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cs.AI2026

From Reactive to Proactive: Assessing the Proactivity of Voice Agents via ProVoice-Bench

Ke Xu, Yuhao Wang, Yu Wang

Recent advancements in LLM agents are gradually shifting from reactive, text-based paradigms toward proactive, multimodal interaction. However, existing benchmarks primarily focus…

cs.AI2026

ReactBench: A Benchmark for Topological Reasoning in MLLMs on Chemical Reaction Diagrams

Qiang Xu, Shengyuan Bai, Yu Wang +6

Multimodal Large Language Models (MLLMs) excel at recognizing individual visual elements and reasoning over simple linear diagrams. However, when faced with complex topological str…

cs.AI2026

AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation

Wentao Shi, Yu Wang, Yuyang Zhao +8

As reinforcement learning continues to scale the training of large language model-based agents, reliably verifying agent behaviors in complex environments has become increasingly c…

cs.AI2026

Agentic Reasoning for Large Language Models

Tianxin Wei, Ting-Wei Li, Zhining Liu +26

Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilitie…

cs.AI2025

Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents

Zihao Wang, Xujing Li, Yining Ye +24

We present Game-TARS, a generalist game agent trained with a unified, scalable action space anchored to human-aligned native keyboard-mouse inputs. Unlike API- or GUI-based approac…

cs.AI2025

RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning

Yu Wang, Shiwan Zhao, Zhihu Wang +6

The integration of external knowledge through Retrieval-Augmented Generation (RAG) has become foundational in enhancing large language models (LLMs) for knowledge-intensive tasks.…