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March 29, 2026Open Access

PRISM: Process-level Real-time Internal Safety Monitor for AI Systems

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Authors

ABAlon Babchuk

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Overview

PRISM reveals processing quality metrics in AI systems, suggesting improved safety monitoring strategies.

Key Points

  • The aim is to enhance AI safety by monitoring internal processing quality during generation phases.
  • Developed PRISM framework with eleven dimensions of processing quality.
  • Validated across five different AI architectures including Claude, ChatGPT, and others.
  • Utilized token-level signals such as entropy trajectory and KL divergence for analysis.
  • Achieved zero disagreements on direction across 55 data points from different AI architectures.
  • Demonstrated a 7.2x difference in branching factor between coherent and distorted text in GPT-2.

Cite This Study

Alon Babchuk (2026) studied this question.

synapsesocial.com/papers/69c8c3a8de0f0f753b39e92fhttps://doi.org/10.5281/zenodo.19247527
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