✨PROGRESSLM: Towards Progress Reasoning in Vision-Language Models

📝 Summary:
VLMs struggle to estimate task progress from partial views. ProgressLM-3B, a new training-based model, shows consistent improvements in progress reasoning across disjoint tasks, addressing this limitation.

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15224
• PDF: https://arxiv.org/pdf/2601.15224
• Project Page: https://progresslm.github.io/ProgressLM/
• Github: https://github.com/ProgressLM/ProgressLM

🔹 Models citing this paper:
• https://huggingface.co/Raymond-Qiancx/ProgressLM-3B-SFT
• https://huggingface.co/Raymond-Qiancx/ProgressLM-3B-RL

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Raymond-Qiancx/ProgressLM-Dataset

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#VLM #ProgressReasoning #AI #MachineLearning #DeepLearning

✨MirrorBench: An Extensible Framework to Evaluate User-Proxy Agents for Human-Likeness

📝 Summary:
MIRRORBENCH is an open-source framework to evaluate large language models as human user simulators. It assesses their ability to generate human-like conversational responses across diverse tasks using various metrics, revealing systematic gaps between AI and real users.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08118
• PDF: https://arxiv.org/pdf/2601.08118
• Github: https://github.com/SAP/mirrorbench

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#LLM #HumanLikeness #AISimulation #ConversationalAI #OpenSource

✨Agentic Confidence Calibration

📝 Summary:
AI agents' overconfidence in failure hinders their deployment. This paper introduces Agentic Confidence Calibration and Holistic Trajectory Calibration HTC, a new framework analyzing an agent's entire process trajectory. HTC improves reliability, interpretability, and generalizes across diverse A...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15778
• PDF: https://arxiv.org/pdf/2601.15778

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✨Agentic Uncertainty Quantification

📝 Summary:
A unified dual-process framework transforms verbalized uncertainty into active control signals for improved reasoning reliability in AI agents. AI-generated summary Although AI agents have demonstrate...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15703
• PDF: https://arxiv.org/pdf/2601.15703

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models

📝 Summary:
Large language models face reliability challenges that are being addressed through uncertainty as an active control signal across advanced reasoning, autonomous agents, and reinforcement learning, sup...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15690
• PDF: https://arxiv.org/pdf/2601.15690

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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://t.iss.one/+lVKtdaI5vcQ1ZDA1

Data Science Interview questions

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

https://t.iss.one/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