9 papers
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +306
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
Breaking the Reversal Curse in Autoregressive Language Models via Identity Bridge
Xutao Ma, Yixiao Huang, Hanlin Zhu +1
Autoregressive large language models (LLMs) have achieved remarkable success in many complex tasks, yet they can still fail in very simple logical reasoning such as the "reversal c…
Transformers Provably Learn to Internalize Chain-of-Thought
Yixiao Huang, Hanlin Zhu, Zixuan Wang +4
Chain-of-Thought (CoT) prompting substantially improves the sample efficiency of transformers, reducing the complexity of tasks like parity learning from exponential to polynomial…
Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning
Ziheng Cheng, Yixiao Huang, Hanlin Zhu +5
Diffusion models are increasingly used as powerful conditional generators, yet real deployments often involve multiple target distributions arising from different tasks, e.g., dive…
AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery
Guiyao Tie, Jiawen Shi, Dingjie Song +20
Scientific research is being reshaped by AI systems that move beyond isolated assistance toward longer-horizon workflows spanning literature grounding, hypothesis generation, exper…
Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers
Yixiao Huang, Hanlin Zhu, Tianyu Guo +5
Large language models (LLMs) can acquire new knowledge through fine-tuning, but this process exhibits a puzzling duality: models can generalize remarkably from new facts, yet are a…