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cs.AI2026

What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents

Xingshan Zeng, Zishan Xu, Boju Zhang +11

LLM agents increasingly rely on generated interaction data to learn how to interact with external environments. Agentic data generation must maintain consistency among environments…

cs.AI2026

EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning

Zishan Xu, Zhiyuan Yao, Yuxin Chen +9

Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verificatio…

cs.AI2026

From Brewing to Resolution: Tracing the Internal Lifecycle of Code Reasoning in LLMs

Siyue Chen, Yifu Guo, Yuquan Lu +9

Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle of code reasoning in which mode…

cs.AI2026

ACE-Router: Generalizing History-Aware Routing from MCP Tools to the Agent Web

Zhiyuan Yao, Zishan Xu, Yifu Guo +6

With the rise of the Agent Web and Model Context Protocol (MCP), the agent ecosystem is evolving into an open collaborative network, exponentially increasing accessible tools. Howe…

cs.AI2025

Octopus: Agentic Multimodal Reasoning with Six-Capability Orchestration

Yifu Guo, Zishan Xu, Zhiyuan Yao +6

Existing multimodal reasoning models and frameworks suffer from fundamental architectural limitations: most lack the human-like ability to autonomously explore diverse reasoning pa…