**Overall Verdict:** Accept — The task is complete, professionally scoped, objectively gradable, and reachable, with one deterministic evaluator-wiring correction needed during deployment.

**Completeness Check:**
- Instruction: Complete — Clearly defines format recovery, the fused Triton implementation, required function signature, prohibited unfused approach, and two deliverables.
- Inputs: Complete — Five valid NPZ cases contain all six promised arrays with consistent dtypes, dimensions, finite numeric data, and group sizes 64 and 128.
- Toolchain: Complete — Linux, Python 3, CUDA-enabled PyTorch, and Triton form a legally obtainable single-machine toolchain; declared licenses and GPU requirements are adequate.
- Reference output: Complete — Includes the quantization oracle, fused reference kernel, deterministic generator, current varied-group grader, and measurements from L4 and A100 hardware. A stale duplicate grader is present but does not require contributor-owned information to correct.
- Rubric: Complete — Defines import and shape gates, relative-error threshold, per-case speed threshold, partial-score formula, hidden workload coverage, warmups, iterations, and median timing methodology.

**File and Task Reachability:**
Attachment classification: reachable. Task classification: reachable. I inspected `B1_w4a16_fused_gemm_triton_INPUT.zip`, including its instructions, JSON manifest, and all five nested NPZ containers; `B1_w4a16_fused_gemm_triton_REFERENCE.zip`, including both graders, format implementation, reference kernel, generator, measurements, task card, and evaluator configuration; and `w4a16_fused_gemm_triton_selftest.txt`. Both ZIP archives and every nested NPZ passed structural and CRC checks, were unencrypted, and contained no unsafe paths, links, device entries, or excessive expansion. No manifest attachment was left uninspected. Python/NumPy and GPU execution were unavailable within the safe review path, so numerical reconstruction and latency measurements were not independently rerun; array headers, primitive payloads, finite-value status, representative values, and internal consistency were checked statically. The completion path is to recover the format from the two probes, implement `output/kernel.py`, document the work in `output/notes.md`, and evaluate six protected cases against the supplied fp32 oracle and colocated unfused baseline. The configured evaluator currently selects a stale `reference/grade.py` that looks for the hidden format module in solver inputs and fixes group size at 128; the supplied `scripts/grade.py` contains the correct protected-reference lookup and 64/128 cases, making this a routine, deterministic evaluator-wiring correction. 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. No affirmative evidence supports an AI-synthetic, public-benchmark, or synthetic-defect-fixture label. Public identifiability was not externally searched because that is outside the permitted evidence boundary.

**Recommended Changes:**
The recovery probes, hidden multi-group workloads, architecture-aware speed threshold, and self-test evidence are substantively sound. No contributor revision is required. During evaluator integration, stage the supplied current grader as the protected reference grader, or update the evaluator selector to use it, and remove the stale duplicate to prevent all submissions from failing before evaluation.