# The learned decision engine (in development)

**Status 2026-10-02: in development, not adopted.** No scan, pre-push check or report uses it.
Every user-facing path stays on today's deterministic one until a compiled program is adopted.
The design is the `learned-decision-engine` specification (maintainer tooling). The code is in
`src/openultrasast/learn/`.

## Why

Before the engine, every detection gain was a hand edit. Each edit was tuned on the cases that
exposed the miss. Engine tuning measured on population v1 did not transfer to v2. [Where we
stand](where-we-stand.md#2-our-approach-to-detection-step-by-step) gives the reasoning and the
step-by-step chain that replaced hand edits.

## Design

- **Signals, not verdicts.** Every instrument contributes a feature record per candidate
  `(path, function, family)`. The instruments are quick rules, the Joern engine, model sink and
  role classification, the plane's verify passes and agreement, and repository facts such as
  callers. The record includes an explicit state when an instrument did not run or failed. No
  instrument decides alone.
- **Labels from ground truth.** `ousast learn labels` derives labels only from sources on a
  fail-closed allow-list (`learn/sources.toml`). Fail-closed means a source not on the list is
  never used. The sources are:
  - the vulnerable and fixed sides of pairs;
  - the declared sites and fix ranges of spent populations;
  - benign deltas;
  - recorded adjudications.

  "Rejected by the plane" is never a negative. Reserved populations (v3) are not on the list.
- **An AI classifier with local memory, compiled in house in the DSPy style.** The program has
  an instruction, a few demonstrations and up to six retrieved labelled examples. Then come the
  candidate's code excerpt and signals. The model (`deepseek-flash`) answers vulnerable / not
  vulnerable / unsure with a rationale. It is sampled `k` times, which gives a score `s`.
  Compilation bootstraps demonstrations and searches instructions on a compile split. A
  pre-registered balanced Brier score (`learn/compile.toml`) scores the candidates. The Brier score
  measures how far predicted probabilities are from the true labels.
- **Retrieval** from the memory store works in three steps:
  1. find nearest neighbours by Gower distance (a distance over mixed feature types) on the
     signal profile;
  2. re-rank them by cosine similarity of code embeddings (OpenAI `text-embedding-3-small`
     through OpenRouter, cached by excerpt hash);
  3. apply a label/family/repository balance rule.

  An evaluation-boundary filter keeps a candidate's own repository group out of what it is
  shown.
- **Repository-grouped evaluation.** The compile split (25% of repository groups) is never
  evaluated. The rest is evaluated by outer grouped 5-fold cross-validation:
  - the Platt calibration map (which turns the score into a probability) is cross-fitted on
    the other folds;
  - the operating points are chosen on the other folds (nested).

  There is one compile per profile, not one per fold. Intervals are 95% repository-cluster
  bootstrap.
- **Two operating points.** BLOCK is offered only when both of these hold:
  - calibration holds: slope CI contains 1, intercept CI contains 0, ECE <= 0.05;
  - a fold reaches the required precision.

  Otherwise the engine is ADVISORY only. An `unsure` answer can never BLOCK. ECE (expected
  calibration error) is the average gap between predicted and observed rates.

## First measured slice (2026-10-01)

Record: `benchmarks/measurements/2026-10-01-decision-engine-injection-slice/record.json` (counts
only). The setup:

- family **injection** only;
- memory of 724 static examples from all families;
- input profile v1, with the engine signals and function length withheld;
- framework priors off.

The signals were withheld because a leak audit found them separating the sides of a pair by
construction (`2026-10-01-decision-engine-leak-audit/`). AUC below is the chance the engine
ranks a real bug above a fixed one.

| Measure | Value (95% repository-cluster CI) |
| --- | --- |
| Candidates evaluated | 110 in 29 repository groups, 58 positive |
| ADVISORY recall | 0.90 (0.82 - 0.97) |
| ADVISORY precision | 0.54 (0.44 - 0.65) |
| AUC of the score `s` | 0.74 (0.65 - 0.85) |
| Majority "vulnerable" verdict | precision 0.86 (0.72 - 1.0), recall 0.43 (0.29 - 0.62) |
| BLOCK | not offered: ECE 0.064 > 0.05, and every fold reported `precision_unreachable` |
| Compiled program | balanced Brier 0.128 on the validation split vs 0.199 for the baseline instruction |
| Canary agreement on re-run | 0.83 (30 candidates) |
| Spend | $0.91 metered at list price for DeepSeek, $0.0040 for embeddings; $0.0032 per evaluated candidate |

## Second slice: six families (2026-10-02)

Record: `benchmarks/measurements/2026-10-02-decision-engine-slice-2/record.json` (counts only).
After the advisory-fix harvest, the memory holds 904 static examples (724 before).

One program was compiled per evaluable family. Injection was recompiled, since the memory
changed. Each program was evaluated on its family's own candidates, out of repository as above:

- compile split never evaluated;
- outer grouped 5-fold;
- calibration cross-fitted;
- operating points nested;
- 95% repository-cluster bootstrap intervals (2,000 resamples);
- input profile v1, priors off, k = 5.

| Family | Candidates (groups; positive) | Pooled AUC of `s` (95% CI) | Within-pair AUC (paired groups) | ADVISORY recall / precision | Canary agreement |
| --- | --- | --- | --- | --- | --- |
| access control | 37 (22; 23) | 0.96 (0.90 - 1.0) | 0.93 (14) | 0.91 / 0.91 | 0.83 |
| deserialization | 46 (30; 30) | 0.90 (0.82 - 0.97) | 0.81 (16) | 0.83 / 0.76 | 0.93 |
| injection | 110 (29; 58) | 0.77 (0.67 - 0.88) | 0.72 (22) | 0.88 / 0.57 | 0.77 |
| output encoding | 44 (27; 29) | 0.91 (0.82 - 0.97) | 0.82 (15) | 0.86 / 0.81 | 0.90 |
| path | 35 (21; 21) | 0.89 (0.79 - 0.97) | 0.83 (14) | 0.86 / 0.69 | 0.90 |
| untrusted destination | 62 (37; 38) | 0.85 (0.76 - 0.94) | 0.72 (18) | 0.84 / 0.73 | 0.87 |

- **BLOCK is offered for no family.** Calibration holds only for output encoding (ECE 0.045).
  There, no fold reaches the required precision (`precision_unreachable`: no fold reaches a
  Wilson lower bound of 0.95). For the other five families, calibration does not hold (ECE
  0.071 to 0.119).
- **The pooled AUC is the more favourable number.** Every family's pool includes candidates
  that carry only a positive label, with no fixed-side counterpart (7 to 19 per family; there
  are no single-label negatives). These positives never had to be told apart from their own
  fix, so they inflate the pooled AUC. The within-pair AUC over the paired groups, 0.72 to
  0.93, is the stricter reading (`paired_groups_check` in the record).
- **Re-run agreement (canary, 30 candidates per family) is below 0.9 for three families:**
  injection 0.77, access control 0.83, untrusted destination 0.87.
- **One memory-size point for injection.** This run is paired: the same program, candidates
  and folds, with a retrieval memory of 724 vs 904 examples. AUC was 0.76 vs 0.77, within the
  intervals. No injection example was added. So the new examples reach an injection prompt
  only as contrast.
- **Spend:** $2.88 metered at list price for DeepSeek under an $8 ceiling, and $0.0009 for
  embeddings. That is $0.0027 to $0.0032 per evaluated candidate. The access-control
  evaluation replayed from the response cache at $0 with identical predictions.
- **An instrument fix is recorded.** Compiled demonstrations ignored the near-duplicate rule
  (cosine >= 0.98). The pre-call boundary assertion caught this and stopped the
  untrusted-destination evaluation. Demonstrations are now swapped or dropped like retrieved
  examples. The stopped part was replayed after the fix.

**Not adopted.** [Where we stand](where-we-stand.md#5-open-items) lists, in one place, what is
still ahead.

## Experiments

Changes to prompts, models, pass counts, features or sampling are compared by controlled
experiments. Each experiment comes from a pre-registered manifest
(`plane/experiments/<id>.yaml`). This way nothing is adopted on one run's number.

Three experiments are registered:

- exp-001 (known callers) has not run.
- exp-002 compared a retrieval ensemble against today's temperature sampling. It ran on 160
  paired candidates in 56 repository groups and was **rejected**. Within-pair AUC was 0.815
  against 0.741 and the cost was $0.0021 against $0.0051 per candidate. The record shows re-run agreement
  0.875 against 0.906 and an interval spanning zero
  (`benchmarks/experiments/exp-002-retrieval-ensemble/result.json`).
- exp-003 compared two BLOCK rules on the same scores. It was **inconclusive**. Neither rule flags anything.

### exp-003: precision-bound blocking (2026-10-03)

The question: is the calibration gate what keeps BLOCK out of reach?
Arm A is today's rule: a calibrated probability, offered only when calibration holds.
Arm B picks a raw-score threshold per family, on training folds only.
It takes the lowest threshold whose Wilson 95% lower bound on precision reaches 0.95.
The held-out repository group is then scored with that threshold.
Arm C relaxes the bound to point precision 0.95 over at least 10 flags.
Arm C was registered as secondary and does not decide.

- **Registered first.** The manifest and units were committed and registered before the run.
- **Primary metric:** arm B's held-out Wilson lower bound on flagged precision, per family.
- **Primary population:** paired units only. Positives-only groups are excluded; pooled figures are secondary.
- **Zero cost.** Scores came only from the response cache, behind a zero-ceiling meter.
  The meter recorded 0 client calls and $0.0.
- **Exclusions.** 142 of 353 units had an uncached response and were excluded, not asked.
  Path lost all 39 units. Injection and access control lost none.
- **Evaluated:** 211 units in 89 repository groups, 138 of them paired.

| Family | Evaluated (excluded) | Paired positives | Flagged A / B / C | Coverage A / B / C |
| --- | --- | --- | --- | --- |
| access control | 41 (0) | 16 | 0 / 0 / 0 | 0.0 / 0.0 / 0.0 |
| deserialization | 14 (34) | 2 | 0 / 0 / 0 | 0.0 / 0.0 / 0.0 |
| injection | 119 (0) | 38 | 0 / 0 / 0 | 0.0 / 0.0 / 0.0 |
| output encoding | 24 (21) | 8 | 0 / 0 / 0 | 0.0 / 0.0 / 0.0 |
| path | 0 (39) | 0 | 0 / 0 / 0 | none |
| untrusted destination | 13 (48) | 4 | 0 / 0 / 0 | 0.0 / 0.0 / 0.0 |

No arm chose a threshold on any fold, so flagged precision is undefined everywhere.
Arm A failed calibration where it was checked: ECE 0.077 (access control), 0.1067 (injection).
The other families had too few units for a calibration check.

**Why B flags nothing.** A Wilson lower bound of 0.95 needs 73 flags without one error.
Injection, the largest family, holds 38 paired positives across all five folds.
The registration predicted this zero before the run.
On this data the bound, not the calibration gate, makes BLOCK unreachable.

**The zero is the rule's, not the instrument's.** The scores are real: 48 distinct values for injection.
In access control, the 10 highest-scored paired units are all positive.
In injection, a negative appears at rank 4 of the paired ranking.
Pooled rankings look cleaner because positives-only units fill the top.
Injection answered `unsure` for 42 of 119 units, and `unsure` never blocks.

Record: `benchmarks/experiments/exp-003-precision-bound-blocking/result.json`.

The reading is on [Where we stand](where-we-stand.md#2-our-approach-to-detection-step-by-step).

## Commands

`ousast learn` is a maintainer command group over the engine's data. Paid steps take
`--budget-usd`. `--replay-only` answers every call from the response cache at no cost.

| Command | Does |
| --- | --- |
| `ousast learn labels` | labels from the allow-listed sources (host only); `--snapshot` writes the counts-only record |
| `ousast learn memory build` | label + feature record + excerpt -> `example` rows (no model) |
| `ousast learn memory embed` | embed the stored excerpts (OpenRouter, metered, cached) |
| `ousast learn compile` | compile the program on the compile split |
| `ousast learn evaluate --program ID` | outer-fold evaluation, cross-fitted calibration, nested operating points |
| `ousast learn curve --program ID` | the metric over memory size on a fixed candidate subset |
| `ousast learn audit-leaks` | how well each signal alone separates the sides of a pair (no model) |
| `ousast learn experiment register\|units\|run\|analyse MANIFEST` | A/B experiments from a pre-registered manifest (`plane/experiments/<id>.yaml`): record its digest, freeze the units (no model), run the arms paired (arm A may replay from the cache at $0), analyse (repository bootstrap, exact McNemar, two looks, the adoption verdict) |

The memory store is the plane's (`OUSAST_MEMORY`; see [ops/ax/README.md](ops/ax/README.md)).
The harvests that produced the plane signals ran as plane Runs generated by
`ousast plane harvest`. Records: `benchmarks/measurements/2026-10-01-decision-engine-harvest/record.json`
and, for the advisory-fix pairs, `2026-10-02-decision-engine-harvest-advisory/record.json`.