Tiancheng Hu

Large Language Model (LLM) agents deployed for real-world tasks face a fundamental dilemma: user requests are underspecified, yet agents must decide whether to act on incomplete information or interrupt users for clarification. Existing approaches either rely on brittle confidence thresholds that...

Understanding how discussion dynamics shape team creativity has been limited by the difficulty of measuring process at scale. We introduce Trace, a corpus of 309 group discussions from 103 teams (460 participants) across six creative problem-solving tasks. The dataset follows an input-process-out...

Reasoning-tuned LLMs utilizing long Chain-of-Thought (CoT) excel at single-answer tasks, yet their ability to model Human Label Variation—which requires capturing probabilistic ambiguity rather than resolving it—remains underexplored. We investigate this through systematic disentanglement experim...

While confidence estimation is a promising direction for mitigating hallucinations in Large Language Models (LLMs), current research overwhelmingly focuses on single-turn settings. The dynamics of model confidence in multi-turn conversations, where context accumulates and ambiguity is progressive...

The “alignment tax” of post-training is typically framed as a drop in task accuracy. We show it also involves a severe loss of calibration, making models overconfident, less reliable, and model outputs less diverse. We demonstrate that this trade-off can be navigated effectively via a simple post...