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20242026
most citedSimulating Financial Market via Large Language Model based Agents

2 citations · 2 across the 16 of their papers we have counts for

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cs.CL2026

Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering

Yifan Wang, Xinkui Lin, Yongxiu Xu +9

Retrieval-augmented generation (RAG) enables large language models (LLMs) to answer questions by accessing external knowledge and has been widely adopted for long-term conversation…

cs.AI2026

When and What to Teach: Budget-Aware Online Adaptation for Web Agents

Jianwei Zhang, Sihan Cao, Pengcheng Zheng +7

Web agents have achieved significant success in automating complex internet tasks but deploying them in real-world environments requires continuous online adaptation. Given that de…

cs.CL2026

DPEPO: Diverse Parallel Exploration Policy Optimization for LLM-based Agents

Junshuo Zhang, Chengrui Huang, Feng Guo +6

Large language model (LLM) agents that follow the sequential "reason-then-act" paradigm have achieved superior performance in many complex tasks.However, these methods suffer from…

cs.IR2026

FAVE: Flow-based Average Velocity Establishment for Sequential Recommendation

Ke Shi, Yao Zhang, Feng Guo +4

Generative recommendation has emerged as a transformative paradigm for capturing the dynamic evolution of user intents in sequential recommendation. While flow-based methods improv…

cs.CL2026

PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning

Ruixiang Feng, Yuntao Wen, Silin Zhou +14

Language Reasoning Models (LRMs) achieve strong performance by scaling test-time computation but often suffer from ``overthinking'', producing excessively long reasoning traces tha…