Detection Without Correction: A Two-Parameter Decomposition of Multi-Stage LLM Pipelines

Researchers discovered that multi-stage LLM pipelines (used for debate, self-correction, and verification) fail due to a specific mechanism: models detect problematic upstream content but fail to correct it, creating a 'detection-without-correction' failure mode. Testing across four model families and four benchmarks reveals conditional miscorrection rates of 53-94%, explaining why accuracy plateaus and debate gains don't replicate on frontier models.

This research identifies a fundamental flaw in how large language models handle multi-stage reasoning pipelines. Rather than assuming models simply lack reasoning capability, the study decomposes downstream agent behavior into detection (recognizing when upstream content is unreliable) and conditional generation (producing correct alternatives). The decomposition reveals models frequently identify problematic inputs but then generate incorrect outputs anyway, indicating the failure isn't in detection mechanisms but in conditional correction quality.

The findings emerge from systematic testing across nine experimental conditions using GSM8K, MATH-500, GPQA-Diamond, and AIME benchmarks. The consistency of 53-94% miscorrection rates across different model families and methods suggests this is a fundamental characteristic of current LLM architectures rather than a training-specific problem. This pattern explains several previously puzzling phenomena: why multi-agent debate shows accuracy reversals across rounds, why self-correction degrades performance, and why different model providers show divergent debate dynamics.

For AI developers and researchers building production systems, this suggests that simply adding debate rounds or self-correction loops won't reliably improve performance without explicitly training models to condition their outputs on detected errors. The research indicates detection thresholds operate as stable model-level regularities, meaning the problem persists across different prompting protocols and methods. Organizations deploying retrieval-augmented generation or multi-agent verification systems should account for this detection-correction gap rather than assuming pipeline depth automatically improves reliability. Future research must focus on decoupling and improving the conditional generation phase specifically.

• →Multi-stage LLM pipelines fail primarily through detection-without-correction, where models identify errors but fail to fix them rather than missing problems entirely.
• →Conditional miscorrection rates consistently dominate (53-94%) across all tested models and benchmarks, indicating a systemic architectural limitation.
• →Detection thresholds operate as stable model-level regularities that persist across different methods and benchmarks, suggesting this is a fundamental LLM characteristic.
• →Current multi-agent debate and self-correction approaches may plateau or reverse accuracy gains because models cannot reliably condition correct outputs on detected errors.
• →Production AI systems using retrieval-augmented generation or verification pipelines require explicit training to improve conditional generation quality, not just pipeline depth.