{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MRDE3URMMZL5SLNDRTYNKK42S4","merge_version":"pith-open-graph-merge-v1","event_count":4,"valid_event_count":4,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"84dfc896efb87d441c25a3c97c8734af1ce8bc95fe026de8944834282de79590","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2026-05-05T20:50:29Z","title_canon_sha256":"e2e699982d7b520709ca44469398fbed35177eb2a17a4f8c20291c24455d1065"},"schema_version":"1.0","source":{"id":"2605.13872","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13872","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13872v1","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13872","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"pith_short_12","alias_value":"MRDE3URMMZL5","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"MRDE3URMMZL5SLND","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"MRDE3URM","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:a54acf93f4954a3f89481346008a4ec31b53ddc1aefe209bbd50abf0fe3995d4","target":"graph","created_at":"2026-05-17T23:39:19Z","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":"Experimental validation on the SAI-UT+ testbench demonstrates that S-AI-Recursive achieves competitive reasoning performance on abstract and symbolic benchmarks with fewer than ten million parameters, confirming the central principle of temporal parsimony: iterative cognitive depth substitutes for architectural width."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that the newly introduced hormones Clarifine and Confusionin, together with the recursive state dynamics, produce genuine iterative refinement and Lyapunov-stable convergence on real reasoning tasks rather than on the specific SAI-UT+ benchmarks alone."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"S-AI-Recursive operationalizes reasoning as a closed-loop hormonal iteration with Clarifine and Confusionin to reach stable equilibrium, achieving competitive benchmark performance with under 10 million parameters via temporal depth instead of width."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Reasoning emerges from iterative hormonal feedback in small AI models rather than from wide feed-forward layers."}],"snapshot_sha256":"c1cbc085f70df8358b1e5248f8314acb99bf447cb075acbceb305da50a22d8ce"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a71f75c6e8e62da3182b9d2e7cf16d9c3c6eee39108748ccc8b3947027b6ddff"},"paper":{"abstract_excerpt":"This article introduces S-AI-Recursive, a bio-inspired Sparse Artificial Intelligence architecture in which reasoning is operationalized as a hormonal closed-loop iteration rather than a single feed-forward pass. Building upon the S-AI foundational framework [1], the hormonal-probabilistic unification doctrine [2], and the formal mathematical methodology established in S-AI-IoT [3], the present work formalizes the Recursive Reasoning Cycle (RRC) as a dynamical system governed by two novel hormones: Clarifine, a convergence signal, and Confusionin, an uncertainty detector, whose antagonistic re","authors_text":"Said Slaoui","cross_cats":["cs.AI"],"headline":"Reasoning emerges from iterative hormonal feedback in small AI models rather than from wide feed-forward layers.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2026-05-05T20:50:29Z","title":"S-AI-Recursive: A Bio-Inspired and Temporal Sparse AI Architecture for Iterative, Introspective, and Energy-Frugal Reasoning"},"references":{"count":49,"internal_anchors":3,"resolved_work":49,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"S-AI: A sparse artificial intelligence system orchestrated by a hormonal MetaAgent and context-aware specialized agents,","work_id":"c493ebe1-0e26-4623-b4ec-db6ddd7a3a3b","year":2025},{"cited_arxiv_id":"","doi":"10.2139/ssrn.5735582","is_internal_anchor":false,"ref_index":2,"title":"From Hormones to Probabilities: A Unified Doctrine of Cognitive Homeostasis in Sparse Artificial Intelligence,","work_id":"e30b5f31-0dae-4ace-aed8-d8c9d047b1bf","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"S-AI-IoT: Formal Agent Specification, Mathematical Modeling, and Stability Analysis of the Hormonal Orchestration Framework,","work_id":"f995a5dd-3cff-4d90-90ed-03a9cc33042e","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Attention is all you need,","work_id":"2148da8e-5ab3-4b4a-bc95-661e6a1bb331","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Language models are few- shot learners,","work_id":"3d7c2f74-335d-4f2a-b32e-4c650c0859e0","year":1901}],"snapshot_sha256":"fc505594e22b07b8a62258fe86685b0dfdb6ee24cb3f6aee21852dc04826bf63"},"source":{"id":"2605.13872","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T06:38:32.006566Z","id":"c469cd17-4ef0-486e-85ef-99861eec48b3","model_set":{"reader":"grok-4.3"},"one_line_summary":"S-AI-Recursive operationalizes reasoning as a closed-loop hormonal iteration with Clarifine and Confusionin to reach stable equilibrium, achieving competitive benchmark performance with under 10 million parameters via temporal depth instead of width.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Reasoning emerges from iterative hormonal feedback in small AI models rather than from wide feed-forward layers.","strongest_claim":"Experimental validation on the SAI-UT+ testbench demonstrates that S-AI-Recursive achieves competitive reasoning performance on abstract and symbolic benchmarks with fewer than ten million parameters, confirming the central principle of temporal parsimony: iterative cognitive depth substitutes for architectural width.","weakest_assumption":"The assumption that the newly introduced hormones Clarifine and Confusionin, together with the recursive state dynamics, produce genuine iterative refinement and Lyapunov-stable convergence on real reasoning tasks rather than on the specific SAI-UT+ benchmarks alone."}},"verdict_id":"c469cd17-4ef0-486e-85ef-99861eec48b3"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:93bb5948606762a3c2463605881a9334a2161c09baebdbc3b44863b612fc3550","target":"record","created_at":"2026-05-17T23:39:19Z","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":"84dfc896efb87d441c25a3c97c8734af1ce8bc95fe026de8944834282de79590","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2026-05-05T20:50:29Z","title_canon_sha256":"e2e699982d7b520709ca44469398fbed35177eb2a17a4f8c20291c24455d1065"},"schema_version":"1.0","source":{"id":"2605.13872","kind":"arxiv","version":1}},"canonical_sha256":"64464dd22c6657d92da38cf0d52b9a9718db5009e4b17a06e9597d367ef722b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"64464dd22c6657d92da38cf0d52b9a9718db5009e4b17a06e9597d367ef722b1","first_computed_at":"2026-05-17T23:39:19.307191Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:19.307191Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XVWCwdQniEizgQosWiMPbdjL9iTOXJ4n1p5gatnuBVXj6RfeAskMOEJMlUkNO7pIW15B4ICekZZ5OGNpQu3NCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:19.307953Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.13872","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:53debcdc6e945eda9749758fd18507b61c867a5c92485538d7105e0f180dc31d","sha256:7229dc3e840fda793f5b4bfa505d2798c8d125220ee33d33fcd9c1dd95b41e9a"]}],"invalid_events":[],"applied_event_ids":["sha256:93bb5948606762a3c2463605881a9334a2161c09baebdbc3b44863b612fc3550","sha256:a54acf93f4954a3f89481346008a4ec31b53ddc1aefe209bbd50abf0fe3995d4"],"state_sha256":"97f6d5d29cca75827dac92682f99464a4631ab9d6448e68182d29e753e1fccbf"}