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Research Paper#LLM Agents, Tool Use, Benchmarking🔬 ResearchAnalyzed: Jan 3, 2026 09:18

MCPAgentBench: Evaluating LLM Agents with Real-World Tools

Published:Dec 31, 2025 02:09
1 min read
ArXiv

Analysis

This paper addresses the limitations of current LLM agent evaluation methods, specifically focusing on tool use via the Model Context Protocol (MCP). It introduces a new benchmark, MCPAgentBench, designed to overcome issues like reliance on external services and lack of difficulty awareness. The benchmark uses real-world MCP definitions, authentic tasks, and a dynamic sandbox environment with distractors to test tool selection and discrimination abilities. The paper's significance lies in providing a more realistic and challenging evaluation framework for LLM agents, which is crucial for advancing their capabilities in complex, multi-step tool invocations.
Reference

“The evaluation employs a dynamic sandbox environment that presents agents with candidate tool lists containing distractors, thereby testing their tool selection and discrimination abilities.”

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Paper#MLLM, Computer Vision, Segmentation🔬 ResearchAnalyzed: Jan 3, 2026 17:05

RSAgent: Agentic MLLM for Text-Guided Segmentation

Published:Dec 30, 2025 06:50
1 min read
ArXiv

Analysis

This paper introduces RSAgent, an agentic MLLM designed to improve text-guided object segmentation. The key innovation is the multi-turn approach, allowing for iterative refinement of segmentation masks through tool invocations and feedback. This addresses limitations of one-shot methods by enabling verification, refocusing, and refinement. The paper's significance lies in its novel agent-based approach to a challenging computer vision task, demonstrating state-of-the-art performance on multiple benchmarks.
Reference

“RSAgent achieves a zero-shot performance of 66.5% gIoU on ReasonSeg test, improving over Seg-Zero-7B by 9%, and reaches 81.5% cIoU on RefCOCOg, demonstrating state-of-the-art performance.”

PermalinkArXiv