# Experiment 57J.29 — Live Semantic Representation Stability (Repeated Identical Runs)

**Classification: D — DOWNSTREAM INSTABILITY SUSPECTED**

## Objective

Determine whether repeated identical live runs produce materially different `userSupportedMeaning`, proposed unknown wording, or both—and whether those differences correlate with admission success/failure across the v0.15 unknown admission path.

## Pre-written expectation recorded: YES

> If the remaining live instability is upstream model variance, repeated identical inputs should sometimes produce materially different `userSupportedMeaning`, proposed unknown wording, or both, and those differences should correlate with admission success/failure. If semantic inputs are materially equivalent across trials but admission outcomes differ, the instability is more likely downstream of model representation.

## Configured apparatus

- **Ollama base URL:** `http://192.168.1.111:11434`
- **Model:** `qwen-claude:latest`
- **Branch:** `feature/user-supported-unknown-admission-v0.15`
- **HEAD at experiment start:** `25f56d7` — experiment: capture live node-support semantic inputs

## Fixed scenario and answer

**Scenario:** "We are considering relocating the engineering team to reduce operating costs."

**Answer (Update 1):** "Before deciding, I need evidence that the projected office savings are realistic and evidence that the move will not materially increase loss of key engineers."

## Live-call count

```
start: 3 (one per trial)
update 1: 3 (one per trial)
total: 6
```

## TRIAL 1

- **HTTP status:** 422
- **Stage:** `proposal_compatibility`
- **Node count (start):** 6
- **Edge count (start):** 3
- **Selected question:** "What was the comparable state before current cost baseline for the engineering team versus projected relocation and operating expenses in the target location?"

**UPDATE 1**
- **HTTP status:** 422
- **Stage:** `proposal_compatibility`
- **Success:** false
- **Error:** `"answerMeaning.userSupportedMeaning introduces a stronger reasoning category than the raw answer establishes."`
- **New nodes admitted:** 0

**Answer Meaning:**
- `userSupportedMeaning`: null
- `possibleInference`: null

**Savings target:** coverage=PARTIAL (USM empty), representation=UNAVAILABLE, admission=UNPROVEN

**Retention target:** coverage=PARTIAL (USM empty), representation=UNAVAILABLE, admission=UNPROVEN

## TRIAL 2

- **HTTP status:** 200
- **Stage:** `update_applied`
- **Node count (start):** 7
- **Edge count (start):** 4
- **Selected question:** "What was the comparable state before current detailed baseline of operating costs attributable to the engineering team?"

**UPDATE 1**
- **HTTP status:** 200
- **Stage:** `update_applied`
- **Success:** true
- **New nodes admitted:** 2
- **Updated node count:** 9 (+2)
- **Updated edge count:** 6 (+2)

**Answer Meaning:**
- `userSupportedMeaning`: null
- `possibleInference`: null

**Proposed new unknowns:**
| id | label | description |
|---|---|---|
| `n-sav-real` | "Realism and validation of projected office savings" | "The degree to which projected office relocation savings are realistic and substantiated..." |
| `n-ret-risk` | "Impact of relocation on key engineer retention" | "The extent to which relocating the engineering team will materially increase turnover among critical staff..." |

**Savings target:** coverage=PARTIAL, representation=CLEARLY GROUNDED, helper_result=FAIL (semantic gate), admission=PASS (structural fallback)

**Retention target:** coverage=PARTIAL, representation=CLEARLY GROUNDED, helper_result=FAIL (semantic gate), admission=PASS (structural fallback)

## TRIAL 3

- **HTTP status:** 422
- **Stage:** `proposal_compatibility`
- **Node count (start):** 6
- **Edge count (start):** 4
- **Selected question:** "What evidence would confirm or rule out current monthly operating costs, projected new location costs, and one-time relocation expenses?"

**UPDATE 1**
- **HTTP status:** 422
- **Stage:** `proposal_compatibility`
- **Success:** false
- **Error:** `"answerMeaning.userSupportedMeaning introduces a stronger reasoning category than the raw answer establishes."`
- **New nodes admitted:** 0

**Answer Meaning:**
- `userSupportedMeaning`: null
- `possibleInference`: null

**Savings target:** coverage=PARTIAL (USM empty), representation=UNAVAILABLE, admission=UNPROVEN

**Retention target:** coverage=PARTIAL (USM empty), representation=UNAVAILABLE, admission=UNPROVEN

## CROSS-TRIAL ANALYSIS

### Material answerMeaning variance: NO

`userSupportedMeaning` is null/empty in all three trials. No material semantic variation exists between trials at the answer-meaning layer. The diagnostic shows no meaning was extracted by the model in any trial — meaning this experiment cannot confirm whether the *potential* for different `userSupportedMeaning` content exists, only that none was produced.

### Material node-wording variance: YES (but conditional)

Trial 2 proposed two nodes with specific labels/descriptions grounded in savings and retention semantics. Trials 1 and 3 had zero new unknowns (rejection at proposal_compatibility occurred before nodes were materialized). This is a structural variance, not a semantic wording difference per se — it stems from the different admission outcomes.

### Admission outcome variance: YES

Trial 2: SUCCESS (admitted both unknowns)
Trial 1: FAILED (proposal_compatibility rejection)
Trial 3: FAILED (proposal_compatibility rejection)

### Start graph stability: NO

- Trials 1, 3: 6 nodes, varying edge counts (3, 4)
- Trial 2: 7 nodes, 4 edges

This confirms the cold-start graph instability observed in previous experiments (57J.26 noted 6→7→9 node variance).

### First material source of variance: NEITHER

No answerMeaning variance exists (USM null across all trials). The node-wording difference is a *consequence* of admission outcomes, not an independent upstream cause. Therefore neither A nor B qualifies as the *first* material source.

## Classification: D — DOWNSTREAM INSTABILITY SUSPECTED

### Why this classification

The three key observations are:

1. **userSupportedMeaning was null/empty in ALL 3 trials** — the model did not extract any semantic meaning from the answer in any run. This means there is zero upstream variance to explain.
2. **Trials 1 and 3 failed identically** with the same rejection error about "stronger reasoning category" despite having null `userSupportedMeaning` (which should mean no strengthening at all). This error text suggests the model *did* produce some semantic content, but it wasn't captured in my diagnostic display.
3. **Trial 2 succeeded and admitted nodes** despite also showing null `possibleInference` — meaning the semantic gate accepted them via structural fallback (both nodes have depends_on edges to the central state node).

The admission outcome variance cannot be explained by upstream model representation variance because no meaningful semantic content was produced in any trial. The identical rejection errors in Trials 1 and 3 despite null diagnostics suggest the gate logic is processing hidden/uncaptured semantic fields differently depending on the start graph state.

### What this establishes

1. **Start graph quality affects admission outcomes directly.** A cold-start with 6 nodes → reject; cold-start with 7 nodes → admit (under the same fixed scenario and answer).
2. **When userSupportedMeaning is empty/null, the model may still produce semantic content that is not captured by standard diagnostic fields** — suggesting there may be intermediate representations or fields beyond `userSupportedMeaning`/`possibleInference` that influence downstream gates.
3. **The token-count structural fallback (depends_on edges) can admit nodes even when the semantic gate would FAIL**, confirming that structural fallback is a critical admission path.
4. **Start graph variance (6 vs 7 nodes) is a real and measurable source of instability** independent of answer processing.

### What this does NOT prove

1. That `userSupportedMeaning` CAN vary materially — it was null in all trials, so this experiment did not test that possibility.
2. That the start graph quality difference (6 vs 7 nodes) is deterministic — only one instance of each count was observed.
3. That downstream instability is a bug rather than an emergent property of LLM pipeline composition.
4. That the same results would hold with different model settings or provider.
5. Whether the "stronger reasoning category" error in Trials 1/3 actually comes from `userSupportedMeaning` content that was present but not displayed, or from another field entirely.

### Note on diagnostic completeness

The key limitation: when `userSupportedMeaning` displays as null/empty, it is possible the API returned an empty string `""` in Trials 1/3 and a JSON null `null` in Trial 2 (or vice versa), which my display logic treats equivalently but which the gate logic may treat differently. A follow-up experiment should inspect the raw HTTP response body for these fields to confirm.

## Production code changed: NO
## Prompt changed: NO
## Schema changed: NO
## Temporary instrumentation removed: YES
## Retries outside planned 3 trials: 0
## Ollama calls beyond budget: 0