EMNLP Findings 2026 FinanceHarness: Autonomous Financial Deep Research Framework Code

Yijia Xiao, Rujun Han, Yanfei Chen, Zifeng Wang, Ke Jiang, Zhongying CuiZhu, Vishy Tirumalashetty, Wei Wang, Burak Gokturk, Tomas Pfister, Chen-Yu Lee

Abstract: FinanceHarness automates financial deep research end to end — environment and data construction, the agent execution loop, and reward modeling — running finance-oriented tools and practitioner-guided workflows. We pair it with FinanceGym, a point-in-time benchmark whose thesis-driven rubrics separate pre-cutoff evidence retrieval from post-cutoff reasoning, validated by professional annotators at an 82% pass rate. With the same open-weight backbone, FinanceHarness lifts the overall rubric score from 25.3% to 32.4%; even paired with frontier models the benchmark stays below 45%, leaving substantial headroom.

Technical Report Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning Trading-R1 Terminal

Yijia Xiao, Edward Sun, Tong Chen, Fang Wu, Di Luo, Wei Wang

Abstract: Trading-R1 is a financially-aware reasoning model that incorporates strategic planning for thesis composition, grounds analysis in heterogeneous evidence, and executes volatility-adjusted decisions. We align its reasoning with trading principles through supervised fine-tuning and a three-stage reinforcement curriculum on the Tauric-TR1-DB corpus. Trading-R1 delivers interpretable, disciplined workflows that will power the forthcoming Trading-R1 Terminal.

Technical Report TradingAgents: Multi-Agents LLM Financial Trading Framework Oral Presentation · Poster

Yijia Xiao, Edward Sun, Di Luo, Wei Wang

Abstract: We present TradingAgents, a pioneering multi-agent LLM framework that revolutionizes autonomous trading by simulating professional trading firm dynamics. Our system orchestrates specialized agents—from analysts to risk managers—in a collaborative decision-making process, achieving up to 30.5% annualized returns, significantly outperforming traditional trading strategies while maintaining robust risk management.

NAACL 2025, Oral CSR-Bench: Benchmarking LLM Agents in Deployment of Computer Science Research Repositories Oral Presentation

Yijia Xiao, Runhui Wang, Luyang Kong, Davor Golac, Wei Wang

Abstract: CSR-Bench is a benchmark for evaluating LLM agents on deploying computer-science research repositories (NLP/CV/AI/ML/DM), scoring accuracy, efficiency, and deployment-script quality. We introduce CSR-Agents, a multi-agent framework that reads a repository’s README and structure and iteratively generates and refines bash commands to set up environments and run experiments—significantly streamlining research-code deployment.

EMNLP Findings 2025 Protein Large Language Models: A Comprehensive Survey

Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang

Abstract: Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. While existing surveys focus on specific aspects or applications, this work provides the first comprehensive overview of Protein LLMs, covering their architectures, training datasets, evaluation metrics, and diverse applications. Through a systematic analysis of over 100 articles, we propose a structured taxonomy of state-of-the-art Protein LLMs, analyze how they leverage large-scale protein sequence data for improved accuracy, and explore their potential in advancing protein engineering and biomedical research.

Spotlight, MLGenX ICLR 2025 ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding Spotlight, MLGenX, ICLR 2025 · Poster

Yijia Xiao, Edward Sun, Yiqiao Jin, Qifan Wang, Wei Wang

Abstract: ProteinGPT enables comprehensive protein analysis by allowing users to upload sequences and structures, providing contextually relevant responses to streamline protein research.

Huggingface Demonstration: https://huggingface.co/spaces/AI-BIO/ProteinGPT-Llama3.

EMNLP 2024 Main Large Language Models Can Be Contextual Privacy Protection Learners

Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng

Abstract: We introduce CPPLM (Contextual Privacy Protection Fine-Tuning for LLM), which emphasizes instruction-based tuning with positive and negative examples, enabling LLMs to capture knowledge while preserving privacy.

Research Benchmark LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts

Yijia Xiao, Edward Sun, Tianyu Liu, Wei Wang

Abstract: LogicVista is an evaluation benchmark designed to assess logical reasoning capabilities of MLLMs in visual contexts, encompassing multiple logical reasoning tasks and capabilities.