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teal-sea / zeta-labstate of record · compiled 28 Sep 2026 · revision e4945c4 · source

Library · hunts/lambda_dh_exact/RUNS.md

RUNS: hunt #119, `lambda_dh_exact`

1,053 words · 182 lines · source

Every run this hunt ever performed, including the ones that produced nothing. Reconstructed 2026-09-12 from the committed artifacts and the session record, because the runs that would normally have written this file died before writing anything.

The hunt ran a theory phase, opened an evaluation phase, and stopped there. The evaluation agents and the adjudicator all terminated on API 529 errors on 2026-08-18. Three consequences, stated once and true of everything below: no verdict was written, no adversary attacked any number, and the nine predictions registered in MISSION.md were never scored.

Run 1: the theory phase

id: R1-theory
hunt: lambda_dh_exact
started: 2026-08-18
finished: 2026-08-18
outcome: completed; derivations and their probes ran, nothing was adjudicated
ran:
  - theory.py
artifacts:
  - theory_results.json
  - MISSION.md sections 1 to 5 and the nine registered predictions

What it produced, each a float-grade measurement on synthetic or already-published configurations and none of them on a new Davenport-Heilbronn zero:

Run 2: the low and middle screens

id: R2-screen
hunt: lambda_dh_exact
started: 2026-08-18
finished: 2026-08-18
outcome: partial; the low screen completed, the middle screen was cut off mid-sweep and no zero was ever located from either
ran:
  - deep_zeros.py validate
  - deep_zeros.py screen 8 10000
  - deep_zeros.py screen 10000 100000
artifacts:
  - deep_zeros.json

Run 3: the screen's own controls

id: R3-control
hunt: lambda_dh_exact
started: 2026-08-18
finished: 2026-08-18
outcome: completed; the screen nests correctly in its inner abscissa
ran:
  - deep_zeros.py screen 8 600 --sigma-c 0.55
  - deep_zeros.py screen 8 600 --sigma-c 0.75
artifacts:
  - deep_zeros_control.json

Both completed. Over t in [8, 600] the flagged-window sets are strictly nested as the inner abscissa drops, which is what the argument principle requires of a contour that is being widened:

inner abscissa Re sflagged windows in [8, 600]total winding
0.8511
0.7577
0.551314

and set-wise 0.85's window is contained in 0.75's, which is contained in 0.55's. The single window the deepest screen flags below height 600 is [228, 248], which is the window holding the pair at gamma = 240.4046 that hunts/flow_repair had already measured. That agreement was not arranged.

Run 4: the height-10^6 screen, the only evaluation datum

id: R4-deep
hunt: lambda_dh_exact
started: 2026-08-18
finished: 2026-08-18
outcome: completed for its window; one zero located, float grade, never enclosure-decided and never adjudicated
ran:
  - deep_zeros.py screen 1000000 1001200
  - deep_zeros.py locate
artifacts:
  - deep_zeros_1e6.json

complete: true over t in [1000000, 1001200], 7 refinements, 762.8 s, exactly one flagged window [1000440, 1000460], and stage_locate did run here. The zero:

quantityvalue
gamma1000459.7433532759
beta0.8583118590734415
depth y0 = beta - 1/20.35831185907344154
winding count1
winding defect2.220446049250313e-16
abs f at the root4.3516753449553316e-11
routefloat64 Euler-Maclaurin + Newton

Read it as one float-grade location with a unit winding count whose defect is at machine precision. It is not an enclosure and the sweep covered 1200 units of height, not a decade.

Run 5: the landing census

id: R5-landing
hunt: lambda_dh_exact
started: 2026-08-18
finished: 2026-08-18
outcome: completed; nine landings with their neighbour distances, used as the model's training and holdout data
ran:
  - landing.py
artifacts:
  - landing.json

The nine hunts/flow_repair pairs with measured nearest and second-nearest neighbour distances, local strip-zero counts and measured densities: the data theory.py's calibration consumes.

Runs that were started and produced nothing

id: R6-dead
hunt: lambda_dh_exact
started: 2026-08-18
finished: 2026-08-18
outcome: null; all four agents terminated on API 529 before writing an artifact
ran:
  - evaluation agent 1, the depth-versus-height law above height 10^4
  - evaluation agent 2, the shave model against new landings
  - evaluation agent 3, the no-creation step as a proof rather than a sample
  - the adjudicator, which would have scored the nine predictions
artifacts:

Recorded because a null run that is not written down looks like a run that was never planned. The five kill conditions in MISSION.md are therefore all unfired rather than survived, and the pre-registered verdict (that the bracket does not collapse) is unevaluated.