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20232026
most citedTool Learning with Foundation Models

33 citations · 56 across the 13 of their papers we have counts for

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Showing cs.CLShow all

10 papers · 1 filter

cs.CL2026

FutureBridge: Token Selection Beyond Local Preference in Collaborative Decoding

Quanquan Li, Hongbo Zhang, Yihe Chi +9

Token-level collaboration allows a large language model (LLM) to assist a small language model (SLM) when their predictions diverge. Existing methods either use LLM-generated inter…

cs.CL2026

DashAttention: Differentiable and Adaptive Sparse Hierarchical Attention

Yuxiang Huang, Nuno M. T. Gonçalves, Federico Alvetreti +5

Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-value (KV) blocks based on coarse attention scores and subsequently apply fine-grain…

cs.CL2025

NOSA: Native and Offloadable Sparse Attention

Yuxiang Huang, Pengjie Wang, Jicheng Han +9

Decoding throughput improvements from larger inference batches are limited by GPU memory, which is largely consumed by the key-value (KV) cache. Prior training-free KV cache offloa…

cs.CL2025

InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long Adaptation

Weilin Zhao, Zihan Zhou, Zhou Su +10

Long-sequence processing is a critical capability for modern large language models. However, the self-attention mechanism in the standard Transformer architecture faces severe comp…

cs.CL2025

FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling

Weilin Zhao, Tengyu Pan, Xu Han +9

Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify…

cs.CL2024

Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads on Consumer-Grade Devices

Yuxiang Huang, Binhang Yuan, Xu Han +2

Scaling the input context length of a large language model (LLM) incurs a significant increase in computation cost and memory footprint to maintain the attention key-value (KV) cac…