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20232026
most citedDocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding

7 citations · 12 across the 18 of their papers we have counts for

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cs.CL2026

From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory

Yishuo Cai, Xingyu Guo, Xuancheng Huang +8

Large language model (LLM) agents are increasingly deployed in long-running settings where improving through experience at test time becomes important. A common approach is to upda…

cs.CL2026

Projection-Free Evolution Strategies for Continuous Prompt Search

Yu Cai, Canxi Huang, Xiaoyu He

Continuous prompt search offers a computationally efficient alternative to conventional parameter tuning in natural language processing tasks. Nevertheless, its practical effective…

cs.CL2025

Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning

Haiyang Yu, Yuchuan Wu, Fan Shi +18

Chinese ancient documents, invaluable carriers of millennia of Chinese history and culture, hold rich knowledge across diverse fields but face challenges in digitization and unders…

cs.CL2025

Post-Completion Learning for Language Models

Xiang Fei, Siqi Wang, Shu Wei +5

Current language model training paradigms typically terminate learning upon reaching the end-of-sequence (<eos>) token, overlooking the potential learning opportunities in the post…

cs.CL2025

Advancing Sequential Numerical Prediction in Autoregressive Models

Xiang Fei, Jinghui Lu, Qi Sun +6

Autoregressive models have become the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlo…

cs.CL2025

Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning

Jinghui Lu, Haiyang Yu, Siliang Xu +9

Recent advancements in reasoning have significantly enhanced the capabilities of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) across diverse tasks. How…