Browsing: Hugging Face
Recently, Zaremba et al. demonstrated that increasing inference-time computation improves robustness in large proprietary reasoning LLMs. In this paper, we…
With the rapid advancement of Large Language Models (LLMs), developing effective critic modules for precise guidance has become crucial yet…
Concept Ablation Fine-Tuning (CAFT) uses interpretability tools to steer LLM generalization away from unintended concepts without altering training data. Fine-tuning…
ObjectGS combines 3D scene reconstruction with semantic understanding by modeling individual objects as neural Gaussians, achieving superior performance in segmentation…
RedOne, a domain-specific LLM, enhances performance across multiple SNS tasks through a three-stage training strategy, improving generalization and reducing harmful…
Mono-InternVL, an advanced monolithic Multimodal Large Language Model, integrates visual experts and improved pre-training strategies to enhance visual learning and…
Diffusion-based large language models (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs, offering faster inference and greater…
A sliding iterative denoising process is proposed to enhance spatio-temporal consistency in 4D diffusion models for high-fidelity view synthesis from…
A residual learning approach enhances Sparse Autoencoders to capture domain-specific features without retraining, improving interpretability and performance on specialized domains.…
AutoSteer, a modular inference-time intervention technology, enhances the safety of Multimodal Large Language Models by reducing attack success rates across…