Toward Controllable and Trustworthy LLM Reasoning: From Failure Mapping to Cognition-inspired Control and Real-world Impact - Zhou, Ben (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Large Language Models (LLMs) have advanced rapidly and raised the bar for what AI is expected to do. However, accompanied with such progress is a stronger consensus that these models consistently fail in out-of-distribution reasoning, especially on tasks that require abstraction, transfer, or long-horizon planning. While acceptable for most consumer use, these issues prevent AI from being safely deployed in high-stakes settings (e.g., healthcare), where stakeholders cannot trust AI models that exhibit uncontrollable and unpredictable failures. In this talk, I will discuss our work and insights on how to make LLM reasoning controllable and trustworthy, by 1) understanding the mechanisms of LLM reasoning and predicting when LLM will fail; 2) improving model reasoning and generalization based on such insights; and 3) moving towards trustworthy AI applications through such improvements, and identifying new problems to form a healthy positive-feedback loop.