✨No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning

📝 Summary:
ECHO is an RL framework addressing stale critics in LLM agent training. It jointly optimizes policy and critic through a co-evolutionary loop and cascaded rollouts. This ensures synchronized feedback, leading to more stable training and higher task success in open-world environments.

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06794
• PDF: https://arxiv.org/pdf/2601.06794

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#ReinforcementLearning #LLMAgents #MachineLearning #AIResearch #OpenWorldAI

✨Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments

📝 Summary:
Flow Equivariant World Models unify self-motion and external object motion as Lie group flows, enabling stable, symmetry-guided representations. They outperform other models in partially observed environments, particularly for long-term prediction and out-of-view dynamics, leading to data-efficie...

🔹 Publication Date: Published on Jan 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01075
• PDF: https://arxiv.org/pdf/2601.01075
• Project Page: https://flowequivariantworldmodels.github.io/
• Github: https://github.com/hlillemark/flowm

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#WorldModels #Equivariance #MachineLearning #AI #DeepLearning

✨sui-1: Grounded and Verifiable Long-Form Summarization

📝 Summary:
sui-1 is a 24B model producing verifiable abstractive summaries with inline citations. It uses synthetic data training to significantly outperform larger models, showing task-specific training beats scale for grounded summarization.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08472
• PDF: https://arxiv.org/pdf/2601.08472

🔹 Models citing this paper:
• https://huggingface.co/ellamind/sui-1-24b
• https://huggingface.co/ellamind/sui-1-24b-fp8

✨ Spaces citing this paper:
• https://huggingface.co/spaces/ellamind/sui-demo

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG

📝 Summary:
OpenDecoder enhances retrieval-augmented generation by explicitly evaluating retrieved information quality through relevance, ranking, and query performance prediction scores, improving robustness to ...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.09028
• PDF: https://arxiv.org/pdf/2601.09028

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning

📝 Summary:
SCALER is an RL framework for language models that sustains effective training signals in reasoning tasks. It uses adaptive environment design and scalable synthesis of diverse problems to prevent reward sparsity and overfitting, enabling sustained performance improvements.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04809
• PDF: https://arxiv.org/pdf/2601.04809

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research