16 citations · 65 across the 48 of their papers we have counts for
14 papers · 1 filter
FineFT: Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Trading
Molei Qin, Xinyu Cai, Yewen Li +5
Futures are contracts obligating the exchange of an asset at a predetermined date and price, notable for their high leverage and liquidity and, therefore, thrive in the Crypto mark…
MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys
Yanchen Deng, Chendong Zhao, Yixuan Li +10
The discovery of advanced metallic alloys is hindered by vast composition spaces, competing property objectives, and real-world constraints on manufacturability. Here we introduce…
The Path of Self-Evolving Large Language Models: Achieving Data-Efficient Learning via Intrinsic Feedback
Hangfan Zhang, Siyuan Xu, Zhimeng Guo +8
Reinforcement learning (RL) has demonstrated potential in enhancing the reasoning capabilities of large language models (LLMs), but such training typically demands substantial effo…
GDBA Revisited: Unleashing the Power of Guided Local Search for Distributed Constraint Optimization
Yanchen Deng, Xinrun Wang, Bo An
Local search is an important class of incomplete algorithms for solving Distributed Constraint Optimization Problems (DCOPs) but it often converges to poor local optima. While Gene…
FinWorld: An All-in-One Open-Source Platform for End-to-End Financial AI Research and Deployment
Wentao Zhang, Yilei Zhao, Chuqiao Zong +2
Financial AI holds great promise for transforming modern finance, with the potential to support a wide range of tasks such as market forecasting, portfolio management, quantitative…
FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation
Qinggang Zhang, Zhishang Xiang, Yilin Xiao +4
Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks. However, these models often struggle wi…
Autonomous Multi-objective Alloy Design through Simulation-guided Optimization
Penghui Yang, Chendong Zhao, Bijun Tang +11
Alloy discovery is constrained by vast compositional spaces, competing objectives, and prohibitive experimental costs. Although simulations and machine learning have each accelerat…
StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley
Weihao Tan, Changjiu Jiang, Yu Duan +5
Autonomous agents navigating human society must master both production activities and social interactions, yet existing benchmarks rarely evaluate these skills simultaneously. To b…
The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants
Yiqun Zhang, Hao Li, Chenxu Wang +11
Proprietary giants are increasingly dominating the race for ever-larger language models. Can open-source, smaller models remain competitive across a broad range of tasks? In this p…
Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction
Junzhe Jiang, Chang Yang, Xinrun Wang +1
Stock market indices serve as fundamental market measurement that quantify systematic market dynamics. However, accurate index price prediction remains challenging, primarily becau…
FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs
Junzhe Jiang, Chang Yang, Aixin Cui +6
Financial tasks are pivotal to global economic stability; however, their execution faces challenges including labor intensive processes, low error tolerance, data fragmentation, an…
Resolving Latency and Inventory Risk in Market Making with Reinforcement Learning
Junzhe Jiang, Chang Yang, Xinrun Wang +3
The latency of the exchanges in Market Making (MM) is inevitable due to hardware limitations, system processing times, delays in receiving data from exchanges, the time required fo…
Nondeterministic Polynomial-time Problem Challenge: An Ever-Scaling Reasoning Benchmark for LLMs
Chang Yang, Ruiyu Wang, Junzhe Jiang +9
Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can…
Stop Overvaluing Multi-Agent Debate -- We Must Rethink Evaluation and Embrace Model Heterogeneity
Hangfan Zhang, Zhiyao Cui, Jianhao Chen +5
Multi-agent debate (MAD) has gained significant attention as a promising line of research to improve the factual accuracy and reasoning capabilities of large language models (LLMs)…