context-degradation

To diagnose context degradation, you can measure attention distribution to identify degraded regions such as lost-in-middle, then surface contradictions and hallucination markers to assess poisoning risks.

The lost-in-middle effect is a context degradation pattern where an agent loses focus on information positioned in the middle of a long conversation, which you can detect by measuring attention distribution.

You detect context poisoning and conflicts by surfacing contradictions, hallucination markers, and error signals within the session, allowing you to assess poisoning risks and support reliable reasoning.

Yes, you can analyze long conversation context using numpy as the sole dependency to compute attention distribution metrics and generate a context health score with remediation recommendations.

The best way to mitigate context clash and distraction is to generate actionable recovery recommendations and remediation procedures that restore stable and accurate agent behavior.

You should run a context health analysis during long chat sessions or extended debugging operations to obtain a degradation score, risk flags, and concrete steps to improve response quality.