Radically Better Reasoning: Elicit's Andreas Stuhlmüller & Jungwon Byun on World Models for Research

About this episode

Andreas Stuhlmüller and Jungwon Byun discuss Elicit's advancements in trusted reasoning workflows for scientific research.

Andreas Stuhlmüller and Jungwon Byun return to discuss how Elicit is building trusted reasoning workflows for scientific research as frontier models grow more powerful but less transparent. They explain process supervision, domain-specific reasoning primitives, and world models that make evidence, causality, and counterfactuals more inspectable. The conversation also covers life sciences use cases, evaluating conflicting evidence, automated software engineering at Elicit, token costs, Gemini, and why legible reasoning may still beat neuralese. Mercury: Command is Mercury’s new conversational interface, giving you natural-language access to your finances and helping you take actions within your existing permissions and approval policies. Visit https://mercury.com to learn more and apply online in minutes. LINKS: Elicit Research Platform Andreas Stuhlmüller Personal Site Jungwon Byun X Profile Ought Research Organization Elicit Founders Previous Episode GPT-4 Technical Report Monitoring Reasoning Models Paper Ought ICE GitHub Repository Hard-to-Verify Tasks Essay Karpathy LLM Wiki Gist Obsidian Knowledge Base App Mixpanel Analytics Platform Amplitude Analytics Platform Anthropic…

People in this episode

Hosts: Erik Torenberg, Nathan Labenz

Guests: Andreas Stuhlmüller, Jungwon Byun

Topics covered

• scientific research
• reasoning workflows
• world models
• evidence evaluation
• automated software engineering
• life sciences

Keywords

• Elicit
• reasoning workflows
• scientific research
• world models
• evidence
• automated software engineering
• Gemini
• Mercury

Sponsors

Mercury

Mentioned in this episode

Organizations: Elicit, Ought Research Organization

Products: Mercury’s new conversational interface, Elicit Research Platform, Gemini, Claude AI Chat Assistant, Elicit API Documentation, Meta Llama AI

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