✨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

✨Qute: Towards Quantum-Native Database

📝 Summary:
This paper envisions a quantum database (Qute) that treats quantum computation as a first-class execution option. Unlike prior simulation-based methods that either run quantum algorithms on classical ...

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14699
• PDF: https://arxiv.org/pdf/2602.14699
• Github: https://github.com/weAIDB/Qute

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents

📝 Summary:
REDSearcher presents a unified framework for optimizing search agents through improved task synthesis, tool-augmented queries, midtraining capability enhancement, and simulated environments to address...

🔹 Publication Date: Published on Feb 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14234
• PDF: https://arxiv.org/pdf/2602.14234
• Project Page: https://redsearchagent.github.io/index/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddings

📝 Summary:
A reasoning-driven universal multimodal embedding framework integrates embedder-guided reinforcement learning with traceability chain-of-thought to enhance cross-modal semantic consistency and retriev...

🔹 Publication Date: Published on Feb 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13823
• PDF: https://arxiv.org/pdf/2602.13823
• Github: https://github.com/ZoengHN/Embed-RL

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨UniWeTok: An Unified Binary Tokenizer with Codebook Size 2^{128} for Unified Multimodal Large Language Model

📝 Summary:
UniWeTok introduces a unified discrete tokenizer with a massive binary codebook and novel training techniques to achieve superior performance in image generation and multimodal tasks while reducing co...

🔹 Publication Date: Published on Feb 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14178
• PDF: https://arxiv.org/pdf/2602.14178
• Github: https://github.com/shallowdream204/BitDance

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LaViDa-R1: Advancing Reasoning for Unified Multimodal Diffusion Language Models

📝 Summary:
LaViDa-R1 is a multimodal reasoning diffusion language model that unifies supervised fine-tuning and multi-task reinforcement learning with novel training techniques for enhanced performance across vi...

🔹 Publication Date: Published on Feb 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14147
• PDF: https://arxiv.org/pdf/2602.14147

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨BrowseComp-V^3: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents

📝 Summary:
A new benchmark called BrowseComp-V3 challenges multimodal large language models with complex, multi-hop reasoning tasks requiring deep search across text and visual modalities, revealing significant ...

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12876
• PDF: https://arxiv.org/pdf/2602.12876

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨FireRed-Image-Edit-1.0 Techinical Report

📝 Summary:
FireRed-Image-Edit uses a diffusion transformer with optimized data curation and training methods to achieve state-of-the-art performance in instruction-based image editing, supported by a comprehensi...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13344
• PDF: https://arxiv.org/pdf/2602.13344
• Project Page: https://huggingface.co/spaces/FireRedTeam/FireRed-Image-Edit-1.0
• Github: https://github.com/FireRedTeam/FireRed-Image-Edit

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨AIDev: Studying AI Coding Agents on GitHub

📝 Summary:
AIDev is a large-scale dataset of agent-authored pull requests from real-world GitHub repositories that captures AI coding agent usage in practical software development scenarios. AI-generated summary...

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.15003
• PDF: https://arxiv.org/pdf/2602.09185
• Project Page: https://huggingface.co/datasets/hao-li/AIDev
• Github: https://huggingface.co/papers?q=GitHub%20repositories

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)

📝 Summary:
Targeted instruction selection for LLM fine-tuning can be improved by systematically analyzing data representation and selection algorithms, with gradient-based representations and greedy round-robin ...

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14696
• PDF: https://arxiv.org/pdf/2602.14696
• Github: https://github.com/dcml-lab/targeted-instruction-selection

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#AI #DataScience #MachineLearning #HuggingFace #Research