LLMs claim massive context windows but choke on long number sequences. A training-free trick called SepSeq fixes attention dispersion and boosts accuracy 35%.
A new benchmark shows LLMs drop 0.3 to 5.9% accuracy on grade-school math when names and places go non-Western. Same arithmetic, different answers.
A new Transformer variant called FBS tries to let LLM inference preview, skim, and skip, instead of grinding through every token. I read the paper…
A paper trains a tiny probe on a model's own hidden states to catch hallucinations at inference time, no judge model required. Here's why that…
Open models closed most of the quality and ops gaps with proprietary APIs. A pragmatic look at what changed and how to decide whether to…
Most reasoning LLM failures aren't hallucinations, they're silently skipped steps. Here's what to measure instead of end-to-end answer accuracy.