Papers1055

FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation

Jibin Song, Mingi Kwon et al.Dec 31arXiv

FlowBlending is a simple way to speed up video diffusion models by smartly choosing when to use a big model and when a small one is enough.

#FlowBlending#stage-aware sampling#video diffusion

Nested Learning: The Illusion of Deep Learning Architectures

Intermediate

Ali Behrouz, Meisam Razaviyayn et al.Dec 31arXiv

The paper introduces Nested Learning, a new way to build AI that learns in layers (like Russian dolls), so each part can update at its own speed and remember different things.

#Nested Learning#Associative Memory#In-Context Learning

Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models

Intermediate

Junru Lu, Jiarui Qin et al.Dec 31arXiv

Youtu-LLM is a small (1.96B) language model that was trained from scratch to think, plan, and act like an agent instead of just copying bigger models.

#lightweight LLM#agentic mid-training#trajectory data

Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space

Intermediate

Xingwei Qu, Shaowen Wang et al.Dec 31arXiv

Language is lumpy: easy stretches and tricky jumps are mixed together, but old models spend the same effort on every word.

#Dynamic Large Concept Models#semantic boundaries#latent reasoning

Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization

Intermediate

Yuchen Shi, Yuzheng Cai et al.Dec 31arXiv

Youtu-Agent is a build-and-grow factory for AI agents that cuts manual setup and keeps agents improving over time.

#LLM agents#automated agent generation#modular architecture

PhyGDPO: Physics-Aware Groupwise Direct Preference Optimization for Physically Consistent Text-to-Video Generation

Intermediate

Yuanhao Cai, Kunpeng Li et al.Dec 31arXiv

This paper teaches text-to-video models to follow real-world physics, so people, balls, water, glass, and fire act the way they should.

#text-to-video generation#physical consistency#direct preference optimization

SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning

Intermediate

Yong Xien Chng, Tao Hu et al.Dec 30arXiv

SenseNova-MARS is a vision-language model that can think step-by-step and use three tools—text search, image search, and image cropping—during its reasoning.

#multimodal agent#vision-language model#reinforcement learning

Figure It Out: Improve the Frontier of Reasoning with Executable Visual States

Intermediate

Meiqi Chen, Fandong Meng et al.Dec 30arXiv

FIGR is a new way for AI to ‘think by drawing,’ using code to build clean, editable diagrams while it reasons.

#executable visual states#diagrammatic reasoning#reinforcement learning for reasoning

Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation

Intermediate

Zhe Huang, Hao Wen et al.Dec 30arXiv

Multimodal Large Language Models (MLLMs) often hallucinate on videos by trusting words and common sense more than what the frames really show.

#multimodal large language model#video understanding#visual hallucination

GR-Dexter Technical Report

Intermediate

Ruoshi Wen, Guangzeng Chen et al.Dec 30arXiv

GR-Dexter is a full package—new robot hands, a smart AI brain, and lots of carefully mixed data—that lets a two-handed robot follow language instructions to do long, tricky tasks.

#vision-language-action#dexterous manipulation#bimanual robotics

GARDO: Reinforcing Diffusion Models without Reward Hacking

Intermediate

Haoran He, Yuxiao Ye et al.Dec 30arXiv

GARDO is a new way to fine-tune text-to-image diffusion models with reinforcement learning without getting tricked by bad reward signals.

#GARDO#reward hacking#gated KL regularization

Factorized Learning for Temporally Grounded Video-Language Models

Intermediate

Wenzheng Zeng, Difei Gao et al.Dec 30arXiv

This paper teaches video-language models to first find when the proof happens in a video and then answer with that proof, instead of mixing both steps together.

#temporal grounding#video-language models#evidence tokens

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