3 citations · 3 across the 7 of their papers we have counts for
8 papers
Steering Black-Box Large Language Model Agents with Pitfall Anticipation World Model
Jiongxiao Wang, Han Ding, Youzhi Luo +4
Frontier black-box Large Language Model (LLM) agents are proficient at long-horizon reasoning and precise action generation. However, when it comes to a specific agentic task, they…
Prism-GRPO: Faster VLA Policy Optimization via Splitting Same-outcome Groups
Zeyun Deng, Yuzhe Lu, Yawei Wang +6
GRPO is increasingly used for reinforcement learning of vision-language-action (VLA) policies because, unlike PPO, it does not require training a critic. This simplification comes…
The Cold-Start Safety Gap in LLM Agents
Chung-En Sun, Linbo Liu, Tsui-Wei Weng
Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a session and become substantially saf…
LLM Agents Already Know When to Call Tools -- Even Without Reasoning
Chung-En Sun, Linbo Liu, Ge Yan +2
Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latency, yet no existing benchmark…
CLEAR: Context Augmentation from Contrastive Learning of Experience via Agentic Reflection
Linbo Liu, Guande Wu, Han Ding +7
Large language model agents rely on effective model context to obtain task-relevant information for decision-making. Many existing context engineering approaches primarily rely on…
MigrationBench: Repository-Level Code Migration Benchmark from Java 8
Linbo Liu, Xinle Liu, Qiang Zhou +8
With the rapid advancement of powerful large language models (LLMs) in recent years, a wide range of software engineering tasks can now be addressed using LLMs, significantly enhan…
QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks
Yaojie Hu, Qiang Zhou, Qihong Chen +6
We introduce QualityFlow, a dynamic agentic workflow for program synthesis. Given the English description of a programming problem and a set of unit tests, the model's goal is to s…
CodeFort: Robust Training for Code Generation Models
Yuhao Zhang, Shiqi Wang, Haifeng Qian +8
Code generation models are not robust to small perturbations, which often lead to incorrect generations and significantly degrade the performance of these models. Although improvin…