What question did this study set out to answer?

The aim is to enhance AI safety by monitoring internal processing quality during generation phases.

March 29, 2026Open Access

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

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.

Abstract

PRISM proposes a complementary approach to AI safety that monitors the quality of processing during generation, not just outputs. The framework comprises eleven dimensions of processing quality validated across five independent AI architectures (Claude, ChatGPT, Gemini, Grok, Copilot) with zero disagreements on direction across 55 data points, grounded computationally in token-level signals including entropy trajectory, branching factor, and KL divergence. A preliminary validation on GPT-2 produced a 7.2x difference in branching factor between coherent and distorted text. The framework is content-agnostic and includes a working proof-of-concept implementation.

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