AI RESEARCH
ATLAS: All-round Testing of Long-context Abilities across Scales
arXiv CS.CL
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ArXi:2605.28079v1 Announce Type: new Long-context language models now advertise context windows up to millions of tokens, yet evaluations typically report a single length or a narrow task family, masking two failure modes: performance can collapse as length grows, and strong retrieval need not transfer to downstream use. We present ATLAS, a benchmarking framework that redefines long-context evaluation as length-dependent capability profiling.