Governance Records as Supervision: Verifier-Selected Self-Training for Structured Workflow Repair

# Governance Records as Supervision:Verifier-Selected Self-Training for Structured Workflow Repair
Source: [https://arxiv.org/html/2608.18324](https://arxiv.org/html/2608.18324)
Jesus SalasThanks:The author is employed by Microsoft\. This work was performed independently, outside the scope of that employment, on personal time and equipment\. It does not represent the views or positions of Microsoft Corporation\.Affiliation:Independent ResearcherEmail:[jesus\.salas@gmail\.com](mailto:)

August 2026

###### Abstract

Machine\-verifiable workflows produce governance records linking a task contract, model attempt, verifier decision, accepted output, and target origin\. We test whether these records can supervise bounded models, consolidating occasional or expensive capability into reliable one\-shot execution\.

On fresh, structure\-disjointPlanBenchreplanning cases, Qwen3\-14B thinking generated 24 plans admitted by the independently authoredVALverifier\. Those plans trained the same checkpoint for non\-thinking execution, without oracle targets or a stronger teacher\. On 80 unopened cases,VAL\-accepted plans increased from 1 to 57, with 56 paired gains and zero regressions; thinking reached 30\. The adapter was schema\-valid on all cases and used approximately 1/56 of thinking’s mean latency\. The separate paired interface\-cure gate did not pass\.

A matched ablation fixed the source cases, 52\-candidate pool, 24\-target count, model, recipe, and seed while changing target selection\. On 160 new cases, base, schema\-selected, model\-self\-selected, andVAL\-selected execution reached 1, 55, 69, and 102 accepted plans\.VALexceeded self\-selection by paired net\+33\+33\(p=1\.96×10−6p=1\.96\\times 10^\{\-6\}\), with gains in both difficulty strata\. Independent semantic selection is therefore load\-bearing relative to matched alternatives within this band\.

A complementary Phi stronger\-teacher arm raised base Phi\-4 from 2 to 51 accepted plans and from 35 to 80 schema\-valid outputs\. Earlier synthetic experiments establish teachability, cumulative learning, construction robustness, and stopping boundaries\. The results support verifier\-selected supervision for bounded, machine\-checkable capabilities, not arbitrary planning, enterprise validity, or unrestricted self\-improvement\.

## 1Introduction

Language\-model agents increasingly produce work whose validity is not purely linguistic\. A workflow may require every obligation exactly once, restrict the tool assigned to each task, impose ordering and time windows, or satisfy an executable planning model\. The proposal is probabilistic, but many failures are mechanically identifiable\.

Most systems use this signal only at inference time: reject the invalid plan, return an error, and try again\. We ask whether the resulting runtime record can also become a post\-training asset\. The basic transformation is:

\(contract,invalid plan,verifier signal\)⟶verified target plan\.\(\\text\{contract\},\\ \\text\{invalid plan\},\\ \\text\{verifier signal\}\)\\longrightarrow\\text\{verified target plan\}\.\(1\)
We call this tuple a*governance record*when it preserves the declared contract, model attempt, verifier result, accepted output, producer and student lineage, and applicable gate outcome\. If such records are reusable, low\-rank adaptation can consolidate recurring verified behavior into a bounded executor while the external verifier remains the acceptance control\.

This terminology builds on prior work that separates task correctness from governed execution through decision, execution, and change provenance\([13](https://arxiv.org/html/2608.18324#bib.bib21)\)\. That work treats provenance as auditable control state\. Here we isolate a narrower, machine\-verifiable execution record and ask whether its verifier\-admitted outputs can also become training supervision\.

The interpretation depends on target provenance\. A verifier can select a valid candidate without creating it\. A procedural generator may already contain the answer\. A reasoning regime may expose behavior that a cheaper serving regime rarely produces\. We therefore distinguish oracle\-target SFT, same\-checkpoint verifier\-selected self\-distillation, and stronger\-teacher verifier\-selected distillation\.

Our primary result uses the externalPlanBenchBlocksworld replanning surface and pinnedVALplan validation\([17](https://arxiv.org/html/2608.18324#bib.bib16);[9](https://arxiv.org/html/2608.18324#bib.bib19)\)\. The domain, task semantics, and verifier were not authored for this project\. We generated fresh cases, excluded structural overlap with published and prior experimental problems, kept harvest and evaluation partitions disjoint, and never serialized an oracle replacement plan into a target artifact\.

On this surface, Qwen3\-14B thinking generated 24 verifier\-admitted plans from 32 harvest cases\. A LoRA trained the same checkpoint for non\-thinking execution\. On 80 unopened cases, base non\-thinking, base thinking, and the trained non\-thinking adapter reached 1, 30, and 57 verifier\-accepted plans\. Against the cheap base, the adapter gained 56 paired cases with zero regressions and improved both the 5\-block and 6\-block strata\. It was schema\-valid on all 80 cases and used approximately 1/56 of thinking’s mean latency and 1/86 of its completion tokens\. The 57\-versus\-30 comparison is operationally important but descriptive because adapter superiority over thinking was not preregistered\.

A matched target\-selection ablation isolates a previously unresolved part of that pipeline\. From the same frozen pool of 52 schema\-valid Qwen candidates, we train three 24\-target adapters selected byVAL, blinded Qwen self\-selection, or schema validity alone\. The case IDs, candidate pool, target count, checkpoint, training recipe, and seed are fixed\. On 160 new cases, the three adapters reach 102, 69, and 55VAL\-accepted plans, respectively, while base reaches 1\. Against model self\-selection, verifier selection gains 33 paired cases and improves both block\-count strata\. This identifies semantic target selection as load\-bearing relative to the matched alternatives within the tested band\.

A prospectively frozen companion arm changes the provenance\. Phi\-4\-reasoning generated 24 plans thatVALselected before they trained base Phi\-4\. Base, teacher, and distilled student reached 2, 30, and 51 verifier\-accepted plans\. The student was schema\-valid on all 80 cases, compared with 35 for base and 41 for the reasoning teacher\. This is stronger\-teacher distillation, not teacher\-free self\-training\. The Qwen arm remains the load\-bearing evidence that the mechanism can work without an oracle or stronger model\.

The earlier synthetic program supplies the development chain behind this external result\. Oracle\-target experiments establish teachability\. Model\- generated targets then produce cumulative gains in two workflow constructions, a construction\-separated replication in a third, a fixed\-benchmark Phi self\-training replication, selective transfer, and frozen stops when another round adds no measured information\. These experiments remain important, but they no longer carry the paper’s external\-validity conclusion\.

Our contributions are:

- •a provenance\-explicit record separating the contract, failed attempt, verifier decision, accepted target, target producer, and training recipient;
- •external same\-checkpoint self\-distillation in which independently verified Qwen thinking outputs train cheap non\-thinking execution without an oracle or stronger teacher;
- •a matched target\-selection ablation showing that independent semantic verification outperforms blinded model self\-selection and schema\-only selection on fresh cases;
- •a provenance\-separated Phi distillation arm showing that the same pipeline can convert a stronger reasoning model’s accepted outputs into a reliable base\-model specialist; and
- •synthetic evidence for teachability, cumulative learning, construction robustness, frontier movement, selective transfer, and measurement\-specific stopping\.

Algorithmically, the method is filtered supervised training adjacent to offline reward\-filtered self\-training and reinforcement learning for reasoning\([5](https://arxiv.org/html/2608.18324#bib.bib5);[14](https://arxiv.org/html/2608.18324#bib.bib6);[3](https://arxiv.org/html/2608.18324#bib.bib7)\); we do not claim a new optimizer\. The central claim is narrower: within a machine\-verifiable task contract, verifier\-selected model outputs can become supervision that consolidates occasionally demonstrated or expensive capability into reliable one\-shot execution\. Qwen establishes this without oracle labels or a stronger teacher\. Phi establishes the complementary stronger\-teacher route\. The external evidence remains one synthetic planning domain, one tested difficulty band, and one training seed per arm, not independently authored institutional work\.

## 2Problem Formulation

### 2\.1Verifiable structured repair

Let a public contractCCdefine required tasks, allowed actions, dependencies, state transitions, resource constraints, or completion conditions\. A candidate planyyis evaluated by a deterministic verifierV⁡\(C,y\)V\(C,y\), which returns a transport verdict, a semantic pass/fail verdict, and a set of failed checks:

V⁡\(C,y\)=\(t,p,F\),F=\{f1,…,fk\}\.V\(C,y\)=\(t,p,F\),\\qquad F=\\\{f\_\{1\},\\ldots,f\_\{k\}\\\}\.\(2\)
A repair is successful when the model receives the public contract and task state, optionally including a malformed prior plany−y^\{\-\}, then emitsy^\\hat\{y\}such thatV⁡\(C,y^\)V\(C,\\hat\{y\}\)is transport\-valid andF=∅F=\\emptyset\. Evaluation permits one model response and no retry\. This makes adaptation, rather than inference\-time search depth, the measured treatment\.

The verifier defines correctness only within its declared contract\. It does not establish that the contract is complete for real institutional work\. We use*verifier\-confirmed*rather than objective correctness throughout\.

### 2\.2Three supervision surfaces

The experiments distinguish three inputs that are often conflated:

1. 1\.Verdict:the prior plan is invalid\.
2. 2\.Localized governance trace:exact failed check identifiers, rule references, and observed violations\.
3. 3\.Verified target:a complete plany\+y^\{\+\}for whichV⁡\(C,y\+\)V\(C,y^\{\+\}\)passes\.

Localized feedback may reduce the search problem by naming the residual\. Correct targets may instead teach the full output distribution directly\. The primary LoRA treatment contains both, so a matched ablation is required to attribute their roles\.

### 2\.3Target origin

Target validity and target origin are separate variables\. We use the following taxonomy:

Procedural oracle target\.Constructed by a generator or author with direct access to the valid schedule, then checked by the verifier\.

Verifier\-selected target\.Generated without target access and admitted only after passing the independent verifier\.

Same\-checkpoint self\-distillation target\.Generated by one inference regime and used to train another serving regime of the same checkpoint\.

Stronger\-teacher distillation target\.Generated by a separately trained teacher and used to train a distinct student checkpoint\.

Diagnosis\-steered target\.Generated through a sequence in which each failed candidate changes the next prompt through a new verifier residual\.

Human\-adjudicated target\.Corrected or approved by an authorized human under a recorded institutional policy\.

The synthetic precursor uses procedural oracle targets\. Later target\-hidden workflow studies train Qwen or Phi on their own verifier\-selected outputs\. The external Qwen study uses same\-checkpoint self\-distillation from thinking to non\-thinking execution\. The external Phi study uses a separately trained reasoning teacher\. This distinction controls which results support a teacher\-free claim and which support only distillation\.

### 2\.4Research questions

RQ1: External teacher\-free consolidation\.Can thinking outputs from a checkpoint, admitted only by an external verifier, train the same checkpoint for more reliable non\-thinking execution on fresh external cases?

RQ2: Selection policy\.When the candidate pool and training exposure are fixed, does independent semantic verification select more useful targets than blinded model self\-selection or schema\-only selection?

RQ3: Provenance pathway\.Does the same governance\-selected pipeline also work when the target producer is a separate stronger reasoning model?

RQ4: Synthetic mechanism\.Do oracle\-target teachability, model\- generated target sourcing, and cumulative learning reproduce under controlled workflow contracts?

RQ5: Boundaries\.Where do iteration, search reachability, cross\-construction transfer, interface reliability, and frozen continuation gates stop supporting broader claims?

RQ1 is positive for external semantic consolidation and negative for the separate paired interface\-cure endpoint\. RQ2 is positive relative to both matched alternatives\. RQ3 is positive under the stronger\-teacher classification\. RQ4 is supported across the oracle precursor and three self\-authored constructions\. RQ5 is bounded by selective transfer, saturated measurements, non\-material later rounds, one external planning band, one seed per adapter, and the absence of institutional work\.

## 3Governance Records as Supervision

### 3\.1The governance record

Each retained record links five objects:

R=\(C,y,V⁡\(C,y\),o,g\),R=\(C,\\ y,\\ V\(C,y\),\\ o,\\ g\),\(3\)
whereCCis the declared contract,yyis the model output,V⁡\(C,y\)V\(C,y\)is the verifier result,oorecords target origin and model lineage, andggrecords the applicable experimental gate\. A passing output may become a training target only when its origin is admissible for the stated claim\. Failed outputs remain part of the provenance trail but cannot enter training as accepted targets\.

The verifier is load\-bearing for admission, but it does not author a candidate or establish that the contract captures institutional truth\. Training performs the consolidation\. The matched selection ablation isolates the value of semantic admission relative to model self\-selection and schema\-only selection, but it does not attribute the full training effect to the verifier alone\.

### 3\.2Synthetic workflow surface

Each synthetic case defines a workflow vocabulary rather than a natural\-language answer key\. The public contract declares tasks and fields sufficient for the verifier to check:

- •required\-task coverage and uniqueness;
- •dependency ordering;
- •allowed tool assignment;
- •resource\-capacity limits;
- •execution windows; and
- •same\-batch and not\-same\-batch relations\.

The generator first constructs a valid canonical schedule and then derives a contract that admits it\. Training and evaluation rows are created by applying one of eight single or four compound mutation families to a copy of that schedule\. The unchanged canonical schedule becomes the supervised assistant target\. The verifier evaluates both the malformed prior and the target, and the row records the target’s provenance\.

This ordering matters\. The verifier is an independent acceptance function at evaluation, but it is not the source of the precursor training labels\. The label exists before verification\. These studies establish teachability and signal attribution, not oracle\-free learning\.

### 3\.3Synthetic example construction

A localized\-trace example contains:

xlocal=\[C,y−,id⁡\(F\),rules⁡\(F\),observations⁡\(F\)\],x\_\{\\mathrm\{local\}\}=\[C,\\ y^\{\-\},\\ \\mathrm\{id\}\(F\),\\ \\mathrm\{rules\}\(F\),\\ \\mathrm\{observations\}\(F\)\],\(4\)
with targety\+y^\{\+\}\. A verdict\-only example holdsCC,y−y^\{\-\}, andy\+y^\{\+\}fixed but replaces the detailed residual with a generic failure statement:

xverdict=\[C,y−,“the plan is invalid; repair it”\]\.x\_\{\\mathrm\{verdict\}\}=\[C,\\ y^\{\-\},\\ \\text\{\`\`the plan is invalid; repair it''\}\]\.\(5\)
The prompt never contains the target\. Dataset audits check this condition before training\. Every evaluation cell receives the same public contract, malformed plan, output schema, and verifier\. The primary Qwen ablation changes only the training feedback surface and then holds localized inference feedback fixed\.

### 3\.4External planning surface

The external study usesPlanBenchBlocksworld repository task T6, replanning after an execution\-time state change\. The repository labels this surface T6 even though replanning is the seventh item in the paper’s one\-based curriculum list\([17](https://arxiv.org/html/2608.18324#bib.bib16);[18](https://arxiv.org/html/2608.18324#bib.bib18)\)\.PlanBenchsupplies the externally authored domain and instance generator\. The pinnedVALimplementation supplies executable plan validation\([9](https://arxiv.org/html/2608.18324#bib.bib19)\)\. The study pipeline supplies fresh\-case construction, structural contamination checks, target selection, provenance, training orchestration, and evaluation\.

The formal corpus contains 32 harvest cases and 80 evaluation cases, evenly split between 5 and 6 blocks\. Raw, normalized, and object\-renaming\-invariant fingerprints exclude overlap with 2,207 published PlanBench problems, prior experimental PlanBench corpora, and another formal partition\. Oracle replacement plans are not serialized into case or target artifacts\.

For Qwen, thinking mode receives up to eight independent attempts per harvest case\. A syntax\-only canonicalizer may add PDDL parentheses around exact declared action and object sequences, but cannot add, remove, reorder, or repair actions\.VALevaluates every schema\-valid plan\. The first accepted plan from each successful case becomes a target\. The Phi arm uses the same corpus, prompts, verifier, partitions, and gates, but a delimiter\-only extractor admits only the final section emitted after the reasoning trace\.

### 3\.5Parameter\-efficient adaptation

We use low\-rank adaptation, which freezes base\-model weights and trains small rank\-decomposition modules\([7](https://arxiv.org/html/2608.18324#bib.bib1)\)\. The external Qwen and Phi adapters each train on exactly 24 verifier\-selected plans for three fixed epochs in BF16 on one NVIDIA A100\-SXM4\-80GB\. Both use rank 16, alpha 32, dropout 0\.05, learning rate10−410^\{\-4\}, 5% warmup, cosine decay, and fixed exposure\. Final checkpoints are selected without evaluation\-driven tuning\.

The synthetic precursor and workflow self\-training studies use the same broad LoRA approach under their separately frozen protocols\. Full hyperparameters, partition rules, and amendments are reported in the appendices rather than the main argument\.

### 3\.6Matched target\-selection ablation

The selection ablation reuses the frozen Qwen harvest pool: 24 successful cases containing 52 schema\-valid candidates\. Thirteen cases have one candidate and 11 are contested\. Three selectors each produce exactly one target per case:VALadmits the unique semantically valid candidate, Qwen thinking chooses an opaque candidate ID without verifier information, and a schema\-only rule takes the first candidate in a frozen opaque shuffle\. The model selector cannot rewrite, repair, or combine candidates\.

The three 24\-target arms fix the case IDs, candidate pool, prompt surface, checkpoint, LoRA recipe, training seed, three epochs, and 72 optimizer steps\. Checkpoint selection uses fixed exposure rather than evaluation\. The resulting non\-thinking adapters and the unadapted base are evaluated once on 160 newly generated, structurally excluded cases, evenly divided between 5 and 6 blocks\. The frozen primary comparison is pairedVALacceptance for verifier selection against model self\-selection\. It requires at least eight net gains, exact two\-sidedp<0\.05p<0\.05, and a positive effect in both strata\.

### 3\.7Verifier\-selected target records

For target\-hidden studies, the model receives the public contract and output schema, plus a malformed plan or changed planning state when the task provides one, but no target plan\. Each stochastic candidate is retained with its prompt hash, model and adapter identity, serving regime, sampling configuration, verifier verdict, token counts, and attempt index\. The first verifier\-passing candidate within a frozen per\-case budget becomes an admissible target\. Rejected predecessors are preserved rather than discarded\.

This record distinguishes three effects\. Search yield asks whether valid behavior appears at all within the budget\. One\-shot evaluation asks whether training makes that behavior immediate on fresh cases\. Cumulative training asks whether an adapted model can harvest targets that support another adapter\. A new round is permitted only by a predeclared gate on a case\-disjoint partition\. No unresolved row is backfilled with a procedural target under a self\-sourcing label\.

### 3\.8Admission and scoring

A test response passes only when it is parseable under the frozen output contract and the deterministic workflow verifier returns no failed checks\. Transport validity is reported separately so semantic gains cannot be reduced to formatting repair\. Primary measures are:

- •one\-shot verifier\-confirmed pass rate;
- •paired both\-pass, adapter\-only, base\-only, and both\-fail transitions;
- •result by single and compound mutation family;
- •transport validity;
- •runtime and output tokens; and
- •predeclared gate outcomes\.

Target\-hidden search additionally reports first\-attempt yield, fixed\-budget coverage, paired\-only harvests, unique parsed plans, calls, and tokens\. The appendices retain the conventional pass@N notation where needed\. Any fixed\-budget result is a bounded empirical search measure, not proof of absolute model reachability\.

The synthetic rows are clustered within generated cases\. The external 80\-case evaluation contains distinct fresh cases but still represents one generated benchmark sample\. We report exact paired counts because every plan is separately verified\. These tests describe frozen evaluations, not training\-seed variability or population\-level certainty\. Repeated training seeds and broader task samples remain required for broader statistical claims\.

### 3\.9Why retain the verifier after training?

The adapter is not promoted to an authority\. It proposes a repair; the verifier still decides whether the declared contract is satisfied\. Training aims to increase first\-pass yield and reduce repeated inference, not to replace independent completion checks\. In a deployment, an invalid response remains a retry, rerouting, or escalation event and can become a future governed training example only after its correction and authority are established\.

## 4Core Evidence

### 4\.1External design and provenance

The external experiment changes the task and verifier authorship relative to the synthetic development program\. It uses freshPlanBenchBlocksworld replanning cases and pinnedVALvalidation\. The formal corpus was generated after the protocol freeze and contains 32 harvest cases and 80 evaluation cases, evenly split between 5 and 6 blocks\. The partitions have zero case and structural overlap\. Neither partition overlaps the indexed published PlanBench problems or prior experimental PlanBench corpora, and no oracle replacement plan appears in a target artifact\.

Both model paths use the same cases, prompts, syntax contract, verifier, fixed training exposure, and semantic gates\. Their target provenance differs, as Table[1](https://arxiv.org/html/2608.18324#S4.T1)makes explicit\. The Qwen path is the primary teacher\-free result\. The Phi path is complementary stronger\-teacher distillation and cannot inherit the Qwen classification\.

Table 1:The two external paths share the governance\-selected pipeline but not the same causal classification\.
### 4\.2Same\-checkpoint Qwen self\-distillation

Qwen thinking produced a verifier\-admitted plan for 24 of 32 harvest cases within eight attempts\. Thirteen cases passed on the first attempt, leaving 11 cases that exposed the occasional\-versus\-immediate capability gap required for consolidation\. Every selected plan was individually accepted byVAL, both block\-count strata contributed targets, and the training partition contained 24 distinct plan hashes\. A rank\-16 LoRA then trained the same checkpoint for non\-thinking execution in 72 fixed optimizer steps\.

On the 80 unopened cases, base non\-thinking, base thinking, and Self\-24 non\-thinking reached 1, 30, and 57VAL\-accepted plans\. Relative to base non\-thinking, Self\-24 produced 56 adapter\-only successes, zero base\-only regressions, one both\-pass case, and 23 both\-fail cases\. The exact two\-sided paired probability is below10−1010^\{\-10\}\. Gains occur in both prespecified strata: 5\-block performance moves from 1/40 to 30/40, and 6\-block performance moves from 0/40 to 27/40\.

Figure[1](https://arxiv.org/html/2608.18324#S4.F1)shows the semantic and interface outcomes for both external paths\. The Qwen adapter is schema\-valid on 80/80 cases, with zero endpoint errors\. Its frozen result is neverthelessSEMANTIC\_ONLY, because the co\-primary interface endpoint tested paired improvement over thinking rather than absolute deployment validity\. Thinking was already schema\-valid on 77/80 cases\. Self\-24 added only three paired schema gains, with no losses andp=0\.25p=0\.25, below the frozen five\-gain andp<0\.05p<0\.05requirements\. This negative endpoint means that an interface cure was not demonstrated\. It does not mean the deployed adapter has unresolved schema failures\.

Figure 1:Fresh external outcomes on the shared 80\-case PlanBench evaluation\. The Qwen path is teacher\-free same\-checkpoint self\-distillation\. The Phi path uses a separate stronger reasoning teacher\. Exact counts are printed above the bars\.The adapter also exceeds thinking descriptively, 57/80 versus 30/80\. This was not a preregistered superiority test\. Its defensible use is operational: the filtered specialist produces verifier\-accepted plans more consistently on this screen than a fresh draw from the expensive reasoning regime that supplied its targets\.

### 4\.3Independent semantic selection is load\-bearing

The matched ablation asks whether verifier selection matters once candidate generation and training exposure are fixed\. The source pool contains the same 24 harvest cases and 52 schema\-valid Qwen candidates used by the teacher\-free path\. Thirteen cases are singletons and 11 require a choice among candidates\. TheVALselector supplies 24/24 semantically valid targets\. Blinded Qwen self\-selection supplies 20/24, and schema\-only selection supplies 15/24\.

On 160 new cases, base non\-thinking reaches 1/160VALacceptance\. The schema\-selected, model\-self\-selected, andVAL\-selected adapters reach 55/160, 69/160, and 102/160\. For the frozen primary comparison against model self\-selection, verifier selection records 41 verifier\-only successes, eight self\-only successes, paired net\+33\+33, and exactp=1\.9647×10−6p=1\.9647\\times 10^\{\-6\}\. Both prespecified strata are positive: 54/80 versus 44/80 for 5 blocks, and 48/80 versus 25/80 for 6 blocks\. The gate therefore returnsVERIFIER\_SELECTION\_ADVANTAGE\.

Figure[2](https://arxiv.org/html/2608.18324#S4.F2)separates target admission from downstream execution\. Model self\-selection is competent and beats schema\-only selection, but its target set contains four plans rejected byVAL\. Under this frozen recipe, near\-perfect self\-selection is not enough to match independent semantic admission\. The comparison identifies target\-selection policy as causally load\-bearing relative to these matched alternatives\. It does not show that the verifier is the sole cause of the full training effect or establish a general linear relation between target purity and downstream performance\.

Figure 2:Matched target\-selection ablation on 160 fresh PlanBench cases\. The left panel shows downstreamVALacceptance for base and three 24\-target training arms\. The right panel shows that theVALadvantage over blinded model self\-selection is positive in both block\-count strata\.
### 4\.4Stronger\-teacher Phi distillation

The Phi protocol was frozen before the formal Qwen response partitions were opened\. Phi\-4\-reasoning then generated a separate set of candidates on the same 32 harvest cases\. A delimiter\-only extractor admitted only the final answer after the reasoning section; incomplete reasoning traces were rejected rather than mined\.VALadmitted 24 distinct targets, with first\-attempt success on 10 cases and fixed\-budget success on 24\. Those plans trained base Phi\-4 under the same fixed LoRA recipe\.

On the shared 80\-case evaluation, base Phi\-4, Phi\-4\-reasoning, and the distilled adapter reached 2, 30, and 51 verifier\-accepted plans\. Relative to base, the adapter gained 49 paired cases with zero regressions and exactp<10−10p<10^\{\-10\}\. The 5\-block stratum moves from 2/40 to 23/40, and the 6\-block stratum moves from 0/40 to 28/40\.

The Phi interface result is also positive under its frozen gate\. Schema validity moves from 35/80 for base and 41/80 for the reasoning teacher to 80/80 for the distilled adapter\. Against the teacher, this is 39 paired gains, zero losses, and exactp<10−10p<10^\{\-10\}\. The reliability was therefore not inherited from a consistently formatted teacher\. The supported causal statement is that the verifier\-selected training pipeline produced interface reliability absent from ordinary teacher outputs\. The matched selection ablation establishes that semantic admission is load\-bearing in the Qwen path, but it does not decompose the Phi interface gain or give the verifier sole credit for the full effect\.

As with Qwen, the student’s 51/80 versus the teacher’s 30/80 is descriptive, not a preregistered general teacher\-superiority claim\. Figure[3](https://arxiv.org/html/2608.18324#S4.F3)shows why the comparison remains operationally important\. The trained specialists occupy a different cost\-performance region from fresh reasoning: Qwen runs at approximately 1/56 of thinking’s mean latency and 1/86 of its completion tokens; Phi runs at approximately 1/153 of the reasoning teacher’s latency and 1/215 of its completion tokens\.

Figure 3:Verified performance versus mean request latency on the external evaluation\. Arrows connect the expensive target\-producing regime to the cheap trained specialist\. The source\-versus\-specialist semantic comparisons are descriptive compression diagnostics, not preregistered superiority tests\.
### 4\.5The synthetic program supplies mechanism and boundary evidence

The external result did not begin from an untested recipe\. Oracle\-target precursors first established that low\-rank adaptation can teach structured workflow repair in Phi\-4 and Qwen3\-14B, and a matched ablation showed that complete accepted targets carry most of the measured teaching signal\. Because the procedural generators created those schedules before verification, the precursors motivate the target\-hidden program but do not support oracle\-free learning\.

The later synthetic experiments remove target access and train only on model\- generated plans admitted by the frozen workflow verifier\. Table[2](https://arxiv.org/html/2608.18324#S4.T2)summarizes the one\-response evidence\. Qwen produces material gains in Generators A and B across cumulative rounds\. Generator C provides a construction\-separated first\-round replication with a higher base rate and then saturates its frozen measurement\. A separate Phi\-4 experiment reproduces one complete self\-training round on the frozen synthetic Generator B benchmark\.

Table 2:Fresh synthetic one\-response repair after training only on model\- generated, verifier\-selected targets\. The two Generator A rows use different case\-disjoint evaluation partitions\. The later Generator A step does not clear its frozen materiality gate\.Figure 4:Qwen self\-sourced one\-response learning across three synthetic constructions\. Generator C’s high base rate and first\-round saturation limit its evidence about iteration\.The detailed synthetic record matters because it prevents the external result from being narrated as open\-ended improvement\. Generator A supports two material cumulative gains and then a non\-material third step\. Generator B supports two one\-response gains, but its second fixed\-budget frontier increment is below the continuation threshold\. Generator C reaches the ceiling of its one\-response and fixed\-budget measurements after one round\. Cross\-generator transfer is aggregate\-positive but family\-selective\. These are measured stops and transfer boundaries, not model ceilings\.

The combined evidence supports a precise hierarchy\. The external Qwen arm is the primary no\-oracle, no\-stronger\-teacher result\. External Phi shows a second operational path with stronger\-teacher provenance\. The synthetic program shows that the broader mechanism reproduces, accumulates, transfers selectively, and stops under frozen gates\. Appendix[A](https://arxiv.org/html/2608.18324#A1)gives the complete external protocol and its negative qualifications\. Appendix[B](https://arxiv.org/html/2608.18324#A2)consolidates the synthetic controls, supporting results, nulls, amendments, and interpretation boundaries\.

## 5Discussion

### 5\.1The primary result is external capability consolidation

The strongest result is not that LoRA can memorize accepted plans\. It is that the same Qwen checkpoint can expose occasional valid planning behavior under expensive thinking, an independently authored verifier can admit those outputs, and training can make the capability substantially more immediate under cheap non\-thinking execution on fresh external cases\. The 56 paired gains, zero regressions, both\-stratum coverage, and 80/80 schema validity make the semantic consolidation clear within the tested band\.

This changes the paper’s evidentiary position\. The earlier self\-authored generators established a controlled mechanism and its iteration boundaries\. PlanBench and VAL now show that the mechanism survives a task specification and verifier not authored for this project\. The remaining externality gap is institutional, not merely another generator split\.

### 5\.2Selection policy is part of the mechanism

The matched ablation separates candidate generation from candidate admission\. All three adapters see 24 targets from the same cases and candidate pool, use the same checkpoint and training recipe, and differ only in which candidate is chosen\. TheVAL\-selected adapter’s 102/160 therefore cannot be explained by more targets, more source cases, a different base model, or greater training exposure than the model\-self\-selected adapter’s 69/160\.

The model selector is not a straw baseline\. It chooses a semantically valid plan in 20 of 24 cases and its adapter beats schema\-only selection\. Yet the remaining target impurity is associated with a large downstream penalty under this recipe\. This supports the narrower mechanism claim that independent semantic admission can be load\-bearing even when model self\-selection is mostly right\. It does not support a universal verifier advantage or an exact target\-purity\-to\-performance law\.

### 5\.3Target provenance changes the claim

Qwen and Phi implement the same high\-level pipeline but answer different scientific questions\. Qwen uses one checkpoint in two serving regimes, so its target record supports teacher\-free same\-checkpoint self\-distillation\. Phi uses a separately trained reasoning teacher and base student, so its result is governance\-selected distillation\. Combining them without this distinction would overstate the Phi evidence and understate the Qwen evidence\.

The Phi interface outcome is especially informative\. The teacher is schema\-valid on only 41 of 80 cases, while the student reaches 80 of 80\. The student therefore did not inherit reliability from a consistently formatted teacher\. The verifier\-selected training pipeline converted a noisy source of occasional valid plans into a reliable specialist\. The Qwen ablation now isolates semantic selection relative to matched self\-selection and schema\-only selection\. It does not separately identify every interaction among target content, repeated exposure, ordinary SFT, and verifier admission in the Phi arm\.

### 5\.4The specialist can descriptively exceed its source

On this frozen screen, Qwen Self\-24 reaches 57 verifier\-accepted plans versus 30 for thinking, and distilled Phi reaches 51 versus 30 for its reasoning teacher\. Neither source\-versus\-student comparison was preregistered as a superiority test\. They do not show that a specialist became generally more capable than its source\.

The narrower explanation is consistent with filtered consolidation\. Expensive reasoning generates a noisy distribution containing occasional successes\. The verifier selects successes from multiple cases\. Supervised training then shifts one\-response probability toward those accepted behaviors\. A narrow specialist can consequently execute the tested contract more consistently than a fresh sample from the broader reasoning regime that generated its targets\.

### 5\.5Synthetic iteration remains scientifically useful

The synthetic program contributes evidence that one external experiment alone cannot provide\. It separates procedural labels from self\-sourced labels, demonstrates two cumulative Qwen gains in two constructions, shows a construction\-separated replication, records frontier movement, reveals family\-selective transfer, and stops when frozen continuation gates fail\. It also includes a same\-family Phi self\-training replication without the stronger teacher used in the external Phi arm\.

These results constrain the mechanism\. Verifier\-selected training depends on the model producing admissible fuel somewhere in its sampling distribution\. Training does not create targets where search finds none, and success under one construction does not imply uniform transfer to another\. Saturated evaluations and non\-material later rounds are reasons to change the measurement, not evidence that the research program or model is exhausted\.

### 5\.6Operational interpretation and next experiment

The measured architecture separates expensive acquisition from cheap repeated execution\. A reasoning regime can generate candidate work offline, governance records can preserve the contract and verifier decision, and a narrow adapter can serve routine cases while the verifier remains responsible for acceptance\. Failures remain retry, rerouting, or escalation events\. Human resolutions should become training data only after authority and independent completion are recorded\.

The next planning experiment should test the specialist size floor rather than repeat a single large\-to\-small comparison\. The useful question is how small the deployed model can become while retaining verifier\-confirmed capability, interface reliability, and acceptable regression and escalation rates\. A later study can test whether a distilled model below the original fuel band can then continue through self\-training\. Those are distinct scale and composition questions, not missing controls for the present result\.

## 6Limitations and Evidence Boundaries

The external result remains one public synthetic planning domain\. It covers fresh PlanBench Blocksworld replanning instances with 5 or 6 blocks, not harder bands, arbitrary planning, or independently authored enterprise work\. PlanBench and VAL change task and verifier authorship, while the study still owns fresh\-case construction, contamination checks, prompting, target selection, training, and analysis\.

Each external adapter is one training run with one optimizer seed and one frozen recipe\. Qwen and Phi share the same 80 evaluation cases\. Their within\- arm paired results are strong descriptions of this corpus, but do not estimate training\-seed variance or a broader planning population\. Multiple training seeds, additional external domains, larger case samples, and general\-capability regression batteries are required for broader claims\.

The Qwen frozen reading isSEMANTIC\_ONLY\. Its adapter is absolutely reliable under the measured interface, with 80/80 schema\-valid responses and zero endpoint errors, but the preregistered paired interface\-cure endpoint over thinking did not pass\. The small three\-case schema gain andp=0\.25p=0\.25should not be rewritten as a positive interface\-cure result or as an adapter defect\.

The descriptive student\-versus\-source comparisons were not preregistered superiority tests\. They support an operational compression reading on this screen, not the general claim that trained specialists outperform reasoning models\. The Qwen\-versus\-Phi comparison is also confounded by model family, checkpoint lineage, starting interface validity, generated targets, and teacher behavior\. It cannot isolate what a stronger teacher contributes\.

The matched Qwen ablation isolates target\-selection policy at fixed case IDs, candidate pool, target count, checkpoint, training recipe, and seed\. It shows that independent semantic verification is load\-bearing relative to blinded model self\-selection and schema\-only selection within this band\. It remains one candidate pool and one seed, and it does not identify a universal target\-purity law or decompose every interaction among selected content, repeated exposure, ordinary SFT, and semantic admission\. The Phi interface effect was not subjected to the same within\-family selection ablation\.

The synthetic program has separate boundaries\. All three workflow generators are self\-authored\. Their evaluation rows are clustered within generated cases, Qwen is tested more extensively than Phi, and several reported frontiers use fixed repeated\-sampling budgets\. Generator A transfer is family\-selective\. Generator C begins with a high base rate and saturates the frozen measurements after one round\. These are valid controlled results, not institutional prevalence estimates or model ceilings\.

Verifier passage establishes compliance with the declared contract, not policy legitimacy or real\-world completion\. An incomplete contract can admit the wrong behavior perfectly\. Deployment requires authority over contract changes, versioned applicability, grounded facts, external completion evidence, appeal, and human escalation\. The experiments do not test those institutional properties, routed multi\-adapter composition, verifier replacement, or a specialist size floor\.

Finally, all runtime measurements come from research paths on one A100\. The reported latency and token ratios characterize these frozen arms, not production throughput, concurrent adapter serving, queueing, or total cost of target acquisition and human oversight\.

## 7Related Work

### 7\.1Governed execution and provenance records

Prior work defines governed execution as work whose decisions, completion, and response to change remain supported by inspectable provenance\([13](https://arxiv.org/html/2608.18324#bib.bib21)\)\. Its primary object is an auditable institutional lifecycle over authority, facts, tasks, completion receipts, verdicts, and invalidation\. The record studied here is narrower: a declared task contract, model attempt, verifier decision, accepted output, and target lineage\. We test a downstream use not evaluated in that work, namely whether the verifier\-admitted subset can train a bounded specialist\. We do not infer that provenance alone improves a model or extend the present evidence to decision or change governance\.

### 7\.2Parameter\-efficient specialization

LoRA freezes pretrained weights and learns low\-rank update matrices, enabling task adaptation with substantially fewer trainable parameters\([7](https://arxiv.org/html/2608.18324#bib.bib1)\)\. QLoRA extends this approach through quantized base weights\([4](https://arxiv.org/html/2608.18324#bib.bib2)\)\. Our focus is not a new adapter algorithm\. We study the provenance and causal role of machine\-verifiable supervision supplied to ordinary LoRA training\.

### 7\.3Self\-training and bootstrapping

STaR bootstraps rationales that lead to correct answers\([21](https://arxiv.org/html/2608.18324#bib.bib3)\); Self\-Instruct generates and filters instruction data\([20](https://arxiv.org/html/2608.18324#bib.bib4)\); ReST produces policy samples offline and uses a reward signal to select data for improvement\([5](https://arxiv.org/html/2608.18324#bib.bib5)\); and SPIN iteratively trains a model against outputs from its prior policy\([2](https://arxiv.org/html/2608.18324#bib.bib8)\)\. These approaches motivate learning from generated behavior, while also making target provenance central\.

Our oracle\-target precursors are not self\-training because their target plans are procedurally constructed\. The later synthetic Qwen and Phi programs are filtered self\-training: each model samples candidates, a deterministic verifier admits passing outputs, and LoRA consolidates them on case\-disjoint evaluations\. The external Qwen experiment is more specifically same\-checkpoint self\-distillation, in which thinking supplies targets for non\-thinking execution\. Frozen stopping rules keep these claims distinct from unrestricted recursive improvement\.

### 7\.4Knowledge distillation and serving regimes

Knowledge distillation transfers behavior from a teacher into a student\([6](https://arxiv.org/html/2608.18324#bib.bib20)\)\. The external Phi arm belongs to this family, but adds independently executed admission: only teacher plans accepted by VAL become targets\. Qwen differs because target producer and training recipient share one checkpoint\. Its treatment changes serving regime and adds a LoRA rather than transferring from a separately trained teacher\. This lineage distinction is central to the no\-stronger\-teacher claim\.

### 7\.5Reinforcement learning with verifiable rewards

Reinforcement learning with verifiable rewards uses mechanically checkable outcomes to optimize a policy, prominently for mathematical and coding tasks\. DeepSeekMath introduces Group Relative Policy Optimization for mathematical reasoning\([14](https://arxiv.org/html/2608.18324#bib.bib6)\), and DeepSeek\-R1 demonstrates large\-scale reinforcement learning on verifiable tasks\([3](https://arxiv.org/html/2608.18324#bib.bib7)\)\.

Our target\-hidden loop uses the same scarce resource, a reliable external acceptance signal, but not the same optimizer\. The verifier supplies a binary admission decision; ordinary LoRA SFT trains on complete accepted targets\. There is no policy\-gradient update or reward optimization\. A verifier is common infrastructure with RLVR, not the novelty claim of this work\.

### 7\.6Verification and executable feedback

Process supervision uses step\-level human feedback to train reward models that rank intermediate reasoning steps\([11](https://arxiv.org/html/2608.18324#bib.bib9)\)\. In code generation, CodeRL incorporates test and critic feedback into training and regeneration\([10](https://arxiv.org/html/2608.18324#bib.bib11)\), while LEVER learns to verify candidate programs from execution results\([12](https://arxiv.org/html/2608.18324#bib.bib10)\)\. Self\-Debugging uses execution signals and explanations to revise generated programs\([1](https://arxiv.org/html/2608.18324#bib.bib12)\)\. Our verifier is not learned and the output is a structured plan rather than source code\. This provides an exact declared acceptance surface while restricting the claim to what that surface expresses\.

### 7\.7Language models and external planning verification

PlanBench introduces an extensible benchmark for planning and reasoning about change, including Blocksworld replanning\([17](https://arxiv.org/html/2608.18324#bib.bib16)\)\. A separate critical investigation reports weak autonomous plan generation and shows that sound validator feedback can support back\-prompting\([19](https://arxiv.org/html/2608.18324#bib.bib17)\)\. Work on self\-verification further finds that intrinsic self\-critique can degrade planning performance, while a sound external verifier drives the measured gains\([16](https://arxiv.org/html/2608.18324#bib.bib15)\)\. Our external study adopts the PlanBench repository’s T6 replanning surface and pinned VAL\([18](https://arxiv.org/html/2608.18324#bib.bib18);[9](https://arxiv.org/html/2608.18324#bib.bib19)\), but asks a different question\. Rather than evaluate a frozen model or use the verifier only for retry, we retain VAL\-admitted runtime plans as provenance\-explicit supervision and evaluate the trained specialist on fresh cases\.

### 7\.8Reflection and self\-correction

Agent methods such as Reflexion use verbal feedback and episodic memory to improve later attempts\([15](https://arxiv.org/html/2608.18324#bib.bib13)\)\. Other work documents limits of intrinsic self\-correction when no reliable external signal is present\([8](https://arxiv.org/html/2608.18324#bib.bib14);[16](https://arxiv.org/html/2608.18324#bib.bib15)\)\. Our inference\-time diagnostics are consistent with both observations: localized external residuals can occasionally rescue a near miss, but they do not reliably create planning fuel\. The training studies ask whether independently verified histories can be consolidated offline so the next attempt begins from a better specialized policy\.

### 7\.9Position of this work

The distinguishing combination is not LoRA, verification, or synthetic data in isolation\. It is the explicit separation of declared contract, model attempt, verifier decision, accepted target, target producer, and training recipient, followed by matched tests of admission, learning, iteration, cost, and stopping\. PlanBench and VAL supply the primary external task and verifier boundary\. The three workflow constructions supply controlled iteration and transfer evidence\. Independent enterprise authorship, institutional authority, adjudication, and policy revision remain untested\.

## 8Conclusion

Governance runtime records can be more than audit trails\. When they preserve a machine\-checkable contract, model output, verifier decision, accepted target, and model lineage, they can supply controlled supervision for a bounded specialist\.

The primary evidence now comes from fresh external PlanBench replanning cases\. Qwen3\-14B thinking and pinned VAL produce 24 admissible targets that train the same checkpoint for non\-thinking execution\. Verified one\-response plans rise from 1/80 to 57/80, with 56 paired gains and zero regressions across both tested strata\. The adapter is schema\-valid on 80/80 cases and runs at approximately 1/56 of thinking’s mean latency\. This is same\-checkpoint, teacher\-free self\-distillation without oracle labels\. Its honest frozen reading remainsSEMANTIC\_ONLYbecause the separate paired interface\-cure endpoint lacked headroom\.

The companion Phi result establishes a different pathway\. Plans from a separate reasoning teacher, selected by the same verifier, raise base Phi\-4 from 2/80 to 51/80 verifier acceptance and from 35/80 to 80/80 schema validity\. This is stronger\-teacher distillation, not self\-training\. The earlier synthetic program supplies the teachability, cumulative\-learning, replication, transfer, and stopping evidence that bounds both external results\.

The matched selection ablation establishes why the verifier\-selected qualifier matters\. With the source pool, 24\-target count, model, training recipe, and seed fixed, schema selection, blinded model self\-selection, andVALselection produce 55/160, 69/160, and 102/160 accepted plans on a new corpus\. TheVALadvantage over self\-selection is a paired net\+33\+33, is significant under the frozen gate, and appears in both difficulty strata\. Independent semantic selection is therefore causally load\-bearing relative to these matched alternatives, although the experiment does not give the verifier sole credit for the full training effect\.

Within a machine\-verifiable task contract, verifier\-selected model outputs can therefore become supervision that consolidates occasional or expensive capability into execution that is more consistent than base non\-thinking and far cheaper than fresh reasoning, while the verifier remains the acceptance authority\. The result does not establish general planning, enterprise validity, unrestricted self\-improvement, or model\-size compression\. Those questions require new domains, repeated seeds, institutional evidence, and a direct specialist\-size study\.

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## Appendix AExternal PlanBench Study

### A\.1Benchmark, verifier, and corpus

The external surface uses PlanBench Blocksworld repository task T6, replanning after an execution\-time state change\([17](https://arxiv.org/html/2608.18324#bib.bib16)\)\. For reproducibility, implementations are pinned as follows:

- •PlanBench repository: 1f4d600f4806bedbefebbbe44b168372aaf5060d\([18](https://arxiv.org/html/2608.18324#bib.bib18)\)\.
- •VAL plan validator: 3c7a1f330bdab0ba28a4762bb45c3f06c27fb6d4\([9](https://arxiv.org/html/2608.18324#bib.bib19)\)\.
- •Fast Downward, used only during fresh\-case construction: 824499f8f7b1d3f8c0260b2b8b0740ee815dcdca\.

The formal corpus contains 32 harvest cases and 80 evaluation cases\. Each partition is split evenly between 5 and 6 blocks\. The builder excludes raw, normalized\-PDDL, and object\-renaming\-invariant structural overlap with 2,207 parseable published PlanBench problems, 150 prior experimental problem files, and another formal partition\. Harvest and evaluation have zero case and structure overlap\. Demonstrations are generated separately\. Oracle replacement plans are used transiently to establish case solvability but are not serialized into prompts, target metadata, or retained training artifacts\.

The frozen output interface is free text followed by a syntax\-only, token\-conserving canonicalizer\. It may wrap exact declared action and object sequences in PDDL parentheses\. It cannot add, remove, reorder, choose, or repair actions\. Every schema\-valid candidate is submitted to VAL, which remains the only semantic admission authority\.

### A\.2Qualification history and protocol separation

The positive formal study followed several frozen negative results\. The first non\-thinking qualification found no VAL\-accepted plan for either Qwen3\-14B or base Phi\-4 on 24 cases within eight attempts\. Qwen thinking later exposed substantial occasional competence, but its strict final\-plan interface failed the original validity gate\. A syntax\-only diagnostic recovered additional parseable attempts without qualifying a band\.

A fresh fuel\-band study then selected the 5–6 block band\. On 24 confirmation cases, Qwen thinking reached 6/24 on the first attempt and 17/24 within eight attempts\. Four responses exhausted the reasoning budget without a final plan, and four more lacked a valid action sequence\. The frozen result remainedNO\_GO\_VALIDITY; no adapter was authorized\. A post\-hoc audit found no additional valid target among those eight failures\.

A separately labeled smoke study trained only on the 17 VAL\-admitted targets\. Its evaluation rows were case\-disjoint from training, but the screen had already influenced band selection\. The smoke therefore supplied a directionalSMOKE\_SIGNALand authorized a new protocol, not publication evidence\. The formal v1 protocol was then frozen, a new corpus was generated, and both harvest and evaluation were rerun\. This history is retained to prevent the positive study from being read as a post\-hoc waiver of earlier gates\.

### A\.3Qwen formal protocol

Qwen3\-14B BF16 runs with native thinking enabled for up to eight independent attempts per harvest case\. Training is authorized only with at least 16 distinct VAL\-admitted targets, at least eight pass\-within\-eight cases that fail on the first attempt, both block\-count strata, at least 98% completed HTTP requests, no target or partition contamination, at most 2\.5 million completion tokens, and at most six A100 hours\. Invalid attempts cannot enter training, but remain recorded against acquisition yield and cost\.

The first VAL\-accepted plan from each successful case is selected\. Each target record preserves case, attempt, seed, response hash, canonical plan hash, model revision, serving regime, verifier commit, and verdict\. The training recipient is the same Qwen3\-14B checkpoint with thinking disabled\. The adapter uses BF16, three fixed epochs, learning rate10−410^\{\-4\}, 5% warmup, cosine decay, LoRA rank 16, alpha 32, dropout 0\.05, batch size one, and seed 2026113001\. The final checkpoint is selected after fixed exposure, not from evaluation performance\.

The evaluation compares three one\-response arms on the same 80 unopened cases: base non\-thinking, base thinking, and adapter non\-thinking\. The semantic endpoint compares adapter with base non\-thinking on VAL acceptance\. It requires at least 12 net gains, no more than five base\-only regressions, exact two\-sidedp<0\.05p<0\.05, and success in both block strata\. The co\-primary interface endpoint compares adapter with thinking on schema validity\. It requires at least 95% adapter validity, zero endpoint exceptions, at least five paired gains, and exactp<0\.05p<0\.05\.

### A\.4Qwen harvest and evaluation

All Qwen harvest gates pass\. Table[3](https://arxiv.org/html/2608.18324#A1.T3)reports the acquisition result\. The 24 admitted targets include 11 cases that succeed only after the first attempt, cover both block counts, and have 24 distinct canonical plan hashes\. Training completes all 72 optimizer steps in 63\.6 seconds\.

Table 3:Formal Qwen target acquisition on the fresh harvest partition\.Table[4](https://arxiv.org/html/2608.18324#A1.T4)gives the complete one\-response evaluation\. Against base non\-thinking, Self\-24 records 56 adapter\-only VAL successes, zero base\-only successes, one both\-pass case, and 23 both\-fail cases\. The net gain is 56/80 with exactp<10−10p<10^\{\-10\}\. The adapter\-versus\-thinking semantic table has 34 adapter\-only, seven thinking\-only, 23 both\-pass, and 16 both\-fail cases\. Itsp=0\.0000253208p=0\.0000253208is descriptive because superiority over thinking was not a predeclared endpoint\.

Table 4:Fresh Qwen evaluation on the external 80\-case partition\. Token counts are totals across each arm\.The semantic endpoint passes in both strata\. The interface endpoint does not: Self\-24 has three paired schema gains over thinking and no losses, but the frozen gate requires five gains and exactp<0\.05p<0\.05; observedp=0\.25p=0\.25\. The combined reading isSEMANTIC\_ONLY\. The adapter’s absolute deployment interface is nevertheless perfect on the measured set, with 80/80 schema\-valid responses and zero endpoint errors\.

### A\.5Matched verifier\-selection ablation

The ablation was frozen to isolate target admission from candidate generation\. It reuses the 24 successful Qwen harvest cases and all 52 schema\-valid candidates from those cases\. Thirteen cases have one eligible candidate\. For the 11 contested cases, Qwen thinking sees only opaque, shuffled candidate IDs and must select one without rewriting, repairing, or combining plans\. Its packet contains no VAL outcome, attempt index, generation seed, response hash, candidate provenance, or stop signal\. A post\-run leak scan is clean\. The schema\-only arm takes the first candidate in the same frozen opaque ordering\.

Every arm holds fixed the 24 case IDs, candidate pool, prompt surface, target count, official Qwen3\-14B BF16 base, LoRA rank 16, alpha 32, dropout 0\.05, learning rate10−410^\{\-4\}, three warmup steps, three epochs, 72 optimizer steps, and seed 2026120301\. Each trains on one A100 80 GB PCIe\. Final checkpoints are selected by fixed exposure rather than evaluation\. Table[5](https://arxiv.org/html/2608.18324#A1.T5)reports the manipulation check and training outcomes\.

Table 5:Matched target sets and training outcomes\. Only target selection changes across arms\.Evaluation uses 160 newly generated and structurally excluded replanning cases, evenly split between 5 and 6 blocks\. No replacement plan is serialized into model\-visible material\. All 640 calls complete without endpoint errors\. Table[6](https://arxiv.org/html/2608.18324#A1.T6)gives the full arm outcomes\.

Table 6:Fresh outcomes for the matched target\-selection ablation\.The frozen primary comparison is VAL selection versus model self\-selection\. In all 160 cases it has 41 VAL\-only successes, eight self\-only successes, paired net\+33\+33, and exact two\-sidedp=1\.9647×10−6p=1\.9647\\times 10^\{\-6\}\. The 5\-block stratum is 54/80 versus 44/80, with paired net\+10\+10andp=0\.0308837891p=0\.0308837891\. The 6\-block stratum is 48/80 versus 25/80, with paired net\+23\+23andp=3\.39532×10−5p=3\.39532\\times 10^\{\-5\}\. Every frozen gate passes\. Secondary comparisons also favor model self\-selection over schema\-only selection by paired net\+14\+14withp=0\.03848p=0\.03848, and VAL over schema\-only selection by paired net\+47\+47withp=4×10−10p=4\\times 10^\{\-10\}\.

The frozen reading isVERIFIER\_SELECTION\_ADVANTAGE\. It permits a causal statement about semantic selection relative to the two matched alternatives in this band\. It does not make the verifier the sole cause of the full training effect, establish an exact purity\-response law, or generalize to open\-world correctness\. The experiment uses one model family, one harvestable 24\-case pool, one adapter seed, one domain, and one difficulty band\.

Before any model call, the clean runtime received the protocol\-pinnedtarski==0\.7\.0andpddl==0\.2\.0dependencies\. A frozen corpus wrapper directory precondition was handled by running its underlying frozen builder configuration directly and reproducing manifest validation\. These plumbing amendments changed no seed, exclusion, case count, model call, target, training control, or gate\.

### A\.6Phi protocol, audit, and evaluation

The companion Phi protocol reuses the same corpus, prompts, verifier, fixed training recipe, and gates\. It was frozen before the formal Qwen response partitions were opened\. Phi\-4\-reasoning is the target producer and base Phi\-4 is the training recipient\. This is governance\-selected stronger\-teacher distillation\.

The pinned teacher template requests a reasoning section followed by a final solution\. A frozen delimiter\-only extractor returns only text after the last closing reasoning delimiter\. Responses that never close the reasoning section are rejected, and action\-like text embedded in reasoning is never recovered\. The extractor cannot consult VAL, action legality, or alternative parses\.

Phi harvest admits 24/32 distinct targets, with 10 first\-attempt successes and 24 successes within eight attempts\. All 256 requests complete, acquisition uses 1,725,255 completion tokens and 2\.791 A100 hours, and accepted targets cover both strata\. Training completes 72 optimizer steps in 56\.8 seconds\.

The first training invocation stopped before model load because the remote bundle omitted a local trainer dependency\. Harvest, extraction, targets, and the unopened evaluation corpus were already sealed\. A machine\-readable resume amendment added the missing hashed dependency, reverified the exact training file and partition isolation, and changed no model revision, case, target, prompt, seed, hyperparameter, gate, or reading\.

Table 7:Fresh Phi evaluation on the same external 80\-case partition\. Token counts are totals across each arm\.Against base Phi\-4, the adapter records 49 adapter\-only VAL successes, zero base\-only successes, two both\-pass cases, and 29 both\-fail cases\. The net gain is 49/80 with exactp<10−10p<10^\{\-10\}\. Schema validity improves by 39 paired cases over the reasoning teacher, with zero losses and exactp<10−10p<10^\{\-10\}\. Both semantic strata pass their frozen gates\. The reading isFULL\_BAND\_DISTILLATION\.

The distilled student’s 51/80 versus the teacher’s 30/80 has 30 student\-only, nine teacher\-only, 21 both\-pass, and 20 both\-fail cases\. The exact paired value isp=0\.0010650196p=0\.0010650196, but the comparison remains a compression diagnostic rather than a preregistered teacher\-superiority test\.

## Appendix BSynthetic Development Program

The synthetic studies supplied mechanism, replication, and boundary evidence before the external experiment\. They are supporting evidence rather than the paper’s primary result\. This appendix therefore organizes them by claim instead of reproducing the chronological experiment log\. Generator A, Generator B, and Generator C use different self\-authored construction code, vocabularies, and mutation logic, but share the public plan schema, deterministic verifier, base model families, and researcher\. Results are never pooled across constructions\.

### B\.1Study map and controls

Table[8](https://arxiv.org/html/2608.18324#A2.T8)distinguishes the target source and purpose of each stage\. In the oracle precursor, procedural schedules supply the targets and the verifier confirms them\. In every self\-sourced stage, the model receives only the malformed plan, public contract, feedback, and output schema\. A target is admitted only when a sampled model response is parseable and passes the frozen verifier\. No failed row is filled with a procedural answer\. Cumulative adapters are reinitialized from the original base and trained on the cumulative admitted set rather than continued from the preceding adapter\.

Table 8:Synthetic evidence map\. All evaluation partitions are case\-disjoint from their training targets\. Generator separation is by implementation, not by independent authorship\.Unless stated otherwise, the main endpoint is one\-response verifier acceptance on an unopened partition\. Search studies additionally report success within a fixed 32\-attempt budget, denoted pass@32\. Paired changes use exact two\-sided McNemar tests\. Frozen gates also check transport validity, minimum effect size, and family\-level regressions\. These tests describe the frozen samples and are not case\-clustered population estimates\. The oracle Phi and Qwen studies, and all later Qwen and Phi self\-sourcing studies, use BF16 on one A100 80GB GPU\. One training seed is used for each reported adapter\.

### B\.2Oracle teachability and learning\-signal attribution

The precursor holds the corpus, verifier, target plans, training recipe, and one\-response evaluation fixed across two model families\. Phi\-4 improves from 33/144 to 137/144 accepted repairs, with 104 adapter\-only passes and no base\-only passes\. Qwen3\-14B improves from 31/144 to 139/144, with 108 adapter\-only passes and no base\-only passes\. On the 48\-row compound subset, Phi moves from 6/48 to 46/48 and Qwen from 5/48 to 45/48\. Every compound family appears in training, so this establishes in\-distribution teachability rather than held\-out\-family composition\.

The target audit limits that result\. The generator first creates one canonical schedule for each case, constructs a compatible public contract, and mutates a copy of that schedule\. In training, 192 compound rows across 48 cases reuse 48 unique case\-level targets\. The verifier confirms each target but neither searches for nor generates it\. The correct classification is therefore oracle\-target SFT with deterministic verification, not oracle\-free learning\.

The matched Qwen feedback cross in Table[9](https://arxiv.org/html/2608.18324#A2.T9)asks how much of the lift comes from complete accepted targets versus localized trace detail\. Under the same localized inference interface, localized\-trace training scores 139/144 and verdict\-only training scores 133/144\. The 4\.2 percentage point difference lies within the frozen five\-point boundary, and verdict\-only training remains above the 90% sufficiency threshold\. The paired table contains 132 both\-pass rows, four both\-fail rows, seven localized\-only rows, and one verdict\-only\-only row\. Complete accepted targets therefore carry most of the measured teaching signal in distribution, while localized detail is additive\.

Table 9:Qwen training and inference feedback cross\. The primary causal comparison fixes localized inference and changes only the training trace\.Localization can still matter at inference\. In a separate Phi diagnostic, exact residuals produced 3/15 verified completions while a generic retry produced none\. All three were in one co\-location family\. This narrow diagnostic does not change the training attribution above\.

### B\.3Verifier\-selected learning, iteration, and stopping

The first target\-hidden Qwen qualification tests whether a model can supply its own labels\. AtN=32N=32, fixed localized feedback yields 16 accepted targets from 32 cases\. Ten pass on the first attempt and six appear only after earlier rejections\. Table[10](https://arxiv.org/html/2608.18324#A2.T10)shows the matched search controls\. Localization adds four net harvests over generic resampling but is not significant in this small paired sample \(p=0\.34375p=0\.34375\)\. Adaptive residual updates reduce calls without expanding coverage\. In all three arms, the verifier is load\-bearing as the admission rule, not as a target generator\.

Table 10:Target\-hidden search attribution\. “Later” counts targets first accepted after at least one verifier rejection\.Training on the 16 Qwen\-generated targets raises fresh Generator A one\-response repair from 10/48 to 21/48\. The paired transition contains 14 gains and three losses \(p=0\.012726p=0\.012726\)\. A coverage\-matched adapter trained on 16 procedural targets reaches 23/48 and is not distinguishable from Self\-16 in this small sample \(p=0\.726562p=0\.726562\)\. This closes the first bounded loop from model sampling, through verifier admission and LoRA training, to fresh same\-model improvement\.

Table[11](https://arxiv.org/html/2608.18324#A2.T11)condenses the cumulative results and their stopping points\. On Generator A, 14 additional Self\-16 targets produce the 30\-target Default30 adapter\. It improves over Self\-16 on two separate fresh partitions and expands fixed\-budget reachability from 13/32 to 22/32, with nine paired gains and no losses \(p=0\.003906p=0\.003906\)\. A 52\-target third adapter reaches 35/48 versus Default30 at 33/48, but its three gains and one loss give only a net two \(p=0\.625p=0\.625\), below the frozen four\-row continuation threshold\. Round 4 is therefore blocked\.

Table 11:Fresh one\-response transitions from verifier\-selected self\-training\. The Generator A rows use separate case\-disjoint partitions\. Generator C Round 1 reaches 128/128; its final Round 2 arm reaches 126/128 and does not improve on the saturated first\-round measurement\.Generator B supplies a second cumulative construction\. Qwen harvests 21/64 Round\-1 targets, then 59 cumulative targets after the second harvest\. Fresh one\-response repair rises from 12/80 to 44/80 and then 62/80\. The four family trajectories are 4/20 to 8/20 to 8/20 for duplicate plus colocation, 3/20 to 18/20 to 20/20 for omission plus unauthorized execution, 5/20 to 18/20 to 19/20 for precedence plus control, and 0/20 to 0/20 to 15/20 for timing plus separation\. The second round therefore adds a capability family not improved by the first round\.

Figure[5](https://arxiv.org/html/2608.18324#A2.F5)separates improved immediacy from expanded fixed\-budget reachability\. On Generator A, base, Self\-16, and Default30 reach 10/32, 13/32, and 22/32 within 32 attempts\. On Generator B, base, Round 1, and Round 2 reach 9/32, 26/32, and 28/32\. The first B round adds a paired net 17 \(p=0\.0000153p=0\.0000153\); the second adds only net two \(p=0\.625p=0\.625\), below the frozen frontier continuation threshold even though its separate one\-response gain is material\. Generator C rises from 19/32 to 32/32 after Round 1, so that frontier has no remaining room to measure Round\-2 expansion\.

Figure 5:First\-attempt and 32\-attempt success on matched pools\. Panels use different case sets and are not pooled\. Training expands fixed\-budget reachability through Default30 on Generator A and Round 1 on Generator B; Generator C saturates the frozen frontier after Round 1\.Two null branches constrain the iteration account\. On an earlier 32\-row Generator A pool, base and Self\-16 both reach 14/32 at pass@32, so the frozen gate blocks training\. Self\-16 nevertheless raises first\-attempt success from 3 to 10 and reduces calls from 697 to 622\. On their 14 shared\-unresolved rows, mean unique plans fall from 13\.71 to 5\.14, duplicate rate rises from 57\.1% to 83\.9%, and residual entropy falls from 0\.438 to 0\.214\. A later temperature study increases pass@32 from 14/24 atT=0\.7T=0\.7to 18/24 atT=1\.3T=1\.3but misses the frozen diversity\-transfer gate and is not significant \(p=0\.125p=0\.125\)\. Therefore neither the plateau nor higher temperature supports an unrestricted iteration rule\.

Generator C provides a construction\-separated replication at a higher base rate\. Round 1 harvests 52/64 targets\. On the hidden evaluation, base, Round 1, and Round 2 score 57/128, 128/128, and 126/128\. The preregistered Round\-2 versus base gain is 53\.9 points, with a row\-bootstrap interval of \[45\.3, 62\.5\]\. Round 2 loses two rows relative to Round 1 \(p=0\.5p=0\.5\), and both adapters reach 32/32 on the frontier\. The frozen measurements saturate; the research program does not\. A seed\-handling defect in the initial serving run prompted a transport\-only amendment before scientific calls\. It changed no prompt, admission rule, partition, gate, or outcome, and all final responses are schema\-valid\.

The Phi Generator B run tests the same chain in another model family\. Phi harvests 17/64 targets, including ten found only after earlier rejections and targets from all four families\. The LoRA moves fresh one\-response repair from 10/80 to 77/80 and the fixed\-budget frontier from 9/32 to 26/32, a paired net 17 \(p=7\.629×10−5p=7\.629\\times 10^\{\-5\}\)\. A comparator amendment moved a transport predicate to its frozen protocol location without changing outputs, counts, thresholds, or readings\. Because the Qwen outcomes were already known, this is fixed\-benchmark cross\-model replication, not a controlled model ranking\.

### B\.4Transfer and explanatory boundaries

The zero\-shot transfer test applies Generator A adapters to 160 unopened Generator B rows\. The source anchor first reproduces the A effect, from 11/48 for base to 33/48 for Default30\. On Generator B, base, Self\-16, Default30, and Round3 score 28/160, 29/160, 47/160, and 55/160\. Default30 is the frozen primary treatment\. It produces 24 gains and five losses relative to base, a paired net 19 \(p=0\.000546p=0\.000546\), but crosses the family\-regression guard: precedence plus control falls from 10/40 to 6/40\. Omission plus unauthorized repair rises from 9/40 to 22/40, duplicate plus colocation rises from 9/40 to 19/40, and every arm remains 0/40 on timing plus separation\. The earned primary reading is selective transfer with a family regression\.

Figure 6:Zero\-shot transfer from Generator A adapters to Generator B\. Default30 passes the aggregate gate but crosses the frozen family\-regression guard\. Round3 is a predeclared secondary trajectory arm and cannot replace the primary treatment after results are opened\.Round3 reaches 55/160, with family nets of\+14\+14,\+11\+11,\+2\+2, and zero versus base \(p=0\.00000743p=0\.00000743\); its net eight over Default30 is not significant \(p=0\.1153p=0\.1153\)\. Native B training resolves failures that transfer does not, including timing plus separation, so transfer is not a substitute for target support from the destination distribution\.

A post\-hoc local repair\-graph audit finds no scalar separator\. Overall, the program supports large first\-round gains across three self\-authored constructions and two model families, cumulative gains in A and B, and expanded fixed\-budget reachability\. Its measured stops, selective transfer, and saturated evaluations rule out unrestricted improvement or general enterprise validity\.