✨Length Value Model: Scalable Value Pretraining for Token-Level Length Modeling

📝 Summary:
LenVM is a token-level framework that models remaining generation length as a value estimation problem. It improves length control and efficiency in autoregressive models, significantly outperforming baselines and enabling continuous control over performance-efficiency trade-offs.

🔹 Publication Date: Published on Apr 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27039
• PDF: https://arxiv.org/pdf/2604.27039
• Project Page: https://length-value-model.github.io/
• Github: https://length-value-model.github.io/demo/index.html

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Efficient Training on Multiple Consumer GPUs with RoundPipe

📝 Summary:
RoundPipe introduces a novel pipeline scheduling approach that eliminates weight binding constraints in LLM fine-tuning, enabling efficient training on consumer GPUs through dynamic stage distribution...

🔹 Publication Date: Published on Apr 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.27085
• PDF: https://arxiv.org/pdf/2604.27085
• Project Page: https://itcarrot.github.io/RoundPipe/
• Github: https://github.com/ITcarrot/RoundPipe

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Claw-Eval-Live: A Live Agent Benchmark for Evolving Real-World Workflows

📝 Summary:
Claw-Eval-Live presents a dynamic benchmark for evaluating workflow agents that tracks evolving demands and verifies task execution through detailed logging and structured assessment methods. AI-gener...

🔹 Publication Date: Published on Apr 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28139
• PDF: https://arxiv.org/pdf/2604.28139
• Project Page: https://claw-eval-live.github.io
• Github: https://claw-eval-live.github.io

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨ViPO: Visual Preference Optimization at Scale

📝 Summary:
ViPO scales visual preference optimization using Poly-DPO for noisy data and constructing ViPO, a large high-quality dataset. This dual approach yields superior performance, emphasizing that algorithmic adaptability and data quality are crucial.

🔹 Publication Date: Published on Apr 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.24953
• PDF: https://arxiv.org/pdf/2604.24953
• Project Page: https://liming-ai.github.io/ViPO
• Github: https://liming-ai.github.io/ViPO

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

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#VisualAI #MachineLearning #DeepLearning #Optimization #DataScience

✨Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization

📝 Summary:
Semi-DPO addresses label noise in multi-dimensional visual preference learning. It treats consistent data as clean and conflicting data as noisy, using iterative refinement via pseudo-labeling. This improves alignment with complex human preferences and achieves state-of-the-art results.

🔹 Publication Date: Published on Apr 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.24952
• PDF: https://arxiv.org/pdf/2604.24952
• Project Page: https://liming-ai.github.io/SemiDPO
• Github: https://liming-ai.github.io/SemiDPO

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

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#MachineLearning #SemiSupervisedLearning #DPO #NoisyData #PreferenceLearning

✨FlashRT: Towards Computationally and Memory Efficient Red-Teaming for Prompt Injection and Knowledge Corruption

📝 Summary:
FlashRT significantly enhances the efficiency of optimization-based prompt injection and knowledge corruption attacks for long-context LLMs. It delivers 2x-7x speedup and 2x-4x GPU memory reduction, enabling systematic and scalable security evaluations.

🔹 Publication Date: Published on Apr 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.28157
• PDF: https://arxiv.org/pdf/2604.28157
• Github: https://github.com/wang-yanting/FlashRT

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs

📝 Summary:
SignRoundV2 is a post-training quantization method for LLMs. It achieves competitive, near full-precision accuracy even at extremely low-bits like 2-bits. This is done via layer-wise bit allocation and pre-tuning scale search.

🔹 Publication Date: Published on Dec 4, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04746
• PDF: https://arxiv.org/pdf/2512.04746
• Project Page: https://github.com/intel/auto-round
• Github: https://github.com/intel/auto-round

🔹 Models citing this paper:
• https://huggingface.co/Intel/MiroThinker-v1.5-30B-gguf-q2ks-mixed-AutoRound
• https://huggingface.co/Intel/DeepSeek-R1-0528-Qwen3-8B-int4-AutoRound

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

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#LLMs #Quantization #DeepLearning #AI #MachineLearning

Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚

This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊

It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖

Free public repository on GitHub. 💻✨

https://github.com/dair-ai/Mathematics-for-ML

#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI

🔖 A huge open-source course on AI Engineering from scratch

In the repository, we've collected:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— AI agents, MCP servers, prompts, and AI skills.

Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. 🚀

⛓️ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch

#AI #MachineLearning #Python #Rust #OpenSource #Tech

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💎 By: https://xn--r1a.website/Udemy26
#MachineLearning #AI #DeepLearning #FreeCourse #Udemy #OnlineLearning

🔥 Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination

💡 A new benchmark for AI-generated video detection reveals that current detectors fail to generalize across realistic crisis-related videos and become less reliable as content spreads socially. Generate...

📅 Published on Aug 14

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2608.14391
• PDF: https://arxiv.org/pdf/2608.14391
• Project Page: https://ra-bench-crisis-video.yxgma811120.chatgpt.site/

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📢 By: https://xn--r1a.website/PaperNexus

#AI #MachineLearning #DataScience #Research #HuggingFace

🔥 Latent On-Policy Self-Distillation

💡 Latent On-Policy Self-Distillation learns privileged teaching context end-to-end from experience to provide dense token-level supervision, improving agent performance and efficiency. Generated by thin...

📅 Published on Aug 13

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2608.13040
• PDF: https://arxiv.org/pdf/2608.13040

🤖 Models citing this paper:
• https://huggingface.co/liunanfu1992/Qwen3-8B-LOPD
• https://huggingface.co/liunanfu1992/OLMo-3-7B-Think-LOPD

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📢 By: https://xn--r1a.website/PaperNexus

#AI #MachineLearning #DataScience #Research #HuggingFace

🔥 LLM-as-a-Verifier: A General-Purpose Verification Framework

💡 LLM-as-a-Verifier introduces a probabilistic verification framework that scales across multiple dimensions to improve solution correctness assessment and agent performance across various benchmarks. G...

📅 Published on Jul 6

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.05391
• PDF: https://arxiv.org/pdf/2607.05391
• Project Page: https://llm-as-a-verifier.com/

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📢 By: https://xn--r1a.website/PaperNexus

#AI #MachineLearning #DataScience #Research #HuggingFace

🔥 VibeWorlding: Can Multimodal Agents Construct 3D Open Worlds End-to-End?

💡 A unified framework benchmarks and trains multimodal agents that infer intent, plan 3D scenes, invoke tools, and reflect on feedback, revealing that reinforcement learning improves open-source models ...

📅 Published on Aug 15

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2608.15265
• PDF: https://arxiv.org/pdf/2608.15265
• Project Page: https://huggingface.co/collections/usail-hkust/vibeworlder

🤖 Models citing this paper:
• https://huggingface.co/usail-hkust/VibeWorlder-8B
• https://huggingface.co/usail-hkust/VibeWorlder-30B-A3B
• https://huggingface.co/usail-hkust/VibeWorlder-Embedding-4B

📊 Datasets citing this paper:
• https://huggingface.co/datasets/usail-hkust/VWE-Bench

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📢 By: https://xn--r1a.website/PaperNexus

#AI #MachineLearning #DataScience #Research #HuggingFace

🔥 HarnessEval-W: Agentifying the Evaluation of Visual Worlds

💡 HarnessEval-W uses hierarchical sub-agents to decompose world-model evaluations into verifiable reasoning chains that justify scores with transparent evidence. Generated by thinkingmachines/Inkling-Sm...

📅 Published on Aug 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2608.16859
• PDF: https://arxiv.org/pdf/2608.16859
• Project Page: https://mirros-lab.github.io/HarnessEval-W

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📢 By: https://xn--r1a.website/PaperNexus

#AI #MachineLearning #DataScience #Research #HuggingFace

🔥 UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations

💡 UI-Mate is a foundation GUI agent that uses environment-grounded training and in-context demonstration learning to improve reliability on long-horizon office tasks, achieving state-of-the-art results ...

📅 Published on Aug 16

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2608.15930
• PDF: https://arxiv.org/pdf/2608.15930
• Project Page: https://ui-mate.github.io/

🤖 Models citing this paper:
• https://huggingface.co/tencent/UI-Mate-27B
• https://huggingface.co/tencent/UI-Mate-9B
• https://huggingface.co/tencent/UI-Mate-democua-27B

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/hugging-apps/ui-mate-27b-demo
• https://huggingface.co/spaces/hugging-apps/ui-mate-9b-demo

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📢 By: https://xn--r1a.website/PaperNexus

#AI #MachineLearning #DataScience #Research #HuggingFace

🔥 Advancing Open and Reproducible Relational Learning: RelArena-α, TabPFN-Rel and RPI

💡 Prior Labs released open-source tools including a unified relational benchmark framework, a TabPFN-based relational model, and a model-agnostic predictive interface to advance reproducible relational ...

📅 Published on Aug 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2608.16319
• PDF: https://arxiv.org/pdf/2608.16319
• Project Page: https://priorlabs.ai/blog-posts/introducing-relarena

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📢 By: https://xn--r1a.website/PaperNexus

#AI #MachineLearning #DataScience #Research #HuggingFace

🔥 GenRouter: Unified Workflow Routing for Agentic Image Generation

💡 GenRouter is a unified routing framework that adaptively directs prompts to optimal agentic image-generation workflows, cutting costs and latency while improving visual alignment and enabling continuo...

📅 Published on Aug 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2608.16721
• PDF: https://arxiv.org/pdf/2608.16721

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📢 By: https://xn--r1a.website/PaperNexus

#AI #MachineLearning #DataScience #Research #HuggingFace

🔥 StateM: Reaching 95.3% Raw Accuracy, or a \$15 Frontier Run, on Terminal-Bench 2.1 via Harness Scaling

💡 StateM is a runtime system that improves long-horizon agent execution through durable states, recoverable runbooks, and enforceable procedural controls without altering model weights. Generated by thi...

📅 Published on Aug 15

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2608.15089
• PDF: https://arxiv.org/pdf/2608.15089
• Project Page: https://henryqin1997.github.io/statem/

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📢 By: https://xn--r1a.website/PaperNexus

#AI #MachineLearning #DataScience #Research #HuggingFace