most citedCoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation

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cs.AI2026

EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds

Runxuan Liu, Jingqi Shi, Baoxin Wang +10

Multi-modal agents have made significant progress in interacting with open worlds. However, their ability to self-evolve remains limited. Supervised fine-tuning enables imitation b…

cs.AI2026

Consistent Plan-Act for Long-Horizon Agentic Tasks

Heng-Zhuang Li, Yi-Kai Zhang, Yu Wang +4

Long-horizon agentic tasks demand strong reasoning and efficient execution across successive interactions with dynamic environments. A common approach decouples high-level planning…

cs.AI2026

AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning

Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao +10

Reinforcement learning (RL) with verifiable rewards constructs trajectory-level advantage estimates, yet it often fails to credit the few pivotal decisions that determine outcomes…

cs.AI2026

Finding the Evidence: Discovering Decision-Supporting Tokens for On-Policy Reasoning Distillation

Jinwei Xiao, Zhuowen Han, Yueqing Sun +6

On-policy distillation transfers reasoning ability through dense token-level supervision, yet the nature of the transferable signal remains unclear. We discover that reasoning chai…

cs.AI2026

MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning

Yuxin Liu, Ziang Ye, Yueqing Sun +6

Current interactive LLM agents rely on goal-conditioned stepwise planning, where environmental understanding is acquired reactively during execution rather than established beforeh…

cs.AI2026

ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Training

Dunwei Tu, Hongyan Hao, Hansi Yang +10

Training generalist agents capable of adapting to diverse scenarios requires interactive environments for self-exploration. However, interactive environments remain critically scar…

cs.AI2026

Cross-Modal Memory Compression for Efficient Multi-Agent Debate

Jing Wu, Yue Sun, Tianpei Xie +7

Multi-agent debate can improve reasoning quality and reduce hallucinations, but it incurs rapidly growing context as debate rounds and agent count increase. Retaining full textual…