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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Fler avsnitt av "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis
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