41 citations · 133 across the 15 of their papers we have counts for
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cs.LG2026
Interpretable Hypergraph Learning via Neural Additive Models
Shihan Feng, Xin Zheng, Shiyi Yang +3
Hypergraphs offer a natural framework for modeling networked data, where dependencies among entities are governed by higher-order interactions. While hypergraph learning methods su…
cs.CL2026
The Asymmetric Harms of LLM Compression
Yuan Wu, Mairui Li, Lesia Semenova +1
Large language models (LLMs) compression reduces deployment costs, but standard aggregate metrics like perplexity and accuracy often mask underlying behavioral shifts. In this work…
cs.CL2026
Where You Inject Diversity Matters: A Unified Framework for Diverse Generation
Cheng Zhang, Rui Xin, Chudi Zhong
Open-ended generation tasks often require a set of meaningfully different outputs, yet large language models often produce similar generations. Existing test-time diversity methods…