Reasoning as Attractor Dynamics: Latent Memory Retrieval via Gibbs-Weighted Energy Minimization
arXiv:2606.24543v1 Announce Type: new Abstract: Large Language Models (LLMs) are traditionally viewed as autoregressive generators. However, from the perspective of collective computation, they function as high-dimensional Dense Associative Memories that store complex reasoning patterns as latent attractors. In this work, we investigate the energy landscape of mathematical reasoning. We posit that correct reasoning chains correspond to deep, wide attractor basins ("flat minima") in the model's output distribution, whereas hallucinations manifest as sharp, unstable local minima. To exploit this
This paper represents a timely advancement in understanding LLM reasoning by applying established concepts from collective computation and associative memory, particularly relevant as LLMs become foundational in various applications.
A strategic reader should care because deeper understanding of LLM reasoning, particularly regarding error mechanisms like hallucinations, is crucial for building more reliable and trustworthy AI systems and for developing better evaluation methods.
The perspective on LLMs shifts from purely autoregressive generators to systems that store complex reasoning patterns as latent attractors, opening new avenues for controlling and improving their logical capabilities.
- · AI researchers
- · LLM developers
- · AI safety researchers
- · Deep learning practitioners
- · Developers solely focused on superficial LLM improvements
- · Companies neglecting foundational AI research
This research provides a theoretical framework to identify and potentially mitigate hallucinations in LLMs.
Improved understanding could lead to more robust AI agents capable of complex decision-making with fewer errors.
The ability to 'read' the energy landscape of LLMs might enable more interpretable and controllable artificial intelligence, accelerating adoption in high-stakes domains.
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Read at arXiv cs.LG