AI Systems
Researchers introduce SETA, a statistical method for identifying which component in complex AI pipelines causes failures—critical for debugging multi-stage systems like video generation workflows.
LLM research
New benchmark reveals surprising findings about multi-LLM collaboration: more AI models deliberating doesn't always improve results. Research identifies when consensus helps and when it hurts.
LLM
New research introduces an evaluation-driven multi-agent workflow that automatically optimizes prompt instructions for improved LLM instruction following performance.
LLM research
New research explores whether deliberation improves LLM-based forecasting, examining how AI agents can leverage collective reasoning to make better predictions through structured discussion.
Multi-Agent AI
A comprehensive technical guide to building production-ready multi-agent AI systems using CrewAI for agent orchestration, LangGraph for workflow graphs, FastAPI for APIs, and Docker for deployment.