Interpreting and Remedying GPT-5’s Surprising Failures in Scientific Reasoning

Research Question: What are the systematic patterns in GPT-5’s scientific reasoning failures, and how can targeted interventions (e.g., fine-tuning, prompt engineering, external reasoning modules) address these weaknesses?

Hypothesis: By cataloguing and analyzing GPT-5’s unexpected failures on domain-specific scientific reasoning tasks, we can design targeted interventions that measurably reduce error rates and improve trustworthiness in high-stakes research applications.

Experiment Plan: Collect a dataset of GPT-5’s incorrect or surprising outputs from real research tasks (e.g., mathematics proofs, ECG interpretations, supplier risk analysis). Categorize failures (e.g., overgeneralization, poor temporal reasoning, data hallucination). Implement and test targeted interventions: additional fine-tuning, prompt modifications, or hybrid symbolic-AI modules. Re-evaluate performance on a hold-out set of tasks and compare with baseline error rates.

References:

• Pandya, V., Ge, A., Ramineni, S., Danilov, A., Kirdar, F., Di Biase, L., Ferrick, K., & Krumerman, A. (2024). Abstract 4142075: From GPT-4 to GPT-4o: Progress and Challenges in ECG Interpretation. Circulation.
• Gupta, G. K., Acharya, N., & Pande, P. (2025). LLM-Based Support for Diabetes Diagnosis: Opportunities, Scenarios, and Challenges with GPT-5. arXiv.org.