AGENTIC-SYSTEMSApril 21, 20261 min read8 views

When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis

Researchers identify 'output stalling' in LLM agents and propose the GEN-PILOT framework, which reduces token costs by up to 72% and eliminates silent failures during large document synthesis.

Computer Science > Artificial Intelligence

Title:When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis

View PDF HTML (experimental)Abstract: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 through three contributions. (1) We introduce Output Generation Capacity (OGC), a formal measure of an agent's effective ability to produce output given its current context state - distinct from and empirically smaller than the raw context window. (2) We prove a Format-Cost Separation Theorem showing that deferred template rendering is always at least as token-efficient as direct generation for any format with overhead multiplier $\mu_f > 1$, and derive tight bounds on the savings. (3) We formalize Adaptive Strategy Selection, a decision framework that maps the ratio of estimated output cost to available OGC into an optimal generation strategy (direct, chunked, or deferred). We validate the theory through controlled experiments across three models (Claude 3.5 Sonnet, GPT-4o, Llama 3.1 70B), four document types, and an ablation study isolating each component's contribution. Deferred rendering reduces LLM generation tokens by 48-72% across all conditions and eliminates output stalling entirely. We instantiate the framework as GEN-PILOT, an open-source MCP server, demonstrating that the theory translates directly into a practical tool.

Source: arXiv cs.AI Recent

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