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20242026
most citedBeyond Performance: Quantifying and Mitigating Label Bias in LLMs

1 citations · 2 across the 9 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.CV2025

VISTA: A Test-Time Self-Improving Video Generation Agent

Do Xuan Long, Xingchen Wan, Hootan Nakhost +3

Despite rapid advances in text-to-video synthesis, generated video quality remains critically dependent on precise user prompts. Existing test-time optimization methods, successful…

cs.AI2025

Maestro: Self-Improving Text-to-Image Generation via Agent Orchestration

Xingchen Wan, Han Zhou, Ruoxi Sun +4

Text-to-image (T2I) models, while offering immense creative potential, are highly reliant on human intervention, posing significant usability challenges that often necessitate manu…

cs.CL2025

DynScaling: Efficient Verifier-free Inference Scaling via Dynamic and Integrated Sampling

Fei Wang, Xingchen Wan, Ruoxi Sun +2

Inference-time scaling has proven effective in boosting large language model (LLM) performance through increased test-time computation. Yet, its practical application is often hind…

cs.LG2025

Visual Planning: Let's Think Only with Images

Yi Xu, Chengzu Li, Han Zhou +4

Recent advancements in Large Language Models (LLMs) and their multimodal extensions (MLLMs) have substantially enhanced machine reasoning across diverse tasks. However, these model…

cs.LG2025★ 1 cited

Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL

Mohammadreza Pourreza, Shayan Talaei, Ruoxi Sun +5

Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query fo…

cs.LG2025

Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies

Han Zhou, Xingchen Wan, Ruoxi Sun +5

Large language models, employed as multiple agents that interact and collaborate with each other, have excelled at solving complex tasks. The agents are programmed with prompts tha…