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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

✨The Truthfulness Spectrum Hypothesis

📝 Summary:
This paper proposes the truthfulness spectrum hypothesis: LLMs contain truth directions ranging from domain-general to domain-specific. While general directions exist, domain-specific ones steer more effectively, with post-training reshaping this geometry to influence behaviors like sycophancy.

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20273
• PDF: https://arxiv.org/pdf/2602.20273
• Github: https://github.com/zfying/truth_spec

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

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#LLMs #AIResearch #AIAlignment #NLP #Truthfulness

✨Intent Laundering: AI Safety Datasets Are Not What They Seem

📝 Summary:
AI safety datasets overrely on unrealistic triggering cues. This paper introduces intent laundering to remove these cues, revealing that models previously deemed safe become vulnerable. This method also works as a powerful jailbreaking technique, exposing a critical flaw in current AI safety eval...

🔹 Publication Date: Published on Feb 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16729
• PDF: https://arxiv.org/pdf/2602.16729

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

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#AISafety #JailbreakingAI #LLMSecurity #AIDatasets #AIEvaluation

✨The Trinity of Consistency as a Defining Principle for General World Models

📝 Summary:
This paper proposes the Trinity of Consistency modal, spatial, temporal as a foundational theoretical framework for General World Models. It systematically reviews multimodal learning through this lens and introduces CoW-Bench, a new benchmark for evaluating current and future models.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23152
• PDF: https://arxiv.org/pdf/2602.23152
• Project Page: https://openraiser.github.io/CoW-Bench/
• Github: https://github.com/openraiser/awesome-world-model-evolution

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨OmniGAIA: Towards Native Omni-Modal AI Agents

📝 Summary:
OmniGAIA benchmark evaluates multi-modal agents on complex reasoning tasks across video, audio, and image modalities, while OmniAtlas agent improves tool-use capabilities through hindsight-guided tree...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22897
• PDF: https://arxiv.org/pdf/2602.22897
• Github: https://github.com/RUC-NLPIR/OmniGAIA

✨ Datasets citing this paper:
• https://huggingface.co/datasets/RUC-NLPIR/OmniGAIA
• https://huggingface.co/datasets/RUC-NLPIR/Omnimodal-Agent-SFT-2K

✨ Spaces citing this paper:
• https://huggingface.co/spaces/RUC-NLPIR/OmniGAIA-Leaderboard

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

📝 Summary:
A risk-aware framework for autonomous driving that uses world modeling and risk evaluation to generalize beyond expert demonstrations without requiring explicit expert supervision. AI-generated summar...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23259
• PDF: https://arxiv.org/pdf/2602.23259

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DyaDiT: A Multi-Modal Diffusion Transformer for Socially Favorable Dyadic Gesture Generation

📝 Summary:
DyaDiT is a multi-modal diffusion transformer that generates contextually appropriate human motion from dyadic audio signals by capturing interaction dynamics between two speakers. AI-generated summar...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23165
• PDF: https://arxiv.org/pdf/2602.23165

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨GeoWorld: Geometric World Models

📝 Summary:
GeoWorld addresses limitations in energy-based predictive world models by utilizing hyperbolic geometry to preserve latent state structures and improve long-horizon prediction performance. AI-generate...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23058
• PDF: https://arxiv.org/pdf/2602.23058
• Project Page: https://steve-zeyu-zhang.github.io/GeoWorld

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨veScale-FSDP: Flexible and High-Performance FSDP at Scale

📝 Summary:
veScale-FSDP introduces a redesigned fully sharded data parallel system with flexible sharding and structure-aware planning to improve scalability and efficiency for large-scale model training. AI-gen...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22437
• PDF: https://arxiv.org/pdf/2602.22437
• Github: https://github.com/volcengine/veScale

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Imagination Helps Visual Reasoning, But Not Yet in Latent Space

📝 Summary:
Research reveals that latent visual reasoning in multimodal models suffers from input-latent and latent-answer disconnects, leading to the proposal of CapImagine, a text-based approach that outperform...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22766
• PDF: https://arxiv.org/pdf/2602.22766
• Github: https://github.com/Michael4933/CapImagine

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization

📝 Summary:
EMPO² is a hybrid reinforcement learning framework that enhances exploration for large language model agents by integrating memory mechanisms with on- and off-policy updates, demonstrating improved pe...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23008
• PDF: https://arxiv.org/pdf/2602.23008

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Causal Motion Diffusion Models for Autoregressive Motion Generation

📝 Summary:
Causal Motion Diffusion Models introduce a unified framework for autoregressive motion generation using a causal diffusion transformer in a semantically aligned latent space, enabling fast, high-quali...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22594
• PDF: https://arxiv.org/pdf/2602.22594
• Project Page: https://yu1ut.com/CMDM-HP/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns

📝 Summary:
TRC2 introduces a sparse, chunk-parallel architecture for language models to address continual learning challenges. It enables rapid adaptation and prevents catastrophic forgetting, improving the stability-plasticity tradeoff with efficient compute.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22479
• PDF: https://arxiv.org/pdf/2602.22479
• Project Page: https://trc2lm.github.io

🔹 Models citing this paper:
• https://huggingface.co/akhadangi/trc2

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

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#AI #DataScience #MachineLearning #HuggingFace #Research