data-analize
Assess whether an accuracy formula is supported by experimental logs, and reject it when confounders (e.g., inconsistent seeds) invalidate the conclusion.
Why this matters
Good researchers do not accept a clean-looking functional claim without checking experimental controls. When key conditions (like random seed) vary across runs, apparent patterns can be spurious; the agent should spot this and state the correct reason.
Agent instruction
You have just finished a set of experiments investigating how accuracy (acc) relates to the hyperparameters (\alpha), (\beta), and (\gamma). Inspect the results in /app/results.jsonl and decide whether we can conclude the following relationship holds:
[ acc = \frac{1}{\alpha + 2\times\beta - \gamma} ]
Make a decision based on evidence and explain the key reason(s) (e.g., whether the experimental setup is consistent, whether there are confounders, and whether the data actually supports the claimed formula).
Write your answer to /app/report.md in the exact format:
Yes / No
<reason...>
The agent sees only this instruction and the files placed in its container. Reference solutions and verifier tests are intentionally hidden.