2 citations · 2 across the 14 of their papers we have counts for
15 papers
Composing Task-specific Agent Harnesses at Test Time with Reusable Primitives
Peng Kuang, Haibo Jin, Dehao Wu +4
Agent harnesses govern how large language models (LLMs) gather context, invoke tools, verify results, preserve state, and terminate, largely affecting agent performance. However, t…
STEVE: Stabilizing Textual Gradient-Based Prompt Optimization via Error-Driven Refinement and Regularized Verification
Yifan Xu, Yixuan Li, Xinzhuo Li +4
Textual-gradient methods automate prompt optimization through natural-language feedback, but their iterative updates can be unstable. We identify two sources of this instability: n…
Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives
Haibo Jin, Suijin Wang, Xucheng Yu +2
Large language models (LLMs) augmented with external tools have demonstrated remarkable capability in solving complex real-world tasks. However, existing approaches suffer from two…
Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior
Yifan Lu, Xiaopeng Yuan, Haohan Wang
Personalized game generation requires inferring a player's abilities and behavioral style from how they play. Large language models have made this inference more attainable than ev…
CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation
Yaoning Yu, Kai-Min Chang, Ye Yu +3
Online credit card discussions provide a natural setting for studying how consumers communicate about financial products. Simulating these discussions requires more than just gener…
QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing
Yijing Zuo, Zhe Fu, Zihan Nie +2
Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and outpu…
KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling
Peng Kuang, Haibo Jin, Xiaoyu Han +5
Process Reward Models (PRMs) have been proven to be highly effective in guiding test-time scaling (TTS) methods, which significantly boost the capabilities of LLM-based multi-agent…
Closing the Loop on Latent Reasoning via Test-Time Reconstruction
Xiaopeng Yuan, Haibo Jin, Ye Yu +4
Recent work moves intermediate reasoning from natural-language traces into latent or cache-level representations to reduce token overhead and avoid a discrete communication bottlen…
ThreadBench: Benchmarking Realism in Agentic Simulation of Real-world Discussions
Yaoning Yu, Ye Yu, Haojing Luo +4
LLM agents are increasingly used to simulate real-world interactions, but it remains unclear whether simulated behaviors preserve the content patterns and interaction dynamics of r…
Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation
Ye Yu, Xiaopeng Yuan, Haibo Jin +3
Recent advances in LLM agents enable systems that autonomously refine workflows, accumulate reusable skills, self-train their underlying models, and maintain persistent memory. How…
Learning to Communicate: Toward End-to-End Optimization of Multi-Agent Language Systems
Ye Yu, Heming Liu, Haibo Jin +3
Multi-agent systems built on large language models have shown strong performance on complex reasoning tasks, yet most work focuses on agent roles and orchestration while treating i…
Synthetic Data-Driven Prompt Tuning for Financial QA over Tables and Documents
Yaoning Yu, Kai-Min Chang, Ye Yu +3
Financial documents like earning reports or balance sheets often involve long tables and multi-page reports. Large language models have become a new tool to help numerical reasonin…
Large Language Model-based Data Science Agent: A Survey
Ke Chen, Peiran Wang, Yaoning Yu +2
The rapid advancement of Large Language Models (LLMs) has driven novel applications across diverse domains, with LLM-based agents emerging as a crucial area of exploration. This su…
PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models
Ye Yu, Yaoning Yu, Haohan Wang
Large reasoning models (LRMs) such as Claude 3.7 Sonnet and OpenAI o1 achieve strong performance on mathematical benchmarks using lengthy chain-of-thought (CoT) reasoning, but the…
SIPDO: Closed-Loop Prompt Optimization via Synthetic Data Feedback
Yaoning Yu, Ye Yu, Peiyan Zhang +3
Prompt quality plays a critical role in the performance of large language models (LLMs), motivating a growing body of work on prompt optimization. Most existing methods optimize pr…