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

• URL: http://arxiv.org/abs/2603.28038v1
• Date: Mon, 30 Mar 2026 05:01:07 GMT
• Title: Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners
• Abstract summary: Large Language Models (LLMs) achieve increasingly sophisticated performance on complex reasoning tasks.<n>Characterizing emergent reasoning is vital for long-term interpretability and safety.<n>We use a custom variant of Genetic Pareto (GEPA) to systematically optimize prompts for scientific reasoning tasks.
• Score: 3.831016688248884
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SymbolLKG: Towards Verifiable Logical Reasoning via Logical Knowledge Graph and Symbolic Solvers [20.356252444311195]
  Large Language Models (LLMs) have demonstrated remarkable proficiency in natural language understanding, yet they struggle with strict multi-step reasoning.<n>Existing solutions like Chain-of-Thought (CoT) lack rigorous verification mechanisms, while standard Retrieval-Augmented Generation (RAG) often misses the complex, structural dependencies inherent in logical tasks.<n>We propose a Neuro-Symbolic architecture that integrates a Logical Knowledge Graph (LKG) with dynamic solver routing.
  arXiv  Detail & Related papers  (2026-08-27T09:06:52Z)
• Advantage-Guided Gate: Reshaping Open-Ended Reasoning for Vision-Based Spatial Intelligence [70.8951332710039]
  Multimodal large language models (MLLMs) have demonstrated significant potential in complex spatial scene understanding and reasoning tasks.<n>However, their open-ended reasoning process is prone to decision errors and error accumulation, leading to instability in answer quality.<n>We propose an advantage-guided gating framework that dynamically intervenes in and corrects deviations during the reasoning process.
  arXiv  Detail & Related papers  (2026-08-08T07:46:04Z)
• Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy [62.32144504442516]
  It is unknown whether SOTA LLMs can grasp the structured, hierarchical complexity of formal languages.<n>ChomskyBench is a benchmark for systematically evaluating LLMs through the lens of Chomsky Hierarchy.<n>ChomskyBench is composed of a comprehensive suite of language recognition and generation tasks designed to test capabilities at each level.
  arXiv  Detail & Related papers  (2026-04-03T04:06:39Z)
• Do LLMs Dream of Discrete Algorithms? [0.7646713951724011]
  Large Language Models (LLMs) have rapidly transformed the landscape of artificial intelligence.<n>Their reliance on probabilistic inference limits their effectiveness in domains requiring strict logical reasoning.<n>This paper proposes a neurosymbolic approach that augments LLMs with logic-based reasoning modules.
  arXiv  Detail & Related papers  (2025-06-29T22:03:01Z)
• LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning [74.0242521818214]
  This paper systematically investigates the comparative dynamics of inductive (System 1) versus abductive/deductive (System 2) inference in large language models (LLMs)<n>We utilize a controlled analogical reasoning environment, varying modality (textual, visual, symbolic), difficulty, and task format (MCQ / free-text)<n>Our analysis reveals System 2 pipelines generally excel, particularly in visual/symbolic modalities and harder tasks, while System 1 is competitive for textual and easier problems.
  arXiv  Detail & Related papers  (2025-02-16T15:54:53Z)
• Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools [19.70178343422698]
  We introduce Agentic Reasoning, a framework that enhances large language model (LLM) reasoning by integrating external tool-using agents.<n>Key innovation in our framework is the Mind-Map agent, which constructs a structured knowledge graph to store reasoning context.<n>When deployed on DeepSeek-R1, our method achieves a new state-of-the-art (SOTA) among public models.
  arXiv  Detail & Related papers  (2025-02-07T04:08:46Z)
• Failure Modes of LLMs for Causal Reasoning on Narratives [51.19592551510628]
  We investigate the interaction between world knowledge and logical reasoning.<n>We find that state-of-the-art large language models (LLMs) often rely on superficial generalizations.<n>We show that simple reformulations of the task can elicit more robust reasoning behavior.
  arXiv  Detail & Related papers  (2024-10-31T12:48:58Z)
• Language Agents Meet Causality -- Bridging LLMs and Causal World Models [50.79984529172807]
  We propose a framework that integrates causal representation learning with large language models. This framework learns a causal world model, with causal variables linked to natural language expressions. We evaluate the framework on causal inference and planning tasks across temporal scales and environmental complexities.
  arXiv  Detail & Related papers  (2024-10-25T18:36:37Z)
• Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up [9.42385235462794]
  Large language models (LLMs) have shown remarkable performance in reasoning tasks but face limitations in mathematical and complex logical reasoning.<n>We propose Reversal of Thought (RoT) to enhance the logical reasoning abilities of LLMs during the warm-up phase prior to batch inference.<n>RoT utilizes a Preference-Guided Reverse Reasoning warm-up strategy, which integrates logical symbols for pseudocode planning.
  arXiv  Detail & Related papers  (2024-10-16T07:44:28Z)
• Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning [1.3003982724617653]
  Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning. This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs. Key contributions include a robust type system with sort management for enhanced logical integrity, explicit representation of rules for clear distinction between factual and inferential knowledge.
  arXiv  Detail & Related papers  (2024-09-25T18:35:45Z)
• Logic Agent: Enhancing Validity with Logic Rule Invocation [24.815341366820753]
  Chain-of-Thought prompting has emerged as a pivotal technique for augmenting the inferential capabilities of language models during reasoning tasks.<n>This paper introduces the Logic Agent (LA), an agent-based framework aimed at enhancing the validity of reasoning processes in Large Language Models.
  arXiv  Detail & Related papers  (2024-04-28T10:02:28Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)
• LogiGAN: Learning Logical Reasoning via Adversarial Pre-training [58.11043285534766]
  We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. Inspired by the facilitation effect of reflective thinking in human learning, we simulate the learning-thinking process with an adversarial Generator-Verifier architecture. Both base and large size language models pre-trained with LogiGAN demonstrate obvious performance improvement on 12 datasets.
  arXiv  Detail & Related papers  (2022-05-18T08:46:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.