LLM Research
New research explores how smaller language models can power AI agent systems while dramatically reducing computational costs and environmental impact without sacrificing capability.
Reinforcement Learning
LinkedIn shares practical insights from training agentic reinforcement learning systems for GPT-OSS, covering infrastructure challenges, reward modeling, and lessons learned from real-world deployment.
AI Agents
Researchers propose a software engineering framework for building AI agents that combines LLM capabilities with codified human expert domain knowledge for improved reliability.
Agentic AI
New comprehensive survey systematically categorizes agentic AI architectures, evaluation frameworks, and taxonomies for large language model agents, providing foundational insights for autonomous AI systems.
Agentic AI
As AI agents tackle complex multi-step tasks, traditional memory systems are hitting fundamental scaling limits. New architectural approaches are emerging to handle persistent context across extended workflows.
LLM Agents
Researchers challenge the assumption that LLM agents work reliably with perfect APIs, revealing how real-world complexity degrades AI performance.
AI Governance
Researchers propose comprehensive framework for governing agentic AI systems, mapping capabilities to risks and establishing safety protocols as autonomous agents become more prevalent.
AI Agents
A technical deep dive into how AI coding agents work, from tool-calling mechanisms and agentic loops to planning systems and memory architectures that enable autonomous code generation.
Google releases an updated version of Gemini Deep Research, its AI-powered research assistant that autonomously explores topics and synthesizes information across sources.
Agentic AI
New research surveys the core architectural patterns enabling autonomous AI agents, from single-agent designs to multi-agent orchestration frameworks that power complex AI workflows.
LLM Agents
New research introduces SelfAI, a framework enabling LLM agents to autonomously generate training data and improve performance without human annotation. The system uses multi-agent collaboration for self-supervised learning.
AI Agents
Docker launches MCP toolkit providing standardized infrastructure for AI agents to access tools and services. Technical deep dive into protocol design, integration patterns, and the future of agentic AI ecosystems.