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Found 21 papers, 13 papers with code
Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning
The planning head utilizes flow matching and a novel masked ESDF loss to minimize collision risks for generating local trajectories, and the odometry head integrates multi-sensor inputs via a transformer encoder to predict the relative pose of the robot.
Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning
Our framework specifically investigates key elements that have been hypothesized to benefit from RL training: (1) plan-following and execution, (2) problem decomposition, and (3) improved reasoning and knowledge utilization.
OmniGenBench: A Benchmark for Omnipotent Multimodal Generation across 50+ Tasks
Recent breakthroughs in large multimodal models (LMMs), such as the impressive GPT-4o-Native, have demonstrated remarkable proficiency in following general-purpose instructions for image generation.
COSMOS: Predictable and Cost-Effective Adaptation of LLMs
Large language models (LLMs) achieve remarkable performance across numerous tasks by using a diverse array of adaptation strategies.
NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images.
Wan: Open and Advanced Large-Scale Video Generative Models
Openness: We open-source the entire series of Wan, including source code and all models, with the goal of fostering the growth of the video generation community.
EgoVid-5M: A Large-Scale Video-Action Dataset for Egocentric Video Generation
Video generation has emerged as a promising tool for world simulation, leveraging visual data to replicate real-world environments.
InternLM2.5-StepProver: Advancing Automated Theorem Proving via Expert Iteration on Large-Scale LEAN Problems
We propose to use large scale LEAN problem datasets Lean-workbook for expert iteration with more than 20, 000 CPU days.
Scaling Behavior for Large Language Models regarding Numeral Systems: An Example using Pythia
Numbers can be tokenized into tokens in various ways by different LLMs and affect the numeric operations performance.
LEAN-GitHub: Compiling GitHub LEAN repositories for a versatile LEAN prover
To address this issue, we propose LEAN-GitHub, a dataset consisting of large-scale formal data extracted from almost all Lean 4 repositories on GitHub.
Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models
Large language models (LLMs) and vision-language models (VLMs) have demonstrated remarkable performance across a wide range of tasks and domains.
Lean Workbook: A large-scale Lean problem set formalized from natural language math problems
Our results indicate that the synthetic data pipeline can provide useful training data and improve the performance of LLMs in translating and understanding complex mathematical problems and proofs.
UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation
First, to reduce the optimization difficulty and ensure temporal coherence, we map the reference image along with the posture guidance and noise video into a common feature space by incorporating a unified video diffusion model.
Grammar-Aligned Decoding
Large Language Models (LLMs) struggle with reliably generating highly structured outputs, such as program code, mathematical formulas, or well-formed markup.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning
We further explore how to use LEAN to solve math problems and study its performance under the setting of multi-task learning which shows the possibility of using LEAN as a unified platform for solving and proving in math.
DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models
To more conveniently specify personalized emotions, a diffusion-based style predictor is utilized to predict the personalized emotion directly from the audio, eliminating the need for extra emotion reference.
Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation
At the structure level, we decompose the T2V task into two steps, including spatial reasoning and temporal reasoning, using a unified denoiser.
Ranked #6 on
Text-to-Video Generation
on MSR-VTT
I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models
By this means, I2VGen-XL can simultaneously enhance the semantic accuracy, continuity of details and clarity of generated videos.
LipFormer: High-Fidelity and Generalizable Talking Face Generation With a Pre-Learned Facial Codebook
Generating a talking face video from the input audio sequence is a practical yet challenging task.
Transformation GAN for Unsupervised Image Synthesis and Representation Learning
To improve both image synthesis quality and representation learning performance under the unsupervised setting, in this paper, we propose a simple yet effective Transformation Generative Adversarial Networks (TrGAN).
