Showing 1–4 of 4 · #long-context

TESTS

How to test whether an AI model really reasons: the contradiction-trap eval

A planted contradiction, blind judges and zero partial credit. A step-by-step method for finding out whether a model tracks state or just sounds like it does.

MOMira OkaforNov 09 · 7 min

PAPERS

Sparse attention explained: sliding windows, global tokens and block sparsity

Why full attention gets expensive, and the three main ideas that make long contexts affordable, with the trade-offs each one makes.

SPSana PatelOct 29 · 6 min

BENCHMARKS

Lost in the middle: why long-context models still forget, and how to test it

Needle tests are easy to pass. Real documents need several facts combined from different places. Here is how to measure long-context recall properly.

MOMira OkaforOct 22 · 6 min

MODELS

Context windows are not memory

A bigger window lets a model read more at once. It does not make it remember, prioritise or forget well. How to design for that difference.

MOMira OkaforOct 09 · 6 min