Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners
Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners explores This paper explores how to optimize prompts for LLMs to understand their scientific reasoning capabilities, aiming for better interpretability and collaboration with future AI systems.. Commercial viability score: 3/10 in LLM Interpretability.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisMind the (DH) Gap! A Contrast in Risky Choices Between Reasoning and Conversational LLMsViability: 2.0
- Prior WorkArgumentation for Explainable and Globally Contestable Decision Support with LLMsViability: 3.0
- Prior WorkFrom Human Cognition to Neural Activations: Probing the Computational Primitives of Spatial Reasoning in LLMsViability: 3.0
- Higher ViabilityAbductive Reasoning with Syllogistic Forms in Large Language ModelsViability: 5.0
- Higher ViabilityReasoning While Asking: Transforming Reasoning Large Language Models from Passive Solvers to Proactive Inquirers