✨Learning to Hint for Reinforcement Learning
📝 Summary:
HiLL is a reinforcement learning framework that adaptively generates hints conditioned on reasoner errors to improve learning signals and transfer performance in group relative policy optimization. AI...
🔹 Publication Date: Published on Apr 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00698
• PDF: https://arxiv.org/pdf/2604.00698
• Github: https://github.com/Andree-9/HiLL
==================================
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📝 Summary:
HiLL is a reinforcement learning framework that adaptively generates hints conditioned on reasoner errors to improve learning signals and transfer performance in group relative policy optimization. AI...
🔹 Publication Date: Published on Apr 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00698
• PDF: https://arxiv.org/pdf/2604.00698
• Github: https://github.com/Andree-9/HiLL
==================================
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✨Tunable Soft Equivariance with Guarantees
📝 Summary:
A general framework for constructing soft equivariant models through weight projection into designed subspaces is proposed, demonstrating improved performance and reduced equivariance error across mul...
🔹 Publication Date: Published on Mar 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26657
• PDF: https://arxiv.org/pdf/2603.26657
• Github: https://github.com/ashiq24/soft-equivariance
==================================
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📝 Summary:
A general framework for constructing soft equivariant models through weight projection into designed subspaces is proposed, demonstrating improved performance and reduced equivariance error across mul...
🔹 Publication Date: Published on Mar 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26657
• PDF: https://arxiv.org/pdf/2603.26657
• Github: https://github.com/ashiq24/soft-equivariance
==================================
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✨A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens
📝 Summary:
DeltaTok encodes visual feature differences as delta tokens and DeltaWorld generates diverse video futures with reduced parameters and computational cost through multi-hypothesis training. AI-generate...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04913
• PDF: https://arxiv.org/pdf/2604.04913
• Project Page: https://deltatok.github.io
• Github: https://huggingface.co/collections/Amazon-FAR/deltatok
🔹 Models citing this paper:
• https://huggingface.co/Amazon-FAR/deltatok-kinetics
• https://huggingface.co/Amazon-FAR/deltaworld-kinetics
• https://huggingface.co/Amazon-FAR/seg-head-vspw
==================================
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📝 Summary:
DeltaTok encodes visual feature differences as delta tokens and DeltaWorld generates diverse video futures with reduced parameters and computational cost through multi-hypothesis training. AI-generate...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04913
• PDF: https://arxiv.org/pdf/2604.04913
• Project Page: https://deltatok.github.io
• Github: https://huggingface.co/collections/Amazon-FAR/deltatok
🔹 Models citing this paper:
• https://huggingface.co/Amazon-FAR/deltatok-kinetics
• https://huggingface.co/Amazon-FAR/deltaworld-kinetics
• https://huggingface.co/Amazon-FAR/seg-head-vspw
==================================
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✨R3PM-Net: Real-time, Robust, Real-world Point Matching Network
📝 Summary:
R3PM-Net is a lightweight, global-aware point matching network that achieves high-speed and accurate point cloud registration with competitive performance on real-world datasets. AI-generated summary ...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05060
• PDF: https://arxiv.org/pdf/2604.05060
• Project Page: https://yasiikb.github.io/R3PM-Net/
• Github: https://github.com/YasiiKB/R3PM-Net
✨ Datasets citing this paper:
• https://huggingface.co/datasets/YasiiKB/R3PM-Net
==================================
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📝 Summary:
R3PM-Net is a lightweight, global-aware point matching network that achieves high-speed and accurate point cloud registration with competitive performance on real-world datasets. AI-generated summary ...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05060
• PDF: https://arxiv.org/pdf/2604.05060
• Project Page: https://yasiikb.github.io/R3PM-Net/
• Github: https://github.com/YasiiKB/R3PM-Net
✨ Datasets citing this paper:
• https://huggingface.co/datasets/YasiiKB/R3PM-Net
==================================
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✨Qualixar OS: A Universal Operating System for AI Agent Orchestration
📝 Summary:
Qualixar OS enables universal AI agent orchestration through a comprehensive runtime environment supporting diverse LLM providers, agent frameworks, and communication protocols, featuring advanced mul...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06392
• PDF: https://arxiv.org/pdf/2604.06392
==================================
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📝 Summary:
Qualixar OS enables universal AI agent orchestration through a comprehensive runtime environment supporting diverse LLM providers, agent frameworks, and communication protocols, featuring advanced mul...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06392
• PDF: https://arxiv.org/pdf/2604.06392
==================================
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✨AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
📝 Summary:
AgentGL is a reinforcement learning-driven framework that enables large language models to navigate and reason over complex relational data by integrating graph-native tools and curriculum learning st...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05846
• PDF: https://arxiv.org/pdf/2604.05846
• Github: https://github.com/sunyuanfu/AgentGL
==================================
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📝 Summary:
AgentGL is a reinforcement learning-driven framework that enables large language models to navigate and reason over complex relational data by integrating graph-native tools and curriculum learning st...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05846
• PDF: https://arxiv.org/pdf/2604.05846
• Github: https://github.com/sunyuanfu/AgentGL
==================================
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✨A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning
📝 Summary:
Multi-modal typography attacks demonstrate significantly higher success rates than unimodal attacks by exploiting cross-modal vulnerabilities in audio-visual multi-modal large language models. AI-gene...
🔹 Publication Date: Published on Apr 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03995
• PDF: https://arxiv.org/pdf/2604.03995
• Project Page: https://cskyl.github.io/MLLM-Typography/
==================================
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📝 Summary:
Multi-modal typography attacks demonstrate significantly higher success rates than unimodal attacks by exploiting cross-modal vulnerabilities in audio-visual multi-modal large language models. AI-gene...
🔹 Publication Date: Published on Apr 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03995
• PDF: https://arxiv.org/pdf/2604.03995
• Project Page: https://cskyl.github.io/MLLM-Typography/
==================================
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✨Graph-Based Chain-of-Thought Pruning for Reducing Redundant Reflections in Reasoning LLMs
📝 Summary:
This paper optimizes LLM chain-of-thought reasoning by addressing redundant reflections and overthinking. It uses a graph-based framework to convert CoT into a DAG and applies dual pruning strategies to remove inefficient reflection patterns. This approach reduces reasoning tokens by 42% while ma...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05643
• PDF: https://arxiv.org/pdf/2604.05643
==================================
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📝 Summary:
This paper optimizes LLM chain-of-thought reasoning by addressing redundant reflections and overthinking. It uses a graph-based framework to convert CoT into a DAG and applies dual pruning strategies to remove inefficient reflection patterns. This approach reduces reasoning tokens by 42% while ma...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05643
• PDF: https://arxiv.org/pdf/2604.05643
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✨GenLCA: 3D Diffusion for Full-Body Avatars from In-the-Wild Videos
📝 Summary:
GenLCA generates photorealistic 3D avatars from text and images using a novel 3D diffusion model. It trains on millions of partially observable 2D videos by using a 3D tokenizer and a visibility-aware strategy to handle incomplete data. This enables superior photorealism and animatability.
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07273
• PDF: https://arxiv.org/pdf/2604.07273
• Project Page: https://onethousandwu.com/GenLCA-Page
==================================
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📝 Summary:
GenLCA generates photorealistic 3D avatars from text and images using a novel 3D diffusion model. It trains on millions of partially observable 2D videos by using a 3D tokenizer and a visibility-aware strategy to handle incomplete data. This enables superior photorealism and animatability.
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07273
• PDF: https://arxiv.org/pdf/2604.07273
• Project Page: https://onethousandwu.com/GenLCA-Page
==================================
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❤1
✨Combee: Scaling Prompt Learning for Self-Improving Language Model Agents
📝 Summary:
Combee scales prompt learning for self-improving language model agents, overcoming previous limitations with high parallelism. It uses parallel scans, augmented shuffling, and dynamic batch size control to achieve up to 17x speedup with better or comparable accuracy.
🔹 Publication Date: Published on Apr 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04247
• PDF: https://arxiv.org/pdf/2604.04247
==================================
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📝 Summary:
Combee scales prompt learning for self-improving language model agents, overcoming previous limitations with high parallelism. It uses parallel scans, augmented shuffling, and dynamic batch size control to achieve up to 17x speedup with better or comparable accuracy.
🔹 Publication Date: Published on Apr 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04247
• PDF: https://arxiv.org/pdf/2604.04247
==================================
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❤1
✨On the Step Length Confounding in LLM Reasoning Data Selection
📝 Summary:
Researchers identified a bias in naturalness-based data selection for reasoning tasks where longer reasoning steps are preferred over higher-quality ones, and proposed two debiasing methods to improve...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06834
• PDF: https://arxiv.org/pdf/2604.06834
• Project Page: https://wangbing1416.github.io/projects/acl2026_lengthbias.html
• Github: https://github.com/wangbing1416/ASLEC
==================================
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📝 Summary:
Researchers identified a bias in naturalness-based data selection for reasoning tasks where longer reasoning steps are preferred over higher-quality ones, and proposed two debiasing methods to improve...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06834
• PDF: https://arxiv.org/pdf/2604.06834
• Project Page: https://wangbing1416.github.io/projects/acl2026_lengthbias.html
• Github: https://github.com/wangbing1416/ASLEC
==================================
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❤1
✨Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
📝 Summary:
LLM agents now increasingly rely on externalized components like memory, skills, and protocols, rather than just modifying model weights. This externalization transforms complex cognitive tasks into more reliably solvable forms. Practical agent progress depends on this external cognitive infrastr...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08224
• PDF: https://arxiv.org/pdf/2604.08224
==================================
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📝 Summary:
LLM agents now increasingly rely on externalized components like memory, skills, and protocols, rather than just modifying model weights. This externalization transforms complex cognitive tasks into more reliably solvable forms. Practical agent progress depends on this external cognitive infrastr...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08224
• PDF: https://arxiv.org/pdf/2604.08224
==================================
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✨Graph of Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills
📝 Summary:
Graph of Skills GoS is an inference-time structural retrieval layer for large skill libraries. It constructs an executable skill graph to retrieve dependency-aware skill bundles, significantly improving performance and reducing token usage. GoS boosts average reward by 43.6 percent and cuts input...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05333
• PDF: https://arxiv.org/pdf/2604.05333
• Github: https://github.com/davidliuk/graph-of-skills
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📝 Summary:
Graph of Skills GoS is an inference-time structural retrieval layer for large skill libraries. It constructs an executable skill graph to retrieve dependency-aware skill bundles, significantly improving performance and reducing token usage. GoS boosts average reward by 43.6 percent and cuts input...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05333
• PDF: https://arxiv.org/pdf/2604.05333
• Github: https://github.com/davidliuk/graph-of-skills
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✨DMax: Aggressive Parallel Decoding for dLLMs
📝 Summary:
DMax introduces a novel approach for efficient diffusion language models that reduces error accumulation during parallel decoding through self-refinement and unified training strategies. AI-generated ...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.08302
• PDF: https://arxiv.org/pdf/2604.08302
• Github: https://github.com/czg1225/DMax
🔹 Models citing this paper:
• https://huggingface.co/Zigeng/DMax-Math-16B
• https://huggingface.co/Zigeng/DMax-Coder-16B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Zigeng/DMax-LLaDA-2.0-Mini-Math-Trajectories
• https://huggingface.co/datasets/Zigeng/DMax-LLaDA-2.0-Mini-Code-Trajectories
==================================
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📝 Summary:
DMax introduces a novel approach for efficient diffusion language models that reduces error accumulation during parallel decoding through self-refinement and unified training strategies. AI-generated ...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.08302
• PDF: https://arxiv.org/pdf/2604.08302
• Github: https://github.com/czg1225/DMax
🔹 Models citing this paper:
• https://huggingface.co/Zigeng/DMax-Math-16B
• https://huggingface.co/Zigeng/DMax-Coder-16B
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Zigeng/DMax-LLaDA-2.0-Mini-Math-Trajectories
• https://huggingface.co/datasets/Zigeng/DMax-LLaDA-2.0-Mini-Code-Trajectories
==================================
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✨KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation
📝 Summary:
KnowU-Bench presents a comprehensive benchmark for personalized mobile agents that evaluates true preference inference and proactive assistance capabilities in real-world GUI environments. AI-generate...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08455
• PDF: https://arxiv.org/pdf/2604.08455
• Project Page: https://zju-real.github.io/KnowU-Bench
• Github: https://github.com/ZJU-REAL/KnowU-Bench
==================================
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📝 Summary:
KnowU-Bench presents a comprehensive benchmark for personalized mobile agents that evaluates true preference inference and proactive assistance capabilities in real-world GUI environments. AI-generate...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08455
• PDF: https://arxiv.org/pdf/2604.08455
• Project Page: https://zju-real.github.io/KnowU-Bench
• Github: https://github.com/ZJU-REAL/KnowU-Bench
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✨Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces
📝 Summary:
OmniBehavior benchmark reveals that current LLMs fail to accurately simulate complex real-world user behaviors due to structural biases and limited behavioral diversity. AI-generated summary The emerg...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08362
• PDF: https://arxiv.org/pdf/2604.08362
• Project Page: https://omnibehavior.github.io/
• Github: https://github.com/icip-cas/OmniBehavior
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📝 Summary:
OmniBehavior benchmark reveals that current LLMs fail to accurately simulate complex real-world user behaviors due to structural biases and limited behavioral diversity. AI-generated summary The emerg...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08362
• PDF: https://arxiv.org/pdf/2604.08362
• Project Page: https://omnibehavior.github.io/
• Github: https://github.com/icip-cas/OmniBehavior
==================================
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✨OmniJigsaw: Enhancing Omni-Modal Reasoning via Modality-Orchestrated Reordering
📝 Summary:
OmniJigsaw presents a self-supervised framework for video-audio understanding and collaborative reasoning through temporal reordering and cross-modal integration strategies. AI-generated summary To ex...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08209
• PDF: https://arxiv.org/pdf/2604.08209
• Project Page: https://aim-uofa.github.io/OmniJigsaw
• Github: https://github.com/aim-uofa/OmniJigsaw
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📝 Summary:
OmniJigsaw presents a self-supervised framework for video-audio understanding and collaborative reasoning through temporal reordering and cross-modal integration strategies. AI-generated summary To ex...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08209
• PDF: https://arxiv.org/pdf/2604.08209
• Project Page: https://aim-uofa.github.io/OmniJigsaw
• Github: https://github.com/aim-uofa/OmniJigsaw
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❤1
✨Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference
📝 Summary:
Flux Attention dynamically optimizes attention computation in LLMs by routing layers to full or sparse attention based on input context, achieving faster inference with minimal training overhead. AI-g...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.07394
• PDF: https://arxiv.org/pdf/2604.07394
• Github: https://github.com/qqtang-code/FluxAttention
🔹 Models citing this paper:
• https://huggingface.co/QQTang1223/full_xattn_Qwen3-8B
• https://huggingface.co/QQTang1223/full_streaming_Llama-3.1-8B-Instruct
• https://huggingface.co/QQTang1223/full_streaming_Qwen3-4B
==================================
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📝 Summary:
Flux Attention dynamically optimizes attention computation in LLMs by routing layers to full or sparse attention based on input context, achieving faster inference with minimal training overhead. AI-g...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.07394
• PDF: https://arxiv.org/pdf/2604.07394
• Github: https://github.com/qqtang-code/FluxAttention
🔹 Models citing this paper:
• https://huggingface.co/QQTang1223/full_xattn_Qwen3-8B
• https://huggingface.co/QQTang1223/full_streaming_Llama-3.1-8B-Instruct
• https://huggingface.co/QQTang1223/full_streaming_Qwen3-4B
==================================
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✨LPM 1.0: Video-based Character Performance Model
📝 Summary:
A large-scale multimodal model for real-time conversational character performance generation that maintains identity consistency while enabling interactive, infinite-length video synthesis. AI-generat...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07823
• PDF: https://arxiv.org/pdf/2604.07823
• Project Page: https://large-performance-model.github.io/
• Github: https://github.com/large-performance-model/large-performance-model.github.io
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📝 Summary:
A large-scale multimodal model for real-time conversational character performance generation that maintains identity consistency while enabling interactive, infinite-length video synthesis. AI-generat...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07823
• PDF: https://arxiv.org/pdf/2604.07823
• Project Page: https://large-performance-model.github.io/
• Github: https://github.com/large-performance-model/large-performance-model.github.io
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✨When Numbers Speak: Aligning Textual Numerals and Visual Instances in Text-to-Video Diffusion Models
📝 Summary:
NUMINA enhances text-to-video diffusion models' numerical accuracy through a training-free framework that identifies layout inconsistencies and guides regeneration via attention modulation. AI-generat...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08546
• PDF: https://arxiv.org/pdf/2604.08546
• Project Page: https://h-embodvis.github.io/NUMINA/
• Github: https://github.com/H-EmbodVis/NUMINA
==================================
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📝 Summary:
NUMINA enhances text-to-video diffusion models' numerical accuracy through a training-free framework that identifies layout inconsistencies and guides regeneration via attention modulation. AI-generat...
🔹 Publication Date: Published on Apr 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.08546
• PDF: https://arxiv.org/pdf/2604.08546
• Project Page: https://h-embodvis.github.io/NUMINA/
• Github: https://github.com/H-EmbodVis/NUMINA
==================================
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✨HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents
📝 Summary:
HY-Embodied-0.5 is a foundation model family for embodied agents featuring Mixture-of-Transformers architecture and iterative post-training for enhanced visual perception and reasoning capabilities. A...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07430
• PDF: https://arxiv.org/pdf/2604.07430
• Github: https://github.com/Tencent-Hunyuan/HY-Embodied
🔹 Models citing this paper:
• https://huggingface.co/tencent/HY-Embodied-0.5
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
HY-Embodied-0.5 is a foundation model family for embodied agents featuring Mixture-of-Transformers architecture and iterative post-training for enhanced visual perception and reasoning capabilities. A...
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07430
• PDF: https://arxiv.org/pdf/2604.07430
• Github: https://github.com/Tencent-Hunyuan/HY-Embodied
🔹 Models citing this paper:
• https://huggingface.co/tencent/HY-Embodied-0.5
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
arXiv.org
HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents
We introduce HY-Embodied-0.5, a family of foundation models specifically designed for real-world embodied agents. To bridge the gap between general Vision-Language Models (VLMs) and the demands of...