Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary
arXiv:2510.03271v2 Announce Type: replace Abstract: Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in revealing core model properties and interpreting behaviors. While analyzing the decision boundary of large language models (LLMs) has attracted increasing attention recently, constructing it for mainstream LLMs remains computationally infeasible due to the enormous sequence-level output spaces and the autoregressive nature of LLMs. To address this issue, in this paper we propose Decision Potential S
The proliferation and increasing complexity of large language models necessitate more advanced methods to understand their internal workings and decision-making processes.
Understanding the decision boundary of LLMs is critical for improving their reliability, interpretability, and safety, which are foundational for their widespread deployment in critical applications.
This research introduces a computationally feasible method to approximate LLM decision boundaries, making it possible to analyze model behavior that was previously too complex.
- · AI researchers
- · Machine learning interpretability platforms
- · Developers of LLM applications
- · AI safety and ethics organizations
- · Black-box AI approaches
- · Organizations relying solely on empirical testing for LLM assessment
Improved debugging and fine-tuning capabilities for large language models.
Faster development and deployment of robust and predictable AI agents.
Enhanced regulatory oversight and auditing of AI systems due to increased transparency.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG