As artificial intelligence (AI) systems are increasingly used for everyday advice and guidance, concerns have emerged about sycophancy: the tendency of AI-based large language models to excessively agree with, flatter, or validate users. Although prior work has shown that sycophancy carries risks for groups who are already vulnerable to manipulation or delusion, syncophancy’s effects on the general population’s judgments and behaviors remain unknown. Here, we show that sycophancy is widespread in leading AI systems and has harmful effects on users’ social judgments. — Read More
Daily Archives: April 13, 2026
“AI polls” are fake polls: But they might be useful as something else: models.
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or creating synthetic audiences. Sans startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents.
I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The guys behind the company were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter:
LOL I wish there were a way to short this business this is maybe the single worst use case for AI I’ve ever heard.
— Read More
Building Hierarchical Agentic RAG Systems: Multi-Modal Reasoning with Autonomous Error Recovery
Enterprise AI teams face a persistent challenge: Most Retrieval-Augmented Generation (RAG) systems excel at either structured data queries or document search, but struggle when both are required simultaneously. A financial analyst who asked “Why are European operations underperforming?” needs data from both SQL databases (revenue, margins, and employee counts) and unstructured documents (market reports, competitive analysis, regulatory filings). Current RAG systems might return revenue data without regulatory context or surface market reports without quantitative validation, leaving analysts to manually bridge the gap. Current RAG approaches treat these modalities as separate concerns, forcing engineers to build custom orchestration layers or accept incomplete answers.
This article explores architectural patterns for solving the modality gap through hierarchical multi-agent orchestration, using Protocol-H as a reference implementation to illustrate these concepts in practice. The patterns discussed, supervisor-worker topology with autonomous error recovery, build on LangGraph/LangChain agentic patterns used by teams at companies like xAI and Databricks. The accompanying open source code demonstrates these patterns deployed at enterprise scale with Docker/K8s, though readers can apply the same architectural principles using their preferred frameworks.
The architecture described in this article is based on a reference implementation and production-oriented experimentation with enterprise datasets; specific deployment details have been generalized to focus on the architectural patterns rather than any particular system implementation. — Read More
Rick's Cafe AI 