5 citations · 12 across the 14 of their papers we have counts for
7 papers · 1 filter
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…