AudioFab is an open-source agent framework designed to create a unified and efficient audio-processing ecosystem by addressing the fragmentation and complex integration issues of existing audio AI tools. It offers a modular design and intelligent tool learning strategies to simplify complex audio tasks for both experts and non-experts.

This paper introduces a new benchmark for evaluating Large Language Model (LLM) agents in planning and execution tasks within industrial automation. It uses the Blocksworld problem with five complexity categories and integrates the Model Context Protocol (MCP) as a standardized tool interface, enabling systematic comparison of diverse LLM agent architectures.

This paper introduces a benchmark for evaluating long context reasoning capabilities of WebAgents through sequentially dependent subtasks that require retrieval and application of information from extended interaction histories. It observes a dramatic performance degradation as context length increases and proposes an implicit RAG approach for modest improvements.