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.
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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 Tracing Thoughts Research
Claude AI Chat Assistant
METR Long Tasks Measurement
Pi Agent Scaffold Repository
Personal AI Infrastructure Repository
Elicit Claude Opus Evaluation
Elicit API Documentation
METR Developer Productivity Study
Elicit Planning Is Unsolved
Rich Sutton Bitter Lesson
Meta Llama AI Models
Recursive San Francisco Event
zero.xyz Agent Tool Access
Anthropic Dynamic Workflows Coverage
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