✨LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR

📝 Summary:
LightOnOCR-2-1B is a 1B-parameter end-to-end multilingual vision-language model for OCR. It converts document images to text, achieving state-of-the-art results while being smaller and faster. It also features improved image localization and robustness.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14251
• PDF: https://arxiv.org/pdf/2601.14251

🔹 Models citing this paper:
• https://huggingface.co/lightonai/LightOnOCR-1B-1025
• https://huggingface.co/lightonai/LightOnOCR-2-1B
• https://huggingface.co/lightonai/LightOnOCR-0.9B-32k-1025

✨ Datasets citing this paper:
• https://huggingface.co/datasets/lightonai/LightOnOCR-mix-0126
• https://huggingface.co/datasets/lightonai/LightOnOCR-bbox-mix-0126

✨ Spaces citing this paper:
• https://huggingface.co/spaces/lightonai/LightOnOCR-2-1B-Demo
• https://huggingface.co/spaces/lightonai/LightOnOCR-1B-Demo
• https://huggingface.co/spaces/lightonai/LightOnOCR-1B-Demo-zero

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

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#OCR #VisionLanguageModel #AI #DeepLearning #MultilingualAI

✨KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning

📝 Summary:
KAGE-Bench introduces KAGE-Env, a fast JAX 2D platformer that isolates visual shifts to systematically study RL generalization. It reveals strong failures for agents facing background or photometric changes, but less impact from agent appearance shifts.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14232
• PDF: https://arxiv.org/pdf/2601.14232
• Github: https://avanturist322.github.io/KAGEBench/

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

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#ReinforcementLearning #RLGeneralization #MachineLearning #ComputerVision #AIResearch

✨FantasyVLN: Unified Multimodal Chain-of-Thought Reasoning for Vision-Language Navigation

📝 Summary:
FantasyVLN proposes an implicit reasoning framework for vision-language navigation, overcoming the real-time issues of explicit Chain-of-Thought methods. It encodes imagined visual observations into a compact latent space during training. This enables real-time, reasoning-aware navigation with im...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13976
• PDF: https://arxiv.org/pdf/2601.13976
• Project Page: https://fantasy-amap.github.io/fantasy-vln/
• Github: https://fantasy-amap.github.io/fantasy-vln/

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

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#VisionLanguageNavigation #ChainOfThought #MultimodalAI #EmbodiedAI #AIResearch

✨METIS: Mentoring Engine for Thoughtful Inquiry & Solutions

📝 Summary:
METIS is an AI mentor for undergraduate research writing, outperforming GPT-5 and Claude Sonnet 4.5. It yields higher student scores and better document-grounded outputs, despite minor tool routing challenges.

🔹 Publication Date: Published on Jan 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13075
• PDF: https://arxiv.org/pdf/2601.13075

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

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#AI #EdTech #LLM #ResearchWriting #Mentoring

✨Ark: An Open-source Python-based Framework for Robot Learning

📝 Summary:
ARK is a Python-first, open-source framework simplifying robotics development by integrating modern imitation learning and seamless simulation-to-physical robot interactions. It provides a Gym-style interface and reusable modules, lowering entry barriers for autonomous robot deployment.

🔹 Publication Date: Published on Jun 24, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.21628
• PDF: https://arxiv.org/pdf/2506.21628
• Project Page: https://robotics-ark.github.io/ark_robotics.github.io/
• Github: https://github.com/orgs/Robotics-Ark/repositories

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging

📝 Summary:
Class-stratified Scheduled Power Predictive Entropy (ClaSP PE) is a novel active learning strategy that improves 3D biomedical image segmentation by addressing class imbalance and selection redundancy...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13677
• PDF: https://arxiv.org/pdf/2601.13677
• Github: https://github.com/MIC-DKFZ/nnActive/tree/nnActive_v2

🔹 Models citing this paper:
• https://huggingface.co/nnActive/Liver
• https://huggingface.co/nnActive/ToothFairy2_All
• https://huggingface.co/nnActive/word

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment

📝 Summary:
Researchers developed the Rank-Surprisal Ratio RSR metric to better select reasoning trajectories for teaching student LLMs. RSR balances alignment and informativeness, strongly correlating with improved student performance and outperforming prior methods in distillation.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14249
• PDF: https://arxiv.org/pdf/2601.14249
• Github: https://github.com/UmeanNever/RankSurprisalRatio

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning

📝 Summary:
GRADFILTERING is an uncertainty-aware data selection framework for instruction tuning that uses gradient signal-to-noise ratio to improve LLM adaptation efficiency and performance. AI-generated summar...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13697
• PDF: https://arxiv.org/pdf/2601.13697

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models

📝 Summary:
This survey presents 'Locate, Steer, and Improve' as an actionable framework for mechanistic interpretability in LLMs. It shifts MI from an observational science to a systematic methodology for optimizing LLMs, leading to tangible improvements in their alignment, capability, and efficiency.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14004
• PDF: https://arxiv.org/pdf/2601.14004

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

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#LLM #MechanisticInterpretability #AI #AIAalignment #MachineLearning

✨InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning

📝 Summary:
Intervention Training improves large language model reasoning by enabling fine-grained credit assignment through targeted corrections that localize errors and enhance reinforcement learning performanc...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14209
• PDF: https://arxiv.org/pdf/2601.14209

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems

📝 Summary:
A comprehensive benchmark for evaluating LLM-based data agents across diverse data science tasks demonstrates superior performance for multimodal agents while highlighting persistent challenges in uns...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13591
• PDF: https://arxiv.org/pdf/2601.13591

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection

📝 Summary:
RemoteVAR is a visual autoregressive framework for remote sensing change detection that improves upon existing methods through multi-resolution feature fusion and autoregressive training tailored for ...

🔹 Publication Date: Published on Jan 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11898
• PDF: https://arxiv.org/pdf/2601.11898
• Github: https://github.com/yilmazkorkmaz1/RemoteVAR

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution

📝 Summary:
DARC is a two-stage framework stabilizing LLM self-play by decoupling question generation and using asymmetric self-distillation. This mitigates instability and bootstrapping errors, significantly improving reasoning performance across benchmarks without human annotations.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13761
• PDF: https://arxiv.org/pdf/2601.13761
• Github: https://github.com/RUCBM/DARC

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement

📝 Summary:
APOLO framework uses automated prompt optimization through multi-agent collaboration to improve emotion diagnosis accuracy and robustness in mental healthcare applications. AI-generated summary Lingui...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13481
• PDF: https://arxiv.org/pdf/2601.13481

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Agentic Reasoning for Large Language Models

📝 Summary:
Agentic reasoning redefines LLMs as autonomous agents that plan, act, and learn through continuous interaction in dynamic environments. This survey organizes agentic reasoning by environmental dynamics, from single-agent capabilities to multi-agent collaboration, bridging thought and action throu...

🔹 Publication Date: Published on Jan 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.12538
• PDF: https://arxiv.org/pdf/2601.12538
• Github: https://github.com/weitianxin/Awesome-Agentic-Reasoning

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning

📝 Summary:
Render-of-Thought framework converts textual reasoning steps into images using vision-language models to improve reasoning traceability and efficiency while maintaining competitive performance. AI-gen...

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14750
• PDF: https://arxiv.org/pdf/2601.14750

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models

📝 Summary:
Research reveals that causal attention in language models creates information bottlenecks when question-answer options follow context, leading to performance drops of over 14 percentage points compare...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14152
• PDF: https://arxiv.org/pdf/2601.14152

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance

📝 Summary:
RebuttalAgent is a multi-agent framework that reframes rebuttal generation as an evidence-centric planning task, improving coverage, faithfulness, and strategic coherence in academic peer review. AI-g...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14171
• PDF: https://arxiv.org/pdf/2601.14171
• Project Page: https://mqleet.github.io/Paper2Rebuttal_ProjectPage/
• Github: https://github.com/AutoLab-SAI-SJTU/Paper2Rebuttal

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨XR: Cross-Modal Agents for Composed Image Retrieval

📝 Summary:
A multi-agent framework for compositional image retrieval that uses specialized agents for generation, filtering, and verification to improve semantic and visual query matching. AI-generated summary R...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14245
• PDF: https://arxiv.org/pdf/2601.14245
• Github: https://01yzzyu.github.io/xr.github.io/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Facilitating Proactive and Reactive Guidance for Decision Making on the Web: A Design Probe with WebSeek

📝 Summary:
WebSeek is a mixed-initiative browser extension that enables interactive web data extraction and analysis with AI-assisted guidance and automation. AI-generated summary Web AI agents such as ChatGPT A...

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15100
• PDF: https://arxiv.org/pdf/2601.15100

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

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#AI #DataScience #MachineLearning #HuggingFace #Research