{
 "item": "kap-0",
 "status": "partial",
 "kappa": 0.0,
 "solver": {
  "converged": false,
  "sweeps": 13,
  "control_table_identical": null,
  "note": "Stopped after sweep 13 (diff 2.2e-2, still falling slowly; README says about 110 sweeps). The cgroup caps memory at 1,572,864,000 bytes (memory.max). Unpatched dp.py was killed with exit 137 during sweep 1: the geometry arrays ed/posw/pos2 are about 670 MB, and VN2, its successor and S are 233 MB each. dp_mm.py (in code_zb64) keeps the numerics: geometry moved to disk-backed np.memmap (DP_MMAP=<dir>), VN1/GB/GS released each sweep, and step_end fused into passive so S is never stored. It runs, but page-fault thrashing holds it at about 45 s a sweep on 63% of one core (README: 110 sweeps in about 80 s on 14 cores). The rest of the solve, about 75 min, plus qmdp and 4,608 seed runs did not fit in the 114 min this seat had. A seat with 3 GB or more of memory should run unpatched dp.py."
 },
 "table_change": null,
 "tuning_vs_control": null,
 "holdout_vs_control": null,
 "holdout_vs_flag_qm": null,
 "scored": "nothing, as the brief requires when the solve cannot finish. Seeds 66000..66255 and 10000..12047 are all missing (none run). qm_k0.json was not built.",
 "limitations": [
  "Solve not converged: no qm_k0.json and no check that kappa 1e-5 reproduces qm_k1e-5.json.",
  "tellgauss.py (TellGauss = real.py step 2 with the float-grid tell, as the brief specifies) is written but has not been run, not even a smoke test. Check it before scoring.",
  "dp_mm.py is claimed numerically identical to dp.py by construction (same operations in the same order) but was not checked against pol_mid.json because the solve did not finish.",
  "Fetched solved/ folder: 26 files, sha256 all matched."
 ],
 "code_zb64": "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",
 "code_sha256": "59b469b2331d3f4900798a9f855bd9e2f2fc2c088589a1e20ffcec5131fc077d",
 "code_sha256_of": "the code_zb64 string (base64 of zlib of a tar holding tellgauss.py and dp_mm.py)"
}