논문해외HF Daily Papers
Where Do Vision Language Models Fail at 3D Reasoning?
Vision-language models can get individual judgments right while struggling to recover and consistently use the exact properties of a 3D object.
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Vision-language models can get individual judgments right while struggling to recover and consistently use the exact properties of a 3D object.
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Many open mixture-of-experts (MoE) language models train with Muon instead of AdamW.
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Reinforcement learning (RL) has shown promise for training multimodal agents to reason and use tools.
Visual agents need to coordinate reasoning and action while their environment evolves during inference.
Jailbreak benchmarks test one checkpoint and treat which jailbreaks break it as a property of its pretraining data and safety training.
Solving long-horizon agentic tasks requires language agents to interact with their environments over multiple steps, and reliable performance often comes from…
Low-rank SVD-based compression offers a powerful strategy to reduce the computational costs of LLMs.
A robot generalist must interpret a scene, act on it, and satisfy every condition of a task.
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