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A Theory of Time-Sensitive Language Generation: Sparse Hallucination Beats Mode Collapse

NeurIPS 2026 · Large language models › Language, safety and interpretability · approach: Controlled text generation · 0 citations (OpenAlex)

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Shows that eventually consistent language generators cannot achieve nontrivial timely density under deadlines, and that allowing vanishing hallucination rate recovers density 1/2 timely generation for any superlinear deadline via a black-box reduction to Kleinberg–Wei's generator.

Problem

Language generation in the limit under a global preference order on strings requires breadth, but generated strings should also be produced early (timeliness) under a deadline function; prior consistent generators may suffer mode collapse under such deadlines.

Approach

Impose per-string deadlines defined by rank in a preference order; prove consistent generators fail, then relax to vanishing hallucination rate and build a randomized speculative black-box reduction (SBG) that achieves timely lower density 1/2 for any superlinear deadline, with hallucination rate matching a near-tight bound.

Key results

Surprising findings

Limitations

Experimental setup

Metrics: timely lower density hallucination rate

Baselines: Kleinberg-Wei eventually consistent generator

Builds on: Kleinberg-Mullainathan language generation in the limit Kleinberg-Wei density Gold language identification Angluin

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