Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
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• 2504.01990 • Published
• 303
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InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Paper • 2504.10479 • Published • 306 -
What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models
Paper • 2503.24235 • Published • 55 -
Seedream 3.0 Technical Report
Paper • 2504.11346 • Published • 70 -
TTRL: Test-Time Reinforcement Learning
Paper • 2504.16084 • Published • 120 -
Rethinking Reflection in Pre-Training
Paper • 2504.04022 • Published • 80 -
AnimeGamer: Infinite Anime Life Simulation with Next Game State Prediction
Paper • 2504.01014 • Published • 70 -
C3PO: Critical-Layer, Core-Expert, Collaborative Pathway Optimization for Test-Time Expert Re-Mixing
Paper • 2504.07964 • Published • 62 -
An Empirical Study of GPT-4o Image Generation Capabilities
Paper • 2504.05979 • Published • 64 -
The Bitter Lesson Learned from 2,000+ Multilingual Benchmarks
Paper • 2504.15521 • Published • 64 -
Inference-Time Scaling for Generalist Reward Modeling
Paper • 2504.02495 • Published • 58 -
One-Minute Video Generation with Test-Time Training
Paper • 2504.05298 • Published • 110 -
DeepSeek-R1 Thoughtology: Let's <think> about LLM Reasoning
Paper • 2504.07128 • Published • 87 -
TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation
Paper • 2411.04709 • Published • 26 -
Improving Video Generation with Human Feedback
Paper • 2501.13918 • Published • 52 -
ReCamMaster: Camera-Controlled Generative Rendering from A Single Video
Paper • 2503.11647 • Published • 146 -
Survey on Evaluation of LLM-based Agents
Paper • 2503.16416 • Published • 96 -
Token-Efficient Long Video Understanding for Multimodal LLMs
Paper • 2503.04130 • Published • 96 -
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 115 -
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps
Paper • 2501.09732 • Published • 72 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 288 -
Reinforcement Learning for Reasoning in Large Language Models with One Training Example
Paper • 2504.20571 • Published • 98 -
Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos
Paper • 2501.04001 • Published • 47 -
A Survey of Interactive Generative Video
Paper • 2504.21853 • Published • 46 -
PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity
Paper • 2503.07677 • Published • 86 -
Scaling Image and Video Generation via Test-Time Evolutionary Search
Paper • 2505.17618 • Published • 41 -
Training-Free Efficient Video Generation via Dynamic Token Carving
Paper • 2505.16864 • Published • 24 -
Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval
Paper • 2505.16967 • Published • 24 -
s3: You Don't Need That Much Data to Train a Search Agent via RL
Paper • 2505.14146 • Published • 19 -
The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT Distillation
Paper • 2505.18759 • Published • 14 -
Synthetic Data RL: Task Definition Is All You Need
Paper • 2505.17063 • Published • 11 -
Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models
Paper • 2505.18773 • Published • 7 -
Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!
Paper • 2505.15656 • Published • 15 -
Shifting AI Efficiency From Model-Centric to Data-Centric Compression
Paper • 2505.19147 • Published • 145 -
rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset
Paper • 2505.21297 • Published • 29 -
Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers
Paper • 2504.20752 • Published • 94 -
Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback
Paper • 2503.22230 • Published • 45 -
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Paper • 2506.09113 • Published • 107 -
Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generation
Paper • 2506.09991 • Published • 55 -
Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models
Paper • 2506.06395 • Published • 133 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Phantom-Data : Towards a General Subject-Consistent Video Generation Dataset
Paper • 2506.18851 • Published • 30 -
Why Language Models Hallucinate
Paper • 2509.04664 • Published • 196 -
Story2Board: A Training-Free Approach for Expressive Storyboard Generation
Paper • 2508.09983 • Published • 70 -
Omni-Effects: Unified and Spatially-Controllable Visual Effects Generation
Paper • 2508.07981 • Published • 63 -
DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI
Paper • 2512.16676 • Published • 219