**Overall Verdict:** Accept — Revision 4 provides a coherent, reproducible robotics task with complete inputs, reference implementation sources, and objective evaluation.

**Completeness Check:**

- Instruction: Complete — The full brief defines all four stages, deliverables, interfaces, operating constraints, and scoring.
- Inputs: Complete — Offline packages, pinned source mirrors, 17 schemas, examples, and documented synthetic recordings are supplied; all 371 input/software checksums match.
- Toolchain: Complete — Ubuntu 22.04, ROS 2 Humble, MoveIt, and the hashed Python runtime have a documented setup path with offline task dependencies.
- Reference output: Complete — Reference sources and reproduction recipes cover every stage; the supplied clean-run report records 166/166 passing checks and a 1.000 score.
- Rubric: Complete — Grading code implements hidden-input verification, continuous credit, negative marking, stage weights, and the time penalty.

**File and Task Reachability:**

Attachment classification: `reachable`. Inspected all three attachments: `ur5e_fr3_motion_planning_stack_selftest_evidence.zip`, `ur5e_fr3_motion_planning_stack_reference.zip`, and `ur5e_fr3_motion_planning_stack_inputs.zip`. Their byte counts match the manifest, ZIP integrity checks pass, and no encryption or unsafe top-level paths were found. Inspection covered archive inventories, schemas, representative source content, dependency metadata, numeric recording structures, hidden fixtures, and evaluation reports. Individual dependency implementations were not exhaustively reviewed.

Task classification: `reachable`. One suitable machine can build the offline stack, produce the specified artifacts, and undergo objective evaluation against separately staged hidden data. Solver-visible recordings exclude the true-state channels present in held-out evidence. The robotics workflow was not executed during this review, and public availability was not independently verified. Supplied runs document a network-namespace limitation within an offline test environment.

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 recordings are explicitly disclosed as deterministic simulation data; this does not establish AI fabrication or a synthetic defect fixture. Public-benchmark status remains unverified.

**Recommended Changes:**

The submission provides substantial reference and grading evidence and transparently distinguishes the earlier self-test from its final-rule regrading. No changes are required.