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20242026
most citedA Survey of Large Language Models

1.5k citations · 1.5k across the 2 of their papers we have counts for

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cs.CL20261.5k cited

A Survey of Large Language Models

Wayne Xin Zhao, Kun Zhou, Junyi Li +19

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for compre…

cs.CL2025

SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis

Shuang Sun, Huatong Song, Yuhao Wang +10

Retrieval-augmented generation (RAG) systems have advanced large language models (LLMs) in complex deep search scenarios requiring multi-step reasoning and iterative information re…

cs.CL2025

InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior

Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3

Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…

cs.CL2025

LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation

Zican Dong, Junyi Li, Jinhao Jiang +4

Large language models (LLMs) have gained extended context windows through scaling positional encodings and lightweight continual pre-training. However, this often leads to degraded…

cs.CL2025

ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework

Lisheng Huang, Yichen Liu, Jinhao Jiang +4

Recent advances in web-augmented large language models (LLMs) have exhibited strong performance in complex reasoning tasks, yet these capabilities are mostly locked in proprietary…

cs.CL2025

R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning

Huatong Song, Jinhao Jiang, Wenqing Tian +7

Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-Augmented Generation (RAG) helps by injecting external information, but cur…