Phase 5 · complete · your three conditions, as code

The recovery loop, rebuilt — and the one place it cannot be faithful

Your three recovery conditions differ by exactly one thing each. Phase 5 reproduces all three from your original prompts, verified character by character. It also found the one boundary this project cannot cross, and chose to record it rather than paper over it.

Conditions4
Tests319
Real cell runAPPLIED
Statedone

What the three conditions actually are

Reading your Python scripts side by side makes something clear that the chapter states but the code makes vivid: each condition adds exactly one thing to the one before it. Nothing else changes.

EACH CONDITION ADDS EXACTLY ONE INPUT Blind Retry the bug report the current file try again, knowing nothing Reflection-only the bug report the current file + your previous try Diagnose+Revise the bug report the current file + your previous try + what really went wrong That last addition — the real test output — is the manipulated variable of your whole study. Everything the dissertation concludes rests on the difference between the last two boxes being only that one input. A test now proves it: the two prompt templates differ by exactly one placeholder, `{real_evidence}`, and nothing else.
“Blind” is stronger than it sounds: the model is not shown its previous attempt at all. So that condition measures how much recovery is explained by simply rolling the dice again — which is the right control.

The prompts were not retyped

The prompt is the experiment. If this rebuild worded things even slightly differently, new results would not be comparable with your Chapter 5 — and no ordinary test would notice, because everything would still work on a subtly different prompt.

So the templates are extracted mechanically from your Python scripts into a committed file, along with the fingerprint of each source file. Two checks run:

CheckWhat it proves
The rebuilt prompt equals the stored template with values filled inNo drift in the reimplementation
The stored template appears verbatim inside your original scriptThe stored copy is genuinely yours, not a paraphrase
Each source file still matches its recorded fingerprintNothing changed underneath since extraction

Your own words, checked by a machine.

This is the same trick as phase 3's byte-identical table, applied to the input rather than the output. Between the two, both ends of your experiment are now pinned: the prompts that went in and the numbers that came out.

Being told which file to fix

Your scripts pick the file to edit out of the reference solution — the known correct patch. The model is told where to look. Your dissertation states this plainly and uses the technical name for it: oracle localisation, in a system described as “single-shot, oracle-localised, single-file patch generation.”

Since it is declared method, phase 5 reproduces it exactly rather than improving it. But two things were worth checking.

Does the answer leak? The model is told the file path. It must never see the reference patch itself, or it would be handed the fix. A test now takes a task whose correct answer is recognisable, builds the real prompt, and asserts the current file is present while the solution is absent.

Your code takes only the first file. If a bug needed two files changed, the second would be dropped silently. I measured every task in the benchmark:

FILES TOUCHED BY THE REFERENCE SOLUTION, ALL 300 TASKS exactly one file — 300 of 300 (100%) Zero tasks need more than one file. So taking the first discards nothing at all — the single-file limit belongs to the benchmark, not to your design. The code now refuses a multi-file task loudly instead of quietly dropping half of it.
A pleasant result: a shortcut in the original turns out to cost nothing, and now there is a measurement saying so rather than an assumption.

The boundary this project cannot cross

Here is the honest difficulty, and it is worth understanding properly because someone may ask you about it.

Diagnose+Revise is defined by giving the model the real result of running its patch against the real test suite. The prompt says so to the model in as many words: “the REAL, ACTUAL result of running your previous patch against the real test suite (ground truth, not a guess)”.

This pipeline cannot run tests. It deliberately uses no Docker — that is the decision that makes everything reproducible on a laptop in seconds. But it means that for any new task, the strongest evidence available is git refusing the patch. Real, but not the same thing.

THREE WAYS TO HANDLE IT 1 · Invent plausible test output Fabrication. Not considered. 2 · Quietly use the apply error, still call it Diagnose+Revise The tempting one, and the worst: the condition would mean two different things depending on the task, and no reader could tell which. Unfalsifiable. 3 · Record where the evidence came from, on every single cell ✓ Each result says whether it got real test output or only an apply error — and whether it can be compared with your Chapter 5 at all.
The same discipline as phase 1's rule that a patch which merely applies can never be reported as “resolved”. When you cannot measure something, say so in the data rather than in a footnote.

Two consequences follow, both deliberate. An apply failure found in your archive is labelled an apply error, not test output — being in the archive does not change what it is. And a cell with no evidence is refused outright and the refusal recorded, rather than run as Reflection-only under a Diagnose+Revise label.

One real cell, start to finish

The proof that this works is not a passing test; it is a real task, a real model and a real verdict from git. Task django__django-11283, the migration bug, on muse_30b: 

template    day12_step1_blindretry_searchreplace.py  sha d2f0d9298c30
prompt      4,015 characters
response    3,361 characters

OUTCOME
  kind               APPLIED
  files changed      django/contrib/auth/migrations/0011_update_proxy_permissions.py
  verification       apply  (resolved is null by type)
  condition honoured true
  comparable to ch5  true

COST
  prompt 899  completion 1352  135,656ms

The model proposed excluding permissions that already exist for the target content type before updating — and git accepted the patch cleanly.

What that does not say.

It does not say the bug is fixed. Nobody ran the tests, because nothing here can. The row says verification: apply and its resolved field is empty by type — the safeguard from phase 1, doing its job on the very first live result. A patch that applies is a real, checkable fact; “this resolves the issue” is a different claim and this pipeline is not entitled to make it.

Every path ends in a record

One function runs every condition, so any difference in results comes from the prompt and nothing else. Your original had a separate script per condition, each edited between runs — which means a difference in handling could never be ruled out as the cause of a difference in outcome.

That function has one absolute rule: it always returns a result. There is no path — refusal, budget stop, provider failure, unreadable response, rejected patch — on which a cell can quietly disappear. This is the defect the whole project exists to prevent, and the one phase 3 found 22 real instances of in your original data. Ten tests, one per path, hold it in place.

Deliberately broken again

Sabotage introducedResult
Mark an apply error as comparable with Chapter 52 tests failed
Let Diagnose+Revise run with no evidence at all2 tests failed
Silently take the first file instead of checking there is only one1 test failed

All three reverted, all 319 pass. Unlike phase 4, every sabotage was caught first time — the lesson from that phase's escaped mutation was applied while writing these tests rather than after.

What was left undone, and why

Your configuration asks for a cleverer context setting: send only the relevant function instead of the whole file, which would cut a 3,300-line file from 31,000 tokens to about 3,000 and is what makes repeat runs affordable.

It is not implemented yet, for a reason worth stating. To send only the relevant function, something has to decide which function — and the reliable source for that is the reference solution's line numbers. That would be giving the system more of the answer than the file path alone. Oracle localisation is already declared method; oracle symbol selection is a further step that changes what the system is, and it deserves a deliberate decision and an explicit disclosure rather than arriving as a quiet default.

Also permanent: your Round 2 cannot be replayed cell-for-cell. It fed the model the full round-1 response, and those raw text files were deleted long ago. The patches survive; the prose does not. New multi-round runs are unaffected, because this pipeline keeps each round's full response.

Where this leaves the project

Six of nine phases done. The pipeline can now take a real task, build your exact prompt, call a real model under an enforced budget, turn the reply into a patch, and get a real verdict from git — recording every step, including the parts it is not entitled to claim.

Next was phase 6: the sweep. Phase 5 proved one cell works; phase 6 is the claim that when hundreds run, none of them can go missing. Two numbers from the live cell size it — 2,251 tokens and 135 seconds each, so a 120-cell arm is about four and a half hours and well inside the budget ceiling.

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