✨Efficient RLVR Training via Weighted Mutual Information Data Selection

📝 Summary:
InSight is a new data sampling method for RL training that improves efficiency. It considers both data difficulty and epistemic uncertainty, unlike prior methods. This Bayesian modeling approach achieves state-of-the-art performance and significantly accelerates training.

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01907
• PDF: https://arxiv.org/pdf/2603.01907

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#ReinforcementLearning #MachineLearning #DataScience #BayesianModeling #AI

✨ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data

📝 Summary:
ProtegoFed is a new federated instruction tuning framework. It detects and removes widespread poisoned data across clients using frequency domain gradient analysis and collaborative clustering, reducing attack success to almost zero while maintaining utility.

🔹 Publication Date: Published on Feb 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00516
• PDF: https://arxiv.org/pdf/2603.00516
• Project Page: https://github.com/dongdongzhaoUP/ProtegoFed
• Github: https://github.com/dongdongzhaoUP/ProtegoFed

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#FederatedLearning #AIsecurity #DataPoisoning #MachineLearning #AIResearch

✨Using Songs to Improve Kazakh Automatic Speech Recognition

📝 Summary:
This study improves Kazakh ASR for low-resource languages by using songs as a novel data source. Fine-tuning models with song data, especially combined with existing corpora, significantly boosts performance and offers meaningful adaptation gains.

🔹 Publication Date: Published on Mar 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00961
• PDF: https://arxiv.org/pdf/2603.00961

✨ Datasets citing this paper:
• https://huggingface.co/datasets/yeshpanovrustem/kazakh_songs_asr

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#KazakhASR #LowResourceNLP #SpeechRecognition #DataInnovation #MachineLearning

✨Synthetic Visual Genome 2: Extracting Large-scale Spatio-Temporal Scene Graphs from Videos

📝 Summary:
A large video scene graph dataset, SVG2, and a new model, TRaSER, are introduced. TRaSER generates spatio-temporal scene graphs, significantly improving relation, object, and attribute prediction, and boosting video question answering accuracy.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23543
• PDF: https://arxiv.org/pdf/2602.23543
• Project Page: https://uwgzq.github.io/papers/SVG2/

🔹 Models citing this paper:
• https://huggingface.co/UWGZQ/TRASER

✨ Datasets citing this paper:
• https://huggingface.co/datasets/UWGZQ/Synthetic_Visual_Genome2

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

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#VideoSceneGraphs #SpatioTemporal #ComputerVision #VideoQA #DeepLearning

✨PhotoBench: Beyond Visual Matching Towards Personalized Intent-Driven Photo Retrieval

📝 Summary:
PhotoBench introduces a new benchmark for personalized, intent-driven photo retrieval from authentic albums, moving beyond visual matching. It shows current models struggle with non-visual constraints and multi-source fusion, stressing the need for robust agentic reasoning systems.

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01493
• PDF: https://arxiv.org/pdf/2603.01493
• Github: https://github.com/LaVieEnRose365/PhotoBench

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Unified Vision-Language Modeling via Concept Space Alignment

📝 Summary:
V-SONAR extends the text-only SONAR embedding space to support vision-language tasks through post-hoc alignment, enabling zero-shot visual concept understanding and outperforming state-of-the-art mode...

🔹 Publication Date: Published on Mar 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01096
• PDF: https://arxiv.org/pdf/2603.01096

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Classroom Final Exam: An Instructor-Tested Reasoning Benchmark

📝 Summary:
Classroom Final Exam CFE is a multimodal benchmark using authentic university STEM exam problems to assess LLM reasoning. Frontier models achieve only ~60% accuracy, struggling with multi-step solutions and maintaining intermediate states. This highlights significant room for improvement.

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19517
• PDF: https://arxiv.org/pdf/2602.19517
• Project Page: https://analogyai.ai/cfe_bench.html
• Github: https://github.com/Analogy-AI/CFE_Bench

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Cryo-Bench: Benchmarking Foundation Models for Cryosphere Applications

📝 Summary:
Cryo-Bench benchmarks Geo-Foundation Models GFMs for cryosphere tasks, addressing a data gap. It evaluates 14 GFMs, finding they adapt well despite limited pretraining. For optimal results, encoder fine-tuning with hyperparameter optimization is recommended.

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01576
• PDF: https://arxiv.org/pdf/2603.01576
• Github: https://github.com/Sk-2103/Cryo-Bench

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Planning from Observation and Interaction

📝 Summary:
This paper presents a planning-based Inverse Reinforcement Learning algorithm for real-world robot manipulation. It learns effectively from observation and interaction alone, without prior knowledge or pre-training. The approach demonstrates superior sample efficiency and enables online transfer ...

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24121
• PDF: https://arxiv.org/pdf/2602.24121
• Project Page: https://uwrobotlearning.github.io/mpail2/
• Github: https://github.com/UWRobotLearning/mpail2

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction

📝 Summary:
CMI-RewardBench establishes a comprehensive ecosystem for evaluating music reward models under compositional multimodal instruction. It provides large-scale datasets, a unified benchmark for various alignment tasks, and CMI reward models that correlate strongly with human judgments.

🔹 Publication Date: Published on Feb 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00610
• PDF: https://arxiv.org/pdf/2603.00610
• Github: https://github.com/Haiwen-Xia/CMI-RewardBench

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities

📝 Summary:
SteerEval is a new hierarchical benchmark to evaluate large language model controllability across language, sentiment, and personality. It shows that control often degrades at finer-grained levels, providing a framework for safer and more controllable LLM behavior.

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02578
• PDF: https://arxiv.org/pdf/2603.02578

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models

📝 Summary:
Generative Reward Models can be improved by structuring Chain-of-Thought reasoning into breadth and depth components and optimizing them through supervised fine-tuning and reinforcement learning with ...

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01571
• PDF: https://arxiv.org/pdf/2603.01571
• Project Page: https://huggingface.co/collections/DonJoey/mix-grm

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DREAM: Where Visual Understanding Meets Text-to-Image Generation

📝 Summary:
DREAM is a unified multimodal framework that combines visual representation learning and text-to-image generation through progressive masking and semantically aligned decoding, achieving superior perf...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02667
• PDF: https://arxiv.org/pdf/2603.02667
• Github: https://github.com/chaoli-charlie/dream

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨PRISM: Pushing the Frontier of Deep Think via Process Reward Model-Guided Inference

📝 Summary:
PRISM is a Process Reward Model-guided inference algorithm that enhances DEEPTHINK systems by using step-level verification to improve population refinement and solution aggregation, achieving strong ...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02479
• PDF: https://arxiv.org/pdf/2603.02479

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨UniG2U-Bench: Do Unified Models Advance Multimodal Understanding?

📝 Summary:
Unified multimodal models generally underperform specialized VLMs in generation-to-understanding tasks. However, they show consistent enhancements in spatial intelligence, visual illusions, and multi-round reasoning. This highlights the need for diverse training data to unlock their full potential.

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03241
• PDF: https://arxiv.org/pdf/2603.03241
• Project Page: https://nssmd.github.io/unig2u.github.io/
• Github: https://github.com/nssmd/UniG2U

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨NOVA: Sparse Control, Dense Synthesis for Pair-Free Video Editing

📝 Summary:
NOVA is a novel unpaired video editing framework that uses sparse semantic guidance and dense synthesis to achieve high-fidelity editing with improved motion preservation and temporal coherence. AI-ge...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02802
• PDF: https://arxiv.org/pdf/2603.02802
• Github: https://github.com/WeChatCV/NovaEdit

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance

📝 Summary:
A scalable data generation pipeline creates high-fidelity video editing training data, and a unified architecture enables improved instruction-following and reference fidelity in controllable video ed...

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02175
• PDF: https://arxiv.org/pdf/2603.02175
• Project Page: https://showlab.github.io/Kiwi-Edit/
• Github: https://github.com/showlab/Kiwi-Edit

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Kling-MotionControl Technical Report

📝 Summary:
Kling-MotionControl is a DiT-based framework for character animation that combines heterogeneous motion representations, adaptive identity-agnostic learning, and advanced acceleration techniques to ac...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03160
• PDF: https://arxiv.org/pdf/2603.03160

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨HateMirage: An Explainable Multi-Dimensional Dataset for Decoding Faux Hate and Subtle Online Abuse

📝 Summary:
HateMirage is a new dataset designed to advance research on hate speech embedded in misinformation by providing multi-dimensional annotations for target, intent, and implication, offering a benchmark ...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02684
• PDF: https://arxiv.org/pdf/2603.02684
• Github: https://github.com/Sai-Kartheek-Reddy/HateMirage

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SGDC: Structurally-Guided Dynamic Convolution for Medical Image Segmentation

📝 Summary:
Structure-Guided Dynamic Convolution enhances medical image segmentation by using explicit structural guidance to preserve fine-grained details lost through traditional average pooling methods. AI-gen...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23496
• PDF: https://arxiv.org/pdf/2602.23496
• Github: https://github.com/solstice0621/SGDC

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Token Reduction via Local and Global Contexts Optimization for Efficient Video Large Language Models

📝 Summary:
AOT framework reduces video token redundancy through local-global optimal transport to preserve informative contexts while achieving efficient spatiotemporal compression in video large language models...

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01400
• PDF: https://arxiv.org/pdf/2603.01400
• Project Page: https://tyroneli.github.io/AOT/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research