1 citations · 1 across the 9 of their papers we have counts for
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Agentic Quantitative Trading: A Survey of Workflows, Systems, and Evaluation
Fengrui Hua, Hengyi Yang, Xinlei Hao +5
Quantitative trading is moving from isolated predictive models toward agentic workflows that combine reasoning, tool use, memory, and feedback. This survey reviews agentic quantita…
Envs-FORGE: Frontier-Optimized Reward-Grounded Environment Synthesis for Agent RL
Xiaojun Wu, Cehao Yang, Honghao Liu +7
Reinforcement learning (RL) for terminal agents needs executable training environments with reliable rewards and useful difficulty. Fixed recipes such as few-shot, Self-Instruct, a…
LazyTrain: Limited-resource Allocation toward Zero-waste Yield Optimization in Large Language Model Training
Xiaojun Wu, Cehao Yang, Honghao Liu +5
Training large language models on limited hardware is increasingly a scheduling problem across GPU compute, host memory, PCIe transfer, and storage bandwidth. Existing offloading s…
BrainPilot: Automating Brain Discovery with Agentic Research
Haoxuan Li, Tianci Gao, Jianhe Li +13
Understanding the brain increasingly depends on integrating evidence across scales, modalities, and disciplines. Addressing a single research question therefore requires a coordina…
Bayesian-Agent: Posterior-Guided Skill Evolution Across LLM Agent Harnesses
Xiaojun Wu, Cehao Yang, Honghao Liu +7
LLM agents increasingly rely on prompts, tools, memory, SOPs, skills, and harness feedback, yet current self-evolution pipelines often update these assets through heuristic reflect…
LoReC: Rethinking Large Language Models for Graph Data Analysis
Hongyu Zhan, Qixin Wang, Yusen Tan +6
The advent of Large Language Models (LLMs) has fundamentally reshaped the way we interact with graphs, giving rise to a new paradigm called GraphLLM. As revealed in recent studies,…