✨Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis

📝 Summary:
A novel distillation framework called DP-DMD is introduced that preserves sample diversity in text-to-image generation by separating the roles of distilled steps, using v-prediction for diversity and ...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03139
• PDF: https://arxiv.org/pdf/2602.03139
• Github: https://github.com/Multimedia-Analytics-Laboratory/dpdmd

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SafeGround: Know When to Trust GUI Grounding Models via Uncertainty Calibration

📝 Summary:
SafeGround is a uncertainty-aware framework for GUI grounding models that uses distribution-aware uncertainty quantification and calibration to enable risk-aware predictions with controlled false disc...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02419
• PDF: https://arxiv.org/pdf/2602.02419
• Github: https://github.com/Cece1031/SAFEGROUND

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨FullStack-Agent: Enhancing Agentic Full-Stack Web Coding via Development-Oriented Testing and Repository Back-Translation

📝 Summary:
FullStack-Agent is a unified AI system assisting non-experts in full-stack web development. It uses a multi-agent framework and a self-improving method, demonstrating significant performance gains over prior state-of-the-art across all web functionalities.

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03798
• PDF: https://arxiv.org/pdf/2602.03798
• Github: https://github.com/mnluzimu/FullStack-Agent

🔹 Models citing this paper:
• https://huggingface.co/luzimu/FullStack-Learn-LM-30B-A3B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/luzimu/FullStack-Bench

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection

📝 Summary:
Token Sparse Attention enables efficient long-context inference by dynamically compressing and decompressing attention tensors at the token level, achieving significant speedup with minimal accuracy l...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03216
• PDF: https://arxiv.org/pdf/2602.03216

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents

📝 Summary:
LRAgent is a KV cache sharing framework for multi-LoRA agents that decomposes cache into shared and adapter-dependent components, reducing memory and compute overhead while maintaining accuracy. AI-ge...

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01053
• PDF: https://arxiv.org/pdf/2602.01053
• Github: https://github.com/hjeon2k/LRAgent

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Evaluating and Aligning CodeLLMs on Human Preference

📝 Summary:
A human-curated benchmark (CodeArena) and a large synthetic instruction corpus (SynCode-Instruct) are introduced to evaluate code LLMs based on human preference alignment, revealing performance differ...

🔹 Publication Date: Published on Dec 6, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2412.05210
• PDF: https://arxiv.org/pdf/2412.05210
• Project Page: https://codearenaeval.github.io/
• Github: https://github.com/QwenLM/Qwen2.5-Coder/tree/main/qwencoder-eval/instruct/CodeArena

✨ Datasets citing this paper:
• https://huggingface.co/datasets/CSJianYang/CodeArena

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨The Necessity of a Unified Framework for LLM-Based Agent Evaluation

📝 Summary:
Current LLM agent evaluations are hindered by confounding factors like prompts, toolsets, and environments, alongside a lack of standardization, leading to unfair and irreproducible results. A unified evaluation framework is essential to ensure rigorous and fair assessment of these advanced agents.

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03238
• PDF: https://arxiv.org/pdf/2602.03238

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

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#LLMAgents #AIEvaluation #Standardization #AIResearch #MachineLearning

✨SimpleGPT: Improving GPT via A Simple Normalization Strategy

📝 Summary:
SimpleNorm is a new normalization strategy for Transformers that stabilizes activation scales and reduces the Hessian spectral norm. This allows for significantly larger stable learning rates, leading to improved training performance and lower loss in large GPT models.

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01212
• PDF: https://arxiv.org/pdf/2602.01212

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

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#GPT #Normalization #Transformers #DeepLearning #AIResearch

✨No Shortcuts to Culture: Indonesian Multi-hop Question Answering for Complex Cultural Understanding

📝 Summary:
This paper introduces ID-MoCQA, the first large-scale multi-hop question answering dataset for assessing cultural understanding in LLMs, using Indonesian traditions. It transforms single-hop questions into complex reasoning chains across diverse clue types. Evaluations reveal significant gaps in ...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03709
• PDF: https://arxiv.org/pdf/2602.03709

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

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#MultiHopQA #LLMs #CulturalAI #IndonesianCulture #NLP

✨Instruction Anchors: Dissecting the Causal Dynamics of Modality Arbitration

📝 Summary:
Instruction tokens act as anchors for modality arbitration in MLLMs, guiding multimodal context use. This involves shallow layers gathering cues and deep layers resolving competition. Manipulating a few specialized attention heads significantly impacts this process.

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03677
• PDF: https://arxiv.org/pdf/2602.03677

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

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#MLLMs #MultimodalAI #AttentionMechanisms #DeepLearning #AIResearch

✨RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment

📝 Summary:
RecGOAT bridges the representational gap between LLMs and recommendation systems. It uses graph attention networks and a dual-granularity semantic alignment framework combining cross-modal contrastive learning and optimal adaptive transport for superior performance.

🔹 Publication Date: Published on Jan 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00682
• PDF: https://arxiv.org/pdf/2602.00682
• Github: https://github.com/6lyc/RecGOAT-LLM4Rec

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

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#RecGOAT #LLM #RecommendationSystems #MultimodalAI #GraphNeuralNetworks

✨POP: Prefill-Only Pruning for Efficient Large Model Inference

📝 Summary:
POP is a new stage-aware pruning method for large models. It omits deep layers during the computationally intensive prefill stage while using the full model for decoding. This achieves up to 1.37 times prefill speedup with minimal accuracy loss, overcoming limitations of prior pruning methods.

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03295
• PDF: https://arxiv.org/pdf/2602.03295

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

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#AI #MachineLearning #LLM #ModelPruning #InferenceOptimization

✨MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training

📝 Summary:
MEG-XL improves brain-to-text decoding by pre-training with 2.5 minutes of MEG context, far exceeding prior methods. This long-context approach dramatically boosts data efficiency, achieving supervised performance with only a fraction of the data and outperforming other models.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02494
• PDF: https://arxiv.org/pdf/2602.02494
• Github: https://github.com/neural-processing-lab/MEG-XL

🔹 Models citing this paper:
• https://huggingface.co/pnpl/MEG-XL

✨ Datasets citing this paper:
• https://huggingface.co/datasets/pnpl/LibriBrain

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

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#BrainToText #MEG #Neuroscience #DeepLearning #AI

✨LangMap: A Hierarchical Benchmark for Open-Vocabulary Goal Navigation

📝 Summary:
HieraNav introduces a multi-granularity, open-vocabulary navigation task. LangMap, its benchmark, uses 3D scans and human annotations across four semantic levels. Evaluations highlight challenges for models in complex navigation goals.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02220
• PDF: https://arxiv.org/pdf/2602.02220
• Project Page: https://bo-miao.github.io/LangMap/
• Github: https://github.com/bo-miao/LangMap

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

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#AINavigation #ComputerVision #Robotics #NLP #Benchmark

✨MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning

📝 Summary:
MedSAM-Agent reformulates medical image segmentation as a multi-step decision-making process using hybrid prompting and a two-stage training pipeline with process rewards to improve autonomous reasoni...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03320
• PDF: https://arxiv.org/pdf/2602.03320
• Github: https://github.com/CUHK-AIM-Group/MedSAM-Agent

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨You Need an Encoder for Native Position-Independent Caching

📝 Summary:
LLM KV caches are inefficient for arbitrary context orders. This paper proposes native PIC by reintroducing an encoder to decoder-only LLMs and developing COMB a PIC-aware caching system. COMB reduces TTFT by 51-94 percent and triples throughput with comparable accuracy.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01519
• PDF: https://arxiv.org/pdf/2602.01519

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

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#LLM #Caching #DeepLearning #AI #Performance

✨Neural Predictor-Corrector: Solving Homotopy Problems with Reinforcement Learning

📝 Summary:
Neural Predictor-Corrector NPC unifies diverse homotopy problems, using reinforcement learning to learn optimal policies. This general neural solver consistently outperforms classical methods in efficiency and stability across tasks.

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03086
• PDF: https://arxiv.org/pdf/2602.03086

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

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#ReinforcementLearning #HomotopyProblems #NeuralNetworks #MachineLearning #AI

✨RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval

📝 Summary:
RANKVIDEO is a reasoning-based reranker for text-to-video retrieval that explicitly analyzes query-video pairs for relevance. It uses a multi-objective training approach and a data synthesis pipeline. RANKVIDEO significantly improves retrieval performance by 31 percent on a large benchmark, outpe...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02444
• PDF: https://arxiv.org/pdf/2602.02444
• Github: https://github.com/tskow99/RANKVIDEO-Reasoning-Reranker

🔹 Models citing this paper:
• https://huggingface.co/hltcoe/RankVideo

✨ Datasets citing this paper:
• https://huggingface.co/datasets/hltcoe/RankVideo-Dataset

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LIVE: Long-horizon Interactive Video World Modeling

📝 Summary:
LIVE is a long-horizon video world model that uses cycle-consistency and diffusion loss to control error accumulation during extended video generation. AI-generated summary Autoregressive video world ...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2602.03747
• PDF: https://arxiv.org/pdf/2602.03747
• Project Page: https://junchao-cs.github.io/LIVE-demo/
• Github: https://junchao-cs.github.io/LIVE-demo/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

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✨Didactic to Constructive: Turning Expert Solutions into Learnable Reasoning

📝 Summary:
DAIL improves LLM reasoning by converting didactic expert solutions into detailed, in-distribution traces via contrastive learning. This method achieves 10-25% performance gains and 2-4x reasoning efficiency using minimal expert data.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02405
• PDF: https://arxiv.org/pdf/2602.02405
• Github: https://github.com/ethanm88/DAIL

✨ Datasets citing this paper:
• https://huggingface.co/datasets/emendes3/e1-proof

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

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#AI #DataScience #MachineLearning #HuggingFace #Research