Drift: What Accuracy and Reward Do Not Show
Scores can look normal while a system drifts. We measure drift with Bi-Predictability (P): how well a system's inputs, actions and outcomes still predict one another. When P moves off its own healthy baseline, drift is under way, detected from data the system already produces.
Drift Detection, Explained
What It Takes to See Drift Early
Drift Detection Without Training
No labels, no model access, no retraining.
The monitor learns a healthy baseline from normal operation and flags when the loop drifts away from it.
Why Scores Miss Drift
Accuracy, reward and confidence report outcomes, not how well the loop still holds together.
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An independent drift layer catches the silent kind: wear, added load, an agent losing its grip, a pipeline losing the thread.
From Output Scores to Loop Drift
Traditional AI chases lagging indicators like accuracy or reward.
Bi-Predictability measures how strongly a system and its environment still predict each other. When that coupling weakens, drift is under way, often while task scores still look normal.
Drift With a Direction
Knowing that a system drifts is step one. Knowing where it drifts comes next.
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​With paired sensor and actuator records, the components of P help separate perception faults from actuator faults, so mitigation can be targeted.
ABOUT US
Measuring Drift with Information Theory
BiPredictability (P) is one drift measure, computed from a system's observations, actions and outcomes. When P moves away from the system's own healthy baseline, the system is drifting, often while task scores still look normal.
The Information Digital Twin (IDT)
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Drift Detection for Machines and AI
The IDT is our drift detector for autonomous systems. It runs alongside your robot, RL agent or LLM pipeline, reads only what goes in and what comes out, and computes P from that stream.
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When drift appears, it shows where: on the sensing side or the acting side, so your team can respond where it matters.
Robot, Agent and LLM Monitoring are its first implementations: no model access, no retraining.
Drift Detection for Human-Machine Loops
The HDT applies the same drift measure to the loop between a human operator and their system, in cockpits, control rooms or clinical settings.
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It reads the interaction stream: actions, observations and outcomes. When operator and system stop predicting each other, the HDT flags the drift.
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Systems can then simplify the interface or alert a supervisor while the loop can still be recovered.
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