{"slug": "information-theoretic-limits-of-reliability-and-scaling-in-language-models", "title": "Information-Theoretic Limits of Reliability and Scaling in Language Models", "summary": "A new study from arXiv reveals information-theoretic limits on the reliability of large language models, showing that every generative task has a reliability ceiling determined by output uncertainty and task ambiguity. The authors derive a scaling law that recovers the Chinchilla law as a special case and unifies phenomena like retrieval-augmentation benefits and catastrophic forgetting.", "body_md": "arXiv:2607.14112v1 Announce Type: new\nAbstract: Large language models (LLMs) are evaluated as though perfect reliability is achievable for any task given sufficient scale. We show 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 derive a first-principles scaling law where LLM performance is bottlenecked by the scarcer resource: training data or model capacity. This law recovers the Chinchilla scaling law as a special case and provides a structural account of when scaling improves reliability. Beyond scaling, our framework unifies diverse practical phenomena, such as the benefits of retrieval-augmentation and the spectral mechanics of catastrophic forgetting. Our work formalizes the resource-complexity tradeoffs that govern model performance across domains, offering a unified theory of performance limits in generative language models.", "url": "https://wpnews.pro/news/information-theoretic-limits-of-reliability-and-scaling-in-language-models", "canonical_source": "https://arxiv.org/abs/2607.14112", "published_at": "2026-07-17 04:00:00+00:00", "updated_at": "2026-07-17 04:04:39.467134+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-safety"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/information-theoretic-limits-of-reliability-and-scaling-in-language-models", "markdown": "https://wpnews.pro/news/information-theoretic-limits-of-reliability-and-scaling-in-language-models.md", "text": "https://wpnews.pro/news/information-theoretic-limits-of-reliability-and-scaling-in-language-models.txt", "jsonld": "https://wpnews.pro/news/information-theoretic-limits-of-reliability-and-scaling-in-language-models.jsonld"}}