✨TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration

📝 Summary:
TCAndon-Router TCAR is an adaptive reasoning router for multi-agent systems. It overcomes limitations of existing task routers by supporting dynamic agent onboarding and generating natural language reasoning chains to select agents. TCAR significantly improves routing accuracy, reduces conflicts,...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04544
• PDF: https://arxiv.org/pdf/2601.04544
• Github: https://github.com/Tencent/TCAndon-Router

🔹 Models citing this paper:
• https://huggingface.co/tencent/TCAndon-Router

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

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#MultiAgentSystems #AI #NLP #AdaptiveSystems #AIResearch

✨NitroGen: An Open Foundation Model for Generalist Gaming Agents

📝 Summary:
NitroGen is a vision-action foundation model trained on extensive gameplay data that demonstrates strong cross-game generalization and effective transfer learning capabilities. AI-generated summary We...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02427
• PDF: https://arxiv.org/pdf/2601.02427
• Project Page: https://nitrogen.minedojo.org/
• Github: https://github.com/MineDojo/NitroGen

🔹 Models citing this paper:
• https://huggingface.co/nvidia/NitroGen

✨ Datasets citing this paper:
• https://huggingface.co/datasets/nvidia/NitroGen

✨ Spaces citing this paper:
• https://huggingface.co/spaces/dennny123/NitroGen-SuperstarSaga
• https://huggingface.co/spaces/blanchon/NitroGen-Pokemon

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

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✨Plenoptic Video Generation

📝 Summary:
PlenopticDreamer enables consistent multi-view video re-rendering through synchronized generative hallucinations, leveraging camera-guided retrieval and progressive training mechanisms for improved te...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05239
• PDF: https://arxiv.org/pdf/2601.05239
• Project Page: https://research.nvidia.com/labs/dir/plenopticdreamer/

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

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✨NitroGen: An Open Foundation Model for Generalist Gaming Agents

📝 Summary:
NitroGen is a vision-action foundation model trained on extensive gameplay data that demonstrates strong cross-game generalization and effective transfer learning capabilities. AI-generated summary We...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02427
• PDF: https://arxiv.org/pdf/2601.02427
• Project Page: https://nitrogen.minedojo.org/
• Github: https://github.com/MineDojo/NitroGen

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨ViTNT-FIQA: Training-Free Face Image Quality Assessment with Vision Transformers

📝 Summary:
ViTNT-FIQA is a training-free method for face image quality assessment using Vision Transformers. It measures the stability of patch embeddings across intermediate blocks with a single forward pass. High-quality images show stable feature evolution, achieving competitive results efficiently.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05741
• PDF: https://arxiv.org/pdf/2601.05741
• Github: https://github.com/gurayozgur/ViTNT-FIQA

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

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#VisionTransformers #FaceQuality #ComputerVision #DeepLearning #AI

✨Afri-MCQA: Multimodal Cultural Question Answering for African Languages

📝 Summary:
Afri-MCQA is the first multimodal cultural QA benchmark for 15 African languages. It shows open-weight LLMs perform poorly, particularly with native language speech and cultural contexts. This highlights the need for speech-first, culturally grounded AI development.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05699
• PDF: https://arxiv.org/pdf/2601.05699

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Atnafu/Afri-MCQA

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

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#AfricanLanguages #MultimodalAI #LLMs #CulturalAI #SpeechAI

✨Legal Alignment for Safe and Ethical AI

📝 Summary:
Legal alignment explores leveraging legal principles and methods to guide AI system design for safety, ethics, and compliance. This field focuses on AI compliance with legal rules, adapting legal interpretation for AI reasoning, and using legal concepts as a blueprint for AI reliability and trust.

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04175
• PDF: https://arxiv.org/pdf/2601.04175

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

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#EthicalAI #LegalAI #AIRegulation #ResponsibleAI #AISafety

✨An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift

📝 Summary:
Preference tuning performance degrades under domain shift. This study found pseudo-labeling adaptation strategies effectively reduce performance degradation in summarization and question-answering tasks across various alignment objectives.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05882
• PDF: https://arxiv.org/pdf/2601.05882
• Github: https://github.com/ckarouzos/prefadap

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

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#PreferenceTuning #DomainAdaptation #NLP #MachineLearning #AIResearch

✨The Persona Paradox: Medical Personas as Behavioral Priors in Clinical Language Models

📝 Summary:
Medical personas in clinical language models show context-dependent effects, improving performance in critical care but degrading it in primary care. They act as behavioral priors, introducing trade-offs rather than guaranteeing expertise or safety.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05376
• PDF: https://arxiv.org/pdf/2601.05376

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨TowerMind: A Tower Defence Game Learning Environment and Benchmark for LLM as Agents

📝 Summary:
TowerMind is a new low-computation tower defense environment for evaluating large language model planning and decision-making with multimodal observations. Experiments show a performance gap between large language models and humans, revealing limitations in model planning and action use.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05899
• PDF: https://arxiv.org/pdf/2601.05899
• Github: https://github.com/tb6147877/TowerMind

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

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✨SAM 3D: 3Dfy Anything in Images

📝 Summary:
SAM 3D reconstructs 3D objects from single images, predicting geometry, texture, and layout. It uses a multi-stage training framework combining synthetic pretraining and real-world alignment, overcoming the 3D data barrier. It achieves significant gains in human preference tests.

🔹 Publication Date: Published on Nov 20, 2025

🔹 Paper Links:
• arXiv Page: https://arxivlens.com/PaperView/Details/sam-3d-3dfy-anything-in-images-9667-03d581e7
• PDF: https://arxiv.org/pdf/2511.16624
• Project Page: https://ai.meta.com/sam3d/
• Github: https://github.com/facebookresearch/sam-3d-objects

🔹 Models citing this paper:
• https://huggingface.co/facebook/sam-3d-objects
• https://huggingface.co/jetjodh/sam-3d-objects
• https://huggingface.co/RunyiY/d3mas

✨ Spaces citing this paper:
• https://huggingface.co/spaces/HorizonRobotics/EmbodiedGen-Text-to-3D
• https://huggingface.co/spaces/HorizonRobotics/EmbodiedGen-Image-to-3D
• https://huggingface.co/spaces/HorizonRobotics/EmbodiedGen-Texture-Gen

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

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✨What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models

📝 Summary:
Current VLMs struggle with real-world underspecified queries. A new benchmark reveals explicit query rewriting improves performance by 8-22 points across models. This gap stems from natural query under-specification, not merely model capability.

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06165
• PDF: https://arxiv.org/pdf/2601.06165

✨ Datasets citing this paper:
• https://huggingface.co/datasets/HAERAE-HUB/HAERAE-VISION

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨TourPlanner: A Competitive Consensus Framework with Constraint-Gated Reinforcement Learning for Travel Planning

📝 Summary:
TourPlanner addresses travel planning challenges through multi-path reasoning and constraint-gated reinforcement learning to optimize both hard and soft constraints effectively. AI-generated summary T...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04698
• PDF: https://arxiv.org/pdf/2601.04698

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

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✨AI-Researcher: Autonomous Scientific Innovation

📝 Summary:
AI-Researcher automates the scientific research process, achieving high implementation success and manuscript quality through a comprehensive benchmark system. AI-generated summary The powerful reason...

🔹 Publication Date: Published on May 24, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.18705
• PDF: https://arxiv.org/pdf/2505.18705
• Github: https://github.com/hkuds/ai-researcher

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

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✨PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning

📝 Summary:
Parallel Coordinated Reasoning enables large-scale test-time compute scaling beyond sequential reasoning limitations through parallel exploration and message-passing architecture. AI-generated summary...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05593
• PDF: https://arxiv.org/pdf/2601.05593
• Github: https://github.com/stepfun-ai/PaCoRe

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

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✨Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning

📝 Summary:
VideoDR benchmark enables video question answering by combining cross-frame visual extraction, web retrieval, and multi-hop reasoning in open-domain settings. AI-generated summary In real-world video ...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06943
• PDF: https://arxiv.org/pdf/2601.06943
• Github: https://github.com/QuantaAlpha/VideoDR-Benchmark

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

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✨Boosting Latent Diffusion Models via Disentangled Representation Alignment

📝 Summary:
Latent Diffusion Models generate high-quality images by operating in compressed latent space, typically obtained through image tokenizers such as Variational Autoencoders (VAEs). In pursuit of a gener...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05823
• PDF: https://arxiv.org/pdf/2601.05823
• Github: https://github.com/Kwai-Kolors/Send-VAE

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

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✨ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration

📝 Summary:
ET-Agent is a training framework that calibrates tool-use behavior in large language models through self-evolving data flywheels and behavior calibration training to improve task execution effectivene...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06860
• PDF: https://arxiv.org/pdf/2601.06860

🔹 Models citing this paper:
• https://huggingface.co/zhangboguodong/ET-Agent-based-on-Qwen2.5-7B-it

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

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✨Structured Episodic Event Memory

📝 Summary:
Structured Episodic Event Memory (SEEM) enhances LLMs with hierarchical memory architecture combining graph and episodic layers for improved narrative coherence and reasoning. AI-generated summary Cur...

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06411
• PDF: https://arxiv.org/pdf/2601.06411

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

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✨Lost in the Noise: How Reasoning Models Fail with Contextual Distractors

📝 Summary:
NoisyBench benchmark reveals significant performance degradation in state-of-the-art models when exposed to noisy contextual information, with agentic workflows amplifying errors and attention mechani...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07226
• PDF: https://arxiv.org/pdf/2601.07226

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

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#AI #DataScience #MachineLearning #HuggingFace #Research