arxiv
PublishedJuly 10, 2026 at 4:00 AM
—neutral
Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench
Publisher summary· verbatim
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. However, existing long-context evaluations - from Needle-in-a-Haystack (NIAH) tests to more recent mul
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivPlanning and Scheduling Business Processes under Control-Flow Uncertainty16harxivSIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection16harxivWAPP: Safe Learning of Positive Security WAF Policies from Live Traffic16harxivPAN: A World Model for General, Actionable, and Long-Horizon World Simulation16hThe Bubble Brief
WEEKLYRead benchmark insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗