Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners

arXiv cs.AI / 3/31/2026

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Key Points

The paper argues that as LLMs improve at complex reasoning, their behavior can act as a proxy for understanding the heuristics inside more capable frontier models, which is important for interpretability and safety.
It uses a modified Genetic Pareto (GEPA) method to systematically optimize prompts for scientific reasoning tasks and then studies what logical/structural heuristics appear in the optimized prompts.
The authors find that improvements in scientific reasoning tend to rely on model-specific (“local”) heuristics that do not generalize reliably to other models or systems.
They assess how transferable and brittle these prompt-induced reasoning patterns are, highlighting that prompting can materially change reasoning behavior.
The work frames prompt optimization as a pathway toward interpretability by mapping the reasoning structures an LLM prefers, as a prerequisite for collaboration with more capable (potentially superhuman) intelligence systems.

Abstract

As Large Language Models (LLMs) achieve increasingly sophisticated performance on complex reasoning tasks, current architectures serve as critical proxies for the internal heuristics of frontier models. Characterizing emergent reasoning is vital for long-term interpretability and safety. Furthermore, understanding how prompting modulates these processes is essential, as natural language will likely be the primary interface for interacting with AGI systems. In this work, we use a custom variant of Genetic Pareto (GEPA) to systematically optimize prompts for scientific reasoning tasks, and analyze how prompting can affect reasoning behavior. We investigate the structural patterns and logical heuristics inherent in GEPA-optimized prompts, and evaluate their transferability and brittleness. Our findings reveal that gains in scientific reasoning often correspond to model-specific heuristics that fail to generalize across systems, which we call "local" logic. By framing prompt optimization as a tool for model interpretability, we argue that mapping these preferred reasoning structures for LLMs is an important prerequisite for effectively collaborating with superhuman intelligence.