1 citations · 3 across the 11 of their papers we have counts for
12 papers
Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards
Yuante Li, Junwei Quan, Kai Qiu +2
Reinforcement learning with verifiable rewards (RLVR) relies on costly model-generated responses, motivating larger rollout batches and square-root learning-rate scaling. Reported…
Diverse Evidence, Better Forecasts: Multi-Agent Deliberation Under Information Asymmetry
Yuante Li, Yicheng Tao, Kate Zhang +3
Multi-agent systems are increasingly used for forecasting future events, as deliberation among multiple LLMs is believed to improve reasoning and calibration. Yet existing approach…
Audio Language Model for Deepfake Detection Grounded in Acoustic Chain-of-Thought
Runkun Chen, Yixiong Fang, Pengyu Chang +3
Deepfake speech detection systems are often limited to binary classification tasks and struggle to generate interpretable reasoning or provide context-rich explanations for their d…
MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants
Zuhao Zhang, Chengyue Yu, Yuante Li +3
With the rapid advancement of Large Language Models (LLMs) in code generation, human-AI interaction is evolving from static text responses to dynamic, interactive HTML-based applic…
From Observations to States: Latent Time Series Forecasting
Jie Yang, Yifan Hu, Yuante Li +3
Deep learning has achieved strong performance in Time Series Forecasting (TSF). However, we identify a critical representation paradox, termed Latent Chaos: models with accurate pr…
Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches
Yicheng Tao, Yuante Li, Yao Qin +1
Recent advances in large language models (LLMs) have significantly improved automated code generation. While existing approaches have achieved strong performance at the function an…
QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance
Haoxue Wang, Keli Wen, Yuante Li +11
Quantitative research increasingly relies on unstructured financial content such as filings, earnings calls, and research notes, yet existing LLM and RAG pipelines struggle with po…
DeepMath-Creative: A Benchmark for Evaluating Mathematical Creativity of Large Language Models
Xiaoyang Chen, Xinan Dai, Yu Du +28
To advance the mathematical proficiency of large language models (LLMs), the DeepMath team has launched an open-source initiative aimed at developing an open mathematical LLM and s…
R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
Yuante Li, Xu Yang, Xiao Yang +4
Financial markets pose fundamental challenges for asset return prediction due to their high dimensionality, non-stationarity, and persistent volatility. Despite advances in large l…
R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science
Xu Yang, Xiao Yang, Shikai Fang +13
Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. Although crowd-sourcing platforms alle…
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting
Yifan Hu, Yuante Li, Peiyuan Liu +6
Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investmen…
FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock Movement Prediction
Yifan Hu, Peiyuan Liu, Yuante Li +5
Recently, combining stock features with inter-stock correlations has become a common and effective approach for stock movement prediction. However, financial data presents signific…