Tool Use

Tiantian He, Yihang Chen, Keyue Jiang et al.

EE-MCP shows how MCP-plus-GUI agents can self-improve by generating environments, synthesizing gap tasks, and accumulating reusable experience, with clear gains across desktop apps.

Shilin Yan, Jintao Tong, Hongwei Xue et al.

Act Wisely separates task accuracy from tool-efficiency rewards so multimodal agents learn when not to call tools, cutting unnecessary invocations by orders of magnitude while improving accuracy, latency, and cost.

Nirajan Acharya, Gaurav Kumar Gupta

First formal security framework for MCP-based AI agents, defining threats and verifiable defenses. Essential for builders deploying LLM agents with external tool access in production environments.

Guan-Ting Lin, Chen Chen, Zhehuai Chen et al.

Voice agents often fail when users stutter, pause, or interrupt, leading to broken API calls and frustrated users. This benchmark uses real human speech to reveal exactly how top models handle these messy realities. It allows developers to test if their voice systems can actually execute tasks reliably in natural conversation.