closed-loop

LLM-AutoSciLab is a closed-loop framework that uses LLMs to iteratively generate hypotheses, select informative experiments, and refine mechanisms, achieving superior accuracy and sample efficiency on physics and biology benchmarks over prior static methods.

Two separate brain implant systems, the ICVP and a closed-loop visual neuroprosthesis, restore partial vision in blind people by directly stimulating the visual cortex, with one device also adapting based on brain signals.

The LEAP framework integrates a domain-specialized large language model with active learning to efficiently prioritize precursor additives for perovskite solar cells, achieving improved power conversion efficiencies in experimental validation.

This paper proposes a memory-augmented reinforcement learning framework for CAD generation agents that integrates geometric kernel toolchains, dual-track memory, and dynamic utility retrieval to handle complex CAD models with long operation sequences and geometric constraints, achieving improved success rate and geometric consistency.

This article presents a method for building self-repairing agent loops using OpenAI's Codex, where agents review, repair, and validate outputs iteratively, with a worked example of fixing stale API documentation.