Trending Research
UFO2: The Desktop AgentOS
Recent Computer-Using Agents (CUAs), powered by multimodal large language models (LLMs), offer a promising direction for automating complex desktop workflows through natural language.
IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System
Recently, large language model (LLM) based text-to-speech (TTS) systems have gradually become the mainstream in the industry due to their high naturalness and powerful zero-shot voice cloning capabilities. Here, we introduce the IndexTTS system, which is mainly based on the XTTS and Tortoise model.
MonSter: Marry Monodepth to Stereo Unleashes Power
junda24/monster
•
•
The refined monodepth is in turn guides stereo effectively at ill-posed regions.
MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation
xu107/mmhcl
•
•
For a comprehensive information exploration from user-product relations, we construct two hypergraphs, i. e. a user-to-user (u2u) hypergraph and an item-to-item (i2i) hypergraph, to mine shared preferences among users and intricate multimodal semantic resemblance among items, respectively.
Take the aTrain. Introducing an Interface for the Accessible Transcription of Interviews
If an entry-level graphics card is available, the transcription speed increases to 20% of the audio duration.
Deep Industrial Image Anomaly Detection: A Survey
m-3lab/awesome-industrial-anomaly-detection
•
•
In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.
Learning to Reason for Long-Form Story Generation
Generating high-quality stories spanning thousands of tokens requires competency across a variety of skills, from tracking plot and character arcs to keeping a consistent and engaging style.
PRIMA.CPP: Speeding Up 70B-Scale LLM Inference on Low-Resource Everyday Home Clusters
Emergency of DeepSeek R1 and QwQ 32B have broken through performance barriers for running frontier large language models (LLMs) on home devices.
Perception-R1: Pioneering Perception Policy with Reinforcement Learning
linkangheng/pr1
•
•
In this work, we return to the fundamentals and explore the effects of RL on different perception tasks.
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
Training large language models (LLMs) as interactive agents presents unique challenges including long-horizon decision making and interacting with stochastic environment feedback.
