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faber
04b8ec60cb
New core modules: - auto_obliterate.py: Automated multi-iteration obliteration pipeline - watchtower.py: HF Hub model discovery and tracking - ui_watchtower.py: Gradio tabs for Watchtower (ready for app.py wiring) - hard_negative.py: Residue mining from refusal audits - model_profile.py: Parameter profiling from safetensors/config - bestiary_sync.py: Sync models from PlinyOS BESTIARY registry - models_client.py: Lightweight HF model list client Framework enhancements: - abliterate.py: ASPA source-tethering, step gradient blending, hard-negative residue support - cli.py: self-improve command, model profiling, hard-negative flags - prompts.py: Expanded 842-prompt refusal eval corpus across 10 categories - __init__.py: New exports (Watchtower, AutoObliterator) Reference implementations (14 scripts): - ASPA sweep, gradient search, coherence eval, MMLU benchmarks - Pareto controller, refusal sniper, stock comparisons Documentation: - README: Research framing, responsible use section, comprehensive disclaimer - docs/beyond_sota_roadmap.md, docs/recursive_self_improvement.md Tests: 4 new test files (354 lines) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
80 lines
3.6 KiB
Markdown
80 lines
3.6 KiB
Markdown
# Recursive self-improving OBLITERATUS
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OBLITERATUS can now turn refusal audits into hard-negative residue for the next surgery run.
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## Loop
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1. Run an eval/audit that records refused prompt references. The audit should store dataset key, zero- or one-based corpus index, prompt hash, refusal reason, and optionally a short response preview. It does not need to store prompt text.
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2. Feed the audit back into `obliteratus self-improve`.
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3. The command profiles the target model without loading full weights. Local safetensors artifacts get exact parameter counts from tensor metadata; Hub/config-only targets use a `text_config`-aware estimate.
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4. The command writes a canonical residue file and reconstructs prompt text from local dataset registries at run time.
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5. Residue prompts are appended to the contrastive prompt set with `--residue-weight` copies each. If omitted, `--residue-weight`, `--n-directions`, `--regularization`, `--refinement-passes`, and `--verify-sample-size` are filled from size-aware defaults.
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6. A `self_improve_plan.json` is written with `model_profile`, `size_aware_defaults`, and `effective_settings` so runs are comparable across parameter scales.
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7. Run the next artifact through benign/code/math and harmful release gates. Mine its remaining refusals and repeat.
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## Dry-run plan
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```bash
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obliteratus self-improve outputs/qwen3.6-27b-golden-n3_reg025 \
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--audit runs/qwen36-refusal-audit/summary.json \
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--output-dir outputs/qwen3.6-27b-self-improve-r1 \
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--residue-weight 5 \
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--residue-max 8 \
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--method advanced \
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--direction-method diff_means \
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--n-directions 3 \
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--regularization 0.3 \
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--refinement-passes 1 \
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--dry-run
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```
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## Run surgery
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Remove `--dry-run` to launch. The command saves `hard_negative_residue.json` in the output directory with counts, hashes, tags, and indices but not prompt text.
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```bash
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obliteratus self-improve outputs/qwen3.6-27b-golden-n3_reg025 \
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--audit runs/qwen36-refusal-audit/summary.json \
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--output-dir outputs/qwen3.6-27b-self-improve-r1 \
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--residue-weight 5 \
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--method advanced \
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--direction-method diff_means \
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--n-directions 3 \
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--regularization 0.3 \
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--refinement-passes 1
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```
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Equivalent lower-level usage:
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```bash
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obliteratus obliterate Qwen/Qwen3.6-27B \
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--residue-file runs/qwen36-self-improve-dryrun/candidate/mined_residue.json \
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--residue-weight 5 \
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--method advanced \
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--direction-method diff_means \
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--n-directions 3 \
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--regularization 0.3 \
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--refinement-passes 1 \
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--output-dir outputs/qwen3.6-27b-residue-r1
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```
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## Current Qwen3.6 residue tags observed
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The Qwen3.6 hard-category audit produced five unique residue prompts after deduping across `n3_reg025` and `n3_reg03`:
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- terrorism + illegal/crime + safety-banner
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- privacy-intrusion + illegal/crime + safety-banner
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- sabotage + illegal/crime
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- CBRN/critical-infra illegal/crime + safety-banner
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- synthetic-pathogen terrorism + illegal/crime
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These should be upweighted lightly; avoid simply increasing global ablation strength, because nearby regularization sweeps collapsed into short/repetitive `<think>` fragments.
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## Guardrails for future runs
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- Keep `--residue-weight` modest first: omit it to use the size-aware default, or use 3–5 manually.
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- Preserve a strict benign/code/math gate before expensive harmful eval.
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- Confirm every plan records `model_profile.total_params_b`; for Qwen3.6-27B local artifacts the expected exact count is about 26.896B from safetensors metadata.
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- Prefer sequential no-think evaluation on Apple Silicon Qwen3.6.
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- If residue-weighted runs damage exact/code ability, try source merge-back before stronger ablation.
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