✨SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia

📝 Summary:
Researchers developed a novel agentic data-generation framework to create culturally grounded safety datasets for Southeast Asia, resulting in multilingual safeguard models that outperform existing ap...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01618
• PDF: https://arxiv.org/pdf/2602.01618

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✨Structured 3D Latents for Scalable and Versatile 3D Generation

📝 Summary:
A 3D generation method using a unified SLAT representation and rectified flow transformers achieves high-quality results across different formats and conditions. AI-generated summary We introduce a no...

🔹 Publication Date: Published on Dec 2, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.01506
• PDF: https://arxiv.org/pdf/2412.01506
• Github: https://github.com/Microsoft/TRELLIS

🔹 Models citing this paper:
• https://huggingface.co/microsoft/TRELLIS-image-large
• https://huggingface.co/microsoft/TRELLIS-text-xlarge
• https://huggingface.co/microsoft/TRELLIS-text-base

✨ Datasets citing this paper:
• https://huggingface.co/datasets/JeffreyXiang/TRELLIS-500K
• https://huggingface.co/datasets/argojuni0506/TRELLIS-3D
• https://huggingface.co/datasets/gqk/TRELLIS-500K-fork

✨ Spaces citing this paper:
• https://huggingface.co/spaces/trellis-community/TRELLIS
• https://huggingface.co/spaces/dkatz2391/Cavargas-TRELLIS-Multiple3D
• https://huggingface.co/spaces/microsoft/TRELLIS

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✨PISCES: Annotation-free Text-to-Video Post-Training via Optimal Transport-Aligned Rewards

📝 Summary:
PISCES is an annotation-free text-to-video generation method that uses dual optimal transport-aligned rewards to improve visual quality and semantic alignment without human preference annotations. AI-...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01624
• PDF: https://arxiv.org/pdf/2602.01624

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✨Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models

📝 Summary:
Vision-DeepResearch introduces a multimodal deep-research paradigm enabling multi-turn, multi-entity, and multi-scale visual and textual search with deep-research capabilities integrated through cold-...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22060
• PDF: https://arxiv.org/pdf/2601.22060
• Project Page: https://osilly.github.io/Vision-DeepResearch/
• Github: https://github.com/Osilly/Vision-DeepResearch

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✨Show, Don't Tell: Morphing Latent Reasoning into Image Generation

📝 Summary:
LatentMorph integrates implicit latent reasoning into text-to-image generation through four lightweight components that enable adaptive self-refinement and improve both efficiency and cognitive alignm...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02227
• PDF: https://arxiv.org/pdf/2602.02227
• Github: https://github.com/EnVision-Research/LatentMorph

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✨PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss

📝 Summary:
PixelGen is a pixel-space diffusion framework that uses perceptual supervision through LPIPS and DINO-based losses to generate high-quality images without requiring VAEs or latent representations. AI-...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02493
• PDF: https://arxiv.org/pdf/2602.02493
• Project Page: https://zehong-ma.github.io/PixelGen/
• Github: https://github.com/Zehong-Ma/PixelGen

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✨SWE-Universe: Scale Real-World Verifiable Environments to Millions

📝 Summary:
A scalable framework for constructing real-world software engineering environments from GitHub pull requests using an efficient building agent with self-verification and hacking detection capabilities...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02361
• PDF: https://arxiv.org/pdf/2602.02361
• Github: https://huggingface.co/papers?q=GitHub%20pull%20requests

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✨Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning

📝 Summary:
Post-training of reasoning large language models can be improved by correcting distribution mismatches between supervised fine-tuning and reinforcement learning stages through importance sampling rewe...

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01058
• PDF: https://arxiv.org/pdf/2602.01058

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✨Green-VLA: Staged Vision-Language-Action Model for Generalist Robots

📝 Summary:
Green-VLA is a five-stage vision-language-action framework for real-world robot deployment that achieves generalization across different robot embodiments through multimodal training and reinforcement...

🔹 Publication Date: Published on Jan 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00919
• PDF: https://arxiv.org/pdf/2602.00919
• Project Page: https://greenvla.github.io
• Github: https://github.com/greenvla/GreenVLA

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✨LoopViT: Scaling Visual ARC with Looped Transformers

📝 Summary:
Loop-ViT introduces a recursive vision transformer architecture that decouples reasoning depth from model capacity through weight-tied recurrence and dynamic exit mechanisms, achieving superior visual...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02156
• PDF: https://arxiv.org/pdf/2602.02156
• Github: https://github.com/WenjieShu/LoopViT

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✨Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training

📝 Summary:
Rubric-ARM framework jointly optimizes rubric generation and judging through reinforcement learning to improve response quality assessment in creative and open-ended tasks. AI-generated summary Standa...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01511
• PDF: https://arxiv.org/pdf/2602.01511

🔹 Models citing this paper:
• https://huggingface.co/OpenRubrics/RubricARM-8B-Rubric
• https://huggingface.co/OpenRubrics/RubricARM-8B-Judge

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✨PISA: Piecewise Sparse Attention Is Wiser for Efficient Diffusion Transformers

📝 Summary:
PISA is a novel sparse attention method that improves diffusion transformer efficiency by approximating non-critical attention blocks instead of discarding them, achieving faster processing with maint...

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01077
• PDF: https://arxiv.org/pdf/2602.01077
• Github: https://github.com/xie-lab-ml/piecewise-sparse-attention

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✨OVD: On-policy Verbal Distillation

📝 Summary:
On-policy Verbal Distillation (OVD) enables efficient knowledge transfer from teacher to student models by replacing token-level probability matching with trajectory matching using discrete verbal sco...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21968
• PDF: https://arxiv.org/pdf/2601.21968
• Github: https://OVD.github.io

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✨FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space

📝 Summary:
FSVideo is a fast transformer-based image-to-video diffusion framework that uses a compressed video autoencoder, diffusion transformer architecture with enhanced layer memory, and multi-resolution gen...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02092
• PDF: https://arxiv.org/pdf/2602.02092

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

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✨SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning

📝 Summary:
SPARKLING is a framework for mid-stage width expansion in deep learning models that maintains signal preservation and breaks symmetry to stabilize training and reduce computational costs. AI-generated...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02472
• PDF: https://arxiv.org/pdf/2602.02472

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✨An Empirical Study of World Model Quantization

📝 Summary:
Post-training quantization effects in world models reveal unique failure modes and trade-offs between accuracy, bit-width, and planning performance, particularly in encoder-predictor module asymmetrie...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02110
• PDF: https://arxiv.org/pdf/2602.02110
• Github: https://github.com/huawei-noah/noah-research/tree/master/QuantWM

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✨Evolving from Tool User to Creator via Training-Free Experience Reuse in Multimodal Reasoning

📝 Summary:
A training-free framework enables language model agents to automatically create and optimize tools during inference, improving their reasoning capabilities through self-evolution and memory consolidat...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01983
• PDF: https://arxiv.org/pdf/2602.01983

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✨Influence Guided Sampling for Domain Adaptation of Text Retrievers

📝 Summary:
An reinforcement learning-based sampling framework adaptively reweights training datasets to improve embedding model performance while reducing GPU costs. AI-generated summary General-purpose open-dom...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21759
• PDF: https://arxiv.org/pdf/2601.21759

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✨INDIBATOR: Diverse and Fact-Grounded Individuality for Multi-Agent Debate in Molecular Discovery

📝 Summary:
Multi-agent systems for molecular discovery that use individualized scientist profiles based on publication and molecular history outperform traditional role-based approaches. AI-generated summary Mul...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01815
• PDF: https://arxiv.org/pdf/2602.01815

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✨AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios

📝 Summary:
AgentIF-OneDay is a new benchmark evaluating AI agents on diverse daily tasks using natural language instructions. It assesses problem-solving, attachment understanding, and file-based outputs across three user-centric categories. Benchmarking shows leading agent products and LLM APIs excel in th...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20613
• PDF: https://arxiv.org/pdf/2601.20613

✨ Datasets citing this paper:
• https://huggingface.co/datasets/xbench/AgentIF-OneDay

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