arXiv:2608. 02940v1 Announce Type: new Abstract: A reproducible compression statistic can still select the wrong candidate.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
A stable compression score can still select the worse model. In our dense study, a split-half reliable path-quadratic score predicted a 16.
arXiv:2607. 27275v1 Announce Type: new Abstract: Post-training quantization to 4-bit weights is widely reported to be nearly lossless.
arXiv:2608. 06564v1 Announce Type: new Abstract: Quantization is how large language models are actually deployed, and below four bits it is known to hurt.
arXiv:2608. 06564v2 Announce Type: replace Abstract: Quantization is how large language models are actually deployed, and below four bits it hurts.
arXiv:2608. 08652v1 Announce Type: cross Abstract: We present \LegoLM{}, a structured weight-sharing compression framework for large language models grounded in a systematic study of why global weight sharing fails and how to fix it.
arXiv:2607. 16721v1 Announce Type: new Abstract: The strongest open-weight coding models are mixture-of-experts (MoE) networks: most of their size comes from large pools of "expert" subnetworks, of which only a few act on any token.