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20242026
most citedSeed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning

1 citations · 1 across the 9 of their papers we have counts for

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cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2025

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning

Zihao Huang, Yu Bao, Qiyang Min +8

While Mixture of Experts (MoE) models achieve remarkable efficiency by activating only subsets of parameters, they suffer from high memory access costs during inference. Memory-lay…

cs.LG2025

Frac-Connections: Fractional Extension of Hyper-Connections

Defa Zhu, Hongzhi Huang, Jundong Zhou +5

Residual connections are central to modern deep learning architectures, enabling the training of very deep networks by mitigating gradient vanishing. Hyper-Connections recently gen…

cs.LG2024

Ultra-Sparse Memory Network

Zihao Huang, Qiyang Min, Hongzhi Huang +4

It is widely acknowledged that the performance of Transformer models is logarithmically related to their number of parameters and computational complexity. While approaches like Mi…

cs.LG2024

Hyper-Connections

Defa Zhu, Hongzhi Huang, Zihao Huang +5

We present hyper-connections, a simple yet effective method that can serve as an alternative to residual connections. This approach specifically addresses common drawbacks observed…