":"2026-06-10T00:08:30.039406+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.09861","created_at":"2026-06-10T00:08:30.039406+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q4F3D4ANFJHQ","created_at":"2026-06-10T00:08:30.039406+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q4F3D4ANFJHQFTCJ","created_at":"2026-06-10T00:08:30.039406+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q4F3D4AN","created_at":"2026-06-10T00:08:30.039406+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/Q4F3D4ANFJHQFTCJ3X7CQNVIG2","json":"https://pith.science/pith/Q4F3D4ANFJHQFTCJ3X7CQNVIG2.json","graph_json":"https://pith.science/api/pith-number/Q4F3D4ANFJHQFTCJ3X7CQNVIG2/graph.json","events_json":"https://pith.science/api/pith-number/Q4F3D4ANFJHQFTCJ3X7CQNVIG2/events.json","paper":"https://pith.science/paper/Q4F3D4AN"},"agent_actions":{"view_html":"https://pith.science/pith/Q4F3D4ANFJHQFTCJ3X7CQNVIG2","download_json":"https://pith.science/pith/Q4F3D4ANFJHQFTCJ3X7CQNVIG2.json","view_paper":"https://pith.science/paper/Q4F3D4AN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.09861&json=true","fetch_graph":"https://pith.science/api/pith-number/Q4F3D4ANFJHQFTCJ3X7CQNVIG2/graph.json","fetch_events":"https://pith.science/api/pith-number/Q4F3D4ANFJHQFTCJ3X7CQNVIG2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q4F3D4ANFJHQFTCJ3X7CQNVIG2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q4F3D4ANFJHQFTCJ3X7CQNVIG2/action/storage_attestation","attest_author":"https://pith.science/pith/Q4F3D4ANFJHQFTCJ3X7CQNVIG2/action/author_attestation","sign_citation":"https://pith.science/pith/Q4F3D4ANFJHQFTCJ3X7CQNVIG2/action/citation_signature","submit_replication":"https://pith.science/pith/Q4F3D4ANFJHQFTCJ3X7CQNVIG2/action/replication_record"}},"created_at":"2026-06-10T00:08:30.039406+00:00","updated_at":"2026-06-10T00:08:30.039406+00:00"}