6 papers
AlphaCrafter: Harnessing Multi-Agent Workflows for Cross-Sectional Quantitative Trading
Yishuo Yuan, Jiayi Sheng, Sirui Zeng +2
Quantitative trading agents have demonstrated substantial promise in automating factor discovery, signal aggregation, and portfolio execution. However, existing agent-based trading…
DR-Eval: Towards Realistic and Reproducible Deep Research Evaluation
Qianqian Xie, Qingheng Xiong, He Zhu +16
Deep Research Agents (DRAs) aim to solve complex, long-horizon research tasks involving planning, retrieval, multimodal understanding, and report generation, yet their evaluation r…
CodeTracer: Towards Traceable Agent States
Han Li, Yifan Yao, Letian Zhu +13
Code agents are advancing rapidly, but debugging them is becoming increasingly difficult. As frameworks orchestrate parallel tool calls and multi-stage workflows over complex tasks…
Long-form RewardBench: Evaluating Reward Models for Long-form Generation
Hui Huang, Yancheng He, Wei Liu +7
The widespread adoption of reinforcement learning-based alignment highlights the growing importance of reward models. Various benchmarks have been built to evaluate reward models i…
Flow-GRPO: Training Flow Matching Models via Online RL
Jie Liu, Gongye Liu, Jiajun Liang +6
We propose Flow-GRPO, the first method to integrate online policy gradient reinforcement learning (RL) into flow matching models. Our approach uses two key strategies: (1) an ODE-t…
DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models
Jianyu Liu, Hangyu Guo, Ranjie Duan +14
Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data, thereby introducing new dimensions of potential attacks…