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Code as Agent Harness
Paper • 2605.18747 • Published • 224 -
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
Paper • 2605.12500 • Published • 194 -
From Context to Skills: Can Language Models Learn from Context Skillfully?
Paper • 2604.27660 • Published • 171 -
PhysBrain 1.0 Technical Report
Paper • 2605.15298 • Published • 145
Collections
Discover the best community collections!
Collections including paper arxiv:2607.09024
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Representation Alignment for Just Image Transformers is not Easier than You Think
Paper • 2603.14366 • Published • 13 -
ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model
Paper • 2603.22281 • Published • 20 -
Steerable Visual Representations
Paper • 2604.02327 • Published • 56 -
Video Generation Models are General-Purpose Vision Learners
Paper • 2607.09024 • Published • 58
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4D Human-Scene Reconstruction from Low-Overlap Captures
Paper • 2607.09125 • Published • 42 -
MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs
Paper • 2505.24858 • Published • 17 -
Metacognition in LLMs: Foundations, Progress, and Opportunities
Paper • 2607.11881 • Published • 13 -
Video Generation Models are General-Purpose Vision Learners
Paper • 2607.09024 • Published • 58
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What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 91 -
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark
Paper • 2405.19707 • Published • 9 -
Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal Representations
Paper • 2410.08049 • Published • 8
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Code as Agent Harness
Paper • 2605.18747 • Published • 224 -
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
Paper • 2605.12500 • Published • 194 -
From Context to Skills: Can Language Models Learn from Context Skillfully?
Paper • 2604.27660 • Published • 171 -
PhysBrain 1.0 Technical Report
Paper • 2605.15298 • Published • 145
-
Representation Alignment for Just Image Transformers is not Easier than You Think
Paper • 2603.14366 • Published • 13 -
ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model
Paper • 2603.22281 • Published • 20 -
Steerable Visual Representations
Paper • 2604.02327 • Published • 56 -
Video Generation Models are General-Purpose Vision Learners
Paper • 2607.09024 • Published • 58
-
4D Human-Scene Reconstruction from Low-Overlap Captures
Paper • 2607.09125 • Published • 42 -
MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs
Paper • 2505.24858 • Published • 17 -
Metacognition in LLMs: Foundations, Progress, and Opportunities
Paper • 2607.11881 • Published • 13 -
Video Generation Models are General-Purpose Vision Learners
Paper • 2607.09024 • Published • 58
-
What matters when building vision-language models?
Paper • 2405.02246 • Published • 104 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 91 -
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark
Paper • 2405.19707 • Published • 9 -
Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal Representations
Paper • 2410.08049 • Published • 8