2026/02/16 by Ivan Kondratyev, Weinan Sun · 1 voice
Computer Science · #Adversarial Robustness in Machine Learning #Explainable Artificial Intelligence (XAI) #Neural Networks and Reservoir Computing
paper · doi:10.64898/2026.02.13.705771
openalex publication_date 2026/02/16 · openalex created_date 2026/02/18 · openalex updated_date 2026/07/14
Abstract AI coding assistants excel at software tasks but lack structured access to laboratory hardware, the physical instruments that define experimental science. We present A taraxis , an open-source framework that provides hardware control capabilities spanning camera acquisition, microcontroller communication, precision timing, and inter-process coordination, while exposing these capabilities to AI agents through Model Context Protocol (MCP) servers and domain-specific skills. Critically, A taraxis separates configuration-time AI assistance from runtime data acquisition , ensuring that experiments run deterministically regardless of AI service availability. We validate this architecture in a two-photon imaging and virtual reality rodent behavior platform, demonstrating up to order-of-magnitude reductions in hardware validation, integration, and personnel onboarding time. By bridging the gap between AI software capabilities and physical instrument control, A taraxis offers a reusable blueprint for AI-assisted scientific instrumentation across experimental disciplines. All code is available at github.com/Sun-Lab-NBB/ataraxis .