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LLM compression reduces inference costs and memory requirements, but selecting a method and configuration remains largely empirical because comparable resource reductions can produce different capability losses. We systematically investigate capability scaling-down laws for LLM compression across pruning, quantization,...
AnyJev is described, which reads a typed decision from one prefill of a pretrained instruction-tuned language model, which divides out a label prior estimated from unlabelled inputs, and it averages log-probabilities over the K cyclic rotations of the option list.
SPECTRA is developed, a training-free, drop-in codec that re-encodes the cache into a coordinate system and concentrates the bit budget on the channels that carry the signal, pushing usable compression past the 2-bit cliff so the same GPU holds longer contexts and larger batches.
The results show that skill representation itself matters, and that simply preserving the structure already present in skill files can substantially improve retrieval.
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