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20242026
most citedReflection-Bench: Evaluating Epistemic Agency in Large Language Models

1 citations · 2 across the 13 of their papers we have counts for

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6 papers · 1 filter

cs.CV2025

Knowledge Completes the Vision: A Multimodal Entity-aware Retrieval-Augmented Generation Framework for News Image Captioning

Xiaoxing You, Qiang Huang, Lingyu Li +4

News image captioning aims to produce journalistically informative descriptions by combining visual content with contextual cues from associated articles. Despite recent advances,…

cs.AI2025

Dr. Bench: A Multidimensional Evaluation for Deep Research Agents, from Answers to Reports

Yang Yao, Yixu Wang, Yuxuan Zhang +9

As an embodiment of intelligence evolution toward interconnected architectures, Deep Research Agents (DRAs) systematically exhibit the capabilities in task decomposition, cross-sou…

cs.CL2025

LinguaSafe: A Comprehensive Multilingual Safety Benchmark for Large Language Models

Zhiyuan Ning, Tianle Gu, Jiaxin Song +8

The widespread adoption and increasing prominence of large language models (LLMs) in global technologies necessitate a rigorous focus on ensuring their safety across a diverse rang…

cs.AI2025

SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law

Shanghai AI Lab, :, Yicheng Bao +115

We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…

cs.AI2025

The Other Mind: How Language Models Exhibit Human Temporal Cognition

Lingyu Li, Yang Yao, Yixu Wang +3

As Large Language Models (LLMs) continue to advance, they exhibit certain cognitive patterns similar to those of humans that are not directly specified in training data. This study…

cs.CV2025

Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?

Yang Yao, Lingyu Li, Jiaxin Song +8

As Multimodal Large Language Models (MLLMs) continue to evolve, their cognitive and reasoning capabilities have seen remarkable progress. However, challenges in visual fine-grained…