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Hejian Sang

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Preprint Oct 2026

A matching lower bound for the sextic large sieve and structured prime moments

We study the contrast between the large-sieve norm and the second moment of structured prime coefficients for sextic residue characters over the Eisenstein integers. For squarefree row and column ideals of norms at most A and B, we prove a lower bound of order (AB)^(-epsilon) times (A + B + A^(5/6) B^(1/3) + A^(1/3) B^...

Yuanda Xu, He-Jian Sang · 0 citations
Preprint Oct 2026

A sharp lower bound for the sextic large sieve

We establish a matching lower bound for the sextic large sieve over the Eisenstein integers in an unbalanced range, with both indices restricted to squarefree ideals. Let $\Xi_6(A,B)$ denote the squared operator norm of the sextic residue-character matrix with row and column norms at most $A$ and $B$, respectively, out...

Yuanda Xu, He-Jian Sang · 0 citations
#artificial intelligence Preprint Oct 2026

AvoKV-E: Payload-Aware KV Cache Eviction for Long Reasoning

AvoKV-E is introduced, a training-free eviction policy that first delays eligibility for recent states and then ranks eligible entries using candidate-normalized read pressure, key redundancy, and value-payload potential, which shows that long-reasoning KV eviction should preserve not only keys that are likely to be re...

Han Yu, Wen-Hui Zhu, Xi-Wen Chen et al. · 0 citations
#artificial intelligence Preprint Sep 2026

LatentPress: Context Compression Beyond Text and Vision

LatentPress is introduced, which writes conversational histories and long documents into a third representation: continuous memory tokens that a frozen decoder reads directly through its input-embedding interface, with no text reconstruction at inference.

Yuanda Xu, Zheng-Ze Zhou, He-Jian Sang et al. · 1 citation
Jul 2026

TREK: Distill to Explore, Reinforce to Refine

TREK (Teacher-Routed Exploration via Forward KL), a simple staged procedure that uses distillation not for imitation but for exploration support expansion, achieves high success rates early in training while unaided GRPO requires substantially more optimization steps to reach comparable levels.

Yuanda Xu, Zhengze Zhou, Kayhan Behdin et al. · 0 citations

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