Large language models often appear to “forget” everything between sessions—not because of model failure, but because today’s systems lack true continuity. Context resets, assumptions vanish, and long-term work quietly degrades.
Fixing Drift introduces a real-world continuity architecture that preserves reasoning state across sessions without modifying the underlying model. By separating in-session stability from cross-session memory and using deterministic handoffs instead of fragile summaries, the system makes reasoning resumable, auditable, and recoverable over time.
This is not a prompt trick or a theoretical proposal. It documents a deployed system built for long-horizon research and high-stakes workflows where starting over isn’t acceptable.
CANARY: A Runtime Integrity Architecture for Detecting and Containing Drift in Long-Running LLM Reasoning
CANARY is a runtime integrity architecture that detects, classifies, and contains reasoning drift in large language models during extended or complex interactions.
Instead of treating hallucinations as isolated output errors, CANARY monitors the gradual degradation of reasoning coherence—including constraint erosion, assumption hardening, and premature convergence—before failures surface as incorrect or fabricated outputs.
CANARY operates as a non-intrusive governance layer. It does not modify model weights or training data. Instead, it provides real-time observability, explicit health signaling, and deterministic containment actions that preserve reasoning stability while a session is active.
Designed for long-running reasoning, research workflows, and production systems, CANARY makes drift visible, auditable, and actionable, turning reasoning stability into a managed system property.
Hallucination is commonly treated as a primary failure of language models—an output problem to be patched with better grounding or verification. This article argues instead that hallucination is often the final symptom of earlier, invisible failures that accumulate during extended use.
By analyzing how constraint loss, assumption hardening, and confidence escalation emerge over time, the piece shows why correctness-based evaluations miss the real failure sequence. Hallucination is reframed not as the cause, but as the observable endpoint of runtime degradation.
Understanding hallucination this way shifts the focus from fixing outputs to stabilizing reasoning itself.
Hallucination in language models is often treated as a knowledge or grounding failure. This article argues instead that it is a structural consequence of forcing models to always participate—always answer, even when uncertainty is high or information is missing.
By examining how output requirements interact with confidence signaling, constraint decay, and runtime drift, the piece explains why hallucination persists even in well-trained systems. The Participation Problem reframes hallucination not as misbehavior, but as an emergent property of systems that prohibit refusal, silence, or uncertainty.
This perspective highlights why many mitigation strategies fail—and why addressing hallucination requires changing interaction contracts, not just improving models.