1 citations · 1 across the 9 of their papers we have counts for
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One Model, Multiple Compute Budgets: Multi-Ratio ConceptMoE for Flexible Compute Across Turns
Yiyang Fu, Zihao Huang, Dongchen Han +4
Large language models typically support different inference budgets through separate model sizes or fixed compute configurations. In multi-turn interactions, however, compute deman…
Improved Large Language Diffusion Models
Shen Nie, Qiyang Min, Shaoxuan Xu +7
Modern large language models are predominantly trained with autoregressive factorization and causal attention. We present \emph{iLLaDA}, an 8B masked diffusion language model train…
Benchmarking Vision-Language Models for Microscopic Plant Image Understanding
Tianqi Wei, Xin Yu, Zhi Chen +2
Microscopic imaging provides essential visual evidence for studying plant biology and pathology at the cellular and subcellular levels. However, existing benchmarks on vision-langu…
ConceptMoE: Adaptive Token-to-Concept Compression for Implicit Compute Allocation
Zihao Huang, Jundong Zhou, Xingwei Qu +2
Large language models allocate uniform computation across all tokens, ignoring that some sequences are trivially predictable while others require deep reasoning. We introduce Conce…
Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space
Xingwei Qu, Shaowen Wang, Zihao Huang +16
Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity…