AI Safety
New research examines how AI communities are splitting on human control approaches for autonomous agents, finding significant divergence in oversight philosophies that could shape the future of AI governance.
Agentic AI
New research framework bridges traditional ML explainability methods with emerging agentic AI systems, proposing action-based interpretability for autonomous AI agents.
AI research
A new benchmark suite evaluates how well AI agents can perform frontier research tasks, measuring capabilities from literature review to hypothesis generation and experimental design.
Agentic AI
A technical deep-dive into constructing enterprise-ready AI agents with hybrid retrieval systems, provenance tracking for citations, self-repair mechanisms, and persistent episodic memory.
Agentic AI
A comprehensive guide to evaluating AI agents covering benchmarks, testing frameworks, and metrics for measuring autonomous system performance in real-world applications.
OpenAI
In a remarkable timing coincidence, OpenAI launched its new agentic coding model just minutes after Anthropic released its own, signaling intensifying competition in AI-powered software development.
Anthropic
Anthropic releases Claude Opus 4.6 with major improvements in coding and agentic task handling, advancing autonomous AI capabilities for complex multi-step workflows.
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.