{"paper":{"title":"S-AI-Recursive: A Bio-Inspired and Temporal Sparse AI Architecture for Iterative, Introspective, and Energy-Frugal Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Reasoning emerges from iterative hormonal feedback in small AI models rather than from wide feed-forward layers.","cross_cats":["cs.AI"],"primary_cat":"cs.NE","authors_text":"Said Slaoui","submitted_at":"2026-05-05T20:50:29Z","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"},"claims":{"count":4,"items":[{"kind":"strongest_claim","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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Reasoning emerges from iterative hormonal feedback in small AI models rather than from wide feed-forward layers.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c1cbc085f70df8358b1e5248f8314acb99bf447cb075acbceb305da50a22d8ce"},"source":{"id":"2605.13872","kind":"arxiv","version":1},"verdict":{"id":"c469cd17-4ef0-486e-85ef-99861eec48b3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T06:38:32.006566Z","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.","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","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.","pith_extraction_headline":"Reasoning emerges from iterative hormonal feedback in small AI models rather than from wide feed-forward layers."},"references":{"count":49,"sample":[{"doi":"","year":2025,"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","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.2139/ssrn.5735582","year":2025,"title":"From Hormones to Probabilities: A Unified Doctrine of Cognitive Homeostasis in Sparse Artificial Intelligence,","work_id":"e30b5f31-0dae-4ace-aed8-d8c9d047b1bf","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"S-AI-IoT: Formal Agent Specification, Mathematical Modeling, and Stability Analysis of the Hormonal Orchestration Framework,","work_id":"f995a5dd-3cff-4d90-90ed-03a9cc33042e","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Attention is all you need,","work_id":"2148da8e-5ab3-4b4a-bc95-661e6a1bb331","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1901,"title":"Language models are few- shot learners,","work_id":"3d7c2f74-335d-4f2a-b32e-4c650c0859e0","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":49,"snapshot_sha256":"fc505594e22b07b8a62258fe86685b0dfdb6ee24cb3f6aee21852dc04826bf63","internal_anchors":3},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a71f75c6e8e62da3182b9d2e7cf16d9c3c6eee39108748ccc8b3947027b6ddff"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}