✨Video Models Reason Early: Exploiting Plan Commitment for Maze Solving

📝 Summary:
Video diffusion models demonstrate emergent reasoning abilities in maze solving through early plan commitment and path length prediction, with improved performance achieved via Chaining with Early Pla...

🔹 Publication Date: Published on Mar 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.30043
• PDF: https://arxiv.org/pdf/2603.30043
• Project Page: https://video-maze-reasoning.github.io/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨MedGemma Technical Report

📝 Summary:
MedGemma, a collection of medical vision-language foundation models, demonstrates advanced medical understanding and reasoning, outperforming similar-sized generative models and approaching task-speci...

🔹 Publication Date: Published on Jul 7, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.05201
• PDF: https://arxiv.org/pdf/2507.05201
• Project Page: https://goo.gle/medgemma
• Github: https://github.com/google-gemini/gemma-cookbook

🔹 Models citing this paper:
• https://huggingface.co/google/medgemma-4b-it
• https://huggingface.co/google/medgemma-1.5-4b-it
• https://huggingface.co/google/medgemma-27b-text-it

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Mateenah/medgemma-4b-hematologic-oncology-blind-spots

✨ Spaces citing this paper:
• https://huggingface.co/spaces/yipengsun/diagnostic-devils-advocate
• https://huggingface.co/spaces/AIencoder/RadAssist-MedGemma
• https://huggingface.co/spaces/google/appoint-ready

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨An Empirical Recipe for Universal Phone Recognition

📝 Summary:
PhoneticXEUS achieves leading performance for universal phone recognition in multilingual and accented speech. This results from large-scale training and an empirical analysis of key factors including SSL representations, data scale, and loss objectives.

🔹 Publication Date: Published on Mar 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.29042
• PDF: https://arxiv.org/pdf/2603.29042
• Github: https://github.com/changelinglab/PhoneticXeus

🔹 Models citing this paper:
• https://huggingface.co/changelinglab/PhoneticXeus

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Signals: Trajectory Sampling and Triage for Agentic Interactions

📝 Summary:
A signal framework efficiently triages agentic interaction trajectories. It computes low-cost signals from live interactions to identify informative samples for post-deployment optimization, achieving 82% informativeness and outperforming other methods.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00356
• PDF: https://arxiv.org/pdf/2604.00356
• Project Page: https://planoai.dev/
• Github: https://github.com/katanemo/plano

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively

📝 Summary:
DeepScientist autonomously conducts scientific discovery through Bayesian Optimization, surpassing human state-of-the-art methods on multiple AI tasks. AI-generated summary While previous AI Scientist...

🔹 Publication Date: Published on Sep 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.26603
• PDF: https://arxiv.org/pdf/2509.26603
• Project Page: https://ai-researcher.net
• Github: https://github.com/ResearAI/DeepScientist

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LOME: Learning Human-Object Manipulation with Action-Conditioned Egocentric World Model

📝 Summary:
LOME is an egocentric world model that generates realistic human-object interactions in videos by combining image, text, and action inputs with joint estimation of spatial human actions and environmen...

🔹 Publication Date: Published on Mar 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.27449
• PDF: https://arxiv.org/pdf/2603.27449
• Project Page: https://zerg-overmind.github.io/LOME.github.io/
• Github: https://github.com/Zerg-Overmind/LOME

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

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✨Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material

📝 Summary:
This tutorial introduces Hunyuan3D 2.1, a system for generating high-fidelity, textured 3D assets to make AI content creation more accessible. It details the full workflow from data preparation to deployment, using Hunyuan3D-DiT for shape and Hunyuan3D-Paint for texture synthesis.

🔹 Publication Date: Published on Jun 18, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.15442
• PDF: https://arxiv.org/pdf/2506.15442
• Github: https://github.com/huggingface/huggingface.js

🔹 Models citing this paper:
• https://huggingface.co/tencent/Hunyuan3D-2.1
• https://huggingface.co/tencent/Hunyuan3D-Omni
• https://huggingface.co/tencent/HY3D-Bench

✨ Datasets citing this paper:
• https://huggingface.co/datasets/tencent/HY3D-Bench

✨ Spaces citing this paper:
• https://huggingface.co/spaces/duranponce/ai-default
• https://huggingface.co/spaces/AliothTalks/Hunyuan3D-2.1
• https://huggingface.co/spaces/joaojack/Hunyuan3D-2.1

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

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#3DGeneration #AI #ComputerGraphics #ImageTo3D #PBRMaterials

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✨RF-DETR: Neural Architecture Search for Real-Time Detection Transformers

📝 Summary:
RF-DETR is a light-weight detection transformer using weight-sharing NAS to optimize real-time accuracy and latency across diverse datasets. It significantly outperforms prior state-of-the-art methods on COCO and Roboflow100-VL, with its largest variant exceeding 60 AP on COCO.

🔹 Publication Date: Published on Nov 12, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09554
• PDF: https://arxiv.org/pdf/2511.09554
• Project Page: https://rfdetr.roboflow.com/1.3.0/
• Github: https://github.com/roboflow/rf-detr

🔹 Models citing this paper:
• https://huggingface.co/mlx-community/rfdetr-base-fp32
• https://huggingface.co/mlx-community/rfdetr-seg-small-fp32
• https://huggingface.co/mlx-community/rfdetr-seg-large-fp32

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

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#ObjectDetection #NeuralArchitectureSearch #DeepLearning #ComputerVision #DETR

✨Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence?

📝 Summary:
Agentic-MME introduces a process-verified benchmark for multimodal agentic capabilities. It evaluates tool usage and efficiency using real-world tasks and stepwise checkpoints, revealing models struggle with complex multimodal problem-solving.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03016
• PDF: https://arxiv.org/pdf/2604.03016
• Project Page: https://agenticmme.github.io/

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

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#AgenticAI #MultimodalAI #AIEvaluation #AIResearch #Benchmarks

✨AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents

📝 Summary:
Computer-use agents pose unique safety risks as harm can emerge from sequences of individually benign actions. AgentHazard is a benchmark with 2,653 instances to evaluate this. Experiments reveal current systems are highly vulnerable, showing model alignment alone doesnt ensure agent safety.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02947
• PDF: https://arxiv.org/pdf/2604.02947
• Project Page: https://yunhao-feng.github.io/AgentHazard/

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

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#AISafety #AgentAI #AIVulnerability #AIethics #AIbenchmark

✨CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning

📝 Summary:
CoME-VL fuses contrastive and self-supervised vision encoders to improve vision-language models. It uses entropy-guided aggregation and RoPE-enhanced attention for better visual understanding and grounding, outperforming single-encoder baselines.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03231
• PDF: https://arxiv.org/pdf/2604.03231
• Project Page: https://mbzuai-oryx.github.io/CoME-VL/
• Github: https://github.com/mbzuai-oryx/CoME-VL

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

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#VisionLanguage #MultimodalAI #ComputerVision #MachineLearning #DeepLearning

✨InCoder-32B-Thinking: Industrial Code World Model for Thinking

📝 Summary:
Industrial software development lacks expert reasoning traces for hardware constraints, so a model was trained on error-driven reasoning chains and domain-specific execution traces to generate high-qu...

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03144
• PDF: https://arxiv.org/pdf/2604.03144

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

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#AI #CodeGeneration #IndustrialAI #WorldModels #SoftwareDevelopment

✨Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

📝 Summary:
XpertBench introduces a benchmark with 1346 expert-curated tasks across 80 domains for evaluating LLMs on complex professional cognition. It uses ShotJudge for scalable human-aligned assessment. Current LLMs achieve only a 66 percent peak success, revealing a significant expert-gap.

🔹 Publication Date: Published on Mar 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02368
• PDF: https://arxiv.org/pdf/2604.02368

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

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#LLM #AIEvaluation #Benchmarking #ArtificialIntelligence #ProfessionalAI

✨MetaChain: A Fully-Automated and Zero-Code Framework for LLM Agents

📝 Summary:
MetaChain is a fully automated, zero-code framework enabling non-technical users to create and deploy LLM agents via natural language. It offers superior performance for multi-agent tasks and retrieval-augmented generation, surpassing current methods.

🔹 Publication Date: Published on Feb 9, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.05957
• PDF: https://arxiv.org/pdf/2502.05957
• Github: https://github.com/HKUDS/MetaChain

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

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#LLMAgents #NoCode #AI #RAG #AIAutomation

✨A Simple Baseline for Streaming Video Understanding

📝 Summary:
A simple sliding-window approach outperforms complex memory-based streaming video methods by using only recent frames. It demonstrates a trade-off between real-time perception and long-term memory, suggesting benchmarks should separate these abilities.

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16655
• PDF: https://arxiv.org/pdf/2604.02317
• Project Page: https://simple-stream.github.io/
• Github: https://simple-stream.github.io/

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

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#VideoUnderstanding #StreamingAI #ComputerVision #RealTimeAI #MachineLearning

✨Self-Distilled RLVR

📝 Summary:
RLSD combines reinforcement learning with verifiable rewards RLVR and self-distillation to overcome sparse feedback. It uses self-distillation for fine-grained update magnitudes and RLVR for reliable update directions. This achieves superior training stability and convergence.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03128
• PDF: https://arxiv.org/pdf/2604.03128

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

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#ReinforcementLearning #SelfDistillation #RLVR #MachineLearning #AI