✨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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✨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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✨Rethinking Video Generation Model for the Embodied World

📝 Summary:
A comprehensive robotics benchmark evaluates video generation models across multiple task domains and embodiments, revealing deficiencies in physical realism and introducing a large-scale dataset to a...

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15282
• PDF: https://arxiv.org/pdf/2601.15282
• Project Page: https://dagroup-pku.github.io/ReVidgen.github.io/
• Github: https://github.com/DAGroup-PKU/ReVidgen/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/DAGroup-PKU/RBench
• https://huggingface.co/datasets/DAGroup-PKU/RoVid-X

✨ Spaces citing this paper:
• https://huggingface.co/spaces/DAGroup-PKU/RBench-Leaderboard

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

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✨FARE: Fast-Slow Agentic Robotic Exploration

📝 Summary:
FARE is a hierarchical exploration framework that combines large language model reasoning with reinforcement learning control to enable efficient autonomous robot navigation in complex environments. A...

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14681
• PDF: https://arxiv.org/pdf/2601.14681

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

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#AI #DataScience #MachineLearning #HuggingFace #Research