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AI research dissects emergent vs. computed functions in cognitive architectures

A new research paper proposes a minimal cognitive architecture to investigate the origins of emergent versus computed functions in AI systems. The study built a system with adaptive halting, homeostatic control, and a value module, finding that while competence and stopping behaviors appear to emerge, they require specific computational mechanisms to be truly effective. The research highlights that second-order decisions, particularly those related to value, must be explicitly computed rather than emerging from standard training processes. AI

IMPACT This research could inform the design of more sophisticated AI systems by clarifying which cognitive functions need explicit computation versus those that can emerge from training.

RANK_REASON The cluster contains a single arXiv paper detailing a novel research approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI research dissects emergent vs. computed functions in cognitive architectures

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The cluster contains a single arXiv paper detailing a novel research approach. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Francisco M. Arrabal-Campos, Francisco G. Montoya, Alfredo Alcayde, Ignacio Fern\'andez ·

    Where Cognition Lives: Dissecting Emergent from Computed Function in a Minimal Complete Cognitive Architecture

    arXiv:2608.22347v1 Announce Type: new Abstract: A cognitive architecture is more than the module that reasons: it must also decide how long to think and what deserves the effort. We built a minimal but complete system - a recurrent reasoner with adaptive halting, a homeostatic co…