aiqbench analysis
This paper proposes S-AI-Recursive, a bio-inspired architecture that replaces standard feed-forward passes with a hormonally regulated closed-loop iteration to improve reasoning efficiency. By modeling convergence and uncertainty as antagonistic signals, the framework achieves significant reductions in iterative depth for specific tasks like mazes and Sudoku while maintaining stability under defined conditions. The approach represents a novel theoretical contribution to sparse AI architectures but does not claim universal superiority over existing independently trained models.
S-AI-Recursive: Convergent Recursive Reasoning
arXiv:2605.13872v2 Announce Type: replace-cross Abstract: This article introduces S-AI-Recursive, a bio-inspired Sparse Artificial Intelligence architecture in which reasoning is implemented as a hormonally regulated closed-loop iteration rather than a single feed-forward pass. The Recursive Reasoning Cycle (RRC) is governed by two recursive hormones: Clarifine, a convergence signal, and Confusionin, a residual-uncertainty signal. Their antagonistic interaction regulates state refinement, stopping, resource allocation, and recursive-engram retrieval. The revised framework distinguishes hormonal-subsystem stability from joint cognitive state-hormone convergence and gives explicit sufficient conditions for coupled contraction on fixed-point-structured tasks. It also includes Lyapunov analysis, conditional entropic contraction, multi-signal stopping, Euler-Maruyama discretization with projection, constrained agent selection, and warm-start memory. Experimental evaluation combines controlled SAI-UT+ simulations with exactly verifiable task-level tests. On convergent Maze instances, adaptive stopping reduces mean depth from 20.00 to 11.31 iterations at unchanged resolution, a 43.4 percent reduction. On compatible recurring Sudoku instances, warm-start reduces mean depth from 18.39 to 2.00 cycles, saving 16.39 cycles at unchanged resolution. ARC-style tasks are used to assess operator portability rather than full benchmark performance. Robustness tests show an advantage over residual-only stopping on deceptive plateaus, but not under homogeneous Gaussian noise. These results support adaptive temporal parsimony, memory-assisted acceleration, and selected robustness under stated conditions, without establishing superiority over independently trained external architectures.
arXiv:2605.13872 (cs)
Abstract:This article introduces S-AI-Recursive, a bio-inspired Sparse Artificial Intelligence architecture in which reasoning is implemented as a hormonally regulated closed-loop iteration rather than a single feed-forward pass. The Recursive Reasoning Cycle (RRC) is governed by two recursive hormones: Clarifine, a convergence signal, and Confusionin, a residual-uncertainty signal. Their antagonistic interaction regulates state refinement, stopping, resource allocation, and recursive-engram retrieval. The revised framework distinguishes hormonal-subsystem stability from joint cognitive state-hormone convergence and gives explicit sufficient conditions for coupled contraction on fixed-point-structured tasks. It also includes Lyapunov analysis, conditional entropic contraction, multi-signal stopping, Euler-Maruyama discretization with projection, constrained agent selection, and warm-start memory. Experimental evaluation combines controlled SAI-UT+ simulations with exactly verifiable task-level tests. On convergent Maze instances, adaptive stopping reduces mean depth from 20.00 to 11.31 iterations at unchanged resolution, a 43.4 percent reduction. On compatible recurring Sudoku instances, warm-start reduces mean depth from 18.39 to 2.00 cycles, saving 16.39 cycles at unchanged resolution. ARC-style tasks are used to assess operator portability rather than full benchmark performance. Robustness tests show an advantage over residual-only stopping on deceptive plateaus, but not under homogeneous Gaussian noise. These results support adaptive temporal parsimony, memory-assisted acceleration, and selected robustness under stated conditions, without establishing superiority over independently trained external architectures.
Preprint. 55 pages. No figures. S-AI-Recursive: Convergent Recursive Reasoning
Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
arXiv:2605.13872 [cs.NE]
(or arXiv:2605.13872v2 [cs.NE] for this version)
From: Said Slaoui Pr. [view email] [v1] Tue, 5 May 2026 20:50:29 UTC (1,331 KB) [v2] Fri, 21 Aug 2026 15:06:46 UTC (1,161 KB)
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Full text under the publisher's open licence · 328 words
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