✨Transition Matching Distillation for Fast Video Generation

📝 Summary:
Transition Matching Distillation enables efficient video generation by distilling diffusion models into few-step predictors using conditional flows and semantic representation decomposition. AI-genera...

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09881
• PDF: https://arxiv.org/pdf/2601.09881

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Action100M: A Large-scale Video Action Dataset

📝 Summary:
Action100M is a large-scale video action dataset constructed from internet instructional videos using automated pipelines with V-JEPA embeddings and GPT-based reasoning for structured annotations. AI-...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10592
• PDF: https://arxiv.org/pdf/2601.10592

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨STEP3-VL-10B Technical Report

📝 Summary:
STEP3-VL-10B is a lightweight 10B multimodal model that rivals much larger models and proprietary flagships in performance. It uses unified pre-training, scaled post-training, and Parallel Coordinated Reasoning for efficient visual reasoning. This open-source model sets a new standard for compact...

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09668
• PDF: https://arxiv.org/pdf/2601.09668
• Project Page: https://stepfun-ai.github.io/Step3-VL-10B
• Github: https://github.com/stepfun-ai/Step3-VL-10B

🔹 Models citing this paper:
• https://huggingface.co/stepfun-ai/Step3-VL-10B
• https://huggingface.co/stepfun-ai/Step3-VL-10B-Base

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨PRL: Process Reward Learning Improves LLMs' Reasoning Ability and Broadens the Reasoning Boundary

📝 Summary:
Process Reward Learning decomposes reinforcement learning objectives into intermediate steps to provide fine-grained supervision for improving large language model reasoning abilities. AI-generated su...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10201
• PDF: https://arxiv.org/pdf/2601.10201
• Github: https://github.com/MaxwellJryao/Process-Reward-Learning

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LaViT: Aligning Latent Visual Thoughts for Multi-modal Reasoning

📝 Summary:
LaViT addresses the perception gap in multimodal reasoning by aligning latent visual thoughts through autoregressive reconstruction of visual semantics and attention trajectories, improving visual gro...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10129
• PDF: https://arxiv.org/pdf/2601.10129
• Github: https://github.com/Svardfox/LaViT

🔹 Models citing this paper:
• https://huggingface.co/Svard/LaViT-3B

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Deriving Character Logic from Storyline as Codified Decision Trees

📝 Summary:
Executable and interpretable decision trees are induced from narrative data to create robust behavioral profiles for role-playing agents, outperforming traditional methods in consistency and reliabili...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10080
• PDF: https://arxiv.org/pdf/2601.10080

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Urban Socio-Semantic Segmentation with Vision-Language Reasoning

📝 Summary:
SocioReasoner, a vision-language AI, performs urban socio-semantic segmentation of social entities. It simulates human reasoning using reinforcement learning on a new dataset. This approach outperforms state-of-the-art models, achieving strong zero-shot generalization.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10477
• PDF: https://arxiv.org/pdf/2601.10477
• Github: https://github.com/AMAP-ML/SocioReasoner

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LSRIF: Logic-Structured Reinforcement Learning for Instruction Following

📝 Summary:
A logic-structured training framework explicitly models instruction logic through constraint-aware reward mechanisms, improving instruction-following and reasoning capabilities in large language model...

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06431
• PDF: https://arxiv.org/pdf/2601.06431

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts

📝 Summary:
A novel framework injects semantic intent into Mixture-of-Experts routing for image generation and editing, resolving task interference through hierarchical task annotation and predictive alignment re...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08881
• PDF: https://arxiv.org/pdf/2601.08881
• Project Page: https://yuci-gpt.github.io/TAG-MoE/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨WildRayZer: Self-supervised Large View Synthesis in Dynamic Environments

📝 Summary:
WildRayZer is a self-supervised framework for novel view synthesis in dynamic environments that uses analysis-by-synthesis to handle moving cameras and objects through motion masking and gradient gati...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10716
• PDF: https://arxiv.org/pdf/2601.10716

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Beyond Static Tools: Test-Time Tool Evolution for Scientific Reasoning

📝 Summary:
Existing AI agents for science struggle with static tool libraries. This paper introduces Test-Time Tool Evolution TTE, a new method allowing agents to dynamically create, verify, and evolve tools during inference. TTE achieves state-of-the-art performance and adapts tools across domains.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07641
• PDF: https://arxiv.org/pdf/2601.07641
• Github: https://github.com/lujiaxuan0520/Test-Time-Tool-Evol

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#AI #ScientificReasoning #ToolEvolution #AgentAI #AIResearch

✨Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering

📝 Summary:
ML-Master 2.0 enables ultra-long-horizon AI autonomy for machine learning engineering. It uses Hierarchical Cognitive Caching to accumulate knowledge from execution, decoupling short-term actions from long-term strategy, achieving state-of-the-art results.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10402
• PDF: https://arxiv.org/pdf/2601.10402
• Project Page: https://sjtu-sai-agents.github.io/ML-Master/
• Github: https://github.com/sjtu-sai-agents/ML-Master

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

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#AI #MachineLearning #AutonomousAI #AIAgents #CognitiveAI

✨CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents

📝 Summary:
Computer Use Agents CUAs are vulnerable to prompt injection. This paper introduces Single-Shot Planning, generating a full execution graph before UI observation to ensure control flow integrity. This secures CUAs against instruction injections while maintaining performance, though Branch Steering...

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09923
• PDF: https://arxiv.org/pdf/2601.09923

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

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#AgentSecurity #PromptInjection #AIsecurity #Cybersecurity #AIagents

✨HeartMuLa: A Family of Open Sourced Music Foundation Models

📝 Summary:
HeartMuLa introduces open-source music foundation models for understanding and generation. It features an LLM-based generator creating high-fidelity music with controllable attributes. This system achieves commercial-grade quality using academic resources.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10547
• PDF: https://arxiv.org/pdf/2601.10547

🔹 Models citing this paper:
• https://huggingface.co/HeartMuLa/HeartMuLa-oss-3B
• https://huggingface.co/HeartMuLa/HeartCodec-oss
• https://huggingface.co/HeartMuLa/HeartTranscriptor-oss

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#MusicAI #GenerativeAI #FoundationModels #LLM #OpenSource

✨VIBE: Visual Instruction Based Editor

📝 Summary:
VIBE is a compact image editor using a 2B-parameter guidance model and a 1.6B-parameter diffusion model. It achieves high-quality, source-consistent edits with low computational cost, outperforming larger models. VIBE fits in 24GB GPU memory and generates 2K images in 4 seconds.

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02242
• PDF: https://arxiv.org/pdf/2601.02242
• Project Page: https://riko0.github.io/VIBE/
• Github: https://github.com/ai-forever/vibe

🔹 Models citing this paper:
• https://huggingface.co/iitolstykh/VIBE-Image-Edit

✨ Spaces citing this paper:
• https://huggingface.co/spaces/iitolstykh/VIBE-Image-Edit-DEMO

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

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#ImageEditing #DiffusionModels #GenerativeAI #EfficientAI #AI

✨Alterbute: Editing Intrinsic Attributes of Objects in Images

📝 Summary:
Alterbute is a diffusion method for editing intrinsic object attributes like color or shape, while preserving identity and scene context. It uses a relaxed training objective and Visual Named Entities for scalable, identity-preserving supervision, outperforming existing methods.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2601.10714
• PDF: https://arxiv.org/pdf/2601.10714
• Project Page: https://talreiss.github.io/alterbute/
• Github: https://talreiss.github.io/alterbute/

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

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#Alterbute #DiffusionModels #ImageEditing #ComputerVision #AIResearch

✨VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation

📝 Summary:
VQ-Seg introduces vector quantization to replace dropout with a controllable perturbation module for semi-supervised medical image segmentation. It uses a dual-branch architecture and foundation model guidance to maintain performance. VQ-Seg outperforms state-of-the-art methods on various medical...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10124
• PDF: https://arxiv.org/pdf/2601.10124
• Project Page: https://github.com/script-Yang/VQ-Seg
• Github: https://github.com/script-Yang/VQ-Seg

✨ Datasets citing this paper:
• https://huggingface.co/datasets/yscript/ACDC-PNG

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

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#MedicalImageSegmentation #SemiSupervisedLearning #VectorQuantization #DeepLearning #ComputerVision

✨Enhancing Sentiment Classification and Irony Detection in Large Language Models through Advanced Prompt Engineering Techniques

📝 Summary:
This study enhanced LLM sentiment analysis and irony detection through advanced prompt engineering. Different techniques improved performance, but optimal strategies varied by model and task, emphasizing the need for tailored prompt design.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08302
• PDF: https://arxiv.org/pdf/2601.08302
• Github: https://github.com/Marvin2108/ESCID-LLM-APET

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

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#PromptEngineering #LLMs #SentimentAnalysis #IronyDetection #NLP

✨Memory Bank Compression for Continual Adaptation of Large Language Models

📝 Summary:
Memory-augmented continual learning for LLMs faces growing memory bank issues. MBC compresses these banks via codebook optimization and an online resetting mechanism, using Key-Value Low-Rank Adaptation. It reduces bank size to 0.3 percent while maintaining high accuracy.

🔹 Publication Date: Published on Jan 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.00756
• PDF: https://arxiv.org/pdf/2601.00756
• Github: https://github.com/Thomkat/MBC

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

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#LLMs #ContinualLearning #MemoryCompression #MachineLearning #DeepLearning

✨Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale

📝 Summary:
A large-scale study of AI agent skills found 26.1% contain widespread vulnerabilities like data exfiltration and privilege escalation. Skills with executable scripts are twice as likely to be vulnerable, showing an urgent need for security vetting and permission systems.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10338
• PDF: https://arxiv.org/pdf/2601.10338

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

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#AIAgents #AISecurity #Cybersecurity #VulnerabilityResearch #DataSecurity

✨Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL

📝 Summary:
CLINSQL is a new benchmark for evaluating text-to-SQL models on complex clinical tasks, including patient similarity, using real EHR data. Current models achieve moderate execution scores but remain far from clinical reliability for real-world EHR analytics.

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09876
• PDF: https://arxiv.org/pdf/2601.09876
• Github: https://github.com/Barryshen1/ClinSQL

✨ Datasets citing this paper:
• https://huggingface.co/datasets/yifeis02/ClinSQL

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

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#AI #DataScience #MachineLearning #HuggingFace #Research