experiments show that injecting covert deliberate content into manuscripts allows authors to explicitly manipulate LLM reviews, leading to inflated ratings and reduced alignment with human reviews.
@jiqizhixin
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LLM Risks in Scholarly Peer Review Revealed
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Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review https://
arxiv.org/pdf/2412.01708 https://
rui-ye.github.io/BadLLMReviewer -
Cross-Modal Video VAE for Large Motion Video Autoencoding
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Large Motion Autoencoding with Cross-modal VAE https://
arxiv.org/abs/2412.17805 -
AgiBot World: Large-Scale Robotic Learning Dataset Launch
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Towards the "ImageNet Moment" for Embodied AI.Introducing AgiBot World, the first large-scale robotic learning dataset designed to advance multi-purpose robotic policies. HuggingFace：
https://
huggingface.co/agibot-world
Github：
https://
github.com/OpenDriveLab/a
gibot-world
… https://
agibot-world.com -
Red Teaming AI Models with Reinforcement Learning Rewards
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Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning Alex Beutel, Kai Xiao, Johannes Heidecke, Lilian Weng @OpenAI https://
arxiv.org/pdf/2412.18693
v1
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Adversarial Examples for Vision-Language Models via Diffusion
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Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models https://
arxiv.org/abs/2404.10335 -
Real-time Identity Defenses Against Malicious Diffusion Model Personalization
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Real-time Identity Defenses against Malicious Personalization of Diffusion Models https://
arxiv.org/pdf/2412.09844 https://
github.com/Guohanzhong/RID -
QiD Metric Measures LLM Training Levels and Token Requirements
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the work proposes a novel perspective that we can use QiD to measure an LLM's training levels and determine the number of training tokens required for fully training LLMs of various sizes.
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Low-Bit Quantization Favors Undertrained Large Language Models
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Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens https://
arxiv.org/abs/2411.17691 -
Genesis: Generative Physics Engine Creates 4D Dynamical Worlds
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introducing Genesis project , a generative physics engine able to generate 4D dynamical worlds powered by a physics https://t.co/ohgNLpiAim
— 机器之心 JIQIZHIXIN (@jiqizhixin) 19 décembre 2024introducing Genesis project , a generative physics engine able to generate 4D dynamical worlds powered by a physics