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20232026
most citedAre We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

9 citations · 16 across the 27 of their papers we have counts for

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8 papers · 1 filter

cs.LG2026

DataMaster: Data-Centric Autonomous AI Research

Yaxin Du, Xiyuan Yang, Zhifan Zhou +12

As model families, training recipes, and compute budgets become increasingly standardized, further gains in machine learning systems depend increasingly on data. Yet data engineeri…

cs.LG2026

PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research

Tingjia Miao, Wenkai Jin, Muhua Zhang +19

The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current scientific benchmarks remain co…

cs.LG2025

VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization

Menglan Chen, Xianghe Pang, Jingjing Dong +3

Aligning Vision-Language Models (VLMs) with safety standards is essential to mitigate risks arising from their multimodal complexity, where integrating vision and language unveils…

cs.LG2024

Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks

Peizhi Niu, Chao Pan, Siheng Chen +1

Graph neural networks (GNNs) have become instrumental in diverse real-world applications, offering powerful graph learning capabilities for tasks such as social networks and medica…

cs.LG20241 cited

Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning

Shuo Tang, Rui Ye, Chenxin Xu +3

Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied…

cs.LG2024

Training-Free Message Passing for Learning on Hypergraphs

Bohan Tang, Zexi Liu, Keyue Jiang +2

Hypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to gen…