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

2026-06-25

AI Summary

This work addresses the limited cognitive autonomy of traditional AI in scientific discovery by introducing AHOIS, a multi-agent AI scientist that incorporates a Socratic questioning mechanism into autonomous physical exploration. By leveraging causal interrogation, counterexample generation, and falsifiability-driven hypothesis refinement, AHOIS autonomously proposes, tests, and revises hypotheses without relying on prior models. The system integrates causal reasoning, constraint verification, sparse measurement optimization, and uncertainty calibration within a closed-loop experimental framework. Deployed on a multimode fiber platform, AHOIS autonomously discovered a stochastic interference encoding scheme, achieving classification accuracies of 76.97% on MNIST and 83.17% on Fashion-MNIST, while effectively diagnosing multiple failure modes and substantially enhancing the consistency and completeness of physical interpretability.

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.

Problem

• autonomous scientific discovery
• epistemic autonomy
• high-dimensional physical systems
• Socratic interrogation
• hypothesis revision

Innovation

• Socratic agents
• autonomous scientific discovery
• multi-agent AI
• causal questioning
• closed-loop experimentation