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Shows AIP improves AI reasoning in research, suggesting enhanced reliability for scientific applications.
The rapid integration of large language models into research processes poses challenges: models misinterpret novel methodologies, flatten ideas, mix content hierarchies, or lose context.We introduce AIP: AI-Interpretation-Protocol, a lightweight, iterative way to correct machine reasoning. Instead of exhaustive documentation, we propose an AIP file: a concise, evolving scratchpad created empirically during interactions with AI. This publication also contains an AIP file (MOBM-AI-Interpretation-Protocol.docx) that: · corrects common reasoning errors made by AI models when interpreting the MOBM v2.0 article (DOI: 10.5281/zenodo.18750681). · includes preliminary notes on improvements planned for MOBM v2.1 (e.g., convergence criteria, module definitions) that help both AI and human readers better understand the direction of future work.
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Arkadiusz Kondratowicz (2026) studied this question.
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