Bi-Predictability: A Real-Time Signal for Monitoring LLM Interaction Integrity

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

A new paper introduces "Bi-Predictability" (P), an information-theoretic measure for real-time monitoring of Large Language Model (LLM) interaction integrity in multi-turn conversations. Current evaluation methods, such as perplexity or semantic entropy, often fail to detect gradual degradation in structural coupling. The authors propose the Information Digital Twin (IDT), a lightweight architecture that estimates P across the context, response, and next prompt loop without requiring secondary inference or embeddings. In experiments involving 4,500 conversational turns between a student model and three frontier teacher models, the IDT achieved 100% sensitivity in detecting injected disruptions. The research highlights that structural coupling and semantic quality are empirically separable, with P aligning with structural consistency in 85% of conditions but with semantic judge scores in only 44%, revealing a "silent uncoupling" phenomenon where LLMs produce high-scoring outputs despite degrading conversational context.

Key takeaway

For research scientists developing or deploying LLMs in high-stakes autonomous workflows, you should integrate bi-predictability monitoring to detect structural degradation. Relying solely on semantic evaluation can mask "silent uncoupling," leading to unreliable system behavior. Consider implementing an Information Digital Twin (IDT) to ensure continuous, real-time interaction integrity, even when semantic outputs appear acceptable.

Key insights

Bi-predictability offers a real-time signal for monitoring LLM interaction integrity, distinct from semantic quality.

Principles

• Structural coupling is separable from semantic quality.
• Real-time monitoring requires lightweight, non-semantic signals.

Method

The Information Digital Twin (IDT) architecture estimates bi-predictability (P) from raw token frequency statistics across conversational turns, without secondary inference or embeddings.

In practice

• Detect "silent uncoupling" in LLM interactions.
• Implement real-time AI assurance for autonomous workflows.

Topics

• Bi-predictability
• Information Digital Twin
• LLM Interaction Monitoring
• Real-Time AI Assurance
• Structural Coupling

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.