✨PACED: Distillation at the Frontier of Student Competence
📝 Summary:
PACED optimizes distillation by focusing training on a student competence frontier using a Beta kernel weighting. Derived from gradient analysis, this avoids wasted compute at extremes, boosting distillation and self-distillation performance.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11178
• PDF: https://arxiv.org/pdf/2603.11178
==================================
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#KnowledgeDistillation #DeepLearning #ModelOptimization #AIResearch #ComputeEfficiency
📝 Summary:
PACED optimizes distillation by focusing training on a student competence frontier using a Beta kernel weighting. Derived from gradient analysis, this avoids wasted compute at extremes, boosting distillation and self-distillation performance.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11178
• PDF: https://arxiv.org/pdf/2603.11178
==================================
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#KnowledgeDistillation #DeepLearning #ModelOptimization #AIResearch #ComputeEfficiency
✨SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
📝 Summary:
SurvHTE-Bench is the first comprehensive benchmark for estimating heterogeneous treatment effects with censored survival data. It offers synthetic, semi-synthetic, and real-world datasets for rigorous and reproducible evaluation of causal survival methods.
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05483
• PDF: https://arxiv.org/pdf/2603.05483
• Github: https://github.com/Shahriarnz14/SurvHTE-Bench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/snoroozi/SurvHTE-Bench
==================================
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✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
SurvHTE-Bench is the first comprehensive benchmark for estimating heterogeneous treatment effects with censored survival data. It offers synthetic, semi-synthetic, and real-world datasets for rigorous and reproducible evaluation of causal survival methods.
🔹 Publication Date: Published on Mar 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05483
• PDF: https://arxiv.org/pdf/2603.05483
• Github: https://github.com/Shahriarnz14/SurvHTE-Bench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/snoroozi/SurvHTE-Bench
==================================
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✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨Meta-Reinforcement Learning with Self-Reflection for Agentic Search
📝 Summary:
MR-Search is a meta-reinforcement learning approach for agentic search that uses self-reflection. It conditions on past episodes to adapt search strategies and improve in-context exploration. This method shows strong generalization and significant performance gains across various benchmarks.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11327
• PDF: https://arxiv.org/pdf/2603.11327
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
MR-Search is a meta-reinforcement learning approach for agentic search that uses self-reflection. It conditions on past episodes to adapt search strategies and improve in-context exploration. This method shows strong generalization and significant performance gains across various benchmarks.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11327
• PDF: https://arxiv.org/pdf/2603.11327
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨RubiCap: Rubric-Guided Reinforcement Learning for Dense Image Captioning
📝 Summary:
RubiCap introduces a reinforcement learning framework for dense image captioning, using LLM-generated rubrics to provide fine-grained reward signals. This method overcomes limitations of supervised learning and prior RL, achieving superior performance on benchmarks and improving vision-language m...
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09160
• PDF: https://arxiv.org/pdf/2603.09160
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
RubiCap introduces a reinforcement learning framework for dense image captioning, using LLM-generated rubrics to provide fine-grained reward signals. This method overcomes limitations of supervised learning and prior RL, achieving superior performance on benchmarks and improving vision-language m...
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09160
• PDF: https://arxiv.org/pdf/2603.09160
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights
📝 Summary:
Large pretrained models have a high density of task-specific experts around their weights. This enables a simple post-training method of random sampling and ensembling to be competitive with complex optimization techniques.
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12228
• PDF: https://arxiv.org/pdf/2603.12228
• Project Page: https://thickets.mit.edu
• Github: https://github.com/sunrainyg/RandOpt
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Large pretrained models have a high density of task-specific experts around their weights. This enables a simple post-training method of random sampling and ensembling to be competitive with complex optimization techniques.
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12228
• PDF: https://arxiv.org/pdf/2603.12228
• Project Page: https://thickets.mit.edu
• Github: https://github.com/sunrainyg/RandOpt
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨CREATE: Testing LLMs for Associative Creativity
📝 Summary:
CREATE is a new benchmark to evaluate LLMs associative creativity by generating diverse and specific concept paths. It scores models on path specificity, diversity, and quantity. Strong models perform well but saturation is hard to achieve, and thinking models dont always improve performance.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09970
• PDF: https://arxiv.org/pdf/2603.09970
• Project Page: https://manyawadhwa.github.io/projects/create/
• Github: https://github.com/ManyaWadhwa/CREATE
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
CREATE is a new benchmark to evaluate LLMs associative creativity by generating diverse and specific concept paths. It scores models on path specificity, diversity, and quantity. Strong models perform well but saturation is hard to achieve, and thinking models dont always improve performance.
🔹 Publication Date: Published on Mar 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09970
• PDF: https://arxiv.org/pdf/2603.09970
• Project Page: https://manyawadhwa.github.io/projects/create/
• Github: https://github.com/ManyaWadhwa/CREATE
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨WaDi: Weight Direction-aware Distillation for One-step Image Synthesis
📝 Summary:
Diffusion model inference is slow. WaDi focuses on weight direction changes during distillation to accelerate models into efficient one-step generators. This achieves state-of-the-art quality with significantly fewer parameters and broad versatility.
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08258
• PDF: https://arxiv.org/pdf/2603.08258
• Github: https://github.com/gudaochangsheng/WaDi
==================================
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#DiffusionModels #ImageSynthesis #ModelAcceleration #DeepLearning #AIResearch
📝 Summary:
Diffusion model inference is slow. WaDi focuses on weight direction changes during distillation to accelerate models into efficient one-step generators. This achieves state-of-the-art quality with significantly fewer parameters and broad versatility.
🔹 Publication Date: Published on Mar 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08258
• PDF: https://arxiv.org/pdf/2603.08258
• Github: https://github.com/gudaochangsheng/WaDi
==================================
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#DiffusionModels #ImageSynthesis #ModelAcceleration #DeepLearning #AIResearch
✨AutoFigure-Edit: Generating Editable Scientific Illustration
📝 Summary:
AutoFigure-Edit generates editable scientific illustrations from text and reference images. It improves editability, style control, and efficiency by combining long-context understanding and native SVG editing for high-quality, flexible refinement.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2603.06674
• PDF: https://arxiv.org/pdf/2603.06674
• Project Page: https://deepscientist.cc/
• Github: https://github.com/ResearAI/AutoFigure-Edit
==================================
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#AI #ScientificIllustration #ImageGeneration #SVG #DeepLearning
📝 Summary:
AutoFigure-Edit generates editable scientific illustrations from text and reference images. It improves editability, style control, and efficiency by combining long-context understanding and native SVG editing for high-quality, flexible refinement.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2603.06674
• PDF: https://arxiv.org/pdf/2603.06674
• Project Page: https://deepscientist.cc/
• Github: https://github.com/ResearAI/AutoFigure-Edit
==================================
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#AI #ScientificIllustration #ImageGeneration #SVG #DeepLearning
✨Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
📝 Summary:
Nemotron 3 Nano is an efficient Mixture-of-Experts hybrid Mamba-Transformer model. It achieves better accuracy and up to 3.3x higher inference throughput than similar models, while using fewer active parameters and supporting 1M token contexts for enhanced agentic reasoning.
🔹 Publication Date: Published on Dec 23, 2025
🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/nemotron-3-nano-open-efficient-mixture-of-experts-hybrid-mamba-transformer-model-for-agentic-reasoning-1072-37bf9190
• PDF: https://arxiv.org/pdf/2512.20848
• Github: https://github.com/NVIDIA-NeMo/Nemotron
🔹 Models citing this paper:
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
✨ Spaces citing this paper:
• https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard
• https://huggingface.co/spaces/hadadxyz/ai
• https://huggingface.co/spaces/hadadxyz/blog
==================================
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✓ https://t.iss.one/DataScienceT
#Nemotron3Nano #MixtureOfExperts #MambaTransformer #AgenticAI #LLM
📝 Summary:
Nemotron 3 Nano is an efficient Mixture-of-Experts hybrid Mamba-Transformer model. It achieves better accuracy and up to 3.3x higher inference throughput than similar models, while using fewer active parameters and supporting 1M token contexts for enhanced agentic reasoning.
🔹 Publication Date: Published on Dec 23, 2025
🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/nemotron-3-nano-open-efficient-mixture-of-experts-hybrid-mamba-transformer-model-for-agentic-reasoning-1072-37bf9190
• PDF: https://arxiv.org/pdf/2512.20848
• Github: https://github.com/NVIDIA-NeMo/Nemotron
🔹 Models citing this paper:
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8
• https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
✨ Spaces citing this paper:
• https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard
• https://huggingface.co/spaces/hadadxyz/ai
• https://huggingface.co/spaces/hadadxyz/blog
==================================
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#Nemotron3Nano #MixtureOfExperts #MambaTransformer #AgenticAI #LLM
Arxivlens
Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning - AI Research Paper Analysis…
AI-powered analysis of 'Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning'. We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained…
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✨Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
📝 Summary:
Vision-R1 is a reasoning MLLM enhancing multimodal reasoning via Reinforcement Learning. It leverages a large, AI-generated multimodal CoT dataset and new training strategies to refine reasoning. This achieves high accuracy on multimodal math benchmarks.
🔹 Publication Date: Published on Mar 9, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.06749
• PDF: https://arxiv.org/pdf/2503.06749
• Github: https://github.com/Osilly/Vision-R1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Yuting6/ttrl
• https://huggingface.co/datasets/LoadingBFX/GeoQA-PLUS-aug-train-Vision-R1-cot-rewrite
• https://huggingface.co/datasets/LoadingBFX/GeoQA-train-Vision-R1-cot-rewrite
==================================
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#MLLM #ReinforcementLearning #AIReasoning #ChainOfThought #ArtificialIntelligence
📝 Summary:
Vision-R1 is a reasoning MLLM enhancing multimodal reasoning via Reinforcement Learning. It leverages a large, AI-generated multimodal CoT dataset and new training strategies to refine reasoning. This achieves high accuracy on multimodal math benchmarks.
🔹 Publication Date: Published on Mar 9, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.06749
• PDF: https://arxiv.org/pdf/2503.06749
• Github: https://github.com/Osilly/Vision-R1
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Yuting6/ttrl
• https://huggingface.co/datasets/LoadingBFX/GeoQA-PLUS-aug-train-Vision-R1-cot-rewrite
• https://huggingface.co/datasets/LoadingBFX/GeoQA-train-Vision-R1-cot-rewrite
==================================
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#MLLM #ReinforcementLearning #AIReasoning #ChainOfThought #ArtificialIntelligence
❤1
✨LMEB: Long-horizon Memory Embedding Benchmark
📝 Summary:
LMEB is a new benchmark for evaluating embedding models' long-horizon memory retrieval abilities, a gap in traditional benchmarks. It assesses complex memory types and reveals that performance in standard passage retrieval does not generalize to these challenging scenarios.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12572
• PDF: https://arxiv.org/pdf/2603.12572
✨ Datasets citing this paper:
• https://huggingface.co/datasets/KaLM-Embedding/LMEB
==================================
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#EmbeddingModels #MemoryRetrieval #Benchmarks #MachineLearning #AIResearch
📝 Summary:
LMEB is a new benchmark for evaluating embedding models' long-horizon memory retrieval abilities, a gap in traditional benchmarks. It assesses complex memory types and reveals that performance in standard passage retrieval does not generalize to these challenging scenarios.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12572
• PDF: https://arxiv.org/pdf/2603.12572
✨ Datasets citing this paper:
• https://huggingface.co/datasets/KaLM-Embedding/LMEB
==================================
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#EmbeddingModels #MemoryRetrieval #Benchmarks #MachineLearning #AIResearch
✨VQQA: An Agentic Approach for Video Evaluation and Quality Improvement
📝 Summary:
VQQA is a multi-agent framework that uses VLM critiques as semantic gradients for efficient, black-box video generation optimization via natural language. It resolves visual artifacts, significantly improving video quality for text-to-video and image-to-video tasks.
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12310
• PDF: https://arxiv.org/pdf/2603.12310
• Project Page: https://yiwen-song.github.io/vqqa/
==================================
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#VideoGeneration #AIAgents #VisionLanguageModels #GenerativeAI #MachineLearning
📝 Summary:
VQQA is a multi-agent framework that uses VLM critiques as semantic gradients for efficient, black-box video generation optimization via natural language. It resolves visual artifacts, significantly improving video quality for text-to-video and image-to-video tasks.
🔹 Publication Date: Published on Mar 12
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12310
• PDF: https://arxiv.org/pdf/2603.12310
• Project Page: https://yiwen-song.github.io/vqqa/
==================================
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#VideoGeneration #AIAgents #VisionLanguageModels #GenerativeAI #MachineLearning
✨daVinci-Env: Open SWE Environment Synthesis at Scale
📝 Summary:
OpenSWE is the largest open framework for training software engineering agents, featuring 45,320 executable Python environments. It achieves state-of-the-art performance on SWE-bench Verified and shows substantial out-of-domain reasoning improvements.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13023
• PDF: https://arxiv.org/pdf/2603.13023
• Github: https://github.com/GAIR-NLP/OpenSWE
==================================
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#SoftwareEngineering #AIagents #MachineLearning #OpenSWE #DeepLearning
📝 Summary:
OpenSWE is the largest open framework for training software engineering agents, featuring 45,320 executable Python environments. It achieves state-of-the-art performance on SWE-bench Verified and shows substantial out-of-domain reasoning improvements.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13023
• PDF: https://arxiv.org/pdf/2603.13023
• Github: https://github.com/GAIR-NLP/OpenSWE
==================================
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#SoftwareEngineering #AIagents #MachineLearning #OpenSWE #DeepLearning
✨From Sparse to Dense: Multi-View GRPO for Flow Models via Augmented Condition Space
📝 Summary:
Multi-View GRPO enhances text-to-image flow model alignment by expanding condition space for richer reward mapping and improved sample relationship exploration. AI-generated summary Group Relative Pol...
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12648
• PDF: https://arxiv.org/pdf/2603.12648
• Project Page: https://bujiazi.github.io/mvgrpo.github.io/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Multi-View GRPO enhances text-to-image flow model alignment by expanding condition space for richer reward mapping and improved sample relationship exploration. AI-generated summary Group Relative Pol...
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12648
• PDF: https://arxiv.org/pdf/2603.12648
• Project Page: https://bujiazi.github.io/mvgrpo.github.io/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Spend Less, Reason Better: Budget-Aware Value Tree Search for LLM Agents
📝 Summary:
The Budget-Aware Value Tree BAVT optimizes LLM agent reasoning by dynamically balancing exploration and exploitation based on remaining compute. It uses a budget-conditioned node selection and residual value predictor for efficient search, outperforming brute-force methods with 4x less resources.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12634
• PDF: https://arxiv.org/pdf/2603.12634
==================================
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#LLMAgents #AIResearch #Optimization #EfficientAI #ValueTreeSearch
📝 Summary:
The Budget-Aware Value Tree BAVT optimizes LLM agent reasoning by dynamically balancing exploration and exploitation based on remaining compute. It uses a budget-conditioned node selection and residual value predictor for efficient search, outperforming brute-force methods with 4x less resources.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12634
• PDF: https://arxiv.org/pdf/2603.12634
==================================
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#LLMAgents #AIResearch #Optimization #EfficientAI #ValueTreeSearch
❤2
✨Visual-ERM: Reward Modeling for Visual Equivalence
📝 Summary:
Visual-ERM is a multimodal generative reward model providing fine-grained visual feedback for vision-to-code tasks. It significantly improves reinforcement learning performance for chart, table, and SVG parsing, demonstrating that fine-grained visual supervision is essential.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13224
• PDF: https://arxiv.org/pdf/2603.13224
• Github: https://github.com/InternLM/Visual-ERM
==================================
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#ReinforcementLearning #ComputerVision #GenerativeAI #AI #DataScience
📝 Summary:
Visual-ERM is a multimodal generative reward model providing fine-grained visual feedback for vision-to-code tasks. It significantly improves reinforcement learning performance for chart, table, and SVG parsing, demonstrating that fine-grained visual supervision is essential.
🔹 Publication Date: Published on Mar 13
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13224
• PDF: https://arxiv.org/pdf/2603.13224
• Github: https://github.com/InternLM/Visual-ERM
==================================
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#ReinforcementLearning #ComputerVision #GenerativeAI #AI #DataScience
✨SimRecon: SimReady Compositional Scene Reconstruction from Real Videos
📝 Summary:
SimRecon reconstructs cluttered scenes from real videos using a Perception-Generation-Simulation pipeline. It employs Active Viewpoint Optimization for visual fidelity and a Scene Graph Synthesizer for physical plausibility. This enables superior compositional scene representations for simulation...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02133
• PDF: https://arxiv.org/pdf/2603.02133
• Project Page: https://xiac20.github.io/SimRecon/
• Github: https://github.com/xiac20/SimRecon
==================================
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#SceneReconstruction #ComputerVision #AI #Simulation #3DReconstruction
📝 Summary:
SimRecon reconstructs cluttered scenes from real videos using a Perception-Generation-Simulation pipeline. It employs Active Viewpoint Optimization for visual fidelity and a Scene Graph Synthesizer for physical plausibility. This enables superior compositional scene representations for simulation...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02133
• PDF: https://arxiv.org/pdf/2603.02133
• Project Page: https://xiac20.github.io/SimRecon/
• Github: https://github.com/xiac20/SimRecon
==================================
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#SceneReconstruction #ComputerVision #AI #Simulation #3DReconstruction
✨LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation
📝 Summary:
LookaheadKV enhances KV cache eviction in LLMs by accurately predicting future importance scores. It uses parameter-efficient modules, avoiding costly draft generation while maintaining high accuracy. This lightweight method significantly reduces eviction overhead and speeds up inference.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10899
• PDF: https://arxiv.org/pdf/2603.10899
• Github: https://github.com/SamsungLabs/LookaheadKV
==================================
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#LLM #KVCache #ModelOptimization #DeepLearning #AI
📝 Summary:
LookaheadKV enhances KV cache eviction in LLMs by accurately predicting future importance scores. It uses parameter-efficient modules, avoiding costly draft generation while maintaining high accuracy. This lightweight method significantly reduces eviction overhead and speeds up inference.
🔹 Publication Date: Published on Mar 11
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10899
• PDF: https://arxiv.org/pdf/2603.10899
• Github: https://github.com/SamsungLabs/LookaheadKV
==================================
For more data science resources:
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#LLM #KVCache #ModelOptimization #DeepLearning #AI