arXiv Machine Learning June 9, 2026 By Faruk Alpay, Hamdi Alakkad

Finite Certificates for In-Context Determinacy and a Threshold Theory of Emergence in Language Models

Read the original on arXiv Machine Learning →

arXiv:2606. 07623v1 Announce Type: new Abstract: This paper develops a model-theoretic framework for verifying context-conditioned language-model behavior by replacing benchmark labels with finite semantic certificates.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

2d ago

Identifiability and Order-Dimension Limits of In-Context Learning on Partial Orders

arXiv:2608. 14004v1 Announce Type: new Abstract: In-context learning is commonly formalized as inference from examples of a function.

By Faizanuddin Ansari, Debanjan Dutta, Swagatam Das

llms

arXiv AI

2d ago

Split the Labor: Separating Evidence Interpretation from Decision Aggregation

arXiv:2608. 14509v1 Announce Type: new Abstract: Systems that ask a language model to reach a conclusion from many sources usually concatenate them into one prompt.

By Zhelun Wu

llms

arXiv AI

Jul 10

Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench

arXiv:2607. 08284v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them.

By Siddhartha Jain, Ameya Velingker

llms nlp benchmarks

arXiv Machine Learning

1d ago

Cross-Entropy Risk Estimation for Language Models: Inconsistency Must Be Dense, and the Holdout Method Is No Exception

arXiv:2608. 15798v1 Announce Type: new Abstract: Language models are compared by their held-out per-token cross-entropy risk---the quantity scaling laws are fitted to.

By Hanti Lin

llms

Hugging Face Trending Papers

Aug 3

Measuring in-context algorithmic reasoning in language models against an exact Bayes-optimal standard

Whether large language models perform genuine algorithmic reasoning or mere pattern completion is hard to test, because most benchmarks lack a ground truth for correct inductive inference. We introduce F-ICL, an in-context-learning benchmark that supplies one exactly.

llms benchmarks safety

arXiv AI

Jul 13

NL-PAC: Specification Ambiguity and Certified Minimax Risk Floors in LLM-Mediated Supervision

arXiv:2607. 08961v1 Announce Type: cross Abstract: Large language models increasingly provide labels, evaluations, and feedback for tasks specified in natural language.

By Berkay Anahtarci

llms

Related stories

Identifiability and Order-Dimension Limits of In-Context Learning on Partial Orders

Split the Labor: Separating Evidence Interpretation from Decision Aggregation

Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench

Cross-Entropy Risk Estimation for Language Models: Inconsistency Must Be Dense, and the Holdout Method Is No Exception

Measuring in-context algorithmic reasoning in language models against an exact Bayes-optimal standard

NL-PAC: Specification Ambiguity and Certified Minimax Risk Floors in LLM-Mediated Supervision