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cs.CL2026

Ripple-Pivot Search: Active Parallel Decoding for Diffusion Large Language Models

Yushi Ye, Xu Chen, Haoyun Jiang +7

Diffusion Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive language models, offering the potential for substantially faster inference throu…

cs.CL2026

Hidden Human-Like Nature of Machine-Generated Texts: Theory and Detection Enhancement

Chenwang Wu, Yiu-ming Cheung, Bo Han +1

Machine-generated texts (MGTs) produced by large language models (LLMs) are increasingly prevalent across various applications, while their potential misuse in fake news propagatio…

cs.CL2026

Rethinking How to Remember: Beyond Atomic Facts in Lifelong LLM Agent Memory

Jingwei Sun, Jianing Zhu, Jiangchao Yao +2

To enable reliable long-term interaction, LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over accumulated dialogue history. M…

cs.CL2026

Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers

Fanqin Zeng, Feng Hong, Geng Yu +6

Diffusion Large Language Models (DLLMs) promise fast parallel generation, yet open-source DLLMs still face a severe quality-speed trade-off: accelerating decoding by revealing mult…

cs.CL2026

EEPO: Exploration-Enhanced Policy Optimization via Sample-Then-Forget

Liang Chen, Xueting Han, Qizhou Wang +4

Balancing exploration and exploitation remains a central challenge in reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs). Current RLVR methods o…