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AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation

Event artwork for AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation

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About this event

What to expect

Explore how reinforcement learning and large language models can automate circuit topology generation in this online Machine Learning Paper Club session on 20 August 2026.

About the session

Sophie will present and discuss “AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation”, a research paper by Prashanth Vijayaraghavan, Luyao Shi, Ehsan Degan, Vandana Mukherjee and Xin Zhang.

The session offers an opportunity to examine research at the intersection of reinforcement learning, LLMs and automated circuit design. Rather than approaching the topic as a general AI introduction, the group will centre its conversation on the published paper and its proposed method for generating circuit topologies. Sophie’s presentation will provide the focal point for a shared technical discussion with other attendees interested in machine learning research.

Format and preparation

This is an online paper club, hosted by Ines C. as part of the Machine Learning Paper Club with nPlan. The meeting begins at 12:30 PM on Thursday 20 August 2026 and will take place over Google Meet. Access details are visible to attendees.

Participants are asked to read the paper before the meetup so they can follow the presentation and contribute to the discussion. The club publishes each selected paper in its GitHub repository, making it easier to prepare ahead of time and explore previous selections.

This session will be most relevant to:

  • Machine learning researchers, engineers and students
  • People following practical and academic developments in reinforcement learning
  • LLM practitioners interested in applications beyond text generation
  • Electronic design and circuit automation professionals exploring AI-led workflows
  • Technical attendees who enjoy reading and discussing current research papers

Because the format revolves around a research publication, some familiarity with machine learning terminology will help. No separate prerequisites are listed, but advance reading is strongly encouraged.

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