✨DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

📝 Summary:
A new solver, DPM-Solver, accelerates sampling from diffusion probabilistic models by analytically solving the diffusion ordinary differential equations, achieving high-quality results with fewer func...

🔹 Publication Date: Published on Jun 2, 2022

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2206.00927
• PDF: https://arxiv.org/pdf/2206.00927
• Project Page: https://huggingface.co/spaces/huggingface-projects/stable-diffusion-latent-upscaler
• Github: https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/DPM_Solver_A_Fast_ODE_Solver_for_Diffusion_Probabilistic_Model_Sampling_in_Around_10_Steps

🔹 Models citing this paper:
• https://huggingface.co/raisahil/scunge-model

✨ Spaces citing this paper:
• https://huggingface.co/spaces/huggingface-projects/stable-diffusion-latent-upscaler
• https://huggingface.co/spaces/Rooni/finetuned_diffusion
• https://huggingface.co/spaces/anzorq/finetuned_diffusion

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

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✨Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

📝 Summary:
Rectified flow is a simple ODE-based method for efficient distribution transport and tasks like generative modeling and domain transfer, achieving high-quality results with minimal computational cost....

🔹 Publication Date: Published on Sep 7, 2022

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2209.03003
• PDF: https://arxiv.org/pdf/2209.03003
• Github: https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Flow_Straight_and_Fast_Learning_to_Generate_and_Transfer_Data_with_Rectified_Flow

🔹 Models citing this paper:
• https://huggingface.co/nvidia/GR00T-N1.5-3B
• https://huggingface.co/XCLiu/2_rectified_flow_from_sd_1_5
• https://huggingface.co/XCLiu/instaflow_0_9B_from_sd_1_5

✨ Spaces citing this paper:
• https://huggingface.co/spaces/APGASU/FlowChef-InstaFlow-InverseProblem-Inpainting
• https://huggingface.co/spaces/APGASU/FlowChef-InstaFlow-Edit
• https://huggingface.co/spaces/XCLiu/InstaFlow

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

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✨A Pragmatic VLA Foundation Model

📝 Summary:
A Vision-Language-Action model trained on extensive real-world robotic data demonstrates superior performance and generalization across multiple platforms while offering enhanced efficiency through op...

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18692
• PDF: https://arxiv.org/pdf/2601.18692
• Project Page: https://technology.robbyant.com/lingbot-vla
• Github: https://github.com/robbyant/lingbot-vla

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

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✨FastNeRF: High-Fidelity Neural Rendering at 200FPS

📝 Summary:
FastNeRF enables high-speed rendering of photorealistic 3D environments by factorizing radiance maps for efficient pixel value estimation. AI-generated summary Recent work on Neural Radiance Fields ( ...

🔹 Publication Date: Published on Mar 18, 2021

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2103.10380
• PDF: https://arxiv.org/pdf/2103.10380
• Github: https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/FastNeRF_High_Fidelity_Neural_Rendering_at_200FPS

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

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✨World Craft: Agentic Framework to Create Visualizable Worlds via Text

📝 Summary:
World Craft enables non-expert users to create executable and visualizable AI environments through textual denoscriptions by combining structured scaffolding and multi-agent intent analysis. AI-generate...

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09150
• PDF: https://arxiv.org/pdf/2601.09150
• Github: https://github.com/HerzogFL/World-Craft

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment

📝 Summary:
TriPlay-RL is a closed-loop reinforcement learning framework for LLM safety alignment. It iteratively improves attacker, defender, and evaluator roles with near-zero manual annotation. This leads to better adversarial effectiveness, enhanced safety performance, and refined judgment.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18292
• PDF: https://arxiv.org/pdf/2601.18292

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

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#LLM #ReinforcementLearning #AISafety #MachineLearning #SelfPlay

✨FABLE: Forest-Based Adaptive Bi-Path LLM-Enhanced Retrieval for Multi-Document Reasoning

📝 Summary:
FABLE is a new retrieval framework enhancing LLM-based multi-document reasoning through hierarchical forest indexes and a bi-path strategy. It outperforms traditional RAG with up to 94 percent token reduction, proving the ongoing need for structured retrieval.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18116
• PDF: https://arxiv.org/pdf/2601.18116

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

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#LLM #InformationRetrieval #MultiDocumentReasoning #RAG #NLP

✨HalluCitation Matters: Revealing the Impact of Hallucinated References with 300 Hallucinated Papers in ACL Conferences

📝 Summary:
Hallucinated citations HalluCitation are a growing problem in NLP papers. This study found nearly 300 papers from 2024-2025 contain HalluCitations, with a rapid increase at EMNLP 2025, threatening scientific reliability and conference credibility.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18724
• PDF: https://arxiv.org/pdf/2601.18724

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

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#HalluCitation #NLP #ResearchIntegrity #AI #AcademicPublishing

✨Benchmarks Saturate When The Model Gets Smarter Than The Judge

📝 Summary:
This paper introduces Omni-MATH-2, a manually audited mathematical benchmark dataset to reduce noise. It reveals that existing judges like Omni-Judge are highly inaccurate, masking real model performance differences. Accurate benchmarks require both high-quality datasets and more competent judges.

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19532
• PDF: https://arxiv.org/pdf/2601.19532

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

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#AI #MachineLearning #Benchmarking #ModelEvaluation #Datasets

✨Post-LayerNorm Is Back: Stable, ExpressivE, and Deep

📝 Summary:
Keel is a novel Post-LayerNorm Transformer using Highway-style connections instead of residual ones. This enables stable training of networks over 1000 layers deep, preventing gradient vanishing and improving expressivity for LLMs.

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19895
• PDF: https://arxiv.org/pdf/2601.19895

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

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#Transformers #DeepLearning #LLM #NeuralNetworks #AIResearch

✨EvolVE: Evolutionary Search for LLM-based Verilog Generation and Optimization

📝 Summary:
EvolVE improves LLM-based Verilog generation and optimization through evolutionary search. It uses MCTS for correctness and IGR for optimization, accelerated by STG. EvolVE achieves state-of-the-art performance and reduces PPA on industry-scale designs.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18067
• PDF: https://arxiv.org/pdf/2601.18067
• Github: https://github.com/weiber2002/ICRTL

✨ Datasets citing this paper:
• https://huggingface.co/datasets/weiber2002/ICRTL

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

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#LLM #Verilog #EvolutionaryAlgorithms #HardwareDesign #AI

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✨DeFM: Learning Foundation Representations from Depth for Robotics

📝 Summary:
DeFM is a self-supervised foundation model for depth representation learning in robotics. It learns geometric and semantic features from 60M depth images, achieving state-of-the-art performance across diverse robotic tasks and strong sim-to-real generalization.

🔹 Publication Date: Published on Jan 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18923
• PDF: https://arxiv.org/pdf/2601.18923
• Github: https://de-fm.github.io/

🔹 Models citing this paper:
• https://huggingface.co/leggedrobotics/defm

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

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#Robotics #FoundationModels #SelfSupervisedLearning #ComputerVision #MachineLearning

✨HyperAlign: Hypernetwork for Efficient Test-Time Alignment of Diffusion Models

📝 Summary:
HyperAlign uses a hypernetwork to efficiently align diffusion models at test-time. It dynamically adjusts denoising trajectories based on input conditions, improving semantic consistency and visual appeal. This outperforms existing methods.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15968
• PDF: https://arxiv.org/pdf/2601.15968

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

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#DiffusionModels #Hypernetworks #GenerativeAI #AIResearch #DeepLearning

✨Towards Pixel-Level VLM Perception via Simple Points Prediction

📝 Summary:
SimpleSeg enables MLLMs to perform pixel-level segmentation by predicting point sequences in language space. A two-stage training with reinforcement learning refines these points. This simple method achieves competitive results, showing MLLMs have inherent low-level perception without specialized...

🔹 Publication Date: Published on Jan 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.19228
• PDF: https://arxiv.org/pdf/2601.19228
• Project Page: https://simpleseg.github.io/
• Github: https://github.com/songtianhui/SimpleSeg

🔹 Models citing this paper:
• https://huggingface.co/sthui/SimpleSeg-Kimi-VL
• https://huggingface.co/sthui/SimpleSeg-Qwen2.5-VL

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

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#VLM #MLLM #ImageSegmentation #DeepLearning #AIResearch