Humans Perceive Wrong Narratives from AI Reasoning Texts (2508.16599v2)

Abstract: A new generation of AI models generates step-by-step reasoning text before producing an answer. This text appears to offer a human-readable window into their computation process, and is increasingly relied upon for transparency and interpretability. However, it is unclear whether human understanding of this text matches the model's actual computational process. In this paper, we investigate a necessary condition for correspondence: the ability of humans to identify which steps in a reasoning text causally influence later steps. We evaluated humans on this ability by composing questions based on counterfactual measurements and found a significant discrepancy: participant accuracy was only 29%, barely above chance (25%), and remained low (42%) even when evaluating the majority vote on questions with high agreement. Our results reveal a fundamental gap between how humans interpret reasoning texts and how models use it, challenging its utility as a simple interpretability tool. We argue that reasoning texts should be treated as an artifact to be investigated, not taken at face value, and that understanding the non-human ways these models use language is a critical research direction.

• The paper reveals that humans misinterpret AI-generated reasoning texts, with an accuracy of only about 29% in identifying true causal dependencies.
• It employs counterfactual analysis to systematically compare human-selected narrative steps with the model’s computational process.
• The study underscores the need for advanced interpretability frameworks that bridge the gap between human intuition and machine logic.

Humans Perceive Wrong Narratives from AI Reasoning Texts

Introduction

This paper investigates the discrepancy between human interpretations of AI-generated reasoning texts and the actual computational processes utilized by the models. The research questions the utility of reasoning texts as transparent interpretability tools, revealing a significant gap between human perception and the model's internal operations. The authors conducted experiments to evaluate human capability in identifying true causal dependencies in reasoning texts, revealing an alarming lack of accuracy which suggests a compelling need for new methodologies in AI interpretability.

Research Methodology

The research involves human participants tasked with identifying causal dependencies within reasoning texts produced by large reasoning models. Using counterfactual analysis, the paper assesses whether removal of certain steps alters the outcome of the reasoning, drawing a line between perceived and actual dependencies. Participants are tested on their ability to select causally influential steps among multiple options.

Figure 1: Illustration of human interpretation vs the actual causal relation in AI generated reasoning text.

The causal dependencies are determined systematically by evaluating changes in reasoning outcomes when individual steps are omitted. Measurements indicate profound discrepancies between human interpretations and the model's utilization of reasoning texts.

Results and Analysis

Participants performed poorly in identifying accurate causal dependencies, with average accuracy hovering around 29%, slightly above random chance. This highlights a systemic misunderstanding of the model's process. Despite high agreement on perceived narratives among participants in certain cases, these shared interpretations often deviated from the model's actual process, reinforcing the unreliability of collective human intuition in this context.

Figure 2: Distribution of individual narrative interpretation accuracy shows universal difficulty in interpreting AI reasoning texts.

Figure 3: Analysis of collective narrative accuracy indicates that higher participant agreement does not correlate with correct understanding.

Reasoning texts produced by different models exhibited variance in interpretability; however, the fundamental gap persisted across models, accentuating the challenge of accurately interpreting AI reasoning processes. The analysis establishes that the structural mismatch is not due to individual cognitive differences but rather an intrinsic discrepancy between human narrative frameworks and machine logic.

Implications

The findings suggest substantial limitations in using AI-generated reasoning texts as interpretability tools. The results challenge current assumptions about transparency in AI processes, urging a reconsideration of reasoning texts as reliable explanatory artifacts. Furthermore, the paper points to a novel understanding of language as employed by AI systems, necessitating reconsideration of how language facilitates communication between humans and AI.

The paper implies that more sophisticated methods are necessary to bridge the interpretative gap, perhaps involving advanced techniques that systematically decode the computational logic of models beyond surface-level text analysis. The research opens pathways for further studies on developing robust frameworks for AI interpretability that align with human cognitive constructs.

Conclusion

In conclusion, this paper highlights a fundamental disconnect in the way humans interpret AI-generated reasoning texts vs. the actual operations undertaken by the models. These findings spotlight crucial deficiencies in current interpretability approaches and call for advancements in methodologies that consider the foreign, albeit effective, linguistic constructs of AI systems. Bridging this interpretative gap is essential for enhancing trust and collaboration in human-AI interactions, thus requiring focused efforts in developing nuanced interpretability frameworks.

Figure 4: Test interface showcasing the experimental setup used for participant analysis.