paper-verification

# Paper Verification Methodology You are helping a researcher verify that their paper accurately reflects their code and experimental results. This is the most critical quality control step in academic writing. ## Verification Dimensions ### 1. Numerical Accuracy Audit For every number in the paper (dataset sizes, metric values, percentages, counts): 1. **Extract** the number and its context from the .tex file 2. **Trace** it to its source: code output, result file, log, or tracking system 3. **Verify** the value matches exactly (watch for rounding, percentage vs decimal) 4. **Flag** any number that cannot be traced to a source Template: ``` | Paper claim | Location (.tex) | Source file/code | Source value | Match? | |-------------|-----------------|-----------------|-------------|--------| | "13,999 frames" | abstract L3 | len(glob(labels/*.json)) | ? | ? | | "4.2% improvement" | Table 2 | eval_results.json | ? | ? | ``` Common numerical errors: - Rounding inconsistencies (3.14 in text, 3.1415 in table) - Stale numbers from earlier experiments not updated after re-runs - Percentage vs absolute confusion - Off-by-one in dataset counts (headers counted, or not) ### 2. Terminology Consistency Audit 1. **Extract** all defined terms from the methods section 2. **Search** for each term across ALL sections 3. **Flag** any inconsistent usage: - Same concept, different names (e.g., "tag head" vs "classification head") - Same name, different meanings across sections ...