{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TDKEGA3H7NPGSOUKGLTPVK3DSV","short_pith_number":"pith:TDKEGA3H","schema_version":"1.0","canonical_sha256":"98d4430367fb5e693a8a32e6faab6395675ba1e9d0c3931bed32a1e550379139","source":{"kind":"arxiv","id":"2410.18959","version":4},"attestation_state":"computed","paper":{"title":"Context is Key: A Benchmark for Forecasting with Essential Textual Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandre Drouin, Alexandre Lacoste, Andrew Robert Williams, Arjun Ashok, \\'Etienne Marcotte, Irina Rish, James Requeima, Jithendaraa Subramanian, Nicolas Chapados, Roland Riachi, Valentina Zantedeschi","submitted_at":"2024-10-24T17:56:08Z","abstract_excerpt":"Forecasting is a critical task in decision-making across numerous domains. While historical numerical data provide a start, they fail to convey the complete context for reliable and accurate predictions. Human forecasters frequently rely on additional information, such as background knowledge and constraints, which can efficiently be communicated through natural language. However, in spite of recent progress with LLM-based forecasters, their ability to effectively integrate this textual information remains an open question. To address this, we introduce \"Context is Key\" (CiK), a time-series fo"},"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":"2410.18959","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-24T17:56:08Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"934d01761005c9234d43218a88d52dc17b7b256fbde0763de2be6d83d36b5e84","abstract_canon_sha256":"9cd22e708eb1d251a91418e3aa78087d589cf0e8c28035866c9bec2acae8f6d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:16:30.198189Z","signature_b64":"EemqT/HL4FGfdKX7n4JCWbfKjVEJAIUXaum/VF2bDtD/3pEPkkMx7gxB4TkF64QNSQrOqgC1PNBdsfnpXP/YAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98d4430367fb5e693a8a32e6faab6395675ba1e9d0c3931bed32a1e550379139","last_reissued_at":"2026-07-05T11:16:30.197631Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:16:30.197631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Context is Key: A Benchmark for Forecasting with Essential Textual Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandre Drouin, Alexandre Lacoste, Andrew Robert Williams, Arjun Ashok, \\'Etienne Marcotte, Irina Rish, James Requeima, Jithendaraa Subramanian, Nicolas Chapados, Roland Riachi, Valentina Zantedeschi","submitted_at":"2024-10-24T17:56:08Z","abstract_excerpt":"Forecasting is a critical task in decision-making across numerous domains. While historical numerical data provide a start, they fail to convey the complete context for reliable and accurate predictions. Human forecasters frequently rely on additional information, such as background knowledge and constraints, which can efficiently be communicated through natural language. However, in spite of recent progress with LLM-based forecasters, their ability to effectively integrate this textual information remains an open question. To address this, we introduce \"Context is Key\" (CiK), a time-series fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.18959","kind":"arxiv","version":4},"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/2410.18959/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":"2410.18959","created_at":"2026-07-05T11:16:30.197697+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.18959v4","created_at":"2026-07-05T11:16:30.197697+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.18959","created_at":"2026-07-05T11:16:30.197697+00:00"},{"alias_kind":"pith_short_12","alias_value":"TDKEGA3H7NPG","created_at":"2026-07-05T11:16:30.197697+00:00"},{"alias_kind":"pith_short_16","alias_value":"TDKEGA3H7NPGSOUK","created_at":"2026-07-05T11:16:30.197697+00:00"},{"alias_kind":"pith_short_8","alias_value":"TDKEGA3H","created_at":"2026-07-05T11:16:30.197697+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00956","citing_title":"Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2506.11512","citing_title":"From Time Series Analysis to Question Answering: A Survey in the LLM Era","ref_index":107,"is_internal_anchor":false},{"citing_arxiv_id":"2509.20823","citing_title":"CaTS-Bench: Can Language Models Describe Time Series?","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10291","citing_title":"TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05859","citing_title":"When Do We Need LLMs? A Diagnostic for Language-Driven Bandits","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17295","citing_title":"LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TDKEGA3H7NPGSOUKGLTPVK3DSV","json":"https://pith.science/pith/TDKEGA3H7NPGSOUKGLTPVK3DSV.json","graph_json":"https://pith.science/api/pith-number/TDKEGA3H7NPGSOUKGLTPVK3DSV/graph.json","events_json":"https://pith.science/api/pith-number/TDKEGA3H7NPGSOUKGLTPVK3DSV/events.json","paper":"https://pith.science/paper/TDKEGA3H"},"agent_actions":{"view_html":"https://pith.science/pith/TDKEGA3H7NPGSOUKGLTPVK3DSV","download_json":"https://pith.science/pith/TDKEGA3H7NPGSOUKGLTPVK3DSV.json","view_paper":"https://pith.science/paper/TDKEGA3H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.18959&json=true","fetch_graph":"https://pith.science/api/pith-number/TDKEGA3H7NPGSOUKGLTPVK3DSV/graph.json","fetch_events":"https://pith.science/api/pith-number/TDKEGA3H7NPGSOUKGLTPVK3DSV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TDKEGA3H7NPGSOUKGLTPVK3DSV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TDKEGA3H7NPGSOUKGLTPVK3DSV/action/storage_attestation","attest_author":"https://pith.science/pith/TDKEGA3H7NPGSOUKGLTPVK3DSV/action/author_attestation","sign_citation":"https://pith.science/pith/TDKEGA3H7NPGSOUKGLTPVK3DSV/action/citation_signature","submit_replication":"https://pith.science/pith/TDKEGA3H7NPGSOUKGLTPVK3DSV/action/replication_record"}},"created_at":"2026-07-05T11:16:30.197697+00:00","updated_at":"2026-07-05T11:16:30.197697+00:00"}