AI Research
When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis
Solving the silent failure of LLM agents through deferred template rendering and output capacity optimization.
LLM-powered coding agents suffer from a poorly understood failure mode we term output stalling: the agent silently produces empty responses when attempting to generate large, format-heavy documents. We present a theoretical framework that explains and prevents this failure throug...
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ArXiv CS.AI
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ArXiv CS.AI
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This research addresses the critical silent failure mode in LLM agents where complex formatting exhausts their effective generation capacity. By shifting from direct generation to deferred template rendering, developers can significantly improve reliability while drastically reducing token consumption.
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