Hugging Face Trending Papers
Jul 9

Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench

Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them. However, existing long-context evaluations - from Needle-in-a-Haystack (NIAH) tests to more recent multi-hop reasoning and summarization tasks - predominantly measure average-case performance, and many are either saturated or lack robustness.

arXiv AI
Aug 13

Behavior and Representation in Open-Weight Large Language Models for Combinatorial Optimization: From Feature Extraction to Algorithm Selection

arXiv:2512. 13374v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) open new perspectives for automation in optimization, yet little is known about whether their internal representations capture problem structure or algorithmic behavior.

By Francesca Da Ros, Luca Di Gaspero, Kevin Roitero