{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OBVIPJ2XS5ZJMJSQITHYKSQFST","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":"79a2b4d80412fe69507815a7056e5372f193d7428ad510dddf646ad2b096a2ab","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-04-14T05:31:40Z","title_canon_sha256":"555417a64cd8ea5864ce2f5016b9697386b9c37b7316548e0d0f4dd2e4447310"},"schema_version":"1.0","source":{"id":"2604.12306","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.12306","created_at":"2026-06-10T01:08:35Z"},{"alias_kind":"arxiv_version","alias_value":"2604.12306v3","created_at":"2026-06-10T01:08:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.12306","created_at":"2026-06-10T01:08:35Z"},{"alias_kind":"pith_short_12","alias_value":"OBVIPJ2XS5ZJ","created_at":"2026-06-10T01:08:35Z"},{"alias_kind":"pith_short_16","alias_value":"OBVIPJ2XS5ZJMJSQ","created_at":"2026-06-10T01:08:35Z"},{"alias_kind":"pith_short_8","alias_value":"OBVIPJ2X","created_at":"2026-06-10T01:08:35Z"}],"graph_snapshots":[{"event_id":"sha256:f898500346891f100dc035fea0853f07fa0db3f67672ed20d5a07e8c23565d3c","target":"graph","created_at":"2026-06-10T01:08:35Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Domain fine-tuning and tool integration substantially improve reliability over general-purpose baselines on climate tasks in the GCC states."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The curated 200k QA pairs and remote-sensing inputs are sufficiently representative and high-quality to ground the agent for real decision support."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A GCC-grounded multimodal dataset and tool-augmented agent improve LLM performance on regional climate analysis tasks over general baselines."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Domain fine-tuning on a GCC-grounded climate dataset plus tool integration raises LLM reliability for regional decision support."}],"snapshot_sha256":"9553a5d7441be93bf7f760ad568738ffe4b3e7d2656d352b67764245ffb94318"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.12306/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geospatial and forecasting tools. We present the GCA framework, which unifies (i) GCA-DS, a curated multimodal dataset grounded in the GCC states, and (ii) Gulf Climate Agent (GCA), a tool-augmented agent for climate analysis. GCA-DS comprises 200k question--answer pairs spanning governmental policies and","authors_text":"Fahad Shahbaz Khan, Khawar Shehzad, Muhammad Haris Khan, Muhammad Umer Sheikh, Salman Khan","cross_cats":["cs.AI"],"headline":"Domain fine-tuning on a GCC-grounded climate dataset plus tool integration raises LLM reliability for regional decision support.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-04-14T05:31:40Z","title":"GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.12306","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-10T16:06:56.894635Z","id":"8e9fa35c-132a-4098-a532-02f7bf9a8779","model_set":{"reader":"grok-4.3"},"one_line_summary":"A GCC-grounded multimodal dataset and tool-augmented agent improve LLM performance on regional climate analysis tasks over general baselines.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Domain fine-tuning on a GCC-grounded climate dataset plus tool integration raises LLM reliability for regional decision support.","strongest_claim":"Domain fine-tuning and tool integration substantially improve reliability over general-purpose baselines on climate tasks in the GCC states.","weakest_assumption":"The curated 200k QA pairs and remote-sensing inputs are sufficiently representative and high-quality to ground the agent for real decision support."}},"verdict_id":"8e9fa35c-132a-4098-a532-02f7bf9a8779"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5f7731a576b45843b42546e4f356ebbe1f69beab741d45052761dcac3663ca2b","target":"record","created_at":"2026-06-10T01:08:35Z","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":"79a2b4d80412fe69507815a7056e5372f193d7428ad510dddf646ad2b096a2ab","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-04-14T05:31:40Z","title_canon_sha256":"555417a64cd8ea5864ce2f5016b9697386b9c37b7316548e0d0f4dd2e4447310"},"schema_version":"1.0","source":{"id":"2604.12306","kind":"arxiv","version":3}},"canonical_sha256":"706a87a757977296265044cf854a0594cfec20ac969d6dc978f19cce92a77502","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"706a87a757977296265044cf854a0594cfec20ac969d6dc978f19cce92a77502","first_computed_at":"2026-06-10T01:08:35.259228Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-10T01:08:35.259228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/S4IGHYI4XeLkjKsMvUVqX+UgrHSjPmY/xhqL9zT3XorCOb6ylh36UtqO7k0JJCI7Nly2cLTFssZ0CRDzBKHAQ==","signature_status":"signed_v1","signed_at":"2026-06-10T01:08:35.260354Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.12306","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5f7731a576b45843b42546e4f356ebbe1f69beab741d45052761dcac3663ca2b","sha256:f898500346891f100dc035fea0853f07fa0db3f67672ed20d5a07e8c23565d3c"],"state_sha256":"ad6bdfb80a7511cc9fb788d78146f05dc79283f06fa4f6734263f8b0d0dcdc84"}