✨Soft Adaptive Policy Optimization

📝 Summary:
Soft Adaptive Policy Optimization SAPO enhances reinforcement learning for LLMs. It uses a smooth, temperature-controlled gate to adaptively attenuate off-policy updates, improving training stability and performance on reasoning tasks.

🔹 Publication Date: Published on Nov 25, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20347
• PDF: https://arxiv.org/pdf/2511.20347
• Project Page: https://swift.readthedocs.io/en/latest/Instruction/GRPO/AdvancedResearch/SAPO.html
• Github: https://github.com/NovaSky-AI/SkyRL/pull/762

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#ReinforcementLearning #LLMs #PolicyOptimization #AI #MachineLearning

✨Accelerating Masked Image Generation by Learning Latent Controlled Dynamics

📝 Summary:
MIGM-Shortcut accelerates masked image generation by learning a lightweight model to predict feature evolution velocity from previous features and sampled tokens. This achieves over 4x speedup with maintained quality on state-of-the-art models.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23996
• PDF: https://arxiv.org/pdf/2602.23996
• Github: https://github.com/Kaiwen-Zhu/MIGM-Shortcut

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#ImageGeneration #DeepLearning #GenerativeAI #ComputerVision #AI

✨SenCache: Accelerating Diffusion Model Inference via Sensitivity-Aware Caching

📝 Summary:
SenCache accelerates diffusion model inference by dynamically selecting cache timesteps based on model output sensitivity to input perturbations. This principled framework improves visual quality over existing heuristic methods within similar computational budgets.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24208
• PDF: https://arxiv.org/pdf/2602.24208
• Github: https://github.com/vita-epfl/SenCache

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#DiffusionModels #AI #MachineLearning #InferenceAcceleration #ComputerVision

✨Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators

📝 Summary:
STATIC accelerates constrained decoding for LLM generative retrieval on hardware accelerators. It transforms prefix trees into sparse matrices, vectorizing operations for massive speedups and low latency. This enables the first production-scale deployment of strictly constrained generative retrie...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22647
• PDF: https://arxiv.org/pdf/2602.22647

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

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#LLM #GenerativeAI #ConstrainedDecoding #AIHardware #DeepLearning

✨MeshSplatting: Differentiable Rendering with Opaque Meshes

📝 Summary:
MeshSplatting enables real-time novel view synthesis by combining mesh-based reconstruction with differentiable rendering to produce high-quality, efficient 3D meshes. AI-generated summary Primitive-b...

🔹 Publication Date: Published on Dec 7, 2025

🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/meshsplatting-differentiable-rendering-with-opaque-meshes-3886-1d3e00da
• PDF: https://arxiv.org/pdf/2512.06818
• Project Page: https://meshsplatting.github.io/
• Github: https://github.com/meshsplatting/mesh-splatting

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Enhancing Spatial Understanding in Image Generation via Reward Modeling

📝 Summary:
Text-to-image models struggle with complex spatial relationships. This paper introduces SpatialScore, a reward model trained on 80k preference pairs, to evaluate and improve spatial accuracy. It significantly enhances spatial understanding in image generation via reinforcement learning.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24233
• PDF: https://arxiv.org/pdf/2602.24233
• Project Page: https://dagroup-pku.github.io/SpatialT2I/
• Github: https://github.com/DAGroup-PKU/SpatialT2I

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

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#ImageGeneration #TextToImage #SpatialAI #RewardModeling #DeepLearning

✨CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

📝 Summary:
CUDA Agent is a large-scale agentic reinforcement learning system for optimizing CUDA kernels. It uses data synthesis, a skill-augmented development environment, and RL to achieve state-of-the-art performance, outperforming torch.compile and other LLMs significantly.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24286
• PDF: https://arxiv.org/pdf/2602.24286
• Project Page: https://cuda-agent.github.io/

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

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#CUDA #ReinforcementLearning #HPC #AI #MachineLearning

✨Memory Caching: RNNs with Growing Memory

📝 Summary:
Memory Caching MC enhances recurrent neural networks by caching memory states, allowing their capacity to grow with sequence length. This bridges the performance gap between RNNs and Transformers in long-context and recall-intensive tasks, outperforming other recurrent models.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24281
• PDF: https://arxiv.org/pdf/2602.24281

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

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#RNNs #Transformers #NeuralNetworks #MemoryCaching #AI

✨Ref-Adv: Exploring MLLM Visual Reasoning in Referring Expression Tasks

📝 Summary:
Ref-Adv is a challenging benchmark for referring expression comprehension that eliminates shortcut solutions by using complex linguistic expressions with minimal identifying information and hard distr...

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23898
• PDF: https://arxiv.org/pdf/2602.23898
• Project Page: https://ref-adv.github.io
• Github: https://github.com/dddraxxx/Ref-Adv

✨ Datasets citing this paper:
• https://huggingface.co/datasets/dddraxxx/ref-adv-s

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DLEBench: Evaluating Small-scale Object Editing Ability for Instruction-based Image Editing Model

📝 Summary:
A new benchmark called DeepLookEditBench is introduced to evaluate instruction-based image editing models' capability in handling small-scale object editing, revealing significant performance gaps in ...

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23622
• PDF: https://arxiv.org/pdf/2602.23622

✨ Datasets citing this paper:
• https://huggingface.co/datasets/SPUH/DLEBench

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨dLLM: Simple Diffusion Language Modeling

📝 Summary:
dLLM is an open-source framework standardizing core components of diffusion language modeling. It addresses the issue of scattered, hard-to-reproduce DLM implementations, enabling easy reproduction, customization, and development of both small and large diffusion language models.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22661
• PDF: https://arxiv.org/pdf/2602.22661
• Project Page: https://github.com/ZHZisZZ/dllm
• Github: https://github.com/ZHZisZZ/dllm

🔹 Models citing this paper:
• https://huggingface.co/dllm-hub/ModernBERT-large-chat-v0.1
• https://huggingface.co/dllm-hub/Qwen3-0.6B-diffusion-mdlm-v0.1
• https://huggingface.co/dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1

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#DiffusionModels #LanguageModeling #LLMs #OpenSourceAI #AIResearch

✨CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era

📝 Summary:
CiteAudit presents a benchmark and multi-agent pipeline to detect fabricated citations, a risk introduced by LLMs in scientific writing. This framework decomposes citation verification into steps and significantly outperforms prior methods, offering scalable tools for trustworthy references.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23452
• PDF: https://arxiv.org/pdf/2602.23452
• Project Page: https://www.checkcitation.com/

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

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#LLM #AcademicIntegrity #CitationVerification #AIethics #ResearchTools

✨Mode Seeking meets Mean Seeking for Fast Long Video Generation

📝 Summary:
This paper introduces a Decoupled Diffusion Transformer combining mode seeking and mean seeking for efficient long video generation. It leverages global flow matching for narrative coherence and local distribution matching against a short-video teacher for realism, effectively bridging the fideli...

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24289
• PDF: https://arxiv.org/pdf/2602.24289
• Project Page: https://primecai.github.io/mmm/
• Github: https://primecai.github.io/mmm/

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

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#VideoGeneration #DiffusionModels #AIResearch #MachineLearning #ComputerVision

✨LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference

📝 Summary:
LMCACHE enables efficient KV cache management for large language models by storing caches outside GPU memory, supporting cache reuse across queries and inference engines while achieving significant th...

🔹 Publication Date: Published on Oct 8, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.09665
• PDF: https://arxiv.org/pdf/2510.09665
• Github: https://github.com/LMCache/LMCache

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets

📝 Summary:
This paper introduces an automated framework for high-quality multilingual translation of benchmarks. It uses test-time compute scaling, specifically Universal Self-Improvement and T-RANK, to prevent semantic drift and context loss. This improves LLM evaluation accuracy beyond existing methods.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://huggingface.co/collections/hannayukhymenko/recovered-in-translation-eacl26-mme
• PDF: https://arxiv.org/pdf/2602.22207
• Project Page: https://ritranslation.insait.ai/
• Github: https://github.com/insait-institute/ritranslation

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#LLMEvaluation #MachineTranslation #NLP #AIResearch #Benchmarks

✨InfoNCE Induces Gaussian Distribution

📝 Summary:
InfoNCE objective induces Gaussian distribution in contrastive learning representations. This is supported by theoretical analysis and experimental validation. It explains observed Gaussianity and enables analytical treatment of representations.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24012
• PDF: https://arxiv.org/pdf/2602.24012
• Project Page: https://rbetser.github.io/InfoNCE-induces-Gaussian-distribution/
• Github: https://github.com/rbetser/InfoNCE-induces-Gaussian-distribution

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding

📝 Summary:
LongVideo-R1 is an MLLM agent for efficient long video understanding with low computational cost. It uses active reasoning and selective clip navigation, avoiding exhaustive search by focusing on informative segments. This achieves superior accuracy and efficiency.

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20913
• PDF: https://arxiv.org/pdf/2602.20913
• Github: https://github.com/qiujihao19/LongVideo-R1

🔹 Models citing this paper:
• https://huggingface.co/ChurchillQAQ/LongVideo-R1-Qwen2.5
• https://huggingface.co/ChurchillQAQ/LongVideo-R1-Qwen3

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ChurchillQAQ/LongVideo-R1-Data

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

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#VideoUnderstanding #MLLM #AI #EfficientAI #DeepLearning

✨LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding

📝 Summary:
LK losses directly optimize speculative decoding acceptance rate, outperforming standard KL divergence training. This boosts speedup, showing consistent gains of up to 10% in average acceptance length across various models and domains with no extra overhead.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23881
• PDF: https://arxiv.org/pdf/2602.23881

🔹 Models citing this paper:
• https://huggingface.co/nebius/MTP-DeepSeek-V3-0324
• https://huggingface.co/nebius/MEDUSA-Llama-3.1-8B-Instruct
• https://huggingface.co/nebius/MLP-Speculator-Llama-3.1-8B-Instruct

✨ Datasets citing this paper:
• https://huggingface.co/datasets/nebius/Llama-3.1-8B-Instruct-Infinity-Instruct-0625
• https://huggingface.co/datasets/nebius/gpt-oss-20b-Infinity-Instruct-0625
• https://huggingface.co/datasets/nebius/DeepSeek-V3-Infinity-Instruct-0625

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

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#SpeculativeDecoding #LLMs #LLMOptimization #DeepLearning #AIResearch

✨Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models

📝 Summary:
Compositional generalization requires neural representations to decompose linearly into orthogonal per-concept components. This Linear Representation Hypothesis is theoretically grounded and empirically supported in vision models.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24264
• PDF: https://arxiv.org/pdf/2602.24264
• Github: https://github.com/oshapio/necessary-compositionality

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#CompositionalGeneralization #VisionModels #NeuralNetworks #MachineLearning #AIResearch