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20242026
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cs.CL2026

Unlocking Parallelism in Autoregressive Language Models via Speculative Decoding with Progressive Tree Drafting

Zipeng Gao, Zhi Zheng, Qingrong Xia +5

Speculative decoding has significantly accelerated Large Language Model (LLM) inference by alleviating memory-bound bottlenecks. However, traditional speculative decoding typically…

cs.CL2026

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory

Derong Xu, Shuochen Liu, Pengfei Luo +8

Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving preferences over long interacti…

cs.CL2026

Align-GRAG: Anchor and Rationale Guided Dual Alignment for Graph Retrieval-Augmented Generation

Derong Xu, Pengyue Jia, Xiaopeng Li +9

Despite the strong abilities, large language models (LLMs) still suffer from hallucinations and reliance on outdated knowledge, raising concerns in knowledge-intensive tasks. Graph…

cs.CL2025

A Survey on Parallel Reasoning

Ziqi Wang, Boye Niu, Zipeng Gao +10

With the increasing capabilities of Large Language Models (LLMs), parallel reasoning has emerged as a new inference paradigm that enhances reasoning robustness by concurrently expl…

cs.CL2025

From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agents

Derong Xu, Yi Wen, Pengyue Jia +8

Large Language Models (LLMs) have recently been widely adopted in conversational agents. However, the increasingly long interactions between users and agents accumulate extensive d…

cs.CL2025

Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation

Derong Xu, Xinhang Li, Ziheng Zhang +7

Large Language Models (LLMs) demonstrate remarkable capabilities, yet struggle with hallucination and outdated knowledge when tasked with complex knowledge reasoning, resulting in…