Trending Research
Agent S: An Open Agentic Framework that Uses Computers Like a Human
We present Agent S, an open agentic framework that enables autonomous interaction with computers through a Graphical User Interface (GUI), aimed at transforming human-computer interaction by automating complex, multi-step tasks.
Visual-RFT: Visual Reinforcement Fine-Tuning
Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1 learns from feedback on its answers, which is especially useful in applications when fine-tuning data is scarce.
Few-Shot Object Detection
Fine-Grained Image Classification
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Executable Code Actions Elicit Better LLM Agents
LLM agents are typically prompted to produce actions by generating JSON or text in a pre-defined format, which is usually limited by constrained action space (e. g., the scope of pre-defined tools) and restricted flexibility (e. g., inability to compose multiple tools).
GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control
Our results demonstrate more precise camera control than prior work, as well as state-of-the-art results in sparse-view novel view synthesis, even in challenging settings such as driving scenes and monocular dynamic video.
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking explicit reasoning processes.
Interpreting and Improving Diffusion Models from an Optimization Perspective
yuanchenyang/smalldiffusion
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Denoising is intuitively related to projection.
AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning
Some studies integrate vision-language models (VLMs) into autonomous driving, but they typically rely on pre-trained models with simple supervised fine-tuning (SFT) on driving data, without further exploration of training strategies or optimizations specifically tailored for planning.
LLM4AD: A Platform for Algorithm Design with Large Language Model
We introduce LLM4AD, a unified Python platform for algorithm design (AD) with large language models (LLMs).
Self-rewarding correction for mathematical reasoning
volcengine/verl
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We study self-rewarding reasoning large language models (LLMs), which can simultaneously generate step-by-step reasoning and evaluate the correctness of their outputs during the inference time-without external feedback.
Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference
Diffusion models generate high-quality images but often lack efficient and universally applicable inpainting capabilities, particularly in community-trained models.
