A Mechanistic Lens on Semantic Conflicts Using Activation Patching to Understand LLM Behavior

Jul 16, 2026

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Large language models used in software engineering may face semantic conflicts when non-executable cues such as comments or identifiers suggest different program behavior than the code itself. This work studies how such conflicts alter model priorities and downstream task performance. A controlled dataset of 45 token-aligned Python triplets isolates cue versus implementation variations. Four open-weight LLMs are evaluated on final-output prediction and unit-test generation, combining behavioral metrics with residual-stream activation patching to locate causally active token-layer states.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

Introduction

Experimental framework and dataset

Tasks and evaluation with activation patching

Findings and mechanistic observations

A Mechanistic Lens on Semantic Conflicts: Using Activation Patching to Understand LLM Behavior Youssef Abdelsalam, Norman Peitek, Anna-Maria Maurer, Marvin Wyrich, and Sven Apel Saarland Informatics Campus, Saarland University Saarbr¨ucken, Germany arXiv :2607 .05587v 1 [ cs . SE] 6 Jul 2026 Abstract—Large language models (LLMs) are increasingly used in software-engineering tasks processing executable code and non-executable semantic cues such as comments or identifiers. These two sources of information can conflict, leading to situations where the semantic cues suggest different program behavior than the code itself. It remains unclear how such semantic conflicts affect LLM behavior and which source of information dominates their outputs. We present the first controlled, mechanistic study of LLM behavior under semantic conflicts. To this end, we construct 45 Python snippet triplets that isolate conflicts by varying either semantic cues or implementation while keeping token-aligned pairs for causal intervention. We evaluate four open-weight LLMson two tasks—final-output prediction and unit-test generation—using both behavioral performance measures and residual-stream activation patching to identify token-layer states that causally contribute to differences in LLM behavior between aligned and conflicting inputs. Our results show that semantic conflicts significantly reduce execution-grounded correctness in both tasks and that all tested LLMs frequently follow (misleading) semantic cues. Residualstream activation patching reveals a consistent pattern for finaloutput prediction: The changed cue/code region and a small set of intermediate tokens carry most of the recoverable causal signal before being aggregated near the output readout. For unit-test generation, this pattern extends beyond the prompt, showing that conflict-related information is not only recoverable at prompt sites but also at generated assertion sites before producing expected values. Overall, our findings show that semantic conflicts affect both program comprehension and downstream tasks, with the relevant information concentrated in a small number of causally active residual-stream states, and demonstrate a framework for mechanistically analyzing how LLMs integrate different sources of code-related information under controlled semantic variations. Index Terms—Large Language Models, Mechanistic Interpretability, Semantic Conflicts, Program Comprehension I. INTRODUCTION Large language models (LLMs) are increasingly used in software-engineering workflows [1], [2], including test generation [3], [4], program repair [5], [6], and agentic development [7], [8] . These workflows expose LLMs to both executable code and non-executable semantic cues, such as comments, identifiers, and other natural-language texts. While these sources typically agree in well-maintained code [9],[10], they often diverge in practice due to outdated documentation, misleading identifiers, or evolving implementations [10]–[12], resulting in semantic conflicts. Such conflicts are inherently ambiguous, as neither source can be assumed authoritative without external validation. This renders LLMs distinctive among software-engineering tools, as they jointly process executable implementations and natural-language cues. In fact, LLMs often rely on semantic cues in their reasoning [13], [14] . When conflicts arise, it remains unclear which source LLMs prioritize and how this affects downstream tasks such as test generation, program repair, and agentic development. Prior work has documented many LLM failures in coding tasks [15], [16], but behavioral outputs alone cannot explain how conflicting information is internally represented or how it influences downstream softwareengineering tasks. We address this gap by studying LLM behavior under semantic conflicts in two tasks: predicting program outputsand generating unit tests. We introduce an experimental framework that combines be

FAQs

What are semantic conflicts in the context of LLMs and code?

Semantic conflicts occur when non-executable cues like comments or identifiers disagree with the executable code about expected behavior. Because neither source is inherently authoritative, the conflict is ambiguous for the model.

How does the paper create inputs to isolate semantic conflicts?

It constructs 45 minimal, token-matched Python triplets, pairing an aligned baseline with two conflicting variants. Conflicts are created by modifying either the semantic cue or the implementation while keeping token-aligned pairs for causal intervention.

What does activation patching reveal about where conflict information is represented?

Residual-stream activation patching shows that, for final-output prediction, the changed cue/code region and a small set of intermediate tokens carry most recoverable causal signal. For unit-test generation, the effect extends beyond prompt sites into generated assertion sites before expected values are produced.

Jul 16, 2026

Upload by Asher