5 citations · 12 across the 14 of their papers we have counts for
16 papers
EvalBuilder: Can LLMs Build Efficient Benchmarks?
Huaisheng Zhu, Hangfan Zhang, Teng Xiao +5
Large language model (LLM) agents are increasingly used for long-horizon research and engineering, yet evaluating these capabilities remains expensive, time-consuming, and heavily…
Long-Horizon Scaling: How Model Capabilities Shape the Returns to Computation
Haoyu Zheng, Zhengyu Chen, Huaisheng Zhu +5
Long-horizon agents improve solutions through sustained interaction, execution, and task feedback. Scaling studies relate performance to resources and capabilities, yet how existin…
Rethinking the Evaluation of Harness Evolution for Agents
Yike Wang, Huaisheng Zhu, Zhengyu Hu +8
Harness evolution is an iterative search procedure that repeatedly evaluates and revises candidate harnesses used for LLM agents using task feedback. We revisit the evaluation of s…
Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
Yiwei Li, Wanli Yang, Hexiang Tan +10
Autonomous agents are increasingly capable of improving models, systems, and other technical artifacts through long-horizon experimentation. To understand the current state of this…
Distilling Temporal Search and Reasoning: Evolving LLMs for Future Prediction via Harness-Assisted Efficient Data Synthesis
Wanxu Cai, Zhengyu Chen, Huaisheng Zhu +3
Future event prediction carries broad social impact yet remains challenging. SOTA approaches augment LLMs with external agent frameworks whose predictive capability vanishes once t…
Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents
Zhengyu Chen, Teng Xiao, Huaisheng Zhu +3
Post-training agents for automated AI research requires optimizing not only model parameters, but also the runtime harness that shapes how research trajectories are generated, eval…
Meta-Reinforcement Learning with Self-Reflection for Agentic Search
Teng Xiao, Yige Yuan, Hamish Ivison +6
This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a policy within a single…
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…
Simple Denoising Diffusion Language Models
Huaisheng Zhu, Zhengyu Chen, Shijie Zhou +8
Recent Uniform State Diffusion Models (USDMs), initialized from a uniform prior, offer the promise of fast text generation due to their inherent self-correction ability compared to…
Optimizing Token Choice for Code Watermarking: An RL Approach
Zhimeng Guo, Huaisheng Zhu, Siyuan Xu +3
Protecting intellectual property on LLM-generated code necessitates effective watermarking systems that can operate within code's highly structured, syntactically constrained natur…
Cal-DPO: Calibrated Direct Preference Optimization for Language Model Alignment
Teng Xiao, Yige Yuan, Huaisheng Zhu +2
We study the problem of aligning large language models (LLMs) with human preference data. Contrastive preference optimization has shown promising results in aligning LLMs with avai…
GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules
Teng Xiao, Chao Cui, Huaisheng Zhu +1
Pretraining molecular representations is crucial for drug and material discovery. Recent methods focus on learning representations from geometric structures, effectively capturing…
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective
Teng Xiao, Mingxiao Li, Yige Yuan +3
This paper introduces a novel generalized self-imitation learning () framework, which effectively and efficiently aligns large language models with offline demonstra…
MolBind: Multimodal Alignment of Language, Molecules, and Proteins
Teng Xiao, Chao Cui, Huaisheng Zhu +1
Recent advancements in biology and chemistry have leveraged multi-modal learning, integrating molecules and their natural language descriptions to enhance drug discovery. However,…
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
Huaisheng Zhu, Teng Xiao, Vasant G Honavar
Generating molecular structures with desired properties is a critical task with broad applications in drug discovery and materials design. We propose 3M-Diffusion, a novel multi-mo…
On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?
Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu +5
Large Language Models (LLMs) have achieved unprecedented performance in Natural Language Generation (NLG) tasks. However, many existing studies have shown that they could be misuse…