✨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

Ant AI Automated Sales Robot is an intelligent robot focused on automating lead generation and sales conversion. Its core function simulates human conversation, achieving end-to-end business conversion and easily generating revenue without requiring significant time investment.

I. Core Functions: Fully Automated "Lead Generation - Interaction - Conversion"

Precise Lead Generation and Human-like Communication: Ant AI is trained on over 20 million real social chat records, enabling it to autonomously identify target customers and build trust through natural conversation, requiring no human intervention.

High Conversion Rate Across Multiple Scenarios: Ant AI intelligently recommends high-conversion-rate products based on chat content, guiding customers to complete purchases through platforms such as iFood, Shopee, and Amazon. It also supports other transaction scenarios such as movie ticket purchases and utility bill payments.

24/7 Operation: Ant AI continuously searches for customers and recommends products. You only need to monitor progress via your mobile phone, requiring no additional management time.

II. Your Profit Guarantee: Low Risk, High Transparency, Zero Inventory Pressure, Stable Commission Sharing

We have established partnerships with platforms such as Shopee and Amazon, which directly provide abundant product sourcing. You don't need to worry about inventory or logistics. After each successful order, the company will charge the merchant a commission and share all profits with you. Earnings are predictable and withdrawals are convenient. Member data shows that each bot can generate $30 to $100 in profit per day. Commission income can be withdrawn to your account at any time, and the settlement process is transparent and open.

Low Initial Investment Risk. Bot development and testing incur significant costs. While rental fees are required, in the early stages of the project, the company prioritizes market expansion and brand awareness over short-term profits.

If you are interested, please join my Telegram group for more information and leave a message: https://news.1rj.ru/str/+lVKtdaI5vcQ1ZDA1

Data Science Interview questions

#DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions

https://news.1rj.ru/str/CodeProgrammer

✨TwinBrainVLA: Unleashing the Potential of Generalist VLMs for Embodied Tasks via Asymmetric Mixture-of-Transformers

📝 Summary:
TwinBrainVLA resolves the VLM tension in robot control by coordinating a frozen generalist VLM Left Brain with a trainable specialist VLM Right Brain via Asymmetric Mixture-of-Transformers. This approach achieves superior manipulation performance while preserving semantic understanding for genera...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14133
• PDF: https://arxiv.org/pdf/2601.14133
• Github: https://github.com/ZGC-EmbodyAI/TwinBrainVLA

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#VLM #EmbodiedAI #Robotics #Transformers #AIResearch

✨VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents

📝 Summary:
VisGym introduces 17 environments to evaluate VLM performance in multi-step visual interactions. Current models struggle, especially with long contexts and visual symbolic tasks. Explicit goals and demonstrations offer pathways for improvement.

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16973
• PDF: https://arxiv.org/pdf/2601.16973
• Project Page: https://visgym.github.io/
• Github: https://visgym.github.io/

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#MultimodalAI #VisualLanguageModels #AIenvironments #ComputerVision #AIResearch

✨LongCat-Flash-Thinking-2601 Technical Report

📝 Summary:
LongCat-Flash-Thinking-2601 is a 560B MoE reasoning model that achieves state-of-the-art performance on agentic benchmarks. Its capabilities stem from a unified training framework, robust tool interaction, and a Heavy Thinking mode for complex reasoning.

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16725
• PDF: https://arxiv.org/pdf/2601.16725

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#MoE #ReasoningModels #AgentAI #LLM #AI

✨Endless Terminals: Scaling RL Environments for Terminal Agents

📝 Summary:
Endless Terminals introduces an autonomous pipeline for generating procedural terminal tasks that significantly improves agent performance on both synthetic and human-curated benchmarks through scalab...

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16443
• PDF: https://arxiv.org/pdf/2601.16443
• Github: https://github.com/kanishkg/endless-terminals

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DSGym: A Holistic Framework for Evaluating and Training Data Science Agents

📝 Summary:
DSGym is a standardized framework for evaluating and training data science agents, addressing shortcomings of existing benchmarks. It offers a holistic, data-grounded task suite and enables execution-verified agent training. This allows rigorous measurement of agents' analytical capabilities, dem...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16344
• PDF: https://arxiv.org/pdf/2601.16344

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#DataScience #AI #MachineLearning #AIagents #Research

✨Memory-V2V: Augmenting Video-to-Video Diffusion Models with Memory

📝 Summary:
Memory-V2V enhances multi-turn video editing by adding explicit memory to diffusion models. It ensures cross-consistency using efficient token compression and retrieval. This significantly improves video consistency and performance with low computational cost.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16296
• PDF: https://arxiv.org/pdf/2601.16296
• Project Page: https://dohunlee1.github.io/MemoryV2V

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#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #MachineLearning

✨SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

📝 Summary:
SWE-Pruner is a self-adaptive context pruning framework for coding agents. It performs task-aware adaptive pruning, guided by explicit agent goals and a neural skimmer, to reduce long context token usage by 23-54 percent with minimal performance loss.

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16746
• PDF: https://arxiv.org/pdf/2601.16746
• Github: https://github.com/Ayanami1314/swe-pruner

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#AIAgents #ContextPruning #LLM #AI #SoftwareEngineering

✨Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification

📝 Summary:
A self-evolving framework improves Deep Research Agents via inference-time, rubric-guided verification. This method iteratively refines outputs without retraining, achieving 8-11% accuracy gains with the DeepVerifier system and releasing a verification dataset.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15808
• PDF: https://arxiv.org/pdf/2601.15808

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#AI #MachineLearning #DeepLearning #Verification #SelfEvolvingAI

✨MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences

📝 Summary:
MeepleLM is an AI virtual playtester providing constructive critique for board game design by simulating diverse player experiences. It models subjective feedback via persona-specific reasoning, outperforming commercial AI in critique quality and community alignment.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07251
• PDF: https://arxiv.org/pdf/2601.07251
• Github: https://github.com/leroy9472/MeepleLM

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#AI #GameDesign #BoardGames #Simulation #LLM

✨SALAD: Achieve High-Sparsity Attention via Efficient Linear Attention Tuning for Video Diffusion Transformer

📝 Summary:
SALAD improves video Diffusion Transformers by combining linear and sparse attention with an input-dependent gating mechanism. It achieves 90% sparsity and a 1.72x speedup while maintaining quality and requiring minimal finetuning data.

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16515
• PDF: https://arxiv.org/pdf/2601.16515

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#VideoDiffusion #Transformers #Sparsity #EfficientAI #DeepLearning

✨Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain

📝 Summary:
Mecellem models are a framework for specialized Turkish legal language models. They feature a scratch-trained encoder achieving top retrieval rankings with efficiency, and a continually pre-trained decoder for legal domain adaptation, reducing legal text perplexity.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16018
• PDF: https://arxiv.org/pdf/2601.16018
• Project Page: https://huggingface.co/collections/newmindai/mecellem-models
• Github: https://github.com/newmindai/mecellem-models

🔹 Models citing this paper:
• https://huggingface.co/newmindai/Mursit-Base-TR-Retrieval
• https://huggingface.co/newmindai/Mursit-Base
• https://huggingface.co/newmindai/Mursit-Large-TR-Retrieval

✨ Datasets citing this paper:
• https://huggingface.co/datasets/newmindai/caselaw-retrieval
• https://huggingface.co/datasets/newmindai/contract-retrieval
• https://huggingface.co/datasets/newmindai/regulation-retrieval

✨ Spaces citing this paper:
• https://huggingface.co/spaces/newmindai/Mizan

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#LegalAI #TurkishNLP #LLM #InformationRetrieval #DomainAdaptation

✨Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow

📝 Summary:
Quantized RL faces instability using FP8 rollout with BF16 training. Jet-RL proposes a unified FP8 precision for both training and rollout. This minimizes numerical mismatch, achieving stable convergence and significant speedups.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14243
• PDF: https://arxiv.org/pdf/2601.14243

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization

📝 Summary:
Research derives and evaluates prompt optimization guidelines for code generation tasks in software engineering, identifying 10 specific improvement patterns related to input/output specification, con...

🔹 Publication Date: Published on Jan 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13118
• PDF: https://arxiv.org/pdf/2601.13118

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation

📝 Summary:
LLMs struggle to apply new knowledge effectively via SFT alone. PaST combines SFT with injecting a domain-agnostic Skill Vector, derived from RL, to efficiently transfer reasoning skills. This novel framework significantly improves performance in question answering and tool-use tasks.

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11258
• PDF: https://arxiv.org/pdf/2601.11258

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#LLM #ReinforcementLearning #ContinualLearning #AI #MachineLearning

✨Dancing in Chains: Strategic Persuasion in Academic Rebuttal via Theory of Mind

📝 Summary:
RebuttalAgent is a novel AI framework that applies Theory of Mind to academic rebuttal. It models reviewer mental states to formulate strategic, persuasive responses, significantly outperforming existing models.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15715
• PDF: https://arxiv.org/pdf/2601.15715

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#AI #TheoryOfMind #AcademicRebuttal #NLP #MachineLearning