{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LVVRTKGV7NRZSCGRIPN4L3PKLT","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":"666d1bf2c278dafa3404d433bf45b001638f3b15614483e0a38e869d909935e7","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-16T15:36:13Z","title_canon_sha256":"12dbf7ffd20e46f029e5e5509b72220ebfa953aa043d30a379b8715f26cfbd1b"},"schema_version":"1.0","source":{"id":"2410.12672","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.12672","created_at":"2026-07-05T10:00:42Z"},{"alias_kind":"arxiv_version","alias_value":"2410.12672v5","created_at":"2026-07-05T10:00:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.12672","created_at":"2026-07-05T10:00:42Z"},{"alias_kind":"pith_short_12","alias_value":"LVVRTKGV7NRZ","created_at":"2026-07-05T10:00:42Z"},{"alias_kind":"pith_short_16","alias_value":"LVVRTKGV7NRZSCGR","created_at":"2026-07-05T10:00:42Z"},{"alias_kind":"pith_short_8","alias_value":"LVVRTKGV","created_at":"2026-07-05T10:00:42Z"}],"graph_snapshots":[{"event_id":"sha256:8d5693fe73172ea2421f9ba1349962a3d1638e4a696c9cece6de44c680203e2c","target":"graph","created_at":"2026-07-05T10:00:42Z","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/2410.12672/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series forecasts are often influenced by exogenous contextual features in addition to their corresponding history. For example, in financial settings, it is hard to accurately predict a stock price without considering public sentiments and policy decisions in the form of news articles, tweets, etc. Though this is common knowledge, the current state-of-the-art (SOTA) forecasting models fail to incorporate such contextual information, owing to its heterogeneity and multimodal nature. To address this, we introduce ContextFormer, a novel plug-and-play method to surgically integrate multimodal","authors_text":"Pulkit Paliwal, Sai Shankar Narasimhan, Sameep Chattopadhyay, Sandeep P. Chinchali, Shubhankar Agarwal","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-16T15:36:13Z","title":"Context Matters: Leveraging Contextual Features for Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.12672","kind":"arxiv","version":5},"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:ce92899432d6e3e57b99e82673062b0940b1613e3ba5a6815ab19c1873496e01","target":"record","created_at":"2026-07-05T10:00:42Z","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":"666d1bf2c278dafa3404d433bf45b001638f3b15614483e0a38e869d909935e7","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-16T15:36:13Z","title_canon_sha256":"12dbf7ffd20e46f029e5e5509b72220ebfa953aa043d30a379b8715f26cfbd1b"},"schema_version":"1.0","source":{"id":"2410.12672","kind":"arxiv","version":5}},"canonical_sha256":"5d6b19a8d5fb639908d143dbc5edea5cc2ae007dc387e7a795c81b212d61f6f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5d6b19a8d5fb639908d143dbc5edea5cc2ae007dc387e7a795c81b212d61f6f7","first_computed_at":"2026-07-05T10:00:42.225293Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:00:42.225293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pzgd5+kXgG1lQdyQGLv5x07T9FbTPKwu40JgPKiZMYtxQPdlDWu7hWvGrVRQJUMUCo+3GYdrF+jdYjW9bI7HDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:00:42.225742Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.12672","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ce92899432d6e3e57b99e82673062b0940b1613e3ba5a6815ab19c1873496e01","sha256:8d5693fe73172ea2421f9ba1349962a3d1638e4a696c9cece6de44c680203e2c"],"state_sha256":"0b920fa194ae03684c34c4984d0f5fc793851d90453283eb43b23f8317b38bbf"}