✨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

✨V-DPM: 4D Video Reconstruction with Dynamic Point Maps

📝 Summary:
Dynamic Point Maps extended to video input through V-DPM framework achieve state-of-the-art 3D and 4D reconstruction by recovering both dynamic depth and full 3D motion of scene points. AI-generated s...

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09499
• PDF: https://arxiv.org/pdf/2601.09499
• Project Page: https://www.robots.ox.ac.uk/~vgg/research/vdpm/
• Github: https://github.com/eldar/vdpm

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution

📝 Summary:
PACEvolve framework addresses key failure modes in LLM evolutionary search through hierarchical context management, momentum-based backtracking, and adaptive sampling policies for improved self-improv...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10657
• PDF: https://arxiv.org/pdf/2601.10657

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨RigMo: Unifying Rig and Motion Learning for Generative Animation

📝 Summary:
RigMo unifies rig and motion learning directly from raw mesh sequences, encoding deformations into compact latent spaces. This framework generates interpretable, plausible 3D animation, offering superior reconstruction and generalization over baselines.

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06378
• PDF: https://arxiv.org/pdf/2601.06378
• Project Page: https://rigmo-page.github.io/
• Github: https://rigmo-page.github.io

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Demystifying the Slash Pattern in Attention: The Role of RoPE

📝 Summary:
Slash-Dominant Heads in LLMs emerge when queries and keys are almost rank-one and Rotary Position Embedding has dominant medium-high frequencies. Theoretical proof shows these conditions, combined with gradient descent, explain their emergence.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08297
• PDF: https://arxiv.org/pdf/2601.08297

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨M^4olGen: Multi-Agent, Multi-Stage Molecular Generation under Precise Multi-Property Constraints

📝 Summary:
M4olGen is a multi-agent, multi-stage framework for precise molecular generation under multiple physicochemical constraints. It uses fragment-level, retrieval-augmented reasoning and RL-based optimization, outperforming LLMs and graph-based methods.

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10131
• PDF: https://arxiv.org/pdf/2601.10131

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

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#AI #DataScience #MachineLearning #HuggingFace #Research