3 papers
cs.AI2026
V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization
Yubo Jiang, Yitong An, Xin Yang +7
We introduce V-tableR1, a process-supervised reinforcement learning framework that elicits rigorous, verifiable reasoning from multimodal large language models (MLLMs). Current MLL…
cs.MA2026
Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems
Yuzhe Zhang, Feiran Liu, Yi Shan +8
Large language models are increasingly deployed in multi-agent systems to overcome context limitations by distributing information across agents. Yet whether agents can reliably co…
cs.CL2026
Towards Self-Robust LLMs: Intrinsic Prompt Noise Resistance via CoIPO
Xin Yang, Letian Li, Abudukelimu Wuerkaixi +5
Large language models (LLMs) have demonstrated remarkable and steadily improving performance across a wide range of tasks. However, LLM performance may be highly sensitive to promp…