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20232026
most citedThe Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism

7 citations · 9 across the 12 of their papers we have counts for

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18 papers · 1 filter

cs.CL2026

DFlare: Scaling Up Draft Capacity for Block Diffusion Speculative Decoding

Jiebin Zhang, Zhenghan Yu, Song Liu +9

Block diffusion speculative decoding accelerates LLM inference by predicting all tokens within a block simultaneously for the target model to verify in parallel. Predicting an enti…

cs.CL2026

Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining

Weimin Xiong, Shuhao Gu, Bowen Ye +5

Recent advances in multimodal large language models have driven growing interest in graphical user interface (GUI) agents, yet their generalization remains constrained by the scarc…

cs.CL2026

Learning to Draft: Adaptive Speculative Decoding with Reinforcement Learning

Jiebin Zhang, Zhenghan Yu, Liang Wang +8

Speculative decoding accelerates large language model (LLM) inference by using a small draft model to generate candidate tokens for a larger target model to verify. The efficacy of…

cs.CL2025

Hierarchical Memory Organization for Wikipedia Generation

Eugene J. Yu, Dawei Zhu, Yifan Song +6

Generating Wikipedia articles autonomously is a challenging task requiring the integration of accurate, comprehensive, and well-structured information from diverse sources. This pa…

cs.CL2025

More Vulnerable than You Think: On the Stability of Tool-Integrated LLM Agents

Weimin Xiong, Ke Wang, Yifan Song +4

Current evaluations of tool-integrated LLM agents typically focus on end-to-end tool-usage evaluation while neglecting their stability. This limits their real-world applicability,…

cs.CL20252 cited

A Comprehensive Survey on Long Context Language Modeling

Jiaheng Liu, Dawei Zhu, Zhiqi Bai +34

Efficient processing of long contexts has been a persistent pursuit in Natural Language Processing. With the growing number of long documents, dialogues, and other textual data, it…