Socratic agents for autonomous scientific discovery in high-dimensional physical systems

• URL: http://arxiv.org/abs/2606.26722v1
• Date: Thu, 25 Jun 2026 08:01:07 GMT
• Title: Socratic agents for autonomous scientific discovery in high-dimensional physical systems
• Abstract summary: AHOIS embeds Socratic midwifery into closed-loop experimentation.<n>A physics-critic agent interrogates hypotheses through causal questioning.<n> AHOIS autonomously proposed and validated a random-interference encoding hypothesis.
• Score: 24.054349223347966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automation of scientific discovery has reached an inflection point. While AI systems now operate instruments, optimize parameters and generate hypotheses, most remain procedural: they execute workflows fixed by human designers. True autonomous science demands epistemic autonomy--the capacity to construct, challenge and revise physical explanations in response to evidence. Here we introduce AHOIS, a multi-agent AI scientist that embeds Socratic midwifery into closed-loop experimentation. A physics-critic agent interrogates hypotheses through causal questioning, constraint checking, counterexample generation and falsification-criteria formulation. We evaluate AHOIS on a real multimode-fibre optical platform, a high-dimensional system with complex wave transformations, indirect detection, environmental drift and multi-modal acquisition. Without prior encoding schemes, classifiers or speckle models, the system autonomously proposed and validated a random-interference encoding hypothesis, discovered task-adaptive sparse-measurement strategies, diagnosed distinct failure modes (encoding instability, fluorescence contamination and detector noise) and translated a published imaging protocol into an executable workflow on a non-original configuration. The discovered encoding yielded 16x16 measurements with effective rank 56.9 and classification accuracies of 76.97% on MNIST and 83.17% on Fashion-MNIST. Ablations show that Socratic interrogation improves physical consistency, hypothesis completeness, uncertainty calibration and experimental-plan validity. These results establish a route from workflow automation towards evidence-grounded, self-correcting autonomous discovery in complex physical environments.

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