arXiv:2605.27824v1 Announce Type: new Abstract: Recent studies have shown that Large Language Models (LLMs) can achieve strong reasoning performance by incorporating functional symbolic representations that abstractly describe graph traversal algorithms and step-by-step reasoning in few-shot learning settings. However, it remains unclear how LLMs genuinely understand the abstract meaning of each reasoning step and the overall algorithm from only a limited number of demonstrations. This work aims to localize the attention heads responsible for individual reasoning steps and characterize the typ
This research emerges as the limitations and 'black box' nature of LLM reasoning become critical bottlenecks for deployment in sensitive or high-stakes applications.
Understanding the algorithmic deductive circuits within LLMs is crucial for improving their reliability, interpretability, and ultimately, their capability in complex reasoning tasks, which impacts wide-ranging AI applications.
The ability to localize and characterize reasoning steps within LLMs could shift development from purely empirical scaling to more targeted architectural improvements and oversight.
- · AI researchers
- · Developers of auditable AI systems
- · Sectors requiring explainable AI
- · Black-box AI models in critical applications
Increased interpretability and trustworthiness of Large Language Models in complex reasoning tasks.
Accelerated development of more robust and less 'hallucinatory' AI agents capable of advanced problem-solving.
Potential for new AI architectures inspired by the identified deductive circuits, leading to more efficient and reliable general AI systems.
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Read at arXiv cs.AI