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

📝 Summary:
MLLMs struggle with hallucinations on counterfactual videos. DualityForge synthesizes counterfactual video data and QA pairs through diffusion-based editing to address this. This method significantly reduces model hallucinations and improves general performance.

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24271
• PDF: https://arxiv.org/pdf/2512.24271
• Project Page: https://amap-ml.github.io/Taming-Hallucinations/
• Github: https://github.com/AMAP-ML/Taming-Hallucinations

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#MLLMs #VideoUnderstanding #AIHallucinations #GenerativeAI #MachineLearning

✨NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos

📝 Summary:
NeoVerse is a 4D world model for reconstruction and video generation. It scales to in-the-wild monocular videos using pose-free feed-forward reconstruction and online degradation simulation, achieving state-of-the-art performance.

🔹 Publication Date: Published on Jan 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00393
• PDF: https://arxiv.org/pdf/2601.00393
• Project Page: https://neoverse-4d.github.io/
• Github: https://neoverse-4d.github.io

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

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#4DWorldModel #VideoGeneration #ComputerVision #DeepLearning #AI

✨MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing

📝 Summary:
MorphAny3D offers a training-free framework for high-quality 3D morphing, even across categories. It leverages Structured Latent representations with novel attention mechanisms MCA, TFSA for structural coherence and temporal consistency. This achieves state-of-the-art results and supports advance...

🔹 Publication Date: Published on Jan 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00204
• PDF: https://arxiv.org/pdf/2601.00204
• Project Page: https://xiaokunsun.github.io/MorphAny3D.github.io
• Github: https://github.com/XiaokunSun/MorphAny3D

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#3DMorphing #ComputerGraphics #DeepLearning #StructuredLatent #AIResearch

✨Nested Learning: The Illusion of Deep Learning Architectures

📝 Summary:
Nested Learning NL models ML as nested optimization problems. It enables expressive algorithms for higher-order learning and continual adaptation, introducing optimizers, self-modifying models, and continuum memory systems.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24695
• PDF: https://arxiv.org/pdf/2512.24695

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#NestedLearning #MachineLearning #DeepLearning #Optimization #AI

nature papers: 1400$

Q1 and  Q2 papers    900$

Q3 and Q4 papers   500$

Doctoral thesis (complete)    700$

M.S thesis         300$

paper simulation   200$

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✨AdaGaR: Adaptive Gabor Representation for Dynamic Scene Reconstruction

📝 Summary:
AdaGaR reconstructs dynamic 3D scenes from monocular video. It introduces an Adaptive Gabor Representation for detail and stability, and Cubic Hermite Splines for temporal continuity. This method achieves state-of-the-art performance.

🔹 Publication Date: Published on Jan 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00796
• PDF: https://arxiv.org/pdf/2601.00796
• Project Page: https://jiewenchan.github.io/AdaGaR/

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

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#3DReconstruction #ComputerVision #DynamicScenes #MonocularVideo #GaborRepresentation

✨InfoSynth: Information-Guided Benchmark Synthesis for LLMs

📝 Summary:
InfoSynth automatically generates novel and diverse coding benchmarks for LLMs. It uses information-theoretic metrics and genetic algorithms to create scalable self-verifying problems, overcoming manual effort and training data contamination.

🔹 Publication Date: Published on Jan 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00575
• PDF: https://arxiv.org/pdf/2601.00575
• Project Page: https://ishirgarg.github.io/infosynth_web/
• Github: https://github.com/ishirgarg/infosynth

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

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#LLM #AI #Benchmarking #GenerativeAI #DeepLearning

✨Diversity or Precision? A Deep Dive into Next Token Prediction

📝 Summary:
This paper proposes a pre-training objective that reshapes the token-output distribution for better RL exploration. It uses reward-shaping to balance diversity and precision in next-token prediction. Contrary to intuition, a precision-oriented prior surprisingly yields a superior exploration spac...

🔹 Publication Date: Published on Dec 28, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22955
• PDF: https://arxiv.org/pdf/2512.22955

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#NextTokenPrediction #ReinforcementLearning #LLM #NLP #AIResearch

✨OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions

📝 Summary:
OmniVCus introduces a system for feedforward multi-subject video customization with multimodal controls. It proposes a data pipeline, VideoCus-Factory, and a diffusion Transformer framework with novel embedding mechanisms. This enables more subjects and precise editing, significantly outperformin...

🔹 Publication Date: Published on Jun 29, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.23361
• PDF: https://arxiv.org/pdf/2506.23361
• Project Page: https://caiyuanhao1998.github.io/project/OmniVCus/
• Github: https://github.com/caiyuanhao1998/Open-OmniVCus

🔹 Models citing this paper:
• https://huggingface.co/CaiYuanhao/OmniVCus

✨ Datasets citing this paper:
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Test
• https://huggingface.co/datasets/CaiYuanhao/OmniVCus-Train

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

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#VideoGeneration #DiffusionModels #MultimodalAI #DeepLearning #ComputerVision

✨Bitnet.cpp: Efficient Edge Inference for Ternary LLMs

📝 Summary:
Bitnet.cpp enhances edge inference for ternary LLMs using a novel mixed-precision matrix multiplication library. This system incorporates Ternary Lookup Tables and Int2 with a Scale for efficient, lossless inference, achieving up to a 6.25x speed increase over baselines.

🔹 Publication Date: Published on Feb 17, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.11880
• PDF: https://arxiv.org/pdf/2502.11880
• Github: https://github.com/microsoft/BitNet/tree/paper

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#LLM #EdgeAI #MachineLearning #DeepLearning #AI

✨BitNet b1.58 2B4T Technical Report

📝 Summary:
BitNet b1.58 2B4T is the first open-source 1-bit Large Language Model with 2 billion parameters. It matches full-precision LLM performance while offering significant improvements in computational efficiency like reduced memory and energy. The model weights are openly released for research.

🔹 Publication Date: Published on Apr 16, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.12285
• PDF: https://arxiv.org/pdf/2504.12285
• Github: https://github.com/microsoft/bitnet

🔹 Models citing this paper:
• https://huggingface.co/microsoft/bitnet-b1.58-2B-4T
• https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-gguf
• https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-bf16

✨ Spaces citing this paper:
• https://huggingface.co/spaces/suayptalha/Chat-with-Bitnet-b1.58-2B-4T
• https://huggingface.co/spaces/aizip-dev/SLM-RAG-Arena
• https://huggingface.co/spaces/Tonic/Native_1-bit_LLM

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

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#LLM #AI #Quantization #OpenSourceAI #DeepLearning

✨Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning

📝 Summary:
This paper addresses Preference Mode Collapse PMC in text-to-image diffusion models, where models lose diversity despite high reward scores. It introduces D^2-Align, a framework that mitigates PMC by directionally correcting the reward signal during optimization. This novel approach maintains gen...

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24146
• PDF: https://arxiv.org/pdf/2512.24146

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

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#DiffusionModels #ReinforcementLearning #GenerativeAI #MachineLearning #AIResearch

✨DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer

📝 Summary:
DreamID-V is a novel video face swapping framework that uses diffusion transformers and curriculum learning. It achieves superior identity preservation and visual realism by bridging the image-to-video gap, outperforming existing methods and enhancing temporal consistency.

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01425
• PDF: https://arxiv.org/pdf/2601.01425
• Project Page: https://guoxu1233.github.io/DreamID-V/
• Github: https://guoxu1233.github.io/DreamID-V/

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

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#FaceSwapping #DiffusionModels #ComputerVision #GenerativeAI #VideoAI

✨BitNet Distillation

📝 Summary:
BitNet Distillation fine-tunes LLMs to 1.58-bit precision using SubLN, attention distillation, and continual pre-training. It achieves comparable performance to full-precision models, offering 10x memory savings and 2.65x faster inference.

🔹 Publication Date: Published on Oct 15, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.13998
• PDF: https://arxiv.org/pdf/2510.13998
• Github: https://github.com/microsoft/BitNet

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

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#LLM #Quantization #ModelCompression #DeepLearning #AI

✨NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation

📝 Summary:
NextFlow is a unified decoder-only transformer enabling fast multimodal understanding and generation. It uses next-token prediction for text and next-scale for images, generating 1024x1024 images in 5 seconds. It achieves state-of-the-art performance among unified models.

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02204
• PDF: https://arxiv.org/pdf/2601.02204
• Github: https://github.com/ByteVisionLab/NextFlow

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

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#AI #DataScience #MachineLearning #HuggingFace #Research