**Overall Verdict:** Strong Accept — The submission is complete, coherent, safely inspectable, and objectively evaluable end to end.

**Completeness Check:**
- Instruction: Complete — Clearly defines the interference-modeling objective, required JSON schema, units, and handling of multi-beam effects.
- Inputs: Complete — Four readable Excel spectra with stated columns and units, plus two valid schematic PNGs.
- Toolchain: Complete — Python, NumPy, SciPy, pandas, and openpyxl provide a legally obtainable single-machine workflow without credentials or network access.
- Reference output: Complete — Includes the exact JSON contract, reference values, derivation, sensitivity analysis, and evaluator-facing outputs.
- Rubric: Complete — Specifies exact keys, finite positive values, SiC tolerance, Si sanity bounds, and case-insensitive multi-beam matching.

**File and Task Reachability:**
Attachment classification: `reachable`. Task classification: `reachable`. I inspected the four Excel files (`SiC_10deg.xlsx`, `SiC_15deg.xlsx`, `Si_10deg.xlsx`, `Si_15deg.xlsx`), `TASK.md`, `INDEX.md`, both schematic PNGs, all reference-output files, and all difficulty-evidence files. The ZIP containers were readable, unencrypted, and free of unsafe archive paths; the workbooks are valid OOXML files with consistent worksheet structure, headers, and approximately 7,469 numeric records each. Submitted derivation and self-test outputs were reviewed, but uploaded scripts were not executed; Python execution was unavailable in this review runtime, so fitting results were not independently recomputed. No required attachment was inaccessible, and no material content remained unreviewed.

The input-to-output path is practical: a contributor can read the spectra, fit dispersive two-beam/Airy models, classify multi-beam behavior, and write the exact JSON artifact for automated evaluation. License keys, accounts, tokens, and other credentials are not required from the contributor. Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.

**Special Screening:**
None — the evidence does not affirmatively establish AI-synthetic core data, a publicly identifiable benchmark, or a synthetic defect fixture. Historical-looking workbook metadata is insufficient to assign a public-benchmark label.

**Recommended Changes:**
The submission already provides authentic-looking usable inputs, a reproducible reference derivation, and objective acceptance checks. No changes are required.