{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SWLUDJPQ43YD6GMU4R3ZRFNYKV","short_pith_number":"pith:SWLUDJPQ","schema_version":"1.0","canonical_sha256":"959741a5f0e6f03f1994e4779895b8554f576eea2f87c6d7d938b952de7935ba","source":{"kind":"arxiv","id":"2607.00454","version":1},"attestation_state":"computed","paper":{"title":"Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.AI","authors_text":"Geetha Charan, Kodur Sai Vinay Sathvik, Manojkumar Patil, Rohit P Suresh, Vedant Balasubramaniam, V Priyanka, Y. Narahari","submitted_at":"2026-07-01T05:18:20Z","abstract_excerpt":"Agricultural advisory systems face a fundamental tension: static agronomic guidelines offer consistent, evidence-based recommendations, yet remain blind to in-season variability and dynamic uncertainties. Recent advisory systems powered by LLMs are liable for a different risk of generating recommendations that are agronomically credible but physiologically unconvincing. Agri-SAGE is a closed-loop framework designed to resolve the above two limitations by integrating retrieval-grounded multi-agent LLM reasoning with APSIM-based biophysical simulation, to generate and validate agronomic advisori"},"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":"2607.00454","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-01T05:18:20Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"ccfb247a5f13959f9cc4f718b84ab8a3237d668a544c1b3f86a53968dff005b4","abstract_canon_sha256":"2a6d071e6d0be61b9a0180d60392e556628f661ea20ca39dabd3fb6b75475df9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:17:43.892304Z","signature_b64":"p1EcsFDvSdhSUENbArsMQlVN4twubFqBD2qq0M/YY+A7pY3lzs2voH77HwYuOfE/NhzS2CFQ18ft8iirbsARCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"959741a5f0e6f03f1994e4779895b8554f576eea2f87c6d7d938b952de7935ba","last_reissued_at":"2026-07-02T01:17:43.891864Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:17:43.891864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.AI","authors_text":"Geetha Charan, Kodur Sai Vinay Sathvik, Manojkumar Patil, Rohit P Suresh, Vedant Balasubramaniam, V Priyanka, Y. Narahari","submitted_at":"2026-07-01T05:18:20Z","abstract_excerpt":"Agricultural advisory systems face a fundamental tension: static agronomic guidelines offer consistent, evidence-based recommendations, yet remain blind to in-season variability and dynamic uncertainties. Recent advisory systems powered by LLMs are liable for a different risk of generating recommendations that are agronomically credible but physiologically unconvincing. Agri-SAGE is a closed-loop framework designed to resolve the above two limitations by integrating retrieval-grounded multi-agent LLM reasoning with APSIM-based biophysical simulation, to generate and validate agronomic advisori"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00454","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/2607.00454/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":"2607.00454","created_at":"2026-07-02T01:17:43.891918+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.00454v1","created_at":"2026-07-02T01:17:43.891918+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00454","created_at":"2026-07-02T01:17:43.891918+00:00"},{"alias_kind":"pith_short_12","alias_value":"SWLUDJPQ43YD","created_at":"2026-07-02T01:17:43.891918+00:00"},{"alias_kind":"pith_short_16","alias_value":"SWLUDJPQ43YD6GMU","created_at":"2026-07-02T01:17:43.891918+00:00"},{"alias_kind":"pith_short_8","alias_value":"SWLUDJPQ","created_at":"2026-07-02T01:17:43.891918+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/SWLUDJPQ43YD6GMU4R3ZRFNYKV","json":"https://pith.science/pith/SWLUDJPQ43YD6GMU4R3ZRFNYKV.json","graph_json":"https://pith.science/api/pith-number/SWLUDJPQ43YD6GMU4R3ZRFNYKV/graph.json","events_json":"https://pith.science/api/pith-number/SWLUDJPQ43YD6GMU4R3ZRFNYKV/events.json","paper":"https://pith.science/paper/SWLUDJPQ"},"agent_actions":{"view_html":"https://pith.science/pith/SWLUDJPQ43YD6GMU4R3ZRFNYKV","download_json":"https://pith.science/pith/SWLUDJPQ43YD6GMU4R3ZRFNYKV.json","view_paper":"https://pith.science/paper/SWLUDJPQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.00454&json=true","fetch_graph":"https://pith.science/api/pith-number/SWLUDJPQ43YD6GMU4R3ZRFNYKV/graph.json","fetch_events":"https://pith.science/api/pith-number/SWLUDJPQ43YD6GMU4R3ZRFNYKV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SWLUDJPQ43YD6GMU4R3ZRFNYKV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SWLUDJPQ43YD6GMU4R3ZRFNYKV/action/storage_attestation","attest_author":"https://pith.science/pith/SWLUDJPQ43YD6GMU4R3ZRFNYKV/action/author_attestation","sign_citation":"https://pith.science/pith/SWLUDJPQ43YD6GMU4R3ZRFNYKV/action/citation_signature","submit_replication":"https://pith.science/pith/SWLUDJPQ43YD6GMU4R3ZRFNYKV/action/replication_record"}},"created_at":"2026-07-02T01:17:43.891918+00:00","updated_at":"2026-07-02T01:17:43.891918+00:00"}