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A Theory of LLM Information Susceptibility

arXiv:2603.23626v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as optimization modules in agentic systems, yet the fundamental limits of such LLM-mediated improvement remain poorly understood. Here we propose a theory of LLM information susceptibility, centred on the hypothesis that when computational resources are sufficiently large, the intervention of a fixed LLM does not increase the performance susceptibility of a strategy set with respect to budget. We develop a multi-variable utility-function framework that generalizes this hypothesis to architectures with multiple co-varying budget channels, and discuss the conditions under which co-scaling can exceed the susceptibility bound. We validate the theory empirically across structurally diverse domains and model scales spanning an order of magnitude, and show that nested, co-scaling architectures open response channels unavailable to fixed configurations. These results clarify when LLM interve

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Zhuo-Yang Song, Hua Xing Zhu · · 1 min read · 5 views
#cs.LG #cond-mat.stat-mech #cs.AI #cs.CL #nlin.AO

arXiv:2603.23626v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as optimization modules in agentic systems, yet the fundamental limits of such LLM-mediated improvement remain poorly understood. Here we propose a theory of LLM information susceptibility, centred on the hypothesis that when computational resources are sufficiently large, the intervention of a fixed LLM does not increase the performance susceptibility of a strategy set with respect to budget. We develop a multi-variable utility-function framework that generalizes this hypothesis to architectures with multiple co-varying budget channels, and discuss the conditions under which co-scaling can exceed the susceptibility bound. We validate the theory empirically across structurally diverse domains and model scales spanning an order of magnitude, and show that nested, co-scaling architectures open response channels unavailable to fixed configurations. These results clarify when LLM intervention helps and when it does not, demonstrating that tools from statistical physics can provide predictive constraints for the design of AI systems. If the susceptibility hypothesis holds generally, the theory suggests that nested architectures may be a necessary structural condition for open-ended agentic self-improvement.

Executive Summary

This article proposes a theory of Large Language Model (LLM) information susceptibility, examining the limits of LLM-mediated improvement in agentic systems. The authors develop a multi-variable utility-function framework, validating the theory across diverse domains and model scales. They demonstrate that nested, co-scaling architectures can open response channels unavailable to fixed configurations. The theory suggests that nested architectures may be necessary for open-ended agentic self-improvement, if the susceptibility hypothesis holds generally. This research has significant implications for AI system design, leveraging tools from statistical physics to provide predictive constraints.

Key Points

  • ▸ The article proposes a theory of LLM information susceptibility, centered on the hypothesis that LLM intervention does not increase performance susceptibility with sufficient computational resources.
  • ▸ The authors develop a multi-variable utility-function framework to generalize the hypothesis to architectures with multiple co-varying budget channels.
  • ▸ The theory is validated empirically across diverse domains and model scales, demonstrating that nested, co-scaling architectures can open response channels unavailable to fixed configurations.

Merits

Strength in Mathematical Rigor

The article demonstrates a strong mathematical foundation, developing a novel utility-function framework to analyze LLM information susceptibility.

Empirical Validation

The authors provide empirical validation across diverse domains and model scales, lending credibility to the proposed theory.

Demerits

Limited Scope

The article focuses on LLM information susceptibility in agentic systems, which may not generalize to other areas of AI research.

Assumed Computational Resources

The hypothesis relies on the assumption of sufficiently large computational resources, which may not always be feasible in real-world scenarios.

Expert Commentary

The article presents a significant contribution to the field of AI research, proposing a novel theory of LLM information susceptibility. The development of a multi-variable utility-function framework demonstrates a high level of mathematical rigor, and the empirical validation across diverse domains and model scales lends credibility to the proposed theory. However, the article's focus on agentic systems may limit its generalizability to other areas of AI research. Furthermore, the assumption of sufficiently large computational resources may not always be feasible in real-world scenarios. Nevertheless, the research has significant implications for AI system design, leveraging tools from statistical physics to provide predictive constraints.

Recommendations

  • ✓ Future research should aim to generalize the theory to other areas of AI research, such as computer vision or reinforcement learning.
  • ✓ Investigations into the potential applications and limitations of nested architectures in real-world scenarios are necessary to inform the development of regulatory frameworks.

Sources

Original: arXiv - cs.LG

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