✨A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Doubao 1.8, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

📝 Summary:
This report evaluated 7 frontier AI models for safety across language, vision-language, and image generation. It found varied safety performance, with GPT-5.2 consistently strong. All models showed significant vulnerability to adversarial attacks, highlighting the multidimensional nature of AI sa...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10527
• PDF: https://arxiv.org/pdf/2601.10527

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✨Think-Then-Generate: Reasoning-Aware Text-to-Image Diffusion with LLM Encoders

📝 Summary:
Text-to-image diffusion models enhanced with language model reasoning capabilities achieve improved factual consistency and semantic alignment through a think-then-generate paradigm with dual-gradient...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10332
• PDF: https://arxiv.org/pdf/2601.10332
• Project Page: https://zhijie-group.github.io/Think-Then-Generate/
• Github: https://github.com/zhijie-group/Think-Then-Generate

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✨Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding

📝 Summary:
Molmo2 is a new open-source video-language model family that achieves state-of-the-art performance through novel datasets and training methods, particularly excelling in video grounding tasks without ...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10611
• PDF: https://arxiv.org/pdf/2601.10611

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✨Inference-time Physics Alignment of Video Generative Models with Latent World Models

📝 Summary:
Latent world models enhance video generation physics plausibility through inference-time alignment and trajectory steering, achieving superior performance in challenging benchmarks. AI-generated summa...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10553
• PDF: https://arxiv.org/pdf/2601.10553

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✨DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset

📝 Summary:
A large-scale Chinese image-text dataset called DanQing is introduced to advance vision-language pretraining, demonstrating superior performance in various downstream tasks through continual pretraini...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10305
• PDF: https://arxiv.org/pdf/2601.10305
• Project Page: https://deepglint.github.io/DanQing/
• Github: https://github.com/deepglint/DanQing

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✨CoF-T2I: Video Models as Pure Visual Reasoners for Text-to-Image Generation

📝 Summary:
Chain-of-Frame reasoning is integrated into text-to-image generation through progressive visual refinement with explicit intermediate steps, achieving superior performance on benchmark datasets. AI-ge...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2601.10061
• PDF: https://arxiv.org/pdf/2601.10061
• Project Page: https://cof-t2i.github.io/
• Github: https://github.com/VisionChengzhuo/CoF-T2I

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✨MatchTIR: Fine-Grained Supervision for Tool-Integrated Reasoning via Bipartite Matching

📝 Summary:
MatchTIR enhances LLM reasoning by introducing fine-grained credit assignment through bipartite matching and dual-level advantage estimation for tool-integrated tasks. AI-generated summary Tool-Integr...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10712
• PDF: https://arxiv.org/pdf/2601.10712
• Project Page: https://huggingface.co/collections/ChangleQu/matchtir
• Github: https://github.com/quchangle1/MatchTIR

🔹 Models citing this paper:
• https://huggingface.co/ChangleQu/Qwen3-8B-MatchTIR-KM
• https://huggingface.co/ChangleQu/Qwen3-8B-MatchTIR-OT

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✨FlowAct-R1: Towards Interactive Humanoid Video Generation

📝 Summary:
FlowAct-R1 enables real-time interactive humanoid video generation with high-fidelity synthesis and low-latency responsiveness through MMDiT architecture and chunkwise diffusion forcing strategies. AI...

🔹 Publication Date: Published on Jan 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.10103
• PDF: https://arxiv.org/pdf/2601.10103

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✨Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs

📝 Summary:
Reinforcement learning for large language models is enhanced by a rollout-level objective that rewards rare high-level reasoning strategies, improving diverse solution discovery without sacrificing in...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08763
• PDF: https://arxiv.org/pdf/2601.08763

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✨Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning

📝 Summary:
Multi-Agent Test-Time Reinforcement Learning (MATTRL) enhances multi-agent reasoning through structured textual experience injection and consensus-based decision making at inference time. AI-generated...

🔹 Publication Date: Published on Jan 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09667
• PDF: https://arxiv.org/pdf/2601.09667

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