✨CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning

📝 Summary:
The paper addresses unreliable multilingual medical reasoning in LLMs, especially for underrepresented languages. It introduces CURE-MED, a curriculum-informed reinforcement learning framework, and CUREMED-BENCH dataset. CURE-MED significantly improves language consistency and logical correctness...

🔹 Publication Date: Published on Jan 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13262
• PDF: https://arxiv.org/pdf/2601.13262
• Project Page: https://cure-med.github.io/

🔹 Models citing this paper:
• https://huggingface.co/Aikyam-Lab/CURE-MED-1.5B
• https://huggingface.co/Aikyam-Lab/CURE-MED-3B
• https://huggingface.co/Aikyam-Lab/CURE-MED-7B

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#LLMs #MedicalAI #ReinforcementLearning #MultilingualNLP #AIResearch

✨Implicit Neural Representation Facilitates Unified Universal Vision Encoding

📝 Summary:
This paper unifies image representation learning for both recognition and generation. It uses a hyper-network for implicit neural representation with knowledge distillation to create compressed embeddings. The model achieves state-of-the-art results and enables generative capabilities.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14256
• PDF: https://arxiv.org/pdf/2601.14256
• Github: https://github.com/tiktok/huvr

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#ComputerVision #DeepLearning #GenerativeAI #RepresentationLearning #VisionEncoding

✨Motion 3-to-4: 3D Motion Reconstruction for 4D Synthesis

📝 Summary:
Motion 3-to-4 synthesizes 4D dynamic objects from monocular video by separating static 3D shape generation from motion reconstruction. It uses a canonical mesh and a transformer to predict temporally coherent vertex trajectories, achieving superior fidelity.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14253
• PDF: https://arxiv.org/pdf/2601.14253

🔹 Models citing this paper:
• https://huggingface.co/River-Chen/Motion324

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#3DReconstruction #4DSynthesis #ComputerVision #DeepLearning #MotionCapture

✨The Flexibility Trap: Why Arbitrary Order Limits Reasoning Potential in Diffusion Language Models

📝 Summary:
Arbitrary order generation in diffusion LLMs, surprisingly, limits reasoning by causing premature solution space collapse. This occurs because dLLMs exploit flexibility to bypass crucial, high-uncertainty tokens. Standard Group Relative Policy Optimization without arbitrary order is more effectiv...

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15165
• PDF: https://arxiv.org/pdf/2601.15165
• Project Page: https://nzl-thu.github.io/the-flexibility-trap
• Github: https://github.com/LeapLabTHU/JustGRPO

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#LLM #DiffusionModels #NLP #AIResearch #MachineLearning

✨Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model

📝 Summary:
Stable-DiffCoder uses block diffusion continual pretraining to significantly outperform autoregressive code models. It achieves superior performance on a wide range of code benchmarks, enhancing structured code modeling and benefiting low-resource languages.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15892
• PDF: https://arxiv.org/pdf/2601.15892
• Project Page: https://bytedance-seed.github.io/Stable-DiffCoder/
• Github: https://github.com/ByteDance-Seed/Stable-DiffCoder

🔹 Models citing this paper:
• https://huggingface.co/ByteDance-Seed/Stable-DiffCoder-8B-Instruct
• https://huggingface.co/ByteDance-Seed/Stable-DiffCoder-8B-Base

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LLM-in-Sandbox Elicits General Agentic Intelligence

📝 Summary:
LLM-in-Sandbox enables large language models to explore a code sandbox, eliciting general agentic intelligence across diverse domains without additional training. LLMs spontaneously access resources, handle long contexts, and execute scripts, showing robust generalization. These capabilities can ...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16206
• PDF: https://arxiv.org/pdf/2601.16206
• Project Page: https://llm-in-sandbox.github.io
• Github: https://github.com/llm-in-sandbox/llm-in-sandbox

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Learning to Discover at Test Time

📝 Summary:
Test-time training enables AI systems to discover optimal solutions for specific scientific problems through continual learning focused on individual challenges rather than generalization. AI-generate...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16175
• PDF: https://arxiv.org/pdf/2601.16175
• Project Page: https://test-time-training.github.io/discover/
• Github: https://github.com/test-time-training/discover

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Qwen3-TTS Technical Report

📝 Summary:
The Qwen3-TTS series presents advanced multilingual text-to-speech models with voice cloning and controllable speech generation capabilities, utilizing dual-track LM architecture and specialized speec...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15621
• PDF: https://arxiv.org/pdf/2601.15621
• Github: https://github.com/QwenLM/Qwen3-TTS

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨OpenVision 3: A Family of Unified Visual Encoder for Both Understanding and Generation

📝 Summary:
An advanced vision encoder named OpenVision 3 learns a unified visual representation for both image understanding and generation by combining VAE-compressed image latents with ViT architecture and joi...

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15369
• PDF: https://arxiv.org/pdf/2601.15369
• Project Page: https://ucsc-vlaa.github.io/OpenVision3/
• Github: https://ucsc-vlaa.github.io/OpenVision3/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

📝 Summary:
Terminal-Bench 2.0 presents a challenging benchmark with 89 terminal-based tasks to evaluate AI agents' capabilities in real-world scenarios. AI-generated summary AI agents may soon become capable of ...

🔹 Publication Date: Published on Jan 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11868
• PDF: https://arxiv.org/pdf/2601.11868
• Github: https://github.com/laude-institute/terminal-bench

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing

📝 Summary:
A novel fine-grained composed image retrieval benchmark is introduced through image editing techniques, revealing significant capability gaps in existing multimodal models and exposing limitations of ...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16125
• PDF: https://arxiv.org/pdf/2601.16125
• Github: https://github.com/SighingSnow/edir

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨BayesianVLA: Bayesian Decomposition of Vision Language Action Models via Latent Action Queries

📝 Summary:
VLA models struggle with generalization due to Information Collapse where language is ignored. BayesianVLA uses Bayesian decomposition and latent action queries. It optimizes conditional PMI to penalize vision shortcuts, significantly improving out-of-distribution generalization.

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15197
• PDF: https://arxiv.org/pdf/2601.15197
• Project Page: https://github.com/ZGC-EmbodyAI/BayesianVLA
• Github: https://github.com/ZGC-EmbodyAI/BayesianVLA

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SAMTok: Representing Any Mask with Two Words

📝 Summary:
SAMTok enables pixel-wise capabilities in multi-modal LLMs through discrete mask tokenization and standard training methods, achieving state-of-the-art performance on various vision-language tasks. AI...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16093
• PDF: https://arxiv.org/pdf/2601.16093
• Project Page: https://github.com/bytedance/Sa2VA/tree/main/projects/samtok

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders

📝 Summary:
Representation Autoencoders (RAEs) demonstrate superior performance over VAEs in large-scale text-to-image generation, showing improved stability, faster convergence, and better quality while enabling...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16208
• PDF: https://arxiv.org/pdf/2601.16208
• Project Page: https://rae-dit.github.io/scale-rae/
• Github: https://github.com/ZitengWangNYU/Scale-RAE

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Wigner's Friend as a Circuit: Inter-Branch Communication Witness Benchmarks on Superconducting Quantum Hardware

📝 Summary:
Implementation and benchmarking of quantum circuits for estimating operational inter-branch communication witnesses on IBM Quantum hardware demonstrates visibility and coherence witness measurements u...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16004
• PDF: https://arxiv.org/pdf/2601.16004
• Github: https://github.com/christopher-altman/ibm-qml-kernel

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning

📝 Summary:
A pretrained video model is adapted into a robot policy through single-stage post-training, enabling direct action generation and planning capabilities without architectural modifications. AI-generate...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16163
• PDF: https://arxiv.org/pdf/2601.16163

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding

📝 Summary:
HERMES enables real-time streaming video understanding by reusing a compact KV cache as hierarchical memory. It provides 10x faster response times and superior accuracy, even with greatly reduced video token input, improving efficiency in resource-constrained settings.

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14724
• PDF: https://arxiv.org/pdf/2601.14724
• Project Page: https://hermes-streaming.github.io/
• Github: https://hermes-streaming.github.io/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨VIOLA: Towards Video In-Context Learning with Minimal Annotations

📝 Summary:
VIOLA enables effective multimodal large language model adaptation in low-resource video domains using minimal expert annotations and abundant unlabeled data. It uses density-uncertainty sampling and confidence-aware retrieval to maximize efficiency and leverage unlabeled data, significantly outp...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15549
• PDF: https://arxiv.org/pdf/2601.15549

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨360Anything: Geometry-Free Lifting of Images and Videos to 360°

📝 Summary:
360Anything is a geometry-free framework using diffusion transformers to lift perspective images and videos to 360 panoramas without camera metadata. It achieves state-of-the-art results and uses circular latent encoding to eliminate seam artifacts.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16192
• PDF: https://arxiv.org/pdf/2601.16192
• Github: https://360anything.github.io/

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

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#ComputerVision #DiffusionModels #360Photography #ImageProcessing #DeepLearning

✨Numba-Accelerated 2D Diffusion-Limited Aggregation: Implementation and Fractal Characterization

📝 Summary:
This paper details a Numba-accelerated Python framework for 2D DLA simulations. It confirms a fractal dimension of 1.71 for dilute regimes but reveals a crossover to 1.87 compact growth in high-density environments. This provides an open-source testbed.

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15440
• PDF: https://arxiv.org/pdf/2601.15440
• Project Page: https://pypi.org/project/dla-ideal-solver/
• Github: https://github.com/sandyherho/dla-ideal-solver

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

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#DLA #Fractals #ScientificComputing #Python #Simulations

✨VideoMaMa: Mask-Guided Video Matting via Generative Prior

📝 Summary:
VideoMaMa uses pretrained video diffusion models to convert coarse masks into accurate alpha mattes, achieving zero-shot generalization. This enabled a scalable pseudo-labeling pipeline to create the large MA-V dataset, significantly improving real-world video matting performance.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14255
• PDF: https://arxiv.org/pdf/2601.14255
• Github: https://cvlab-kaist.github.io/VideoMaMa/

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#VideoMatting #ComputerVision #DeepLearning #DiffusionModels #AIResearch