**Overall Verdict:** Accept — The task is complete, professionally scoped, self-contained, and objectively evaluable, with only minor nonblocking data and verifier inconsistencies.

**Completeness Check:**
- Instruction: Complete — The objective, LQ survival model, modeling expectations, executable interface, offline constraint, output schema, and extrapolation requirement are clear.
- Inputs: Complete — The three documented CSV inputs are readable and usable, containing 203 training experiment identifiers, 42 cell lines, and 1,456 survival measurements with positive uncertainties and no missing fields. One identifier represents two nearly identical LET conditions because 17.58 and 17.59 were both rounded to `17.6`; this is nonblocking for the intended global pointwise fit.
- Toolchain: Complete — The pinned Python scientific stack is open source, CPU-compatible, and adequately provisioned by the supplied Linux container definition.
- Reference output: Complete — A reference prediction program, packaging wrapper, 68 held-out queries, 436 ground-truth survival rows, calibration notes, and the independent evaluator are supplied. The referenced dataset-generation source programs are not included, so the generation and baseline claims cannot be reproduced from first principles, but this is not required for task completion or fixed-data evaluation.
- Rubric: Complete — Seven executable checks cover required predictions, finite positive parameters, physical plausibility, RBE errors at two survival levels, log-survival error, and improvement over the clinical baseline. The verifier is slightly looser than the written contract because it permits extra columns, extra or duplicate rows, and uses `0.95` as the RBE-max tolerance.

**File and Task Reachability:**
The attachment classification is `reachable`, and the task classification is `reachable`. Both `proton-rbe-input-materials.zip` and `proton-rbe-reference-evaluation.zip` were inspected: they are readable, unencrypted ZIP archives matching their manifested sizes, with 7 and 14 safe text, CSV, Python, shell, JSON, TOML, and container-definition entries respectively. All attached content was reviewed; submitted programs were inspected statically and not executed. Training and held-out cell lines are disjoint, held-out alpha/beta and LET ranges genuinely extrapolate beyond training, and query metadata agrees with every ground-truth row. Solver-visible training assets are separated from reference solutions and evaluator-only truth. The end-to-end path is viable on one ordinary CPU machine: fit from staged survival data, write the required prediction program, run it on hidden query rows, and evaluate its output against supplied truth with deterministic numerical thresholds. 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

**Recommended Changes:**
The scientific workflow, reference method, hidden extrapolation design, and independent scoring path are valid. No material changes are required. Optional hardening would assign unique experiment identifiers to the two rounded LET conditions, enforce the exact output columns and row cardinality in the verifier, align the RBE-max tolerance with the written requirement, and include the referenced generation sources if full provenance reproduction is intended.