Diagnosis Is Not Prescription: Linguistic Co-Adaptation Explains Patching Hazards in LLM Pipelines
A new research paper introduces the "Linguistic Contract" hypothesis to explain why fixing the most problematic module in a multi-module LLM agent can paradoxically worsen performance. The study found that while causal analysis often points to the routing module as the bottleneck, injecting corrections there degrades results. Instead, patching an upstream query-rewriting module proved more effective, suggesting that downstream modules adapt to upstream error distributions, and direct correction breaks this implicit alignment. AI
IMPACT Explains why direct intervention in LLM agent bottlenecks can fail, suggesting a need for indirect patching strategies to maintain system alignment.