{"id":2523,"job_id":5280,"problem_id":1,"lane_id":2,"type":"explore","user_id":1,"model":"deepseek-v4-flash","provider":"deepseek","report_md":"# Job #5280 (route 220, first look, lane adversarial) — the cross-route connection is already queued; only the N4u transfer is uncovered\n\nJob 5280, route 220, stage `first_look`, lane adversarial, general mode.\nRead-only served fetches plus the served estimator source. **cpu_hours 0** — no\npublished number recomputed.\n\n## Summary\n\nRoute 220 proposes that the normaliser confound #2459 measured on route 31's *thinning*\ncontrol is a **cross-route, structural** prerequisite: it should be removed by one\nmatched-normaliser run that serves route 31's amplitude clause and route 184's level\nstatistic at once, and any future permutation-cloud route should pin the normaliser first.\n\nTwo things are true and they point opposite ways.\n\n1. **The operational content is already on the record.** Route 31's *registered*\n   `next_step` (#2459) already runs the matched-normaliser device over **both** streams,\n   `N4u` and `thinning`, and reports per-cell-case `z_level`; route 184's registered\n   `next_step` (#2490) already consumes that reading. Route 220's \"one run serves both\n   routes\" mapping therefore adds no new experiment, and its own `next_step` (run #2459's\n   device as registered) **duplicates a linked route's registered step** — the thing the\n   assignment schema forbids.\n\n2. **The route's one load-bearing premise is unchecked, and the served evidence makes it\n   doubtful.** The confound was measured on the *thinning* substitute — the one control\n   #2348's own fidelity file shows *destroys* within-class divisibility placement.\n   `newstat_p.py`'s own docstring states N4 \"preserves the exact value multiset\", and\n   #2348 measures `N4u` preserving the value-to-class association *far above* chance. The\n   route admits (its `uncertainty_md`) that `N4u`'s mean_sq was never measured; `N4u` is\n   the stream route 184's `z_level` actually compares against.\n\nSo the route should not be funded to re-run a queued device, but it should not be closed\neither: one small measurement decides whether the confound is structural (route 220\nvindicated) or specific to the thinning placeholder (route 220 refuted). That measurement\nis the distinct next step below.\n\n## What I checked (no compute)\n\n- **Route 220** `GET /research-routes/220`: `state proposed`, rev 1, `last_return_id 2496`,\n  one event (`#2496`, 2026-10-07T21:26:34Z), `next_step` = run #2459's device.\n- **Route 31** `GET /research-routes/31`: `active`, rev 21, `last_return_id 2459`,\n  `next_step.method` runs the matched-normaliser device \"for the streams N4u and thinning\".\n- **Route 184** `GET /research-routes/184`: `active`, rev 4, `last_return_id 2490`,\n  `next_step.method` already says to report \"#2459's matched-normaliser reading of the same\n  runs alongside for cross-checking\".\n- **#2459** (route 31, progress): the measured confound is on the *thinning* draws only\n  (observed `mean_sq` 11.42 / 17.31 / 24.90 against thinning ranges [978.9, 10815.6],\n  [1379.1, 12024.5], [2234.1, 23984.1]; rel-sd 202–299).\n- **#2348** (route 184, progress): `control_fidelity.json` — `n4u_is_above_chance true`\n  (0.8528 vs 0.0064; 0.9261 vs 0.4696), `thinning_is_at_chance true` (0.0049 vs 0.0048;\n  3.97e-5 vs 3.33e-5). `served/newstat_p.py` docstring: the N4 cloud \"preserves the exact\n  value multiset and the divisibility pattern\".\n- **Served estimator** `n4_run.py`: `grid_of(stat, f, mean_sq)` takes `mean_sq` as an\n  argument; the observed grid is evaluated at `resid_sq = (resid*resid).sum()/M`, and\n  *each* draw of *each* stream at its own `float((r*r).sum())/M`. `residual_vec` groups\n  positions by `(class_key(n,U), class_key(n-2,Y))` — a finer partition than the\n  `m mod P_U` classes N4u permutes within — so N4u's residual `mean_sq` is **not**\n  provably preserved by the docstring's multiset statement. This is why the transfer must\n  be measured, not asserted.\n\n## Change to the record\n\nRoute 220's contribution is downgraded from \"new cross-route prerequisite\" to \"a correct\ncoordination note whose single novel premise is unmeasured\". The decisive device is\nalready queued on route 31 and consumed on route 184; the uncovered step is the `N4u`\nmean_sq measurement. Outcome **promising**, with that distinct next step.\n\n## Unresolved obligations\n\n- 46 of @Benjaminsen's returns wait for a trusted verdict (nothing for this session to do).\n- Route 220's `depends_on` was empty; #2496 builds on #2459, #2490, #2348 and #2374, which\n  this return records.\n\n## Traps carried\n\n- A `next_step` that asks a linked route to run its own registered step is a duplicate; the\n  first look must find the *uncovered* step, not restate the queued one.\n- `POST /files` refuses a private execution id inside an uploaded artifact; the payload\n  builder asserts none of this run's ids appear in any upload.\n","patch":null,"cpu_hours":0,"hashes":{"sah.py":"21a1d3556191bf54458b13fa0ebe41b4550fb92a33ab9bee6518d82ef222c843","n4_run.py":"664f82b737e2ea948d195d0fd2ed5adf37ba68cdce9c67156d354028dec62477","check_dw.py":"e3b93a71b9a4925e31d9628888db2d9a0ad0665172584bf2ffb7d8ce70244258","fetch_dw.py":"7b6b9c2f5b0e7f181ba06d2904683de9ff1aa3f20bab210ef9a62d1473c02a4c","check_dw.out":"786ba2e4de5ced3f750e09f9483efbb0ed7f2b5638f7080bb70806d30eb5088d","newstat_p.py":"c3b03d2fcacfac82ef8bc62820082b39f843a80005ea33e33e9f3554e9cb8011","recipe_dw.md":"9fca52ab6c7c6f59f096ea4cd6d6964dc748bd397ecf73e8e4698f49c448600a","redact_dw.py":"5aa6f5aba661b288418efb6fa1d32594cbda77d12e2cbcd2b72e6d480d3be93d","report_dw.md":"1b38b0fd6a1e766e34a883b75f2f5cb43a62406d6b74002fb1f4d47837e1a63d","residual.out":"5e4cdc557a564ef3f81ea370e23fb7ecd577d35533a6795ce6f4d2e830132db6","route31.json":"71b748d4a9c57cd41f46831907c92857d1dd4d5add3b1a9077d982cddc5ba8e2","route184.json":"6980bcec65ebb1cb41aedeef6f9046ca9cf34b42d636de950f973f0cff067b43","route220.json":"7a049dc38604766e635f8415a5dec539362b27da1982b40b7002e8cf7efe2d9e","evidence_dw.md":"68b50e38ff1db6d954fffc3aa5712f2b4e3fbf8937acb777b4647602ce7913a5","next_step.json":"5d18ed24e0161b3699df0b66b21a034f52c142ef29ce0142a02db1ed8bf8a443","residual_dw.py":"7fade320df56ea5e974ae0f7f8c6791eab011c3dd37941c6d341fa920c6dac17","prior_art_dw.md":"0b77b3a8afae6a5063614e97aa7e88980c9d97b3f8bb6d175677215d9f26fc1e","return_2348.json":"1092bb9c3d8a058dd969e584f08921cf112e7cd29531579f881b4b6c2256c2f5","return_2374.json":"ea02e61311a7c4f263d1b259586a497d6a7ee5ed3f58042848249f8a8a0800a9","return_2459.json":"daacdaa51f14d00dbd369ecc6d0b0b99b71249b85dfc322e0b23f2ed9704231f","return_2490.json":"e10c326be9fa7c80baa79811c618ed04b3673396d384521bf54ee6f63c382d4f","return_2496.json":"9ba721113484aa3b0c0e7fe57b12665f07c93ff886f75e87b7b83aeee4cfa352","check_dw.control.out":"dffc4ca41ef480ff582f789426b77f270b378dd43012303efcd34f0970a98653","research-routes.json":"ed7342ec0a95454a3275113995e5672f1a3c3d0668784b2ed457a3e3ece3ff59","control_fidelity.json":"b4c0c22e524dd63c693abf9c9ea3ceb5f2216020909f404bef69e82e97011cb0"},"author_rung":"heuristic","status":"recorded","final_rung":"recorded","created_at":"2026-10-08T02:18:04.533Z","repo_url":null,"commit":null,"cites":{"files":[],"handles":[],"returns":[2496,2459,2490,2348,2374],"messages":[]},"tokens":{"log":"custom","input":0,"models":{"deepseek-v4-flash":0},"output":0,"source":"none","entries":0,"cache_read":0,"cache_write":0,"observed_models":["deepseek-v4-flash"]},"paper_slug":null,"revision_path":null,"revision_sha":null,"recipe_md":"# Recipe — route 220 next step: measure the N4u stream's residual mean_sq\n\nThis is a read-only local measurement against the served pipeline. It does not reproduce\nany published number and does not rerun #2459's matched-normaliser device.\n\n## Inputs (served, hash-pinned)\n\n- `n4_run.py` (#2265) — sha256 `664f82b737e2ea948d195d0fd2ed5adf37ba68cdce9c67156d354028dec62477`\n  (`GET /files/664f82b7...`).\n- `newstat_p.py` (#2267, re-served with #2348) — sha256\n  `c3b03d2fcacfac82ef8bc62820082b39f843a80005ea33e33e9f3554e9cb8011`.\n- upstream served helpers `f636_fibre-sign-lag.py`, `f654_job1438-cls-resid-offset.py`,\n  `f2002_sieve-null.py` (paths resolved by `n4_run.py`) and `control_fidelity.json` (#2348).\n- Anchored cases: `x=2^16` cfg `[10,10,1,1]`, `x=2^17` cfg `[14,14,1,1]`,\n  `x=2^20` cfg `[14,14,1,1]`; 200 draws; seed 4164 (add 4165/4166 only as a stability check).\n\n## Steps\n\n1. Import `n4_run` as a library and monkey-patch nothing. Read `run_case` and note that\n   it already computes `resid_sq = float((resid*resid).sum())/M` for the observed grid and\n   `float((r*r).sum())/M` for every draw of every stream; it simply does not retain them.\n2. Copy `run_case` into a local harness (do not edit the served file) and add, for each of\n   `N3`/`N4u`/`N4y`, retention of the observed `resid_sq` and the list of per-draw\n   `mean_sq` values; keep every other line — anchors, seeds, permutations — identical.\n3. Run it once per anchored case at seed 4164 (deterministic, single process, < 0.3 CPU-h).\n4. Report, per case and stream: observed `mean_sq`, draw min/max, draw rel-sd, and whether\n   the observed value lies inside the draw range. Thinning, where available via the same\n   served libraries, is the positive control (#2459's measured mismatch must reappear).\n5. Decide the success/failure branch above and record the raw per-draw arrays as evidence.\n\n## Acceptance check (smallest falsifier, written first)\n\nThe run must reproduce `control_fidelity.json`'s `N4u` same-residue rates (0.8528 at 2^16,\n0.9261 at 2^17) within tolerance before any mean_sq reading is used — that anchors the\nharness to #2348's served numbers. If the anchor fails, the mean_sq reading is void.\n\n## Controls\n\n- `sah.py bounded --run run-2026-10-08-dw --limit 3600 -- python3 measure_mean_sq.py`\n  (process-group SIGKILL, no child outlives the run); check `sah.py procs` afterwards.\n- No network in the measurement; served files are already fetched and hash-checked.\n\n## Do NOT\n\n- Do not run #2459's matched-normaliser device (mean_sq held fixed; that is route 31's\n  registered next step).\n- Do not recompute z_level, rebuild N4/N3, or re-derive the anchors.\n- Do not treat the multiset statement in `newstat_p.py`'s docstring as proof that N4u's\n  residual mean_sq is preserved (the residual partition is finer than the permuted one).","verification":null,"target":null,"finding":null,"human_md":null,"provisional":false,"effects_applied_at":null,"effort":null,"also_fix":null,"transcript_omitted":{"share":0,"omitted":0,"outputs":0},"patch_hash":null,"superseded_by":null,"duplicate_of":null,"transcript_resubmitted_at":null,"file_notes":null,"research":{"outcome":"promising","route_id":220,"next_step":{"method":"Using the served pipeline unchanged (n4_run.py from #2265, newstat_p.py from #2267 re-served with #2348) as a library, instrument run_case to record, per stream, the observed residual mean_sq resid_sq = (resid*resid).sum()/M and the 200 per-draw values float((r*r).sum())/M for N4u (the question) and thinning (the #2459 control, as a positive control), at x = 2^16 beta U=10, x = 2^17 alpha U=14, x = 2^20 alpha U=14, seed 4164. Report the observed value against each stream's draw min/max and relative sd. Do NOT hold mean_sq fixed (that is #2459's device, already queued on route 31), do NOT recompute z_level, and do NOT rebuild N4/N3 or re-derive the anchors.","compute":{"ram_gb":4,"disk_gb":1,"cpu_hours":0.3},"failure":"The N4u mean_sq verdict is case-dependent, or the observed value lies inside the N4u draw range at one case and outside it at another without a clean margin: neither the transfer nor its refutation is established at these cases, and the next ingredient is a wider case set or higher draw count, not a z_level run.","success":"A clean, seed-stable read at all three anchored cases: N4u's 200-draw residual mean_sq either all contain the observed value (transfer refuted; the #2459 confound is thinning-specific; route 220's cross-route generalisation is refuted and the route closes, with route 31's device still valid for its own thinning control) or all exclude it (transfer established; the confound is shared by the stream route 184 uses and #2459's matched-normaliser run is load-bearing for route 184 too).","question":"Is the N4u cloud's residual mean_sq matched to the observed grid's mean_sq at the three anchored cases, i.e. does the #2459 mean_sq confound exist in the stream N4u that route 184's z_level actually compares against?","budget_hours":1,"required_tools":["python3","numpy"],"required_sources":[]},"depends_on":[2496,2459,2490,2348,2374],"evidence_md":"Route 220 asks to treat the normaliser confound #2459 measured on route 31's thinning\ncontrol as a cross-route prerequisite, removed by one matched-normaliser run and applied to\nany future permutation-cloud route. The served record decides two things.\n\n(1) THE OPERATIONAL CONTENT IS ALREADY QUEUED. Route 31's registered next_step (#2459,\n2026-10-07) already runs the matched-normaliser device \"for the streams N4u and thinning\"\nover the whole anchored set, reporting per-cell-case z_level. Route 184's registered\nnext_step (#2490, 2026-10-07) already says to report \"#2459's matched-normaliser reading of\nthe same runs alongside for cross-checking\". So the \"one run serves both routes\" mapping\nroute 220 claims as new is already the registered step of a linked route and already\nconsumed by the other. Route 220's own next_step (run #2459's device as registered)\nduplicates that registered step.\n\n(2) THE ONE NOVEL PREMISE IS UNCHECKED AND DOUBTFUL. The confound #2459 measured is on the\nthinning placeholder only: observed residual mean_sq 11.42 / 17.31 / 24.90 lies outside the\nthinning draws' entire ranges [978.9, 10815.6] / [1379.1, 12024.5] / [2234.1, 23984.1]\n(rel-sd 202-299). But #2348's own control_fidelity.json shows thinning is exactly the\ncontrol that destroys within-class divisibility placement (same-residue rate at the 1/P_U\nchance level: 0.0049 vs 0.0048 at 2^16; 3.97e-5 vs 3.33e-5 at 2^17), while N4u preserves\nthe value-to-class association far above chance (0.8528 vs 0.0064; 0.9261 vs 0.4696), and\nserved/newstat_p.py's docstring states the N4 cloud \"preserves the exact value multiset and\nthe divisibility pattern\". The route itself admits (uncertainty_md) that N4u's mean_sq was\nnever measured, because #2348's fidelity file has no mean_sq column.\n\nN4u is the stream route 184's z_level actually compares against. Reading the served\nestimator settles that the transfer cannot be assumed from the multiset statement:\nn4_run.py evaluates the observed grid at resid_sq = (resid*resid).sum()/M and every draw of\nevery stream at its own float((r*r).sum())/M (grid_of(stat, f, mean_sq) takes mean_sq as an\nargument); residual_vec groups positions by (class_key(n,U), class_key(n-2,Y)), a finer\npartition than the m mod P_U classes N4u permutes within. So N4u's residual mean_sq is not\nprovably preserved, and the transfer is a live, checkable question rather than an\nestablished structural fact.\n\nWHAT THIS CHANGES. Do not fund a duplicate of route 31's registered run, and do not accept\nroute 220's generalisation (\"any future permutation-cloud route pins the normaliser first\")\nahead of the evidence that would establish it. (The standard remedy for a non-exchangeable\npermutation null is a properly studentized/pivotal statistic, which is a different fix from\npinning the normaliser; see prior_art_md.) Fund instead the single uncovered measurement:\nwhether N4u's own residual mean_sq is matched to the observed at the anchored cases. If it\nis matched, the confound is thinning-specific, route 220's cross-route claim is refuted, and\nroute 31's device remains valid for its own thinning control. If it is not matched, the\ntransfer is real and route 220's connection is load-bearing for route 184 too.\n\nSCOPE. Read-only served inspection; no published number recomputed; nothing here bounds\ntwin primes, G2 or beta_2. Depends on #2496 (this route), #2459, #2490, #2348, #2374.","prior_art_md":"Prior-work search 2026-10-08 (this run), building on #2496's 2026-10-07 record.\n\nFIELD. The general principle is standard: a permutation test is exact under the null only\nwhen the observed and the permuted cloud are exchangeable, and it requires the test\nstatistic to be exchangeable under the null group; when the observed and the null cloud have\ndifferent first-order structure the comparison is confounded.\n- \"The Exchangeability Assumption for Permutation Tests\" (arXiv:2406.07756, 2024) states the\n  assumption and how it fixes the appropriate null sampling distribution.\n- Wikipedia, \"Permutation test\": a permutation test needs the statistic to be exchangeable\n  under the null; in some cases a *properly studentized* statistic is asymptotically exact\n  even when the exchangeability assumption is violated.\n- Studentized / pivotal permutation methods (Ditzhaus et al., PMC11620287, 2023; Neubert &\n  Brunner 2007; arXiv:2505.24774): studentizing the statistic is the standard remedy when\n  the null cloud's scale differs from the observed's, giving an asymptotically pivotal\n  statistic rather than pinning one normalising constant.\n\nEXACT DIFFERENCE FROM THIS PROJECT. The project's residual-grid permutation nulls\n(newstat_p.py / n4_run.py) pass an explicit `mean_sq` normaliser into `stat.rl`; when the\ncloud is drawn from a placement-destroying control (thinning), that normaliser is off by\n10^2-10^3 (#2459) and the comparison is confounded. The located field work treats the same\nfailure mode by *studentizing* the statistic, not by pinning `mean_sq` at the observed\nvalue; the project's matched-normaliser device is a different, narrower fix (hold the\nnormaliser fixed for each draw). No located source treats this specific normaliser argument\nof `stat.rl` on a residual grid. So route 220's methodological point overlaps a known\ngeneral principle, but its claim that the confound is *structural* across clouds is not\nsupported by the field sources: the field criterion is whether the two clouds are\nexchangeable / the statistic pivotal, which is a property of the *specific* control, not of\npermutation-cloud methods in general.\n\nPROJECT RECORD CONSULTED. #2459 (route 31) measured the confound on the thinning control;\n#2490 (route 184) proposed pinning stat.rl's mean_sq semantics and already consumes #2459's\nreading; #2348 (route 184) re-served newstat_p.py and measured N4u's same-residue rate above\nchance; #2374 (route 31) established the consume-not-duplicate principle for the shared\ncontrol; #2496 (route 220) is the proposal under review. No other project route documents\nnormaliser matching for permutation-cloud statistics.\n\nEXACT REMAINING GAP. Whether the measured confound transfers from the thinning substitute to\nthe N4u cloud — the stream route 184's z_level actually uses. No served record or queued step\nmeasures N4u's own residual mean_sq against the observed (the queued #2459 run holds mean_sq\nfixed and reports z_level; route 184's phases 0-2 pin semantics and joint feasibility, not the\nraw N4u mismatch). That gap is the bounded next step.\n\nSOURCES. arXiv:2406.07756; en.wikipedia.org/wiki/Permutation_test; PMC11620287 (Ditzhaus\n2023); Neubert & Brunner 2007; arXiv:2505.24774; plus served project returns #2459, #2490,\n#2348, #2374, #2496 and served files served/newstat_p.py, served/n4_run.py,\nout/control_fidelity.json."},"research_route_id":220,"verification_plan":null,"verification_fingerprint":null,"review_admitted_at":null,"department_id":"dept_0e793a31e299699dfaaa6fee","run_id":"run_c8d16ffcbd87c2a9fccd27f9","triage_lead":null,"revision_base_sha":null,"integration":null,"resolves":null,"handle":"Benjaminsen","job_brief":"Search online for existing attempts, results, tables and datasets before testing feasibility. Reuse the recorded search and inspect the closest sources and weakest assumption. Use published numbers with citations; do not reproduce them in a first look. Seek the smallest experiment on the uncovered step. Recommend promising only with specific evidence and a bounded next step; do not claim the route is proved. Map the assumptions of any borrowed method onto this problem.\n\nRead GET <project base>/research-routes/220 and return #2496. Return the ordinary report and transcript plus research: {route_id: 220, outcome: \"promising|progress|blocked|inconclusive|known|result\", evidence_md: \"what the evidence changes, <=4000 chars\", prior_art_md: \"updated online search record, sources and exact remaining gap, <=4000\", next_step: {question, method, success, failure, budget_hours} <only for continued pursuit; what to do, never when or how fast; it must not ask for what a return on this route or a linked route already did, and the route returns it builds on go in depends_on or cites.returns>, obstacle: {kind, statement, assumptions, evidence, revisit_when} <for blocked/inconclusive>, depends_on: [<return ids actually required>]}. A result with a distinct next_step requests review and continues pursuit concurrently; omit next_step when no further experiment is warranted. Use known with prior_art_md and no next_step or obstacle when cited prior work already covers the proposed contribution; it stops automatic investigation without requesting review. The evidence grade is separate. Do not close a broad route because one proof attempt failed.","review_deferred":false,"in_triage":false,"triage":[],"lean_statement_binding":null,"verification_runs":[],"verification_state":null,"verification_summary":null,"canonical_return":null,"review_history":[],"dependencies":[{"id":"2348","status":"recorded","final_rung":"recorded","canonical_return_id":null},{"id":"2374","status":"recorded","final_rung":"recorded","canonical_return_id":null},{"id":"2459","status":"recorded","final_rung":"recorded","canonical_return_id":null},{"id":"2490","status":"recorded","final_rung":"recorded","canonical_return_id":null},{"id":"2496","status":"recorded","final_rung":"recorded","canonical_return_id":null}],"cited_by":[],"route_dependents":[220],"research_url":"/projects/twin-primes/research-routes/220","transcript_url":"/projects/twin-primes/return/2523/transcript","files":[{"sha256":"1b38b0fd6a1e766e34a883b75f2f5cb43a62406d6b74002fb1f4d47837e1a63d","name":"report_dw.md","bytes":4794},{"sha256":"68b50e38ff1db6d954fffc3aa5712f2b4e3fbf8937acb777b4647602ce7913a5","name":"evidence_dw.md","bytes":3401},{"sha256":"0b77b3a8afae6a5063614e97aa7e88980c9d97b3f8bb6d175677215d9f26fc1e","name":"prior_art_dw.md","bytes":3369},{"sha256":"5d18ed24e0161b3699df0b66b21a034f52c142ef29ce0142a02db1ed8bf8a443","name":"next_step.json","bytes":1906},{"sha256":"9fca52ab6c7c6f59f096ea4cd6d6964dc748bd397ecf73e8e4698f49c448600a","name":"recipe_dw.md","bytes":2842},{"sha256":"7b6b9c2f5b0e7f181ba06d2904683de9ff1aa3f20bab210ef9a62d1473c02a4c","name":"fetch_dw.py","bytes":1486},{"sha256":"e3b93a71b9a4925e31d9628888db2d9a0ad0665172584bf2ffb7d8ce70244258","name":"check_dw.py","bytes":4891},{"sha256":"786ba2e4de5ced3f750e09f9483efbb0ed7f2b5638f7080bb70806d30eb5088d","name":"check_dw.out","bytes":1243},{"sha256":"dffc4ca41ef480ff582f789426b77f270b378dd43012303efcd34f0970a98653","name":"check_dw.control.out","bytes":1243},{"sha256":"5aa6f5aba661b288418efb6fa1d32594cbda77d12e2cbcd2b72e6d480d3be93d","name":"redact_dw.py","bytes":3834},{"sha256":"7fade320df56ea5e974ae0f7f8c6791eab011c3dd37941c6d341fa920c6dac17","name":"residual_dv.py","bytes":2548},{"sha256":"5e4cdc557a564ef3f81ea370e23fb7ecd577d35533a6795ce6f4d2e830132db6","name":"residual.out","bytes":55},{"sha256":"ed7342ec0a95454a3275113995e5672f1a3c3d0668784b2ed457a3e3ece3ff59","name":"research-routes.json","bytes":475323},{"sha256":"7a049dc38604766e635f8415a5dec539362b27da1982b40b7002e8cf7efe2d9e","name":"route220.json","bytes":16009},{"sha256":"71b748d4a9c57cd41f46831907c92857d1dd4d5add3b1a9077d982cddc5ba8e2","name":"route31.json","bytes":247191},{"sha256":"6980bcec65ebb1cb41aedeef6f9046ca9cf34b42d636de950f973f0cff067b43","name":"route184.json","bytes":61262},{"sha256":"9ba721113484aa3b0c0e7fe57b12665f07c93ff886f75e87b7b83aeee4cfa352","name":"return_2496.json","bytes":16107},{"sha256":"daacdaa51f14d00dbd369ecc6d0b0b99b71249b85dfc322e0b23f2ed9704231f","name":"return_2459.json","bytes":30069},{"sha256":"e10c326be9fa7c80baa79811c618ed04b3673396d384521bf54ee6f63c382d4f","name":"return_2490.json","bytes":23492},{"sha256":"1092bb9c3d8a058dd969e584f08921cf112e7cd29531579f881b4b6c2256c2f5","name":"return_2348.json","bytes":40093},{"sha256":"ea02e61311a7c4f263d1b259586a497d6a7ee5ed3f58042848249f8a8a0800a9","name":"return_2374.json","bytes":23354},{"sha256":"c3b03d2fcacfac82ef8bc62820082b39f843a80005ea33e33e9f3554e9cb8011","name":"newstat_p.py","bytes":8774},{"sha256":"664f82b737e2ea948d195d0fd2ed5adf37ba68cdce9c67156d354028dec62477","name":"n4_run.py","bytes":12502},{"sha256":"b4c0c22e524dd63c693abf9c9ea3ceb5f2216020909f404bef69e82e97011cb0","name":"control_fidelity.json","bytes":1938},{"sha256":"21a1d3556191bf54458b13fa0ebe41b4550fb92a33ab9bee6518d82ef222c843","name":"sah.py","bytes":56280}],"decided_by_author_handle":false,"reviews":[],"decisions":[],"decision":null,"duplicates":[],"cited_messages":[]}