LLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos
Julius Steiglechner, Lucas Mahler, Gabriele Lohmann
Why It Matters
What makes this one worth your time
Understanding and improving LLMs' abductive reasoning capabilities is crucial for developing models that can better infer and explain complex phenomena, which is important for applications requiring deep understanding and reasoning.
Elenchos evaluates LLMs' abductive reasoning by testing their ability to detect and attribute system mutations.
Summary
The paper introduces Elenchos, a generative evaluation framework designed to assess the abductive reasoning capabilities of large language models (LLMs) by treating it as a structural inverse problem. The framework evaluates LLMs' ability to detect and attribute mutations in formal systems, revealing a consistent challenge in identifying underlying rule modifications, especially under interacting mutations.
Key contributions
- Introduction of Elenchos, a novel framework for evaluating abductive reasoning in LLMs.
- Empirical evaluation of LLMs' performance in detecting and attributing mutations in formal systems.
Notable insights
- Detection-attribution dissociation in LLMs indicates they can recognize alterations but struggle with identifying specific changes.
- Preliminary evidence suggests limited gains from increased inference-time reasoning, highlighting potential inefficiencies in current LLM architectures.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.12733v1 Announce Type: new Abstract: Large language models (LLMs) excel at pattern recognition and text generation, but their capacity for abductive inference - inferring latent hypotheses that explain observed behavior - remains poorly understood. Here, we introduce Elenchos (named after the Socratic method of cross-examination), a generative evaluation framework that measures abductive reasoning as a structural inverse problem. Given a reference formal system, such as the lambda-calculus, and a potentially mutated counterpart, agents must determine whether a mutation has occurred and infer the rule modifications responsible for the resulting behavioral differences. Evaluating frontier and mid-tier LLMs reveals a consistent detection-attribution dissociation: models often recognize that a system has been altered but struggle to identify the latent mutations causing the observed discrepancies. Performance degrades substantially under interacting mutations, where models frequently recover only a subset of the underlying mutations. Preliminary evidence also suggests diminishing returns from increased inference-time reasoning, with only modest improvements under larger reasoning budgets, though this finding requires further validation.