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✨Utonia: Toward One Encoder for All Point Clouds
📝 Summary:
Utonia introduces a unified self-supervised transformer encoder for diverse point cloud domains. It enhances perception and aids embodied and multimodal reasoning, aiming for foundation models in sparse 3D data.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03283
• PDF: https://arxiv.org/pdf/2603.03283
• Project Page: https://pointcept.github.io/Utonia/
• Github: https://github.com/Pointcept/Utonia
🔹 Models citing this paper:
• https://huggingface.co/Pointcept/Utonia
✨ Spaces citing this paper:
• https://huggingface.co/spaces/pointcept-bot/Utonia
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Utonia introduces a unified self-supervised transformer encoder for diverse point cloud domains. It enhances perception and aids embodied and multimodal reasoning, aiming for foundation models in sparse 3D data.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03283
• PDF: https://arxiv.org/pdf/2603.03283
• Project Page: https://pointcept.github.io/Utonia/
• Github: https://github.com/Pointcept/Utonia
🔹 Models citing this paper:
• https://huggingface.co/Pointcept/Utonia
✨ Spaces citing this paper:
• https://huggingface.co/spaces/pointcept-bot/Utonia
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Qwen3-Coder-Next Technical Report
📝 Summary:
Qwen3-Coder-Next is an 80-billion-parameter language model that activates only 3 billion parameters during inference, achieving strong coding capabilities through agentic training with verifiable task...
🔹 Publication Date: Published on Feb 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00729
• PDF: https://arxiv.org/pdf/2603.00729
• Project Page: https://github.com/QwenLM/Qwen3-Coder
• Github: https://github.com/QwenLM/Qwen3-Coder
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Qwen3-Coder-Next is an 80-billion-parameter language model that activates only 3 billion parameters during inference, achieving strong coding capabilities through agentic training with verifiable task...
🔹 Publication Date: Published on Feb 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00729
• PDF: https://arxiv.org/pdf/2603.00729
• Project Page: https://github.com/QwenLM/Qwen3-Coder
• Github: https://github.com/QwenLM/Qwen3-Coder
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning
📝 Summary:
AReaL, a fully asynchronous reinforcement learning system, decouples generation and training to achieve higher GPU utilization and up to 2.57x training speedup for large language models on reasoning t...
🔹 Publication Date: Published on May 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.24298
• PDF: https://arxiv.org/pdf/2505.24298
• Github: https://github.com/inclusionAI/AReaL
🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/AReaL-boba-2-8B
• https://huggingface.co/inclusionAI/AReaL-boba-2-14B
• https://huggingface.co/inclusionAI/AReaL-boba-2-8B-Open
✨ Datasets citing this paper:
• https://huggingface.co/datasets/inclusionAI/AReaL-tau2-data
✨ Spaces citing this paper:
• https://huggingface.co/spaces/rzvn/Medieval-Village-AI
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📝 Summary:
AReaL, a fully asynchronous reinforcement learning system, decouples generation and training to achieve higher GPU utilization and up to 2.57x training speedup for large language models on reasoning t...
🔹 Publication Date: Published on May 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.24298
• PDF: https://arxiv.org/pdf/2505.24298
• Github: https://github.com/inclusionAI/AReaL
🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/AReaL-boba-2-8B
• https://huggingface.co/inclusionAI/AReaL-boba-2-14B
• https://huggingface.co/inclusionAI/AReaL-boba-2-8B-Open
✨ Datasets citing this paper:
• https://huggingface.co/datasets/inclusionAI/AReaL-tau2-data
✨ Spaces citing this paper:
• https://huggingface.co/spaces/rzvn/Medieval-Village-AI
==================================
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arXiv.org
AReaL: A Large-Scale Asynchronous Reinforcement Learning System...
Reinforcement learning (RL) has become a dominant paradigm for training large language models (LLMs), particularly for reasoning tasks. Effective RL for LLMs requires massive parallelization and...
✨InfoPO: Information-Driven Policy Optimization for User-Centric Agents
📝 Summary:
InfoPO optimizes agent-user collaboration for underspecified requests. It uses an information-gain reward to credit valuable turns that reduce uncertainty, improving decision-making and outperforming multi-turn RL baselines.
🔹 Publication Date: Published on Feb 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00656
• PDF: https://arxiv.org/pdf/2603.00656
• Github: https://github.com/kfq20/InfoPO
==================================
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#ReinforcementLearning #AI #HumanComputerInteraction #InformationTheory #AIagents
📝 Summary:
InfoPO optimizes agent-user collaboration for underspecified requests. It uses an information-gain reward to credit valuable turns that reduce uncertainty, improving decision-making and outperforming multi-turn RL baselines.
🔹 Publication Date: Published on Feb 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00656
• PDF: https://arxiv.org/pdf/2603.00656
• Github: https://github.com/kfq20/InfoPO
==================================
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#ReinforcementLearning #AI #HumanComputerInteraction #InformationTheory #AIagents
✨Chain of World: World Model Thinking in Latent Motion
📝 Summary:
CoWVLA unifies world-model temporal reasoning with disentangled latent motion representation to improve visuomotor learning efficiency. This new approach overcomes limitations of existing VLA models and outperforms them on robotic simulation benchmarks.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03195
• PDF: https://arxiv.org/pdf/2603.03195
• Project Page: https://fx-hit.github.io/cowvla-io/
• Github: https://fx-hit.github.io/cowvla-io/
🔹 Models citing this paper:
• https://huggingface.co/hitfx/CoWVLA
==================================
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#WorldModels #Robotics #MachineLearning #VisuomotorLearning #DeepLearning
📝 Summary:
CoWVLA unifies world-model temporal reasoning with disentangled latent motion representation to improve visuomotor learning efficiency. This new approach overcomes limitations of existing VLA models and outperforms them on robotic simulation benchmarks.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03195
• PDF: https://arxiv.org/pdf/2603.03195
• Project Page: https://fx-hit.github.io/cowvla-io/
• Github: https://fx-hit.github.io/cowvla-io/
🔹 Models citing this paper:
• https://huggingface.co/hitfx/CoWVLA
==================================
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#WorldModels #Robotics #MachineLearning #VisuomotorLearning #DeepLearning
✨Surgical Post-Training: Cutting Errors, Keeping Knowledge
📝 Summary:
Surgical Post-Training SPoT efficiently improves LLM reasoning while preventing catastrophic forgetting. It employs data rectification with an Oracle and a novel binary cross-entropy objective. SPoT enhanced Qwen3-8B accuracy by 6.2 percent using minimal data and training time.
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01683
• PDF: https://arxiv.org/pdf/2603.01683
• Github: https://github.com/Visual-AI/SPoT
==================================
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#LLM #CatastrophicForgetting #MachineLearning #AI #DeepLearning
📝 Summary:
Surgical Post-Training SPoT efficiently improves LLM reasoning while preventing catastrophic forgetting. It employs data rectification with an Oracle and a novel binary cross-entropy objective. SPoT enhanced Qwen3-8B accuracy by 6.2 percent using minimal data and training time.
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01683
• PDF: https://arxiv.org/pdf/2603.01683
• Github: https://github.com/Visual-AI/SPoT
==================================
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#LLM #CatastrophicForgetting #MachineLearning #AI #DeepLearning
✨Whisper-RIR-Mega: A Paired Clean-Reverberant Speech Benchmark for ASR Robustness to Room Acoustics
📝 Summary:
Whisper-RIR-Mega dataset evaluates ASR model robustness to reverberation by pairing clean and reverberant speech samples with stratified splits based on RT60 and DRR metrics. AI-generated summary We i...
🔹 Publication Date: Published on Feb 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02252
• PDF: https://arxiv.org/pdf/2603.02252
• Project Page: https://huggingface.co/datasets/mandipgoswami/whisper-rirmega-bench
• Github: https://github.com/mandip42/whisper-rirmega-bench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/mandipgoswami/whisper-rirmega-bench
✨ Spaces citing this paper:
• https://huggingface.co/spaces/mandipgoswami/whisper-rirmega-benchmark
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Whisper-RIR-Mega dataset evaluates ASR model robustness to reverberation by pairing clean and reverberant speech samples with stratified splits based on RT60 and DRR metrics. AI-generated summary We i...
🔹 Publication Date: Published on Feb 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02252
• PDF: https://arxiv.org/pdf/2603.02252
• Project Page: https://huggingface.co/datasets/mandipgoswami/whisper-rirmega-bench
• Github: https://github.com/mandip42/whisper-rirmega-bench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/mandipgoswami/whisper-rirmega-bench
✨ Spaces citing this paper:
• https://huggingface.co/spaces/mandipgoswami/whisper-rirmega-benchmark
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use
📝 Summary:
MOSAIC is a framework aligning agentic models for safe multi-step tool use, employing explicit safety reasoning and refusal. It significantly reduces harmful actions, increases refusal for unsafe tasks, cuts privacy leakage, and preserves benign performance.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03205
• PDF: https://arxiv.org/pdf/2603.03205
• Project Page: https://aradhye2002.github.io/mosaic-agent-safety/
==================================
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#AISafety #AIAgents #ResponsibleAI #LLMs #AIAlignment
📝 Summary:
MOSAIC is a framework aligning agentic models for safe multi-step tool use, employing explicit safety reasoning and refusal. It significantly reduces harmful actions, increases refusal for unsafe tasks, cuts privacy leakage, and preserves benign performance.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03205
• PDF: https://arxiv.org/pdf/2603.03205
• Project Page: https://aradhye2002.github.io/mosaic-agent-safety/
==================================
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#AISafety #AIAgents #ResponsibleAI #LLMs #AIAlignment
❤1
✨Spilled Energy in Large Language Models
📝 Summary:
Reinterpreting LLM softmax as an Energy-Based Model enables training-free hallucination detection. New energy metrics from output logits identify errors and biases without training overhead, demonstrating robust cross-task generalization.
🔹 Publication Date: Published on Feb 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18671
• PDF: https://arxiv.org/pdf/2602.18671
• Github: https://github.com/OmnAI-Lab/spilled-energy
==================================
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#LLM #EnergyBasedModels #HallucinationDetection #AISafety #ArtificialIntelligence
📝 Summary:
Reinterpreting LLM softmax as an Energy-Based Model enables training-free hallucination detection. New energy metrics from output logits identify errors and biases without training overhead, demonstrating robust cross-task generalization.
🔹 Publication Date: Published on Feb 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18671
• PDF: https://arxiv.org/pdf/2602.18671
• Github: https://github.com/OmnAI-Lab/spilled-energy
==================================
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#LLM #EnergyBasedModels #HallucinationDetection #AISafety #ArtificialIntelligence
✨Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction
📝 Summary:
CCP evaluates LLMs simulating social media users. Supervised fine-tuning improves text structure but degrades semantic accuracy, as models infer from behavioral histories without explicit conditioning. Prioritize authentic behavioral traces.
🔹 Publication Date: Published on Feb 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22752
• PDF: https://arxiv.org/pdf/2602.22752
• Project Page: https://nsschw.github.io/Turing-TWONy/
• Github: https://github.com/nsschw/Conditioned-Comment-Prediction
🔹 Models citing this paper:
• https://huggingface.co/nsschw/echo-Llama-3.1-8B-Instruct-eng
• https://huggingface.co/nsschw/echo-Llama-3.1-8B-Instruct-ger
==================================
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#LLMs #SocialMedia #AISimulation #NLP #AIResearch
📝 Summary:
CCP evaluates LLMs simulating social media users. Supervised fine-tuning improves text structure but degrades semantic accuracy, as models infer from behavioral histories without explicit conditioning. Prioritize authentic behavioral traces.
🔹 Publication Date: Published on Feb 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22752
• PDF: https://arxiv.org/pdf/2602.22752
• Project Page: https://nsschw.github.io/Turing-TWONy/
• Github: https://github.com/nsschw/Conditioned-Comment-Prediction
🔹 Models citing this paper:
• https://huggingface.co/nsschw/echo-Llama-3.1-8B-Instruct-eng
• https://huggingface.co/nsschw/echo-Llama-3.1-8B-Instruct-ger
==================================
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#LLMs #SocialMedia #AISimulation #NLP #AIResearch
✨Conditioned Activation Transport for T2I Safety Steering
📝 Summary:
Current T2I models generate unsafe content, and linear steering degrades image quality. This paper proposes Conditioned Activation Transport CAT, which uses geometric conditioning and nonlinear transport maps to activate only in unsafe regions. CAT significantly reduces unsafe content generation ...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03163
• PDF: https://arxiv.org/pdf/2603.03163
• Github: https://github.com/NASK-AISafety/conditional-activation-transport
✨ Datasets citing this paper:
• https://huggingface.co/datasets/NASK-PIB/SafeSteerDataset
==================================
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#AISafety #TextToImage #GenerativeAI #DeepLearning #AIethics
📝 Summary:
Current T2I models generate unsafe content, and linear steering degrades image quality. This paper proposes Conditioned Activation Transport CAT, which uses geometric conditioning and nonlinear transport maps to activate only in unsafe regions. CAT significantly reduces unsafe content generation ...
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03163
• PDF: https://arxiv.org/pdf/2603.03163
• Github: https://github.com/NASK-AISafety/conditional-activation-transport
✨ Datasets citing this paper:
• https://huggingface.co/datasets/NASK-PIB/SafeSteerDataset
==================================
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#AISafety #TextToImage #GenerativeAI #DeepLearning #AIethics
✨Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling
📝 Summary:
A machine learning framework using generative flow networks with experience replay, uniform exploration, and physics-based masking enables fast and accurate radio propagation path sampling with signif...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01655
• PDF: https://arxiv.org/pdf/2603.01655
• Project Page: https://differt.rtfd.io/npjwt2026/notebooks/sampling-paths.html
• Github: https://github.com/jeertmans/sampling-paths
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
A machine learning framework using generative flow networks with experience replay, uniform exploration, and physics-based masking enables fast and accurate radio propagation path sampling with signif...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01655
• PDF: https://arxiv.org/pdf/2603.01655
• Project Page: https://differt.rtfd.io/npjwt2026/notebooks/sampling-paths.html
• Github: https://github.com/jeertmans/sampling-paths
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨MNN: A Universal and Efficient Inference Engine
📝 Summary:
MNN is an efficient deep learning inference engine for mobile devices. It addresses compatibility and resource limits through pre-inference, kernel optimization, and backend abstraction, outperforming other lightweight frameworks.
🔹 Publication Date: Published on Feb 27, 2020
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2002.12418
• PDF: https://arxiv.org/pdf/2002.12418
• Github: https://github.com/alibaba/MNN
==================================
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#DeepLearning #MobileAI #EdgeAI #Optimization #MachineLearning
📝 Summary:
MNN is an efficient deep learning inference engine for mobile devices. It addresses compatibility and resource limits through pre-inference, kernel optimization, and backend abstraction, outperforming other lightweight frameworks.
🔹 Publication Date: Published on Feb 27, 2020
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2002.12418
• PDF: https://arxiv.org/pdf/2002.12418
• Github: https://github.com/alibaba/MNN
==================================
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#DeepLearning #MobileAI #EdgeAI #Optimization #MachineLearning
❤1
✨CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance
📝 Summary:
This paper reinterprets Classifier-Free Guidance CFG as a control system for diffusion models. It introduces Sliding Mode Control CFG SMC-CFG to overcome instability in existing linear CFG methods. SMC-CFG improves semantic alignment and stability across various guidance scales.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03281
• PDF: https://arxiv.org/pdf/2603.03281
• Project Page: https://hanyang-21.github.io/CFG-Ctrl
• Github: https://github.com/hanyang-21/CFG-Ctrl
==================================
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#DiffusionModels #GenerativeAI #ControlSystems #MachineLearning #AIResearch
📝 Summary:
This paper reinterprets Classifier-Free Guidance CFG as a control system for diffusion models. It introduces Sliding Mode Control CFG SMC-CFG to overcome instability in existing linear CFG methods. SMC-CFG improves semantic alignment and stability across various guidance scales.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03281
• PDF: https://arxiv.org/pdf/2603.03281
• Project Page: https://hanyang-21.github.io/CFG-Ctrl
• Github: https://github.com/hanyang-21/CFG-Ctrl
==================================
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#DiffusionModels #GenerativeAI #ControlSystems #MachineLearning #AIResearch
✨Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
📝 Summary:
This paper proposes GraphGlue, a framework that uses Riemannian geometry and neural manifold gluing to integrate knowledge from diverse graph domains. It merges datasets into a unified manifold for systematic understanding of knowledge transfer. GraphGlue achieves superior performance and shows t...
🔹 Publication Date: Published on Feb 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00618
• PDF: https://arxiv.org/pdf/2603.00618
• Github: https://github.com/RiemannGraph/GraphGlue
==================================
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#GraphFoundationModels #RiemannianGeometry #GraphAI #KnowledgeTransfer #MachineLearning
📝 Summary:
This paper proposes GraphGlue, a framework that uses Riemannian geometry and neural manifold gluing to integrate knowledge from diverse graph domains. It merges datasets into a unified manifold for systematic understanding of knowledge transfer. GraphGlue achieves superior performance and shows t...
🔹 Publication Date: Published on Feb 28
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00618
• PDF: https://arxiv.org/pdf/2603.00618
• Github: https://github.com/RiemannGraph/GraphGlue
==================================
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#GraphFoundationModels #RiemannianGeometry #GraphAI #KnowledgeTransfer #MachineLearning
✨Next Embedding Prediction Makes World Models Stronger
📝 Summary:
NE-Dreamer uses a temporal transformer to predict next-step encoder embeddings, enabling strong model-based reinforcement learning without decoders. This approach learns coherent state representations and achieves strong performance on DeepMind Control Suite and challenging DMLab tasks.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02765
• PDF: https://arxiv.org/pdf/2603.02765
• Project Page: https://corl-team.github.io/nedreamer/
• Github: https://github.com/corl-team/nedreamer
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
NE-Dreamer uses a temporal transformer to predict next-step encoder embeddings, enabling strong model-based reinforcement learning without decoders. This approach learns coherent state representations and achieves strong performance on DeepMind Control Suite and challenging DMLab tasks.
🔹 Publication Date: Published on Mar 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02765
• PDF: https://arxiv.org/pdf/2603.02765
• Project Page: https://corl-team.github.io/nedreamer/
• Github: https://github.com/corl-team/nedreamer
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨DynaMoE: Dynamic Token-Level Expert Activation with Layer-Wise Adaptive Capacity for Mixture-of-Experts Neural Networks
📝 Summary:
DynaMoE presents a dynamic Mixture-of-Experts framework that adapts expert activation and capacity allocation based on input complexity and task requirements, improving parameter efficiency and traini...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01697
• PDF: https://arxiv.org/pdf/2603.01697
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DynaMoE presents a dynamic Mixture-of-Experts framework that adapts expert activation and capacity allocation based on input complexity and task requirements, improving parameter efficiency and traini...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01697
• PDF: https://arxiv.org/pdf/2603.01697
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨SciDER: Scientific Data-centric End-to-end Researcher
📝 Summary:
SciDER automates scientific research by processing raw experimental data through collaborative agents that generate hypotheses and experimental designs while executing code, demonstrating superior per...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01421
• PDF: https://arxiv.org/pdf/2603.01421
• Project Page: https://harryluumn.github.io/scider-proj-page/
• Github: https://github.com/leonardodalinky/SciDER
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
SciDER automates scientific research by processing raw experimental data through collaborative agents that generate hypotheses and experimental designs while executing code, demonstrating superior per...
🔹 Publication Date: Published on Mar 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01421
• PDF: https://arxiv.org/pdf/2603.01421
• Project Page: https://harryluumn.github.io/scider-proj-page/
• Github: https://github.com/leonardodalinky/SciDER
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
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✨BBQ-to-Image: Numeric Bounding Box and Qolor Control in Large-Scale Text-to-Image Models
📝 Summary:
BBQ is a text-to-image model that enables precise numeric control over object attributes through structured-text conditioning without architectural changes. AI-generated summary Text-to-image models h...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20672
• PDF: https://arxiv.org/pdf/2602.20672
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📝 Summary:
BBQ is a text-to-image model that enables precise numeric control over object attributes through structured-text conditioning without architectural changes. AI-generated summary Text-to-image models h...
🔹 Publication Date: Published on Feb 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20672
• PDF: https://arxiv.org/pdf/2602.20672
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
✨GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant
📝 Summary:
GroupGPT is a token-efficient and privacy-preserving framework for multi-user chat assistance that uses a small-large model collaboration approach to improve intervention timing and response accuracy ...
🔹 Publication Date: Published on Mar 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01059
• PDF: https://arxiv.org/pdf/2603.01059
• Github: https://github.com/Eliot-Shen/GroupGPT
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
GroupGPT is a token-efficient and privacy-preserving framework for multi-user chat assistance that uses a small-large model collaboration approach to improve intervention timing and response accuracy ...
🔹 Publication Date: Published on Mar 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01059
• PDF: https://arxiv.org/pdf/2603.01059
• Github: https://github.com/Eliot-Shen/GroupGPT
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research