{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4GIZXSVXJN675BTMYGK7REFTRK","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":"1dc948c004b3eefcc057010e661550899ecd56930fddb9941fb355a38755af29","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-25T16:37:46Z","title_canon_sha256":"515283df41466ab23de34608d6250830f1d63e2d2424f05db2731d6339032032"},"schema_version":"1.0","source":{"id":"2506.20598","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.20598","created_at":"2026-07-05T11:27:08Z"},{"alias_kind":"arxiv_version","alias_value":"2506.20598v1","created_at":"2026-07-05T11:27:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20598","created_at":"2026-07-05T11:27:08Z"},{"alias_kind":"pith_short_12","alias_value":"4GIZXSVXJN67","created_at":"2026-07-05T11:27:08Z"},{"alias_kind":"pith_short_16","alias_value":"4GIZXSVXJN675BTM","created_at":"2026-07-05T11:27:08Z"},{"alias_kind":"pith_short_8","alias_value":"4GIZXSVX","created_at":"2026-07-05T11:27:08Z"}],"graph_snapshots":[{"event_id":"sha256:7eff435469d45d2b10fa464c407325034b2b21c0ee726cd286cc7a8912ff14b2","target":"graph","created_at":"2026-07-05T11:27:08Z","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/2506.20598/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The global demand for sustainable protein sources has accelerated the need for intelligent tools that can rapidly process and synthesise domain-specific scientific knowledge. In this study, we present a proof-of-concept multi-agent Artificial Intelligence (AI) framework designed to support sustainable protein production research, with an initial focus on microbial protein sources. Our Retrieval-Augmented Generation (RAG)-oriented system consists of two GPT-based LLM agents: (1) a literature search agent that retrieves relevant scientific literature on microbial protein production for a specifi","authors_text":"Alexander D. Kalian, Christer Hogstrand, Jaewook Lee, Lennart Otte, Miao Guo, Stefan P. Johannesson","cross_cats":["cs.SY","eess.SY"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-25T16:37:46Z","title":"Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20598","kind":"arxiv","version":1},"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:cc3f3721b8c97f6f9099d78b255bc23bcac1d7b51c492bbf8f8e5b548af53274","target":"record","created_at":"2026-07-05T11:27:08Z","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":"1dc948c004b3eefcc057010e661550899ecd56930fddb9941fb355a38755af29","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-25T16:37:46Z","title_canon_sha256":"515283df41466ab23de34608d6250830f1d63e2d2424f05db2731d6339032032"},"schema_version":"1.0","source":{"id":"2506.20598","kind":"arxiv","version":1}},"canonical_sha256":"e1919bcab74b7dfe866cc195f890b38a9254c34983d4e9ef58af529195bba34b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1919bcab74b7dfe866cc195f890b38a9254c34983d4e9ef58af529195bba34b","first_computed_at":"2026-07-05T11:27:08.682912Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:27:08.682912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gScqs2piOrk5RUu/fedsTdLToG1GkU3lX3XlQEcfKaK/Ydg+f9CETKyHaCC37Kc5ouEJ7SyPUP+xysTu68kZCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:27:08.683393Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.20598","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cc3f3721b8c97f6f9099d78b255bc23bcac1d7b51c492bbf8f8e5b548af53274","sha256:7eff435469d45d2b10fa464c407325034b2b21c0ee726cd286cc7a8912ff14b2"],"state_sha256":"55820364b23858d75eae6a70c5ed0301feb938a7e14e5a13d3c99caff19dd977"}