context-degradation

Long-context degradation reduces model reliability as conversation length grows, causing lost-in-middle risks where information is ignored. It occurs when attention distribution fails across the context window, degrading agent performance during lengthy multi-turn sessions.

You detect lost-in-middle and context poisoning by measuring attention distribution across the context window. This analysis surfaces degraded regions and distraction indicators, providing actionable mitigation recommendations for your agent workflows.

You score context health by running the context-degradation analyzer on your session logs. This monitors degradation risk across multi-turn agent workflows, scoring context health and generating remediation recommendations to maintain robust performance.

The best way to mitigate context degradation is by isolating context or summarizing content. Analyzing attention distribution identifies degraded regions, guiding architectural decisions to maintain robust performance in lengthy multi-turn assistant sessions.

Yes, you can use numpy to support the analysis of attention patterns for context degradation. The analyzer requires numpy to measure attention distribution, detect degradation, and generate remediation recommendations across multi-turn agent workflows.

You should not rely on context health scoring alone when isolated context or summarization is insufficient to fix poisoning indicators. If degradation risks persist across multi-turn workflows, further architectural changes beyond scoring are required.