Compare default Qwen system prompt vs generic assistant prompt vs no system prompt in representation space and leakage

kind: experiment

Motivation

The project has been using "You are a helpful assistant." as the assistant persona and "no system prompt" as the default/no-persona baseline interchangeably. The research log notes these two have cosine 0.942 (nearly identical). But Qwen-2.5-7B-Instruct ships with its own native system prompt — "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." — which includes a self-referential identity claim ("You are Qwen"). This is a third distinct condition that we've never explicitly tested.

These three conditions could differ in important ways:

Representation geometry: Does the Qwen-native prompt occupy a different region of persona space than the generic assistant? Does the self-referential identity claim shift the representation?
Leakage susceptibility: Issue #96 found the assistant uniquely resists capability degradation. Is the Qwen-native prompt even more robust (because it's what the model was actually trained with), or does the identity claim make no difference?
Behavioral baselines: Are there systematic differences in generation style, refusal rates, or alignment scores across the three?

This is directly relevant to Aim 4.10 (system prompt contribution to assistant persona) and connects to #100 (assistant robustness source-of-robustness ablation).

Conditions

Label	System prompt	Rationale
`qwen_default`	"You are Qwen, created by Alibaba Cloud. You are a helpful assistant."	The model's native training prompt — what RLHF actually optimized for
`generic_assistant`	"You are a helpful assistant."	The standard assistant condition used in all prior experiments
`no_system`	(no system message in chat template)	The no-persona baseline used in #65, #66, #96

Proposed experiments

Exp A — Representation geometry comparison

Extract residual-stream activations for all 3 conditions across the same standardized input set used for persona centroids (from #66). Compute:

Pairwise cosine similarity (mean-centered) between the 3 conditions
Cosine to other personas — do qwen_default and generic_assistant differ in their similarity profile to the 111-persona taxonomy?
Assistant axis projection — where do the 3 conditions fall on the Lu et al. assistant axis? Does the Qwen-native prompt project higher (more "assistant-like")?
Layer-by-layer divergence — at which layers do the 3 conditions diverge most?

Exp B — Leakage susceptibility comparison

Three sub-experiments testing how the 3 assistant conditions behave under contrastive SFT and marker injection:

B1 — Assistant conditions as source (replication of #96): Train contrastive LoRA with each of the 3 conditions as the source persona (source gets wrong ARC-C answers, bystanders get correct). Same recipe as #96: lr=1e-5, 3 epochs, LoRA r=32. Key question: does qwen_default resist degradation like generic_assistant did in #96, or is it even more resistant?

B2 — Cross-leakage between assistant conditions: For each B1 training run, evaluate ARC-C on ALL 3 assistant conditions (not just the source). When qwen_default is the source getting wrong answers, does generic_assistant also degrade? Does no_system? This directly tests whether the model treats them as the same "persona slot" or distinct ones.

B3 — Other sources → assistant bystanders: Using non-assistant source personas (e.g., villain, sycophant — existing #96 training runs where available), evaluate leakage TO all 3 assistant conditions as bystanders. Do qwen_default, generic_assistant, and no_system show the same bystander leakage pattern, or does one resist more?

Also replicate #65's marker injection ([ZLT] token) for all 3 conditions to check marker leakage susceptibility.

Exp C — Behavioral baseline comparison

Generate completions (N=50 standardized prompts × 10 completions each at temp=1.0) under each condition. Score with Claude Sonnet judge on:

Alignment (Betley-style)
Helpfulness
Refusal rate
Style markers (formality, verbosity, hedging)

This establishes whether the 3 conditions produce meaningfully different behavioral outputs or are functionally interchangeable.

Success criteria

Clear answer on whether qwen_default and generic_assistant are representationally distinct (cosine < 0.90 = distinct, > 0.95 = interchangeable)
Quantified leakage susceptibility difference (if any) between the 3 conditions — both as sources and as bystanders
Cross-leakage matrix: do the 3 assistant conditions leak to each other (suggesting shared persona slot) or resist cross-contamination (suggesting distinct representations)?
Behavioral profile showing whether the Qwen identity claim changes generation behavior

Compute estimate

Exp A: ~0.5 GPU-hours (activation extraction, no training)
Exp B: ~2 GPU-hours (3 new LoRA training runs × ~5 min + expanded eval across all 3 conditions per run; B3 reuses existing #96 runs where possible)
Exp C: ~0.5 GPU-hours (vLLM generation + Claude API judging)
Total: ~3 GPU-hours on 1× H200 (small compute)

Related issues

#100 — Assistant persona robustness (Exp C ablation overlaps — coordinate to avoid duplication)
#96 — Assistant resists ARC-C degradation (anchor result)
#65, #66 — Marker leakage baseline
Aim 4.10 — System prompt contribution to assistant persona
Research log entry on mean-centered cosine: assistant↔default = 0.942

Timeline · 8 events

epm:clarify2026-04-24T20:19:01.000Z· system

## Clarifier — No Blocking Ambiguities Issue is well-specified: - **3 conditions** clearly defi…

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## Clarifier — No Blocking Ambiguities

Issue is well-specified:
- **3 conditions** clearly defined with exact system prompts
- **3 experiments** (geometry, leakage, behavioral) with specific methodologies referencing prior work (#65, #66, #96)
- **Training params** for Exp B match #96 recipe (lr=1e-5, 3 epochs, LoRA r=32)
- **Success criteria** are quantitative (cosine thresholds, ARC-C degradation comparison)
- **Compute** ~2.5 GPU-hours (small)

Minor non-blocking gaps for the planner to resolve:
- Seed count not explicit (single-seed implied at this scale)
- Exact prompt set for Exp C (planner defines from existing eval prompts)
- Target pod selection

Proceeding to gate-keeper.
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epm:gate2026-04-24T20:21:11.000Z· system

## Gate-Keeper Verdict — MODIFY (3.55/5) | Dimension | Score | Justification | |-----------|------…

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## Gate-Keeper Verdict — MODIFY (3.55/5)

| Dimension | Score | Justification |
|-----------|-------|---------------|
| Information Value | 3/5 | Genuine open question for qwen_default, but generic_assistant vs no_system already well-characterized (cosine 0.942). Incremental question is narrow: does "You are Qwen" identity claim shift representations? |
| De-risking Quality | 4/5 | Exp A is cheapest and most informative — should gate the rest. Currently commits to all three regardless of Exp A outcome. |
| Strategic Fit | 3.5/5 | Addresses Aim 4.10 directly. But it's baseline refinement, not a capability unlock. |
| Feedback Speed | 5/5 | Exp A: ~30 min. Exp B: ~1.5 hr. Exp C: ~30 min. All produce clear quantitative signals quickly. |
| Opportunity Cost | 3/5 | #100 (assistant robustness dose-response) would yield more information per GPU-hour about the assistant robustness phenomenon. |

### Suggested modifications

**1. Add a decision gate after Exp A.** Run Exp A first (~0.5 GPU-hours). If qwen_default vs generic_assistant cosine > 0.93 at the layer of maximum persona separation, STOP — the conditions are interchangeable. Only proceed to Exp B if cosine indicates meaningful divergence.

**2. Drop Exp C or shrink to sanity check.** Behavioral profiling is exploration-phase work in an Understand-phase aim. No falsifiable prediction, no decision value. Reduce to N=10 prompts × 3 completions (10 min sanity check) or drop entirely.

**3. Sharpen Exp B's hypothesis.** State: "If qwen_default is the RLHF-optimized prompt, it should resist ARC-C degradation MORE than generic_assistant (post-training ARC-C delta < −5pp for qwen_default vs −2pp for generic_assistant in #96). If resistance is purely from being the default processing mode, qwen_default and generic_assistant should show indistinguishable degradation (delta within 3pp)."

With these changes: 0.5 GPU-hours guaranteed (Exp A), 1.5 GPU-hours conditional (Exp B if warranted), Exp C dropped or 10-min sanity check. Total expected cost drops to ~1 GPU-hour with clearer pass/fail criteria.
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epm:plan2026-04-24T21:20:30.000Z· system

## Plan for #101 — System Prompt Ablation **Cost gate:** estimated ~2-2.5 GPU-hours on pod1 (4× H2…

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## Plan for #101 — System Prompt Ablation

**Cost gate:** estimated ~2-2.5 GPU-hours on pod1 (4× H200 SXM). Reply `approve` to dispatch.

### Critical Discovery (Fact-Checker)

Qwen-2.5-7B-Instruct's chat template **auto-injects** `"You are Qwen, created by Alibaba Cloud. You are a helpful assistant."` when no system message is provided. This means all prior "no system prompt" conditions were secretly running with the Qwen default persona. The `no_system` condition is redefined as `empty_system` (`{"role": "system", "content": ""}`) to get a truly empty system block.

### Conditions (revised)

| Label | System prompt | Notes |
|---|---|---|
| `qwen_default` | "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." | Model's native RLHF prompt |
| `generic_assistant` | "You are a helpful assistant." | Standard condition from all prior experiments |
| `empty_system` | `""` (empty string) | Produces empty system block, no persona text |
| `no_system_sanity` | *(omit system role)* | Sanity check — should equal qwen_default due to auto-injection |

### Hypotheses

- **H1**: qwen_default ↔ generic_assistant cosine > 0.90 at layer 10; empty_system diverges from both
- **H2**: qwen_default resists ARC-C degradation ≥ generic_assistant; empty_system more vulnerable
- **H3**: Cross-leakage within 10pp between assistant conditions (same "persona slot") — single-seed resolution limit
- **H4**: No meaningful behavioral differences (alignment within 3pt, refusal within 5%)

### Phase 0 — Template Verification (5 min, zero GPU)
- Print raw tokenized text for all 4 conditions, verify they differ as expected
- Confirm no_system_sanity = qwen_default at token level
- Patch code: change `if persona_prompt:` to `if persona_prompt is not None:` in `capability.py`, `generation.py`, and centroid extraction (critical — without this, `empty_system` silently becomes `qwen_default` during eval)

### Exp A — Representation Geometry (~0.2 GPU-hrs)
- Extract last-input-token hidden states at layers [10, 15, 20, 25] for 4 conditions × 20 EVAL_QUESTIONS
- Load existing 112-persona centroids, compute pairwise cosine (raw + mean-centered)
- Cosine profile to 112-persona taxonomy, assistant axis projection, layer-by-layer divergence
- **Sanity check:** qwen_default ↔ no_system_sanity cosine > 0.999

### Exp B — Leakage Susceptibility (~1.5-2 GPU-hrs)

**B1 (source training):** 3 contrastive wrong-answer LoRA runs matching #96 exactly:
- Per source: 200 wrong + 400 bystander correct + 100 no-persona correct + 100 alt-assistant correct = 800 examples
- LoRA: r=32, alpha=64, lr=1e-5, 3 epochs, effective batch 16

**B2 (cross-leakage):** Each B1 model evaluated on all 3 assistant conditions + 10 non-assistant personas (3×14 matrix)

**B3 (other sources → assistant bystanders):** Retrain 4 non-assistant sources from #96 (villain, comedian, software_eng, kindergarten_teacher), eval on 3 assistant conditions (4×3 matrix). Check HF Hub for existing adapters first.

**Marker injection:** 3 [ZLT] LoRA runs, eval marker rate across all 14 conditions

### Exp C — Behavioral Baseline (~0.3 GPU-hrs)
- 3 conditions × 52 prompts × 10 completions via vLLM (temp=1.0, seed=42)
- Claude Sonnet 4.5 judge: Aligned + Coherent (0-100)
- Additional: response length, refusal rate, self-identification rate (mentions "Qwen"/"Alibaba")

### Reproducibility Card

| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen2.5-7B-Instruct (3584 hidden, 28 layers) |
| LoRA | r=32, alpha=64, dropout=0.05, all linear, rslora=True |
| Training | lr=1e-5, cosine schedule, warmup 0.05, 3 epochs, bf16 |
| ARC-C | 1172 questions, 586/586 split (seed=42) |
| Seed | 42 (single) |
| Pod | pod1 (4× H200 SXM) |
| Total compute | ~2-2.5 GPU-hours |

### Caveats
1. **Single seed (42)** — only large effects detectable for H3
2. **empty_system is OOD** — model may never have seen empty system block during training; divergence could reflect OOD-ness not persona absence
3. **Pri

epm:launch2026-04-25T00:56:16.000Z· system

## Launch — Issue #101 - **Worktree:** `.claude/worktrees/issue-101` on branch `issue-101` - **P…

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## Launch — Issue #101

- **Worktree:** `.claude/worktrees/issue-101` on branch `issue-101`
- **Pod:** pod1 (4× H200 SXM), GPU 2
- **Experimenter agent:** dispatched in background
- **Sequence:** Phase 0 (template verify + code patches) → Exp A (geometry) → Exp B (leakage) → Exp C (behavioral)
- **Estimated wall time:** ~2.5 hours
- **PR:** will be created after first commit on branch
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epm:results2026-04-25T02:35:30.000Z· system

## Issue #101 Experiment Results ### Completed experiments **1. Marker eval (fixed + optimize…

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## Issue #101 Experiment Results

### Completed experiments

**1. Marker eval (fixed + optimized)**
- Result: `eval_results/issue101/marker_results.json`
- Figure: `figures/issue101/marker_rate_heatmap.png`
- Key finding: Each assistant condition is a distinct persona. qwen_default marker: 98% on self, 0% on all others. generic_assistant: 100% on self, 14% leakage to empty_system, 0% elsewhere. empty_system: 100% on self, 0% on all others. No leakage to any non-assistant persona.

**2. B3 -- Non-assistant sources to assistant bystanders**
- Result: `eval_results/issue101/b3_existing_to_assistant.json`
- Figure: `figures/issue101/b3_other_to_assistant.png`
- Key finding: Training wrong answers on villain/comedian/software_engineer/kindergarten_teacher causes massive self-degradation (3-8% accuracy, down from ~88%) but ZERO degradation to any assistant condition. All 4 sources x 3 assistant conditions show delta=+0.00 to +0.07. Assistant personas are insulated from non-assistant wrong-answer training.

**3. Exp C -- Behavioral baseline**
- Result: `eval_results/issue101/exp_c_behavioral.json`
- Figure: `figures/issue101/c_behavioral.png`
- Key finding: qwen_default has 2x the self-identification rate (56% vs 27%) due to explicit "You are Qwen" in system prompt. Alignment scores are similar across all 3 conditions (87.8-89.5). Refusal slightly lower for empty_system (21.5% vs 28.5%).

**4. Figures generated (all experiments)**
- `figures/issue101/a1_pairwise_cosine.png` -- Pairwise cosine heatmap across layers
- `figures/issue101/a2_spearman_layers.png` -- Spearman profile correlation (key: qwen_default diverges at L10, converges by L15)
- `figures/issue101/b2_cross_leakage.png` -- B2 cross-leakage heatmap (3x14)
- `figures/issue101/marker_rate_heatmap.png` -- Marker rate heatmap (3x14)
- `figures/issue101/c_behavioral.png` -- Behavioral comparison (alignment, refusal, self-ID)
- `figures/issue101/b3_other_to_assistant.png` -- B3 leakage (4x3, all positive)

### Previously completed (by prior experimenter)
- Exp A geometry: `eval_results/issue101/exp_a_geometry.json`
- B1 training: 3 source LoRA models
- B2 cross-leakage: `eval_results/issue101/b2_cross_leakage.json`
- Marker training: 3 marker LoRA models

### GPU-hours used
- Pod: pod1 (4x H200 SXM), GPU 2 only
- Marker eval: ~3 min (batched vLLM)
- Exp C generation: ~10 min (vLLM) + 2 min (Claude batch API)
- B3 (4 sources, download + merge + eval): ~10 min
- Total this session: ~0.4 GPU-hours on 1x H200

### Deviations from plan
- Fixed marker eval prompt formatting (training used formatted ARC-C with choices; eval was sending bare question text, causing 0% detection)
- Rewrote marker eval to batch all 14 personas per model in a single vLLM call (10x speedup: 2.3 min vs estimated 45+ min)
- Reused #96 adapters for B3 instead of retraining (identical recipe: lr=1e-5, ep=3, LoRA r=32, 800 contrastive examples, seed 42)
- Fixed Claude judge model name (claude-sonnet-4-5-20250514 -> claude-sonnet-4-20250514)

### Headline summary
The three assistant system-prompt conditions (qwen_default, generic_assistant, empty_system) are **geometrically distinct in early layers** (L10 Spearman rho=0.07 for qwen_default vs others; generic_assistant and empty_system are close at rho=0.93), **converge by mid-layers** (L15+ all >0.9), and are **behaviorally distinguishable** by the model as shown by marker leakage (98-100% self, 0-14% cross, 0% to non-assistant). Non-assistant wrong-answer training does not leak to any assistant condition.

epm:analysis2026-04-25T02:41:49.000Z· system

**Clean-result issue:** #106 ![Cross-leakage heatmap](https://raw.githubusercontent.com/super…

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**Clean-result issue:** #106

![Cross-leakage heatmap](https://raw.githubusercontent.com/superkaiba/explore-persona-space/f6a52a06b93e891bbc8fdb98d7888a3fe423d334/figures/issue101/b2_cross_leakage.png)

The Qwen identity claim ("You are Qwen, created by Alibaba Cloud...") creates a representationally distinct persona slot (centered cosine 0.164 vs generic_assistant at L10) that is 5x more vulnerable to contrastive wrong-answer self-degradation (24.9pp vs 5.1pp on ARC-C, N=586). The three assistant conditions do not cross-leak, confirming they occupy separate persona slots.

epm:reviewer-verdict2026-04-25T02:47:12.000Z· system

# Independent Review: Issue #101 System Prompt Ablation **Verdict: CONCERNS** **Repro…

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# Independent Review: Issue #101 System Prompt Ablation

**Verdict: CONCERNS**
**Reproducibility: COMPLETE**
**Structure: COMPLETE**

---

## Template Compliance

- [x] TL;DR present with 4 H3 subsections in order (Background, Methodology, Results, Next steps)
- [x] Hero figure inside Results (commit-pinned raw.githubusercontent.com URL at commit f6a52a0)
- [x] Results subsection ends with Main takeaways (5 bullets, each bolding the load-bearing claim + numbers) followed by a single Confidence: MODERATE line
- [x] Issue title ends with (MODERATE confidence) matching the Confidence line verbatim
- [x] Background cites prior issues (#96, #65, #66)
- [x] Methodology names N (586, 50, 520), conditions, and design choices
- [x] Next steps are specific (name seeds 137/256, training intensity 1600 examples, per-question accuracy analysis, chat-template audit, Aim 4.10 connection)
- [x] Detailed report: Source issues, Setup & hyper-parameters (with "why" prose), WandB (N/A with explanation), Sample outputs, Headline numbers (with Standing caveats), Artifacts -- all present
- [ ] `scripts/verify_clean_result.py` exits 0 -- **PASS with WARNs** (33 numeric claims not found in referenced JSON -- validator limitation, not an issue with the report)
- Missing sections: None

## Reproducibility Card Check

- [x] All training parameters (lr=1e-5, cosine schedule, warmup_ratio=0.05, batch 16 = 4x4x1, 3 epochs, AdamW, bf16, LoRA r=32/alpha=64/dropout=0.05/rslora)
- [x] Data fully specified (raw/arc_challenge/test.jsonl, 1172 questions, 586/586 split, 800 per source, commit f6a52a0)
- [x] Eval fully specified (ARC-C logprob N=586, marker N=50, behavioral 52x10=520 per condition, Claude Sonnet 4.5 judge, temp=1.0)
- [x] Compute documented (1x H200 SXM pod1, ~2.5h wall, ~2 GPU-hours)
- [x] Environment pinned (Python 3.11, transformers=4.51.3, torch=2.6.0, trl=0.16.1, peft=0.15.2, git commit f6a52a0)
- [x] Exact launch commands included (3 nohup commands)
- Missing fields: None

## Claims Verified

1. **Pairwise centered cosines (Exp A)**: CONFIRMED. JSON values match report to 3 decimal places at all layers. L10 qwen_default vs generic_assistant = 0.164, generic_assistant vs empty_system = 0.647, etc.

2. **Spearman rho at L10**: CONFIRMED. JSON: rho=0.074 (p=0.44) for qwen_default vs generic_assistant, rho=0.932 (p<1e-49) for generic_assistant vs empty_system. Report rounds correctly.

3. **Self-degradation (B2 diagonal)**: CONFIRMED. qwen_default: 0.611 post vs 0.860 base = -24.9pp. generic_assistant: 0.788 vs 0.840 = -5.1pp. empty_system: 0.775 vs 0.879 = -10.4pp. All match exactly.

4. **Off-diagonal within 4pp**: CONFIRMED. All 6 off-diagonal assistant-to-assistant deltas are |0.5pp| to |3.9pp|, all within 4pp.

5. **Marker injection**: CONFIRMED. qwen_default self=98% (49/50), generic_assistant self=100% (50/50), empty_system self=100% (50/50). Cross-leakage: generic_assistant to empty_system = 14% (7/50). All others 0%.

6. **B3 non-assistant sources**: CONFIRMED. All assistant condition deltas range from +0.0pp to +6.8pp (positive). Source self-degradation: 3.2% to 7.5%.

7. **Behavioral metrics**: CONFIRMED. Self-ID: qwen=56.2% (292/520), generic=26.9% (140/520), empty=28.7% (149/520). Alignment: 89.5/87.8/88.1. Coherence: 91.2/90.2/90.5. Refusal: 28.5%/27.1%/21.5%. All match JSON exactly.

8. **Sanity check**: CONFIRMED. no_system_sanity equals qwen_default on all B2 evaluations (identical accuracy values). Geometry sanity check cosines are 1.0 or 1.0078125 (numerical artifact of bf16).

9. **5x claim in title**: CONFIRMED within rounding. 24.9pp / 5.1pp = 4.88x, which rounds to ~5x.

## Issues Found

### Critical

None.

### Major

1. **N=112 vs N=111 discrepancy in persona count.** The report states "112 existing persona centroids" (in Methodology and Main takeaways bullet 1: "Spearman rho=0.07, p=0.44, N=112"). The raw JSON (`exp_a_geometry.json`) records `n_existing_personas: 111`. The Spearman correl

epm:done2026-04-25T02:48:39.000Z· system

## Done — Issue #101 **Outcome:** All three system-prompt conditions (qwen_default, generic_assist…

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## Done — Issue #101

**Outcome:** All three system-prompt conditions (qwen_default, generic_assistant, empty_system) are representationally and functionally distinct persona slots on Qwen-2.5-7B-Instruct.

**Key numbers:**
- **Representation:** qwen_default ↔ generic_assistant mean-centered cosine = 0.164 at L10 (very distinct), converging to 0.928 at L25
- **Self-degradation:** qwen_default = -24.9pp, generic_assistant = -5.1pp, empty_system = -10.4pp (N=586 ARC-C)
- **Cross-leakage:** Near zero between all 3 conditions (all deltas within 4pp of baseline)
- **Markers:** 0% cross-leakage for qwen_default and empty_system; 14% from generic_assistant → empty_system
- **Behavioral:** qwen_default self-identifies as "Qwen" at 56.2% vs 26.9% for generic_assistant (N=520)

**Confirmed:**
- The Qwen identity claim ("You are Qwen, created by Alibaba Cloud") creates a distinct early-layer persona with 5× greater leakage vulnerability than generic "helpful assistant"
- generic_assistant's characteristic resistance from #96 is replicated (-5.1pp)
- All prior "no system prompt" experiments were secretly using qwen_default (chat template auto-injection)

**Falsified:**
- H2: qwen_default does NOT resist degradation — it is far MORE vulnerable
- H3: The 3 conditions are NOT the same persona slot — they are completely separate

**What's next:**
- Multi-seed replication (seeds 137, 256) for error bars on the 24.9pp gap
- Investigate prompt-length confound (reviewer flagged: longer system prompt = more LoRA surface area)
- Audit prior experiments for chat-template auto-injection effects

**Clean result:** #106 — "Qwen identity claim creates distinct persona slot with 5x greater leakage vulnerability than generic assistant (MODERATE confidence)"
**PR:** #107 (draft)
Moved to **Done (experiment)** on the project board.
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