✨GeneralVLA: Generalizable Vision-Language-Action Models with Knowledge-Guided Trajectory Planning

📝 Summary:
GeneralVLA is a hierarchical vision-language-action model that enables zero-shot robotic manipulation through knowledge-guided trajectory planning. It requires no real-world data collection and outperforms existing methods, also generating robust training data.

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04315
• PDF: https://arxiv.org/pdf/2602.04315
• Project Page: https://aigeeksgroup.github.io/GeneralVLA
• Github: https://aigeeksgroup.github.io/GeneralVLA

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Less is Enough: Synthesizing Diverse Data in Feature Space of LLMs

📝 Summary:
Feature Activation Coverage measures data diversity in an interpretable feature space and enables diversity-driven data synthesis that improves downstream performance across multiple language model ar...

🔹 Publication Date: Published on Feb 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2602.10388
• PDF: https://arxiv.org/pdf/2602.10388
• Project Page: https://website-sigma-three-35.vercel.app/
• Github: https://github.com/Zhongzhi660/FAC-Synthesis

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨What does RL improve for Visual Reasoning? A Frankenstein-Style Analysis

📝 Summary:
Reinforcement learning (RL) with verifiable rewards has become a standard post-training stage for boosting visual reasoning in vision-language models, yet it remains unclear what capabilities RL actua...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12395
• PDF: https://arxiv.org/pdf/2602.12395
• Project Page: https://github.com/tianyi-lab/Frankenstein
• Github: https://github.com/tianyi-lab/Frankenstein

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨CoPE-VideoLM: Codec Primitives For Efficient Video Language Models

📝 Summary:
Video Language Models (VideoLMs) empower AI systems to understand temporal dynamics in videos. To fit to the maximum context window constraint, current methods use keyframe sampling which can miss bot...

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13191
• PDF: https://arxiv.org/pdf/2602.13191
• Project Page: https://sayands.github.io/cope/
• Github: https://sayands.github.io/cope/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models

📝 Summary:
Bit-Plane Decomposition Quantization (BPDQ) improves low-bit quantization by using variable quantization grids derived from bit-planes and scalar coefficients, achieving better accuracy than tradition...

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04163
• PDF: https://arxiv.org/pdf/2602.04163

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨RLinf-Co: Reinforcement Learning-Based Sim-Real Co-Training for VLA Models

📝 Summary:
Reinforcement learning-based sim-real co-training framework improves vision-language-action policy performance through interactive simulation and real-world data anchoring. AI-generated summary Simula...

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12628
• PDF: https://arxiv.org/pdf/2602.12628

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

📝 Summary:
Diffusion large language models (dLLMs) for CUDA kernel generation achieve superior performance through a specialized dataset and reinforcement learning framework. AI-generated summary Diffusion large...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11715
• PDF: https://arxiv.org/pdf/2602.11715
• Project Page: https://deadlykitten4.github.io/DICE/
• Github: https://github.com/deadlykitten4/DICE

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

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✨Self-EvolveRec: Self-Evolving Recommender Systems with LLM-based Directional Feedback

📝 Summary:
Self-EvolveRec improves recommender system design via a directional feedback loop. It uses a User Simulator for qualitative critiques and a Model Diagnosis Tool for quantitative verification, with adaptive evaluation. It outperforms existing methods.

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12612
• PDF: https://arxiv.org/pdf/2602.12612
• Github: https://github.com/Sein-Kim/self_evolverec

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#RecommenderSystems #LLM #MachineLearning #ArtificialIntelligence #DeepLearning

✨AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets

📝 Summary:
AI-Trader introduces the first fully automated live benchmark for evaluating LLM agents in financial decision-making. It reveals that general AI does not ensure trading success, with most agents showing poor returns and weak risk management. Risk control proves crucial, and liquid markets offer b...

🔹 Publication Date: Published on Dec 1, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10971
• PDF: https://arxiv.org/pdf/2512.10971
• Project Page: https://ai4trade.ai/
• Github: https://github.com/HKUDS/AI-Trader

✨ Datasets citing this paper:
• https://huggingface.co/datasets/T1anyu/AI-Trader

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

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#AI #LLMAgents #FinTech #AlgorithmicTrading #FinancialAI

✨Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost

📝 Summary:
Quantized LLMs are difficult to fine-tune directly using existing methods. Quantized Evolution Strategies QES enables full-parameter fine-tuning of quantized LLMs. It uses error feedback and seed replay for high-precision optimization at low memory cost, outperforming prior methods.

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03120
• PDF: https://arxiv.org/pdf/2602.03120
• Github: https://github.com/dibbla/Quantized-Evolution-Strategies

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#LLM #Quantization #FineTuning #EvolutionStrategies #AI

✨Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels

📝 Summary:
This study trains deep learning models to segment individual tree crowns from aerial imagery. It uses enhanced pseudo-labels derived from ALS data, improved by SAM 2, to eliminate manual annotation. This method produces superior, domain-specific segmentation models.

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13022
• PDF: https://arxiv.org/pdf/2602.13022

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

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#DeepLearning #ImageSegmentation #RemoteSensing #Forestry #ComputerVision

✨Favia: Forensic Agent for Vulnerability-fix Identification and Analysis

📝 Summary:
Favia is an agent-based framework that identifies vulnerability-fixing commits by combining scalable ranking with deep semantic reasoning via LLM agents. It uses specialized tools and environmental context to robustly identify complex fixes, outperforming existing methods and achieving better pre...

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12500
• PDF: https://arxiv.org/pdf/2602.12500
• Github: https://github.com/andstor/agentic-security-patch-classification-replication-package

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#Cybersecurity #LLMAgents #VulnerabilityManagement #SoftwareSecurity #AIResearch

✨Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching

📝 Summary:
UniDFlow is a unified discrete flow-matching framework for multimodal understanding, generation, and editing. It decouples understanding and generation via low-rank adapters and improves tasks with reference-based alignment without retraining. This achieves SOTA performance and strong zero-shot g...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12221
• PDF: https://arxiv.org/pdf/2602.12221
• Project Page: https://plan-lab.github.io/unidflow

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#MultimodalAI #GenerativeAI #FlowMatching #MachineLearning #DeepLearning

✨SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise

📝 Summary:
SQuTR is a new robustness benchmark for spoken query to text retrieval. It uses 37k diverse queries, real speaker profiles, and 17 noise categories to test systems. Experiments show all systems struggle under extreme noise, making robustness a key bottleneck.

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12783
• PDF: https://arxiv.org/pdf/2602.12783
• Github: https://github.com/ttoyekk1a/SQuTR-Spoken-Query-to-Text-Retrieval

✨ Datasets citing this paper:
• https://huggingface.co/datasets/SLLMCommunity/SQuTR

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

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#SQTR #Robustness #NLP #SpeechRecognition #Benchmarking

✨OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report

📝 Summary:
OpenLIDv3 improves language identification for closely related and low resource languages. It uses enhanced training data, cluster merging, and noise detection. This significantly boosts precision over prior tools.

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13139
• PDF: https://arxiv.org/pdf/2602.13139
• Project Page: https://huggingface.co/HPLT/OpenLID-v3
• Github: https://github.com/hplt-project/openlid

🔹 Models citing this paper:
• https://huggingface.co/HPLT/OpenLID-v3

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

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#LanguageIdentification #NLP #LowResourceLanguages #MachineLearning #AIResearch

✨A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

📝 Summary:
This survey explores self evolving AI agents that adapt to dynamic environments through automatic enhancement using interaction data and feedback. It provides a unified framework, reviews techniques, and discusses safety and ethics, aiming to advance adaptive lifelong agentic systems.

🔹 Publication Date: Published on Aug 10, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.07407
• PDF: https://arxiv.org/pdf/2508.07407
• Project Page: https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents
• Github: https://github.com/EvoAgentX/Awesome-Self-Evolving-Agents

✨ Spaces citing this paper:
• https://huggingface.co/spaces/X-iZhang/Awesome-Self-Evolving-Agents

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

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#AIAgents #SelfEvolvingAI #FoundationModels #LifelongLearning #AIResearch

✨SemanticMoments: Training-Free Motion Similarity via Third Moment Features

📝 Summary:
Existing video models struggle with semantic motion often biased towards appearance. SemanticMoments addresses this with a training-free method using temporal statistics on semantic features to consistently outperform other approaches for motion-centric video understanding.

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09146
• PDF: https://arxiv.org/pdf/2602.09146
• Project Page: https://x.com/HubermanSaar/status/2023485404280672498?s=20

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

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#SemanticMoments #VideoUnderstanding #ComputerVision #MachineLearning #MotionAnalysis

✨Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation

📝 Summary:
This paper introduces a novel framework for generating high-quality synthetic data for LLMs in recommender systems. This synthetic data significantly outperforms real data and enables the first robust power-law scaling for LLMs in recommendation, allowing for predictable capability development.

🔹 Publication Date: Published on Feb 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07298
• PDF: https://arxiv.org/pdf/2602.07298

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨scPilot: Large Language Model Reasoning Toward Automated Single-Cell Analysis and Discovery

📝 Summary:
scPilot enables large language models to directly analyze single-cell RNA-seq data through omics-native reasoning. This framework improves accuracy in cell-type annotation and developmental trajectory reconstruction via step-by-step reasoning, providing auditable and interpretable analyses.

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11609
• PDF: https://arxiv.org/pdf/2602.11609
• Github: https://github.com/maitrix-org/scPilot

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Steer2Edit: From Activation Steering to Component-Level Editing

📝 Summary:
Steer2Edit transforms LLM steering signals into training-free, component-level weight edits. This method precisely targets attention heads and MLP neurons, improving safety, truthfulness, and efficiency with better attribute-utility trade-offs than global steering.

🔹 Publication Date: Published on Feb 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09870
• PDF: https://arxiv.org/pdf/2602.09870

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

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#AI #DataScience #MachineLearning #HuggingFace #Research