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Jia-Mu Zhang

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#machine learning Preprint Sep 2026

AnyJev Technical Report

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.

Jia-Mu Zhang, Tian-Ze Yang, Yu-Cheng Shi et al. · 2 citations
Preprint Aug 2026

SPECTRA: Pushing the KV Cache Beyond the 2-Bit Cliff via Spectral Transform Coding

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.

Jiamu Zhang, Liang Wu, Kelly Wan et al. · 1 citation
#machine learning Preprint Sep 2026

Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems

This work introduces a controlled and MAS-demanding diagnostic benchmark for representative MAS efficiency methods and shows that many reported gains are setup-dependent and may arise from structural collapse, disabled tool pathways, or starting systems where random pruning already preserves accuracy, rather than robus...

Jia-Mu Zhang, Ling-Xi Zhang, Peng-Jun Lu et al. · 2 citations

WiSP: A Working-Set View of Mixture-of-Experts Serving on Extremely Low-Resource Hardware

MV-WSA (Marginal-Value Working-Set Allocation), which splits memory by marginal latency benefit per byte while enforcing a KV-admission floor, is implemented in WiSP (Working-Set Paging), a routing-aware expert pager that plugs into an unmodified serving engine and preserves byte-identical outputs.

Jiamu Zhang, Liang Wu, Mayank Darbari et al. · 2 citations · ⚡1

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