arXiv AI
A Quantitative Study of Sustained Focus in Large Language Models via Repetitive Deterministic Prediction Tasks
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arXiv Machine Learning
Aug 18
The Quantum Shortcut: Complex Phase-State Dynamics Reduce the Optimization Steps of Sequence Models
arXiv:2608. 14691v1 Announce Type: new Abstract: Sequence models are conventionally distinguished by their backbone, the mechanism that routes information across positions, such as attention or recurrence.
arXiv AI
Sep 10
Limits of Reliability and Scaling in Language Models
The paper argues that large language models cannot achieve perfect reliability for any task, even with unlimited scale. It establishes that each generative task has an inherent reliability ceiling set by how much output uncertainty can be resolved from observable context, with a resolvable part that can be improved by more context and a subjective part tied to task ambiguity. The authors derive a scaling law showing that performance is limited by the scarcer resource—either training data or model capacity—and explain how this law explains phenomena such as retrieval augmentation and catastrophic forgetting.
arXiv AI
Jul 17
Information-Theoretic Limits of Reliability and Scaling in Language Models
arXiv:2607. 14112v1 Announce Type: cross Abstract: Large language models (LLMs) are evaluated as though perfect reliability is achievable for any task given sufficient scale.
arXiv Machine Learning
Jul 7
How Much is Left? LLMs Linearly Encode Their Remaining Output Length
arXiv:2607. 05316v1 Announce Type: cross Abstract: Large language models generate one token at a time, yet their responses show remarkably consistent length structure: step-by-step solutions converge in predictable token counts, retrievals stop after a few sentences, retractions extend responses by measurable amounts.
arXiv AI
Jun 29
Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
arXiv:2606. 27472v1 Announce Type: cross Abstract: Large language model (LLM) agents operate over long, multi-session interactions in which facts change: a user moves, a price updates, a plan is revised.
arXiv AI
Sep 15
LLM Probability Concentration: How Alignment Shrinks the Generative Horizon
arXiv:2506.17871v4 Announce Type: replace-cross Abstract: Despite their impressive capabilities, aligned large language models (LLMs) often generate outputs that lack diversity. What drives this cons...