9 papers
CogEvol: Towards Efficient and Reliable Learning Environment Generation
Shangqing Tu, Daniel Zhang-Li, Yucheng Wang +20
We present CogEvol, a family of models trained specifically for Learning Environment Generation: turning a course brief into a finished learning artifact (structured-JSON slides or…
AgentRewind: Recoverable Execution for Long-Horizon LLM Agents
Yu Zhuang, Kefei Chen, Yitong Duan +3
Many real-world tasks require LLM agents to interact with their environments over long execution horizons. Errors that occur early in execution may propagate through both the agent…
M3: A State-Event Generative Foundation Model for Market Microstructure Dynamics
Yanzhi Zhang, Yu Ma, Yilin Cheng +2
Market microstructure simulation aims to model how liquidity, prices, and order flow evolve in electronic financial markets. Since market data reveal only one realized trajectory,…
PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding
Yunhe Han, Yunqi Gao, Bing Hu +4
Speculative decoding can significantly accelerate LLM inference, especially given that its cloud-edge collaborative deployment offers cloud workload offloading, offline robustness,…
FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards
Zhixin Han, Yanzhi Zhang, Chuyang Wei +11
Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using large language model-based agent…
Harnessing Pre-Resolution Signals for Future Prediction Agents
Chuyang Wei, Maohang Gao, Zhixin Han +12
Many high-stakes decisions depend on forecasts made before outcomes are known. In this future prediction setting, the central challenge is that public evidence evolves over time, w…
Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning
Yanzhi Zhang, Yitong Duan, Zhaoxi Zhang +2
Test-time scaling has emerged as a promising direction for enhancing the reasoning capabilities of Large Language Models in last few years. In this work, we propose Population-Evol…
One Tool Is Enough: Reinforcement Learning for Repository-Level LLM Agents
Zhaoxi Zhang, Yitong Duan, Yanzhi Zhang +9
Locating files and functions requiring modification in large software repositories is challenging due to their scale and structural complexity. Existing LLM-based methods typically…
No Free Lunch: Rethinking Internal Feedback for LLM Reasoning
Yanzhi Zhang, Zhaoxi Zhang, Haoxiang Guan +6
Reinforcement learning has emerged as a powerful paradigm for post-training large language models (LLMs) to improve reasoning. Approaches like Reinforcement Learning from Human Fee…