Ask precisely.
Test carefully.
Publish clearly.

Ask: Which patients, scanners, and tasks appear in the evaluation? What evidence would be needed before clinical use?

Try: Create a table with the task, dataset, baseline, metric, and limitation. Use public examples; do not upload patient information.

Ask: How well do predictions generalize to variants and biological settings outside the training distribution?

Try: Draw the path from a DNA variant to a predicted molecular effect. Mark every place where experimental validation is still needed.

Ask: Does a claimed safeguard work against realistic failures, or only against the test cases used to build it?

Try: Choose one risk and map the evidence, proposed mitigation, residual uncertainty, and responsible decision-maker.

Ask: How do the reward, training data, and evaluation set affect what “reasoning” means in this experiment?

Try: Compare a direct answer and a reasoned answer on a small, fixed question set. Score correctness and latency independently.

Ask: Which molecular interactions improved, and what failure cases or structural uncertainties remain?

Try: Compare a predicted structure with an experimentally measured structure. Identify what each can and cannot establish.