Limits of Reliability and Scaling in Language Models
Large language models (LLMs) are trained and evaluated as though perfect reliability is achievable for any task given sufficient scale. We show that this assumption is information-theoretically unjustified. Every generative task has a reliability ceiling that no model can exceed, determined by how much output uncertainty is resolvable from observable context. The gap decomposes into a resolvable component closable with additional context and a subjective component inherent to task ambiguity. Autoregressive generation further degrades this ceiling at a rate governed by the task's dependency kernel, which quantifies inter-token correlations in the output. From these two primitives, we deriv...
| Authors | Subhabrata Majumdar |
|---|---|
| Primary category | cs.CL |
| Published | 8 May 2026 |
| Last updated at source | 17 September 2026 |
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