Houston Machine Learning

We are introducing the V-JEPA 2, the first world model and new benchmarks for physical reasoning from Meta:
Meta introduced V-JEPA 2 in June 2025, the first world model trained on video that enables state-of-the-art understanding and prediction, as well as zero-shot planning and robot control in new environments. As we work toward our goal of achieving advanced machine intelligence (AMI), it will be important that we have AI systems that can learn about the world as humans do, plan how to execute unfamiliar tasks, and efficiently adapt to the ever-changing world around us.

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.

Text-to-SQL for Enterprise Data Analytics

The introduction of large language models has brought rapid progress on Text-to-SQL benchmarks, but it is not yet easy to build a working enterprise solution. In this paper, we present insights from building an internal chatbot that enables LinkedIn’s product managers, engineers, and operations teams to self-serve data insights from a large, dynamic data lake. Our approach features three components.

First, we construct a knowledge graph that captures up-to-date semantics by indexing database metadata, historical query logs, wikis, and code. We apply clustering to identify relevant tables for each team or product area.

Second, we build a Text-to-SQL agent that retrieves and ranks context from the knowledge graph, writes a query, and automatically corrects hallucinations and syntax errors.

Third, we build an interactive chatbot that supports various user intents, from data discovery to query writing to debugging, and displays responses in rich UI elements to encourage follow-up chats. Our chatbot has over 300 weekly users. Expert review shows that 53% of its responses are correct or close to correct on an internal benchmark set. Through ablation studies, we identify the most important knowledge graph and modeling components, offering a practical path for developing enterprise Text-to-SQL solutions.

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.

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.