✨Tool Verification for Test-Time Reinforcement Learning

📝 Summary:
Test-time reinforcement learning with tool verification addresses consensus bias in large reasoning models by using external validation to improve reward estimation and model stability. AI-generated s...

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02203
• PDF: https://arxiv.org/pdf/2603.02203

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨ArtLLM: Generating Articulated Assets via 3D LLM

📝 Summary:
ArtLLM generates articulated 3D assets from meshes using a 3D multimodal large language model that predicts part layouts and joints while synthesizing high-fidelity geometries. AI-generated summary Cr...

🔹 Publication Date: Published on Mar 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01142
• PDF: https://arxiv.org/pdf/2603.01142

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨MicroVerse: A Preliminary Exploration Toward a Micro-World Simulation

📝 Summary:
Current video generation models struggle with microscale simulation tasks, prompting the development of MicroVerse, a specialized video generation model trained on expert-verified simulation data to a...

🔹 Publication Date: Published on Feb 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00585
• PDF: https://arxiv.org/pdf/2603.00585

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨Recursive Think-Answer Process for LLMs and VLMs

📝 Summary:
Recursive Think-Answer Process enables iterative reasoning cycles that improve accuracy and reduce self-reflective errors in language and vision-language models through confidence-based reinforcement ...

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02099
• PDF: https://arxiv.org/pdf/2603.02099
• Project Page: https://litcoderr.github.io/rtap_page/

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨From Scale to Speed: Adaptive Test-Time Scaling for Image Editing

📝 Summary:
Image-CoT methods are extended to image editing with ADE-CoT, which improves efficiency and performance through adaptive resource allocation, edit-specific verification, and opportunistic stopping mec...

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00141
• PDF: https://arxiv.org/pdf/2603.00141

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning

📝 Summary:
A synthetic reasoning dataset called CHIMERA is introduced to overcome data-centric challenges in training large language models for cross-domain reasoning, achieving performance comparable to much la...

🔹 Publication Date: Published on Mar 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00889
• PDF: https://arxiv.org/pdf/2603.00889
• Project Page: https://huggingface.co/datasets/TianHongZXY/CHIMERA

✨ Datasets citing this paper:
• https://huggingface.co/datasets/TianHongZXY/CHIMERA

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation

📝 Summary:
SeeThrough3D generates 3D layout-conditioned scenes with explicit occlusion modeling using translucent 3D boxes and visual tokens derived from rendered representations. AI-generated summary We identif...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23359
• PDF: https://arxiv.org/pdf/2602.23359

🔹 Models citing this paper:
• https://huggingface.co/va1bhavagrawa1/seethrough3d

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research

✨WorldStereo: Bridging Camera-Guided Video Generation and Scene Reconstruction via 3D Geometric Memories

📝 Summary:
WorldStereo integrates camera-guided video generation and 3D reconstruction using geometric memory modules. These provide camera control and structural priors for multi-view consistent videos, enabling high-quality 3D scene reconstruction.

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02049
• PDF: https://arxiv.org/pdf/2603.02049
• Project Page: https://3d.hunyuan.tencent.com/sceneTo3D
• Github: https://github.com/FuchengSu/WorldStereo

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

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

#VideoGeneration #3DReconstruction #ComputerVision #DeepLearning #NeuralRendering

✨Reasoning Core: A Scalable Procedural Data Generation Suite for Symbolic Pre-training and Post-Training

📝 Summary:
Reasoning Core is a new scalable system that procedurally generates verifiable symbolic reasoning data across diverse formal domains. Mixing this data into pre-training improves language model reasoning abilities while preserving language modeling quality.

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.02208
• PDF: https://arxiv.org/pdf/2603.02208
• Project Page: https://github.com/sileod/reasoning_core/
• Github: https://github.com/sileod/reasoning_core

✨ Datasets citing this paper:
• https://huggingface.co/datasets/reasoning-core/symbolic-pretraining-pile
• https://huggingface.co/datasets/reasoning-core/symbolic-reasoning-env

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

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

#AI #LLM #SymbolicReasoning #DataGeneration #MachineLearning

✨Spectral Attention Steering for Prompt Highlighting

📝 Summary:
SEKA and AdaSEKA introduce training-free attention steering by editing key embeddings using spectral decomposition. This amplifies attention for specific tokens, outperforming baselines with less memory and latency, compatible with optimized attention.

🔹 Publication Date: Published on Mar 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01281
• PDF: https://arxiv.org/pdf/2603.01281
• Github: https://github.com/waylonli/SEKA

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

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

#AttentionMechanisms #NLP #DeepLearning #MachineLearning #AI

✨OpenAutoNLU: Open Source AutoML Library for NLU

📝 Summary:
OpenAutoNLU is an open-source AutoML library for NLU tasks like text classification and named entity recognition. Its key innovation is data-aware training selection requiring no manual configuration. It also offers integrated diagnostics, out-of-distribution detection, and LLM features through a...

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01824
• PDF: https://arxiv.org/pdf/2603.01824
• Project Page: https://openautonlu.dev
• Github: https://github.com/mts-ai/OpenAutoNLU

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

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

#AutoML #NLU #LLM #OpenSource #MachineLearning

✨SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale

📝 Summary:
SWE-rebench V2 presents a new language-agnostic automated pipeline to create a large-scale dataset of over 32,000 software engineering tasks across 20 languages and 3,600 repositories. It provides reproducible environments and reliable tests, validated by LLMs, to advance training for SWE agents.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23866
• PDF: https://arxiv.org/pdf/2602.23866
• Github: https://huggingface.co/collections/nebius/swe-rebench-v2

✨ Datasets citing this paper:
• https://huggingface.co/datasets/nebius/SWE-rebench-V2
• https://huggingface.co/datasets/nebius/SWE-rebench-V2-PRs

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

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

#SoftwareEngineering #LLMs #AI #Dataset #SWEAgents

✨Monocular Mesh Recovery and Body Measurement of Female Saanen Goats

📝 Summary:
This paper introduces a novel 3D body measurement system for Saanen goats. It uses a new parametric shape model and a multi-view RGBD dataset to enable accurate single-view 3D reconstruction and automated measurement of key body dimensions, improving precision livestock farming.

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19896
• PDF: https://arxiv.org/pdf/2602.19896
• Github: https://github.com/bojin-nwafu/Female-Saanen-Goats

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

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

#3DReconstruction #ComputerVision #PrecisionLivestock #AnimalScience #AgriTech

✨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

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

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

#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

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

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

#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

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

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

#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

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

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

#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

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

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

#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

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

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

#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

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

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

#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

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

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

#AI #DataScience #MachineLearning #HuggingFace #Research