Houston Machine Learning

A Multi-Agent Collaboration Framework for Complex IT Query Support

This paper presents a multi-agent collaboration framework (MACF) powered by large language models (LLMs) for handling complex information technology (IT) support and technical queries. Our system implements a hierarchical workflow that decomposes user queries into manageable sub-tasks, orchestrates multiple specialized agents for parallel execution, and synthesizes their outputs into concise and clear responses. The framework features four key components: a planner node for query decomposition and agent selection, an execution node managing parallel sub-agent operations, a summarization node for result consolidation, and an output node for response generation. We incorporate human-in-the-loop feedback mechanisms and support interactive follow-up conversations to ensure accuracy and user satisfaction. To evaluate the planner’s accuracy and effectiveness of the workflow, we build an expert grounded complex IT Q&A dataset that includes 100 question and answer pairs. Four metrics were evaluated in the experiment, planner accuracy evaluated by human expert, helpfulness, clarity and factual accuracy evaluated by LLM respectively. Experimental results demonstrate that the framework effectively handles a wide range of technical support scenarios with fast and efficient execution.

Slides for past meetups posted: Github
Recordings have been posted at: YanAITalk

Feel free to reach out if you want to present a paper or a use case at upcoming meetups!

Note: You must have a Zoom account to login (free account is sufficient). Link and password will be shared three days before the meeting.

We are going to talk about how Claude Code is built, looking into the principals behind agentic AI coding!

Slides for past meetups posted: Github
Recordings have been posted at: YanAITalk

Feel free to reach out if you want to present a paper or a use case at upcoming meetups!

Note: You must have a Zoom account to login (free account is sufficient). Link and password will be shared three days before the meeting.

Multi-Agent Proactive Information Seeking with Adaptive LLM Orchestration for Non-Factoid Question Answering

The proliferation of complex non-factoid questions in modern information seeking (IS) systems exposes critical limitations in conventional Retrieval-Augmented Generation (RAG) approaches, particularly their static search strategies and the lack of systematic multi-source information integration capabilities. Facing these limitations, we present PASS (Proactive Agent-driven Search System), a novel multi-agent framework that operationalizes human-like proactive search strategies through five specialized agents: Revealer for intent analysis, Navigator for search planning, Seeker/Reader for adaptive retrieval, and Writer for response synthesis, systematically expanding the search space through iterative query refinement and multi-perspective knowledge integration. Crucially, our framework demonstrates remarkable adaptability to mid-sized LLMs, demonstrating its scalability in resource-constrained environments.

Slides for past meetups posted: Github
Recordings have been posted at: YanAITalk

Feel free to reach out if you want to present a paper or a use case at upcoming meetups!

Note: You must have a Zoom account to login (free account is sufficient). Link and password will be shared three days before the meeting.