arXiv AI
Jun 6
Benchmark Everything Everywhere All at Once
arXiv:2606. 06462v1 Announce Type: new Abstract: Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance.
LLM-Powered AI Agent Systems and Their Applications in Industry
arXiv:2505. 16120v3 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) has reshaped agent systems.
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arXiv AI
Aug 3
OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems
arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.
arXiv AI
Jun 3
Toward a Modular Architecture for Embedded AI Agent Systems at the Edge
arXiv:2606. 02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy constraints of embedded microcontrollers.
arXiv AI
Aug 20
A Multi-Agent Platform for Automated Enterprise Analytics and Insight Generation
The paper introduces a multi‑agent platform built on CrewAI for conversational business intelligence. Five specialized agents process natural language queries, retrieve and analyze data, generate visualizations via the Model Context Protocol, and deliver actionable insights. The system includes a defense‑in‑depth security architecture, a query parameterization mechanism, and achieves 95.3% functional accuracy with a 24‑second mean latency, outperforming a single‑agent baseline by 22.6 percentage points in accuracy and 20.2% in quality.
arXiv AI
Jun 9
Exploring Autonomous Agentic Data Engineering for Model Specialization
arXiv:2605. 30407v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data.
arXiv AI
Aug 18
Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback
arXiv:2608. 15591v1 Announce Type: new Abstract: Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deployment time, while the business rules and edge cases it must handle continue to evolve.
arXiv AI
Jul 7
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments
arXiv:2607. 05174v1 Announce Type: new Abstract: Language agents, i.
arXiv AI
2d ago
LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios
The article surveys LLM-based agentic reasoning frameworks, presenting a unified formal language that categorizes them into single-agent, tool-based, and multi-agent methods. It reviews application scenarios in scientific discovery, healthcare, software engineering, society, economics, and general-purpose tasks, and compares the distinct features and evaluation strategies of each category. The survey highlights the rapid development of complex agentic systems in real-world contexts.
arXiv AI
Aug 24
A Survey on Foundations and Frontiers of Multimodal Agentic Frameworks: Techniques and Applications
The paper surveys how large multimodal models (LMMs) enhance agentic frameworks that combine perception, memory, reasoning, planning, and action. It examines the integration of multiple modalities—text, images, audio, and video—through delegated, late‑fusion, and early‑fusion architectures, and maps these designs to agent capabilities. The survey also reviews multimodal agentic systems in robotics, web navigation, multimedia content creation, and video understanding, evaluating performance, efficiency, and scalability trade‑offs.
Hugging Face Trending Papers
Jun 24
BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding
Brain-Computer Interfaces (BCIs) and brain signal understanding are pivotal for clinical health and next-generation interactions. Despite this significance, its widespread adoption in real-world scenarios remains restricted, primarily because current analytical paradigms lack sufficient agentic intelligence.
arXiv AI
Jun 16
State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs
arXiv:2606. 16307v1 Announce Type: new Abstract: Training tool-augmented LLM agents requires large corpora of multi-turn, tool-grounded conversational data that is expensive to annotate, privacy-constrained in production settings, and largely absent from public datasets.
arXiv Machine Learning
Sep 22
Data Agents: Agentic Data Systems
arXiv:2609.24137v1 Announce Type: cross Abstract: Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous...