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20232026
most citedEnhancing Efficiency and Propulsion in Bio-mimetic Robotic Fish through End-to-End Deep Reinforcement Learning

25 citations · 40 across the 23 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

Remember and Reweight: Enhancing Multi-Agent Debate with Experience Memory and Confidence Estimation

Xuanfa Jin, Zhijian Ma, Yongcheng Zeng +3

Multi-agent debate (MAD) improves the reasoning capabilities of large language models by having multiple agents iteratively refine their responses through discussion. However, MAD…

cs.CL2026

UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams

Siyu Xia, Chenheng Zhang, Yanting Wu +8

Memory is essential for LLM agents to accumulate task experience and reuse task-specific execution strategies. However, real-world deployment over boundary-agnostic and evolving ta…

cs.CL2025

From Experience to Strategy: Empowering LLM Agents with Trainable Graph Memory

Siyu Xia, Zekun Xu, Jiajun Chai +7

Large Language Models (LLMs) based agents have demonstrated remarkable potential in autonomous task-solving across complex, open-ended environments. A promising approach for improv…

cs.CL2025

GTA: Grouped-head latenT Attention

Luoyang Sun, Cheng Deng, Jiwen Jiang +5

Attention mechanisms underpin the success of large language models (LLMs), yet their substantial computational and memory overhead poses challenges for optimizing efficiency and pe…

cs.CL2025

PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing

Cheng Deng, Luoyang Sun, Jiwen Jiang +10

While scaling laws have been continuously validated in large language models (LLMs) with increasing model parameters, the inherent tension between the inference demands of LLMs and…

cs.CL2025★ 1 cited

Evolving LLMs' Self-Refinement Capability via Synergistic Training-Inference Optimization

Yongcheng Zeng, Xinyu Cui, Xuanfa Jin +11

Self-Refinement refers to a model's ability to revise its own responses to produce improved outputs. This capability can also serve as a fundamental mechanism for Self-Improvement,…

cs.CL2024

Token-level Direct Preference Optimization

Yongcheng Zeng, Guoqing Liu, Weiyu Ma +3

Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions. This process often utilizes methods like pairwise comparisons and…