✨Mantis: A Versatile Vision-Language-Action Model with Disentangled Visual Foresight

📝 Summary:
Mantis is a VLA framework with Disentangled Visual Foresight DVF and a diffusion Transformer. DVF decouples visual foresight from the backbone, improving action prediction, comprehension, and reasoning while reducing training complexity. Mantis achieves high success rates and strong instruction-f...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16175
• PDF: https://arxiv.org/pdf/2511.16175
• Github: https://github.com/zhijie-group/Mantis

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#AI #ComputerVision #Robotics #VLAModels #DeepLearning

✨VisMem: Latent Vision Memory Unlocks Potential of Vision-Language Models

📝 Summary:
VisMem equips Vision-Language Models with dynamic latent vision memories, inspired by human cognition. This framework helps VLMs maintain perceptual fidelity and semantic consistency, significantly boosting performance on complex visual tasks.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11007
• PDF: https://arxiv.org/pdf/2511.11007
• Github: https://github.com/YU-deep/VisMem.git

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#VisMem #VisionLanguageModels #AI #DeepLearning #ComputerVision

✨General Agentic Memory Via Deep Research

📝 Summary:
GAM is a novel framework for AI memory addressing information loss in static systems. It uses JIT principles with a memorizer and researcher to create optimized contexts at runtime. This improves memory efficiency and task completion, leveraging LLMs and reinforcement learning.

🔹 Publication Date: Published on Nov 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18423
• PDF: https://arxiv.org/pdf/2511.18423
• Github: https://github.com/VectorSpaceLab/general-agentic-memory

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#AI #LLMs #ReinforcementLearning #AIMemory #DeepLearning

✨In-Video Instructions: Visual Signals as Generative Control

📝 Summary:
This paper introduces In-Video Instruction for controllable image-to-video generation. It embeds visual signals like text or arrows directly into frames as instructions, offering precise, spatial-aware control over object actions. Experiments show video models reliably execute these visual cues.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19401
• PDF: https://arxiv.org/pdf/2511.19401
• Project Page: https://fangggf.github.io/In-Video/
• Github: https://fangggf.github.io/In-Video/

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#VideoGeneration #GenerativeAI #ComputerVision #AIResearch #DeepLearning

✨DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation

📝 Summary:
DeCo is a frequency-decoupled pixel diffusion framework that improves image generation by separating high-frequency details and low-frequency semantics. It uses a lightweight pixel decoder for details and a DiT for semantics, achieving superior efficiency and quality over existing pixel diffusion...

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19365
• PDF: https://arxiv.org/pdf/2511.19365
• Project Page: https://zehong-ma.github.io/DeCo/
• Github: https://github.com/Zehong-Ma/DeCo

🔹 Models citing this paper:
• https://huggingface.co/zehongma/DeCo

✨ Spaces citing this paper:
• https://huggingface.co/spaces/zehongma/DeCo

==================================

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#ImageGeneration #DiffusionModels #ComputerVision #DeepLearning #DeCo

✨Controllable Layer Decomposition for Reversible Multi-Layer Image Generation

📝 Summary:
Controllable Layer Decomposition CLD enables fine-grained, controllable separation of raster images into editable RGBA layers, overcoming traditional compositing limitations. Using LD-DiT and MLCA, CLD surpasses existing methods in quality and control. It produces layers directly usable in design...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16249
• PDF: https://arxiv.org/pdf/2511.16249
• Github: https://github.com/monkek123King/CLD

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#ImageGeneration #DeepLearning #ComputerVision #ImageEditing #LayerDecomposition

✨Plan-X: Instruct Video Generation via Semantic Planning

📝 Summary:
Plan-X improves instruction-aligned video generation by integrating a Semantic Planner with diffusion models. The planner generates semantic tokens that guide video synthesis, reducing visual hallucinations. This framework combines language models for reasoning with diffusion models for photoreal...

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17986
• PDF: https://arxiv.org/pdf/2511.17986
• Project Page: https://byteaigc.github.io/Plan-X/

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#VideoGeneration #DiffusionModels #AI #ComputerVision #DeepLearning

✨Continuous Thought Machines

📝 Summary:
The Continuous Thought Machine CTM reintroduces neural timing and synchronization to deep learning for complex sequential reasoning and biologically plausible AI. It uses neuron-level temporal processing and synchronization as a latent representation, performing well on diverse tasks with adaptiv...

🔹 Publication Date: Published on May 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.05522
• PDF: https://arxiv.org/pdf/2505.05522
• Github: https://github.com/SakanaAI/continuous-thought-machines

🔹 Models citing this paper:
• https://huggingface.co/SakanaAI/ctm-imagenet
• https://huggingface.co/SakanaAI/ctm-maze-large

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Uday/ctm-energy-based-halting

==================================

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#AI #DeepLearning #NeuralNetworks #BiologicallyInspiredAI #TemporalAI

✨LucidFlux: Caption-Free Universal Image Restoration via a Large-Scale Diffusion Transformer

📝 Summary:
LucidFlux is a caption-free universal image restoration framework using a large diffusion transformer. It employs a dual-branch conditioner and adaptive modulation for robust restoration, avoiding text prompts by using SigLIP features. This approach outperforms existing methods by intelligently c...

🔹 Publication Date: Published on Sep 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.22414
• PDF: https://arxiv.org/pdf/2509.22414
• Project Page: https://w2genai-lab.github.io/LucidFlux/
• Github: https://github.com/W2GenAI-Lab/LucidFlux

🔹 Models citing this paper:
• https://huggingface.co/W2GenAI/LucidFlux

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#ImageRestoration #DiffusionModels #ComputerVision #DeepLearning #GenerativeAI

✨DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research

📝 Summary:
RLER is introduced to train deep research models for long-form tasks by using rubrics that co-evolve with the policy model. Enabling DR Tulu-8B to outperform open models and match proprietary systems while being more cost-effective.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19399
• PDF: https://arxiv.org/pdf/2511.19399
• Project Page: https://github.com/rlresearch/dr-tulu
• Github: https://github.com/rlresearch/dr-tulu

🔹 Models citing this paper:
• https://huggingface.co/rl-research/DR-Tulu-8B
• https://huggingface.co/rl-research/DR-Tulu-SFT-8B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/rl-research/dr-tulu-sft-data
• https://huggingface.co/datasets/rl-research/dr-tulu-rl-data

==================================

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#ReinforcementLearning #LLMs #DeepLearning #AIResearch #MachineLearning

✨HunyuanVideo 1.5 Technical Report

📝 Summary:
HunyuanVideo 1.5 is a lightweight, open-source video generation model achieving state-of-the-art visual quality and motion coherence. It employs an advanced DiT architecture with SSTA and an efficient video super-resolution network, enabling high-quality video creation on consumer GPUs.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18870
• PDF: https://arxiv.org/pdf/2511.18870
• Github: https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5

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#VideoGeneration #AI #DeepLearning #OpenSource #DiffusionModels

✨Flow Map Distillation Without Data

📝 Summary:
This paper introduces a data-free framework for flow map distillation, eliminating the need for external datasets. By sampling only from the prior distribution, it avoids data mismatch risks and achieves state-of-the-art fidelity with minimal sampling steps, surpassing all data-based alternatives.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19428
• PDF: https://arxiv.org/pdf/2511.19428

==================================

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#FlowMapDistillation #DataFreeLearning #MachineLearning #DeepLearning #AIResearch

✨Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO

📝 Summary:
Training multi-agent systems with distinct LLMs faces optimization challenges. M-GRPO, a hierarchical GRPO extension, addresses this by aligning heterogeneous trajectories and decoupling agent training. This improves stability and sample efficiency for tool-augmented reasoning tasks.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13288
• PDF: https://arxiv.org/pdf/2511.13288

==================================

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#MultiAgentSystems #ReinforcementLearning #DeepLearning #LLM #AI

✨Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems

📝 Summary:
A Sparse Autoencoder extracts interaction-aware monosemantic concepts from recommender embeddings. Its prediction-aware training aligns these with model predictions, enabling controllable personalization and interpretability.

🔹 Publication Date: Published on Nov 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18024
• PDF: https://arxiv.org/pdf/2511.18024

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#RecommenderSystems #DeepLearning #AI #Interpretability #Personalization

✨MIST: Mutual Information Via Supervised Training

📝 Summary:
MIST is a data-driven neural network that estimates mutual information. Trained on synthetic data, it uses attention and quantile regression for uncertainty. It outperforms classical methods, offering faster and more reliable MI estimation.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18945
• PDF: https://arxiv.org/pdf/2511.18945
• Github: https://github.com/grgera/mist

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#MutualInformation #NeuralNetworks #MachineLearning #DeepLearning #DataScience

✨PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC

📝 Summary:
PC-Agent is a hierarchical multi-agent framework improving MLLM-based GUI agents for complex PC tasks. It uses an Active Perception Module and a hierarchical decision-making architecture with Manager, Progress, and Decision agents. A Reflection agent provides feedback. It achieved a 32% task succ...

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.14282
• PDF: https://arxiv.org/pdf/2502.14282
• Github: https://github.com/X-PLUG/MobileAgent/tree/main/PC-Agent

✨ Spaces citing this paper:
• https://huggingface.co/spaces/junyangwang0410/PC-Agent

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#MultiAgentSystems #AIAgents #MLLMs #PCAutomation #DeepLearning

✨MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection

📝 Summary:
MSRNet proposes a Multi-Scale Recursive Network for camouflaged object detection. It uses a Pyramid Vision Transformer and recursive feature refinement to overcome challenges with small and multiple objects, achieving state-of-the-art results.

🔹 Publication Date: Published on Nov 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12810
• PDF: https://arxiv.org/pdf/2511.12810

🔹 Models citing this paper:
• https://huggingface.co/linaa98/MSRNet

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#CamouflagedObjectDetection #ObjectDetection #ComputerVision #DeepLearning #AIResearch

✨Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling

📝 Summary:
Upsample Anything is a novel test-time optimization framework that enhances low-resolution features to high-resolution outputs without training. It learns an anisotropic Gaussian kernel per image, acting as a universal edge-aware operator. This method achieves state-of-the-art results in tasks li...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16301
• PDF: https://arxiv.org/pdf/2511.16301

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#Upsampling #ComputerVision #ImageProcessing #DeepLearning #AI

✨EvoVLA: Self-Evolving Vision-Language-Action Model

📝 Summary:
EvoVLA is a self-supervised VLA framework tackling stage hallucination in long-horizon robotic manipulation. It uses triplet contrastive learning, pose-based exploration, and memory to prevent shortcuts. EvoVLA significantly improves success, sample efficiency, and reduces hallucination in sim an...

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16166
• PDF: https://arxiv.org/pdf/2511.16166
• Project Page: https://aigeeksgroup.github.io/EvoVLA/
• Github: https://aigeeksgroup.github.io/EvoVLA/

==================================

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#Robotics #VisionLanguageAction #SelfSupervisedLearning #AI #DeepLearning

✨One4D: Unified 4D Generation and Reconstruction via Decoupled LoRA Control

📝 Summary:
One4D is a unified framework for 4D generation and reconstruction, producing synchronized RGB frames and pointmaps. It uses Unified Masked Conditioning for varying input sparsities and Decoupled LoRA Control to achieve high-quality results across diverse tasks.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.18922
• PDF: https://arxiv.org/pdf/2511.18922
• Project Page: https://mizhenxing.github.io/One4D
• Github: https://mizhenxing.github.io/One4D

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#4DGeneration #4DReconstruction #ComputerVision #DeepLearning #GenerativeAI