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20232026
most citedFed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer

4 citations · 7 across the 20 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Revisiting Lossy Verification in Speculative Decoding: Mechanisms, Trade-offs, and Failure Modes

Tianyu Wang, Yuxuan Zhou, Heng Li +4

Speculative Decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose tokens that are subsequently verified in parallel by a larger…

cs.CL2026

Qwen-AgentWorld: Language World Models for General Agents

Yuxin Zuo, Zikai Xiao, Li Sheng +30

A world model predicts environment dynamics based on current observations and actions, serving as a core cognitive mechanism for reasoning and planning. In this work, we investigat…

cs.CL2025

LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability

Zikai Xiao, Fei Huang, Jianhong Tu +7

Generating long, informative, and factual outputs remains a major challenge for Large Language Models (LLMs). Existing benchmarks for long-form generation typically assess real-wor…

cs.CL2025

MedEthicsQA: A Comprehensive Question Answering Benchmark for Medical Ethics Evaluation of LLMs

Jianhui Wei, Zijie Meng, Zikai Xiao +5

While Medical Large Language Models (MedLLMs) have demonstrated remarkable potential in clinical tasks, their ethical safety remains insufficiently explored. This paper introduces…

cs.CL2025

Mitigating Posterior Salience Attenuation in Long-Context LLMs with Positional Contrastive Decoding

Zikai Xiao, Ziyang Wang, Wen Ma +5

While Large Language Models (LLMs) support long contexts, they struggle with performance degradation within the context window. Current solutions incur prohibitive training costs,…

cs.CL2024

Large Language Model Bias Mitigation from the Perspective of Knowledge Editing

Ruizhe Chen, Yichen Li, Zikai Xiao +1

Existing debiasing methods inevitably make unreasonable or undesired predictions as they are designated and evaluated to achieve parity across different social groups but leave asi…