4 papers
Does Your Reasoning Model Implicitly Know When to Stop Thinking?
Zixuan Huang, Xin Xia, Yuxi Ren +11
Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thought (CoTs). However, this approa…
Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative Signals
Zihan Dong, Zhixian Zhang, Yang Zhou +3
Evaluating mathematical reasoning in LLMs is constrained by limited benchmark sizes and inherent model stochasticity, yielding high-variance accuracy estimates and unstable ranking…
Unified Inference Framework for Single and Multi-Player Performative Prediction: Method and Asymptotic Optimality
Zhixian Zhang, Xiaotian Hou, Linjun Zhang
Performative prediction characterizes environments where predictive models alter the very data distributions they aim to forecast, triggering complex feedback loops. While prior re…
Semi-structured LLM Reasoners Can Be Rigorously Audited
Jixuan Leng, Cassandra A. Cohen, Zhixian Zhang +2
Although Large Language Models (LLMs) have become capable reasoners, the problem of faithfulness persists: their reasoning can contain errors and omissions that are difficult to de…