✨Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions

📝 Summary:
Foundation model agents rely on natural language tool descriptions for effective interaction with external systems, but poor description quality significantly impacts performance and efficiency. AI-ge...

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14878
• PDF: https://arxiv.org/pdf/2602.14878

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨UniVBench: Towards Unified Evaluation for Video Foundation Models

📝 Summary:
UniVBench introduces a comprehensive benchmark for evaluating video foundation models across multiple capabilities including understanding, generation, editing, and reconstruction using high-quality, ...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21835
• PDF: https://arxiv.org/pdf/2602.21835
• Github: https://github.com/JianhuiWei7/UniVBench

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨The Design Space of Tri-Modal Masked Diffusion Models

📝 Summary:
A large-scale study of tri-modal discrete diffusion models demonstrates improved performance across text, image, and speech generation tasks through systematic analysis of scaling laws and optimized i...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21472
• PDF: https://arxiv.org/pdf/2602.21472

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Solaris: Building a Multiplayer Video World Model in Minecraft

📝 Summary:
Solaris is a multiplayer video world model that simulates consistent multi-view observations through a novel data collection system and staged training approach. AI-generated summary Existing action-c...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22208
• PDF: https://arxiv.org/pdf/2602.22208
• Project Page: https://solaris-wm.github.io/
• Github: https://github.com/solaris-wm/solaris

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation

📝 Summary:
DreamID-Omni is a unified framework for controllable human-centric audio-video generation that uses a symmetric conditional diffusion transformer with dual-level disentanglement and multi-task progres...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12160
• PDF: https://arxiv.org/pdf/2602.12160
• Project Page: https://guoxu1233.github.io/DreamID-Omni/
• Github: https://github.com/Guoxu1233/DreamID-Omni

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

📝 Summary:
GUI-Libra addresses limitations in open-source GUI agents through specialized training methods that improve reasoning-grounding alignment and reinforcement learning under partial verifiability, demons...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22190
• PDF: https://arxiv.org/pdf/2602.22190
• Project Page: https://gui-libra.github.io
• Github: https://github.com/GUI-Libra/GUI-Libra

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors

📝 Summary:
Object hallucinations in LVLMs are primarily caused by language decoder priors, leading to the development of a training-free framework that suppresses these priors to reduce hallucinations. AI-genera...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22144
• PDF: https://arxiv.org/pdf/2602.22144
• Github: https://github.com/lingfengren/NoLan

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨MoBind: Motion Binding for Fine-Grained IMU-Video Pose Alignment

📝 Summary:
MoBind learns joint representations between IMU signals and 2D pose sequences through hierarchical contrastive learning to achieve cross-modal retrieval, temporal synchronization, and action recogniti...

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19004
• PDF: https://arxiv.org/pdf/2602.19004
• Github: https://github.com/bbvisual/MoBind

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨NanoKnow: How to Know What Your Language Model Knows

📝 Summary:
NanoKnow is a benchmark using open pre-training data to analyze how LLMs acquire knowledge. It shows accuracy relies on pre-training frequency, which external evidence can mitigate, and that parametric and external knowledge are complementary, but irrelevant data is harmful.

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20122
• PDF: https://arxiv.org/pdf/2602.20122
• Github: https://github.com/castorini/NanoKnow/tree/main

✨ Datasets citing this paper:
• https://huggingface.co/datasets/LingweiGu/NanoKnow-Fineweb-Edu-Index
• https://huggingface.co/datasets/LingweiGu/NanoKnow_Benchmark

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Image Generation with a Sphere Encoder

📝 Summary:
The Sphere Encoder is an efficient generative model that maps images to a spherical latent space. It produces high-quality images in a single pass, matching diffusion models at a fraction of the inference cost.

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.15030
• PDF: https://arxiv.org/pdf/2602.15030
• Project Page: https://sphere-encoder.github.io

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models

📝 Summary:
Spectral-Evolution-Aware Cache (SeaCache) improves diffusion model inference speed by using spectrally aligned representations to optimize intermediate output reuse, achieving better latency-quality t...

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18993
• PDF: https://arxiv.org/pdf/2602.18993
• Project Page: https://jiwoogit.github.io/SeaCache/
• Github: https://github.com/jiwoogit/SeaCache

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨VecGlypher: Unified Vector Glyph Generation with Language Models

📝 Summary:
VecGlypher is a multimodal language model that generates high-fidelity vector glyphs directly from text or images by emitting SVG path tokens. This bypasses raster processes, creating editable outlines in one pass. It outperforms prior methods, simplifying font design.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21461
• PDF: https://arxiv.org/pdf/2602.21461
• Project Page: https://xk-huang.github.io/VecGlypher/
• Github: https://github.com/xk-huang/VecGlypher

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

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#VectorGraphics #LLM #FontDesign #GenerativeAI #AI

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✨Functional Continuous Decomposition

📝 Summary:
Functional Continuous Decomposition FCD is a new framework for parametric, continuous optimization of time-series data. It extracts M modes capturing local and global patterns, improving feature extraction. FCD features enhance machine learning models, leading to faster convergence and higher acc...

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20857
• PDF: https://arxiv.org/pdf/2602.20857
• Project Page: https://arxiv.org/abs/2602.20857
• Github: https://github.com/Tima-a/fcd

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

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#FCD #TimeSeries #Optimization #FeatureExtraction #MachineLearning

✨MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models

📝 Summary:
MolHIT presents a hierarchical discrete diffusion model for molecular graph generation. It achieves state-of-the-art performance with near-perfect chemical validity and strong property-guided synthesis, surpassing existing methods.

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17602
• PDF: https://arxiv.org/pdf/2602.17602

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

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#MolHIT #MolecularGraphs #DiffusionModels #DrugDiscovery #Cheminformatics

✨DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference

📝 Summary:
DualPath addresses KV-cache I/O bottlenecks in LLM inference with dual-path loading. It loads KV-cache into decode engines, transfers it to prefill engines, and dynamically balances load to boost throughput up to 1.96 times.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21548
• PDF: https://arxiv.org/pdf/2602.21548

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

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#LLM #AI #MachineLearning #PerformanceOptimization #SystemDesign

✨Yor-Sarc: A gold-standard dataset for sarcasm detection in a low-resource African language

📝 Summary:
Yor-Sarc introduces the first gold-standard dataset for sarcasm detection in Yorùbá, a low-resource African language. It offers 436 expertly annotated instances with high inter-annotator agreement and soft labels, designed to advance NLP for African languages.

🔹 Publication Date: Published on Feb 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18964
• PDF: https://arxiv.org/pdf/2602.18964
• Project Page: https://arxiv.org/abs/2602.18964
• Github: https://github.com/toheebadura/yor-sarc

✨ Datasets citing this paper:
• https://huggingface.co/datasets/toheebadura/yor-sarc

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

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#NLP #SarcasmDetection #Yoruba #LowResourceLanguages #AfricanLanguages

✨SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models

📝 Summary:
Spectral-Evolution-Aware Cache (SeaCache) improves diffusion model inference speed by using spectrally aligned representations to optimize intermediate output reuse, achieving better latency-quality t...

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18993
• PDF: https://arxiv.org/pdf/2602.18993
• Project Page: https://jiwoogit.github.io/SeaCache/
• Github: https://github.com/jiwoogit/SeaCache

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨From Statics to Dynamics: Physics-Aware Image Editing with Latent Transition Priors

📝 Summary:
PhysicEdit addresses physically implausible image editing by modeling edits as predictive physical state transitions. It uses a dual-thinking diffusion framework guided by a vision-language model, greatly enhancing physical realism.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21778
• PDF: https://arxiv.org/pdf/2602.21778
• Project Page: https://liangbingzhao.github.io/statics2dynamics/
• Github: https://github.com/liangbingzhao/PhysicEdit

✨ Datasets citing this paper:
• https://huggingface.co/datasets/metazlb/PhysicTran38K

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

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#ImageEditing #DiffusionModels #ComputerVision #PhysicsAI #AIResearch

✨DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction

📝 Summary:
DM4CT benchmarks diffusion models for CT reconstruction, tackling practical challenges like noise and artifacts. It evaluates ten diffusion methods against baselines on diverse real-world and synthetic CT datasets, offering detailed performance insights.

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18589
• PDF: https://arxiv.org/pdf/2602.18589
• Project Page: https://dm4ct.github.io/DM4CT/
• Github: https://github.com/DM4CT/DM4CT

🔹 Models citing this paper:
• https://huggingface.co/jiayangshi/lodochallenge_pixel_diffusion
• https://huggingface.co/jiayangshi/lodochallenge_latent_diffusion
• https://huggingface.co/jiayangshi/lodoind_pixel_diffusion

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

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#DiffusionModels #CTReconstruction #MedicalImaging #AIResearch #DeepLearning

✨ISO-Bench: Can Coding Agents Optimize Real-World Inference Workloads?

📝 Summary:
ISO-Bench evaluates coding agents on real-world LLM inference optimization tasks using combined execution and LLM metrics. Agents often identify bottlenecks but fail to execute working solutions, highlighting that scaffolding is as important as the model itself.

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19594
• PDF: https://arxiv.org/pdf/2602.19594

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

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#CodingAgents #LLMOptimization #AIResearch #Benchmarking #LargeLanguageModels