{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XLRAJSRMIUQGTHSYMCG25TOFNN","short_pith_number":"pith:XLRAJSRM","canonical_record":{"source":{"id":"2508.09191","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-08T03:51:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4895c1cf8c509283a1eb25509b51b6159c8b1bbbf0db93bee6e5af8c4612d8bf","abstract_canon_sha256":"da186ece1f459d2813035956b91d21d12a54d04e58d986eb2c144e5717a06729"},"schema_version":"1.0"},"canonical_sha256":"bae204ca2c4520699e58608daecdc56b70b0241c05949237f7c03d8f52f28fa6","source":{"kind":"arxiv","id":"2508.09191","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.09191","created_at":"2026-06-19T16:11:12Z"},{"alias_kind":"arxiv_version","alias_value":"2508.09191v2","created_at":"2026-06-19T16:11:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09191","created_at":"2026-06-19T16:11:12Z"},{"alias_kind":"pith_short_12","alias_value":"XLRAJSRMIUQG","created_at":"2026-06-19T16:11:12Z"},{"alias_kind":"pith_short_16","alias_value":"XLRAJSRMIUQGTHSY","created_at":"2026-06-19T16:11:12Z"},{"alias_kind":"pith_short_8","alias_value":"XLRAJSRM","created_at":"2026-06-19T16:11:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XLRAJSRMIUQGTHSYMCG25TOFNN","target":"record","payload":{"canonical_record":{"source":{"id":"2508.09191","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-08T03:51:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4895c1cf8c509283a1eb25509b51b6159c8b1bbbf0db93bee6e5af8c4612d8bf","abstract_canon_sha256":"da186ece1f459d2813035956b91d21d12a54d04e58d986eb2c144e5717a06729"},"schema_version":"1.0"},"canonical_sha256":"bae204ca2c4520699e58608daecdc56b70b0241c05949237f7c03d8f52f28fa6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:11:12.220388Z","signature_b64":"q7EYgy4z7roqCDaD54zry8XdlC1YhWjtCgFE4oolOskaUXvIpwwkpY7cfMBR8+nRQIRMVwvASKW2dwtwYVaYAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bae204ca2c4520699e58608daecdc56b70b0241c05949237f7c03d8f52f28fa6","last_reissued_at":"2026-06-19T16:11:12.219949Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:11:12.219949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2508.09191","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-19T16:11:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3BRS4Rm1DF7nBh+wBP9AA0CusAUGOq+e3xkxJx5ntu8lIz0w/2iddHJISFV5YL/srVkCUxFhaRwW5etUfSHjCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T19:52:55.795257Z"},"content_sha256":"0c3ab5b46f5549c6c96b2ea484f62688d41cffe8536d47a4604a03b788c43283","schema_version":"1.0","event_id":"sha256:0c3ab5b46f5549c6c96b2ea484f62688d41cffe8536d47a4604a03b788c43283"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XLRAJSRMIUQGTHSYMCG25TOFNN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bokai Pan, Changqing Zhang, Daoyu Wang, Mingyue Cheng, Shijin Wang, Shilong Zhang, Tingyue Pan, Xiaoyu Tao","submitted_at":"2025-08-08T03:51:08Z","abstract_excerpt":"Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent advances, forecasting accuracy remains limited due to the challenge of integrating historical numerical sequences with contextual features, which often comprise unstructured textual data. To address this challenge, we propose TokenCast, a large language model (LLM) driven framework that leverages language-based symbolic representations as a unified intermediary for context-aware time series forecasting. Specifically, To"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09191","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/2508.09191/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":"2026-06-19T16:11:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EHviWyMGGVFJBzAKEb2ouMHLdpNerQ5Ce2zd1F8ry8aE1P5MjCk3QINbySX62yx6T2qCyjkKKwnPWIYXdrliDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T19:52:55.795661Z"},"content_sha256":"c305747256cc4a3787682c08138fb7934dc2756aa9dd55c9fe203bf6c96de907","schema_version":"1.0","event_id":"sha256:c305747256cc4a3787682c08138fb7934dc2756aa9dd55c9fe203bf6c96de907"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XLRAJSRMIUQGTHSYMCG25TOFNN/bundle.json","state_url":"https://pith.science/pith/XLRAJSRMIUQGTHSYMCG25TOFNN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XLRAJSRMIUQGTHSYMCG25TOFNN/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":"2026-06-28T19:52:55Z","links":{"resolver":"https://pith.science/pith/XLRAJSRMIUQGTHSYMCG25TOFNN","bundle":"https://pith.science/pith/XLRAJSRMIUQGTHSYMCG25TOFNN/bundle.json","state":"https://pith.science/pith/XLRAJSRMIUQGTHSYMCG25TOFNN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XLRAJSRMIUQGTHSYMCG25TOFNN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XLRAJSRMIUQGTHSYMCG25TOFNN","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":"da186ece1f459d2813035956b91d21d12a54d04e58d986eb2c144e5717a06729","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-08T03:51:08Z","title_canon_sha256":"4895c1cf8c509283a1eb25509b51b6159c8b1bbbf0db93bee6e5af8c4612d8bf"},"schema_version":"1.0","source":{"id":"2508.09191","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.09191","created_at":"2026-06-19T16:11:12Z"},{"alias_kind":"arxiv_version","alias_value":"2508.09191v2","created_at":"2026-06-19T16:11:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09191","created_at":"2026-06-19T16:11:12Z"},{"alias_kind":"pith_short_12","alias_value":"XLRAJSRMIUQG","created_at":"2026-06-19T16:11:12Z"},{"alias_kind":"pith_short_16","alias_value":"XLRAJSRMIUQGTHSY","created_at":"2026-06-19T16:11:12Z"},{"alias_kind":"pith_short_8","alias_value":"XLRAJSRM","created_at":"2026-06-19T16:11:12Z"}],"graph_snapshots":[{"event_id":"sha256:c305747256cc4a3787682c08138fb7934dc2756aa9dd55c9fe203bf6c96de907","target":"graph","created_at":"2026-06-19T16:11:12Z","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/2508.09191/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent advances, forecasting accuracy remains limited due to the challenge of integrating historical numerical sequences with contextual features, which often comprise unstructured textual data. To address this challenge, we propose TokenCast, a large language model (LLM) driven framework that leverages language-based symbolic representations as a unified intermediary for context-aware time series forecasting. Specifically, To","authors_text":"Bokai Pan, Changqing Zhang, Daoyu Wang, Mingyue Cheng, Shijin Wang, Shilong Zhang, Tingyue Pan, Xiaoyu Tao","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-08T03:51:08Z","title":"From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09191","kind":"arxiv","version":2},"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":"sha256:0c3ab5b46f5549c6c96b2ea484f62688d41cffe8536d47a4604a03b788c43283","target":"record","created_at":"2026-06-19T16:11:12Z","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":"da186ece1f459d2813035956b91d21d12a54d04e58d986eb2c144e5717a06729","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-08T03:51:08Z","title_canon_sha256":"4895c1cf8c509283a1eb25509b51b6159c8b1bbbf0db93bee6e5af8c4612d8bf"},"schema_version":"1.0","source":{"id":"2508.09191","kind":"arxiv","version":2}},"canonical_sha256":"bae204ca2c4520699e58608daecdc56b70b0241c05949237f7c03d8f52f28fa6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bae204ca2c4520699e58608daecdc56b70b0241c05949237f7c03d8f52f28fa6","first_computed_at":"2026-06-19T16:11:12.219949Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:11:12.219949Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q7EYgy4z7roqCDaD54zry8XdlC1YhWjtCgFE4oolOskaUXvIpwwkpY7cfMBR8+nRQIRMVwvASKW2dwtwYVaYAw==","signature_status":"signed_v1","signed_at":"2026-06-19T16:11:12.220388Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.09191","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c3ab5b46f5549c6c96b2ea484f62688d41cffe8536d47a4604a03b788c43283","sha256:c305747256cc4a3787682c08138fb7934dc2756aa9dd55c9fe203bf6c96de907"],"state_sha256":"956e725ee8f8ef774a98c4043ad94828b4e82ec818af3c838c1659de59f8f558"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EyxDoXMbnqcNISAqV1c0Bai9PtUk5H09Yo6h9cBldNnnQMDhHwjjK3VRMYPNHwBHTAQd8/aKAqg+1ko7jYftCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T19:52:55.797650Z","bundle_sha256":"42b0a227abbaef4b553ced7b60d56f4fc278f25b70a34c90bab9c4063192501b"}}