✨One View Is Enough! Monocular Training for In-the-Wild Novel View Generation

📝 Summary:
OVIE enables monocular novel-view synthesis from single images by generating pseudo-target views via a geometric scaffold. This eliminates the need for multi-view supervision, allowing training on massive unpaired datasets. OVIE achieves superior zero-shot performance and is significantly faster ...

🔹 Publication Date: Published on Mar 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23488
• PDF: https://arxiv.org/pdf/2603.23488
• Github: https://github.com/AdrienRR/ovie

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#NovelViewSynthesis #MonocularVision #ComputerVision #DeepLearning #3DVision

✨Fair splits flip the leaderboard: CHANRG reveals limited generalization in RNA secondary-structure prediction

📝 Summary:
The CHANRG benchmark reveals RNA foundation models achieve high held-out accuracy but lose significant robustness out-of-distribution. This new benchmark provides a stricter framework for evaluating RNA secondary structure prediction.

🔹 Publication Date: Published on Mar 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22330
• PDF: https://arxiv.org/pdf/2603.22330
• Project Page: https://huggingface.co/datasets/multimolecule/chanrg
• Github: https://github.com/MultiMolecule/multimolecule

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#RNAstructure #MachineLearning #FoundationModels #Bioinformatics #ModelRobustness

✨CanViT: Toward Active-Vision Foundation Models

📝 Summary:
CanViT represents the first task- and policy-agnostic Active-Vision Foundation Model that efficiently processes visual scenes through sequential glimpses using a retinotopic Vision Transformer backbon...

🔹 Publication Date: Published on Mar 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22570
• PDF: https://arxiv.org/pdf/2603.22570
• Github: https://github.com/m2b3/CanViT-PyTorch

🔹 Models citing this paper:
• https://huggingface.co/canvit/canvitb16-add-vpe-pretrain-g128px-s512px-in21k-dv3b16-2026-02-02

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Abstraction as a Memory-Efficient Inductive Bias for Continual Learning

📝 Summary:
Abstraction-Augmented Training AAT improves continual learning by jointly optimizing concrete and abstract representations. This memory-efficient method captures latent structures, eliminating replay buffers. AAT performs comparably to experience replay with zero extra memory.

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17198
• PDF: https://arxiv.org/pdf/2603.17198

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Abstraction as a Memory-Efficient Inductive Bias for Continual Learning

📝 Summary:
Abstraction-Augmented Training AAT improves continual learning by jointly optimizing concrete and abstract representations. This memory-efficient method captures latent structures, eliminating replay buffers. AAT performs comparably to experience replay with zero extra memory.

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17198
• PDF: https://arxiv.org/pdf/2603.17198

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents

📝 Summary:
A comprehensive benchmark evaluates enterprise data agents' ability to integrate and analyze multi-database data through natural language, revealing significant challenges in real-world applications. ...

🔹 Publication Date: Published on Mar 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.20576
• PDF: https://arxiv.org/pdf/2603.20576
• Project Page: https://ucbepic.github.io/DataAgentBench/
• Github: https://github.com/ucbepic/DataAgentBench

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨SHAMISA: SHAped Modeling of Implicit Structural Associations for Self-supervised No-Reference Image Quality Assessment

📝 Summary:
SHAMISA is a self-supervised NR-IQA framework learning from unlabeled distorted images. It uses implicit structural associations and a compositional distortion engine to group images for training, achieving strong performance and generalization without human labels or contrastive losses.

🔹 Publication Date: Published on Mar 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13669
• PDF: https://arxiv.org/pdf/2603.13669
• Github: https://github.com/Mahdi-Naseri/SHAMISA

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨CarePilot: A Multi-Agent Framework for Long-Horizon Computer Task Automation in Healthcare

📝 Summary:
CarePilot is a multi-agent framework that uses actor-critic methods and dual-memory to automate complex, long-horizon tasks in healthcare. It addresses the limitations of existing models on the new CareFlow benchmark. CarePilot achieves state-of-the-art performance.

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24157
• PDF: https://arxiv.org/pdf/2603.24157
• Project Page: https://akashghosh.github.io/Care-Pilot/
• Github: https://github.com/AkashGhosh/CarePilot

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Can LLM Agents Be CFOs? A Benchmark for Resource Allocation in Dynamic Enterprise Environments

📝 Summary:
EnterpriseArena benchmark evaluates large language models on long-horizon enterprise resource allocation, revealing significant challenges in sustained decision-making under uncertainty. AI-generated ...

🔹 Publication Date: Published on Mar 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23638
• PDF: https://arxiv.org/pdf/2603.23638

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents

📝 Summary:
GameplayQA is a framework evaluating multimodal LLMs in 3D multi-agent environments using densely annotated gameplay videos and diagnostic QA. It reveals a significant performance gap between current MLLMs and humans, particularly in temporal grounding and agent attribution. This emphasizes the n...

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24329
• PDF: https://arxiv.org/pdf/2603.24329
• Project Page: https://hats-ict.github.io/gameplayqa/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/wangyz1999/GameplayQA

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨When Models Judge Themselves: Unsupervised Self-Evolution for Multimodal Reasoning

📝 Summary:
This paper proposes an unsupervised self-evolution framework for multimodal reasoning. It uses self-consistency and group-relative policy optimization to improve performance without labeled data or external models. This method consistently improves reasoning, offering a scalable path for self-evo...

🔹 Publication Date: Published on Mar 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.21289
• PDF: https://arxiv.org/pdf/2603.21289
• Project Page: https://dingwu1021.github.io/SelfJudge/
• Github: https://github.com/OPPO-Mente-Lab/LLM-Self-Judge

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Toward Physically Consistent Driving Video World Models under Challenging Trajectories

📝 Summary:
PhyGenesis is a world model that generates high-fidelity driving videos with physical consistency by transforming invalid trajectories into plausible conditions and using a physics-enhanced video gene...

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24506
• PDF: https://arxiv.org/pdf/2603.24506

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience

📝 Summary:
A two-stage self-evolving mobile GUI agent named UI-Voyager is proposed, featuring rejection fine-tuning and group relative self-distillation to improve efficiency and performance in GUI automation ta...

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24533
• PDF: https://arxiv.org/pdf/2603.24533
• Github: https://github.com/ui-voyager/UI-Voyager

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨OmniWeaving: Towards Unified Video Generation with Free-form Composition and Reasoning

📝 Summary:
OmniWeaving is an open-source video generation model that unifies multimodal inputs and complex reasoning capabilities through large-scale pretraining and intelligent agent inference. AI-generated sum...

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24458
• PDF: https://arxiv.org/pdf/2603.24458
• Project Page: https://omniweaving.github.io/

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨CUA-Suite: Massive Human-annotated Video Demonstrations for Computer-Use Agents

📝 Summary:
CUA-Suite introduces a large-scale ecosystem of expert video demonstrations and annotations for computer-use agents, providing continuous screen recordings and detailed reasoning annotations to advanc...

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24440
• PDF: https://arxiv.org/pdf/2603.24440

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨4DGS360: 360° Gaussian Reconstruction of Dynamic Objects from a Single Video

📝 Summary:
4DGS360 presents a diffusion-free approach for 360° dynamic object reconstruction using 3D-native initialization and a 3D tracker called AnchorTAP3D to improve geometric consistency and handle occlusi...

🔹 Publication Date: Published on Mar 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.21618
• PDF: https://arxiv.org/pdf/2603.21618
• Project Page: https://jaewon040.github.io/4dgs360/

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?

📝 Summary:
Self-distillation in large language models can degrade mathematical reasoning performance by suppressing uncertainty expression, particularly affecting out-of-distribution tasks. AI-generated summary ...

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24472
• PDF: https://arxiv.org/pdf/2603.24472
• Project Page: https://beanie00.notion.site/why-does-self-distillation-degrade-reasoning
• Github: https://github.com/beanie00/self-distillation-analysis

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search

📝 Summary:
T-MAP, a trajectory-aware evolutionary search method, discovers adversarial prompts that bypass safety measures and achieve harmful outcomes through tool interactions in LLM agents. AI-generated summa...

🔹 Publication Date: Published on Mar 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22341
• PDF: https://arxiv.org/pdf/2603.22341
• Github: https://github.com/pwnhyo/T-MAP

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨UniFunc3D: Unified Active Spatial-Temporal Grounding for 3D Functionality Segmentation

📝 Summary:
UniFunc3D enables 3D scene functionality segmentation by treating multimodal large language models as active observers that perform joint semantic, temporal, and spatial reasoning through adaptive fra...

🔹 Publication Date: Published on Mar 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23478
• PDF: https://arxiv.org/pdf/2603.23478
• Project Page: https://jiaying.link/unifunc3d/

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨EVA: Efficient Reinforcement Learning for End-to-End Video Agent

📝 Summary:
EVA is an RL framework enabling efficient, adaptive video understanding by autonomously deciding what and how to watch. It uses iterative planning to handle long video sequences. EVA significantly outperforms existing MLLM and adaptive agent methods on multiple video benchmarks.

🔹 Publication Date: Published on Mar 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22918
• PDF: https://arxiv.org/pdf/2603.22918
• Project Page: https://huggingface.co/WRHC/EfficientVideoAgent/
• Github: https://github.com/wangruohui/EfficientVideoAgent

🔹 Models citing this paper:
• https://huggingface.co/WRHC/EfficientVideoAgent

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research

✨PLDR-LLMs Reason At Self-Organized Criticality

📝 Summary:
PLDR-LLMs exhibit reasoning capabilities at self-organized criticality through metastable steady states that mirror second-order phase transitions, enabling generalization without benchmark evaluation...

🔹 Publication Date: Published on Mar 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23539
• PDF: https://arxiv.org/pdf/2603.23539
• Project Page: https://huggingface.co/fromthesky
• Github: https://github.com/burcgokden/PLDR-LLM-Self-Organized-Criticality

🔹 Models citing this paper:
• https://huggingface.co/fromthesky/PLDR-LLM-v51-SOC-110M-1
• https://huggingface.co/fromthesky/PLDR-LLM-v51-SOC-110M-2
• https://huggingface.co/fromthesky/PLDR-LLM-v51-SOC-110M-3

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AI #DataScience #MachineLearning #HuggingFace #Research