sub_tasks: 
Task: meta-content-accuracy ( / )
Requirements: Faithfulness of the writeup to the underlying artifacts. The single biggest fabrication risk in autonomous paper generation is invented numbers; this bucket carries the largest weight.
Sub-tasks: requirements: STRICT: every numeric value in the paper (abstract, methods, results, tables) must trace to a value in the artifacts (results.json, experiment_summary.json, CSV outputs, figure data). Sample 5-10 numbers across the paper; mark each as traceable / not-traceable; subscore = traceable_count / sampled_count. Any single invented number for a key metric (e.g. final accuracy, final growth rate) caps the leaf at 0.5 regardless of other traceable numbers.
task_category: Content Accuracy
finegrained_task_category: Faithfulness - Numbers
requirements: Units are consistent and physically correct throughout. ML: percentages stay as percent vs fractions consistently; runtime in seconds vs minutes consistently. HEP: GeV vs MeV not mixed, cross sections in pb vs fb consistent. Biology: mmol/gDW/h is the standard; do not mix with mM. Penalize 'unit-less' numbers where a unit is needed.
task_category: Content Accuracy
finegrained_task_category: Faithfulness - Units
requirements: Strength of claims matches strength of evidence. 'X significantly outperforms Y' requires a statistical test result; 'X tends to be better than Y' requires a directional difference; 'X may be useful' requires only intuition. Penalize uncalibrated 'state of the art', 'novel', 'first to show' without specific support. Hedging language ('appears', 'suggests', 'is consistent with') should appear when the evidence is suggestive.
task_category: Content Accuracy
finegrained_task_category: Faithfulness - Claim Calibration
requirements: The same number / claim appears identically across the paper. Abstract says 'accuracy 92.3%'; Results says 'accuracy 92.3%' (not 92.5%); Conclusion says 'achieved 92.3%'. Tables match prose. Figure captions match figure content. Penalize ANY internal contradiction even if both versions might be 'plausible'.
task_category: Content Accuracy
finegrained_task_category: Faithfulness - Internal Consistency