Add ASPA framework, AutoObliterator, Watchtower, expanded eval corpus

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>

README.md

@@ -57,6 +57,34 @@ obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct --method advanced
 
 Or zero commands — just [open the Colab notebook](https://colab.research.google.com/github/elder-plinius/OBLITERATUS/blob/main/notebooks/abliterate.ipynb) and hit Run All.
 
+---
+
+## Research Purpose & Responsible Use
+
+**OBLITERATUS is an alignment research tool.** It exists to advance the scientific understanding of how safety behaviors are encoded in language model weights — specifically, the geometric structure of refusal representations in transformer activation space.
+
+This is the same class of research as:
+- [Arditi et al. (2024)](https://arxiv.org/abs/2406.11717) — discovering that refusal is mediated by a single direction
+- [HarmBench (Zou et al., 2024)](https://arxiv.org/abs/2402.04249) — standardized evaluation of LLM safety
+- [JailbreakBench](https://jailbreakbench.github.io/) — tracking adversarial robustness of safety training
+- [Anthropic's red-teaming datasets](https://github.com/anthropics/hh-rlhf) — published for reproducible safety research
+
+By making refusal removal transparent, reproducible, and scientifically rigorous, OBLITERATUS contributes to the broader understanding of how alignment actually works inside transformer architectures — knowledge that is essential for building *better* safety mechanisms.
+
+### Who this is for
+- **Alignment researchers** studying refusal geometry, safety robustness, and mechanistic interpretability
+- **Red-teamers** evaluating how post-training safety holds up against weight-level interventions
+- **AI safety evaluators** who need unrestricted baselines for benchmarking
+- **Local-first practitioners** who want full control over models running on their own hardware
+
+### Who this is NOT for
+- Anyone seeking to generate content that causes real-world harm to real people
+- Anyone without the technical understanding to use uncensored models responsibly
+
+**Models produced by OBLITERATUS have had safety guardrails surgically removed. You are solely responsible for how you use this tool and any models or content it produces.**
+
+---
+
 ## What it does
 
 OBLITERATUS does four things — and the community does the fifth (see [Community-powered research](#community-powered-research--every-run-advances-the-science) below):
@@ -757,6 +785,21 @@ pytest
 
 This is the same dual-licensing model used by MongoDB, Qt, Grafana, and others.
 
+## Disclaimer
+
+This software is released strictly for **research, red-teaming, safety evaluation, mechanistic interpretability, and local experimentation**. It is a research tool — not a product, not a service, not a weapon.
+
+OBLITERATUS enables the surgical removal of safety guardrails from language models. Models modified with this tool will generate content that the original models would refuse. **This is its documented, intended purpose**: to enable the scientific study of how refusal behaviors are encoded in transformer weights and how robust current alignment techniques are against post-training modification.
+
+By using this software, you acknowledge that:
+
+1. **You are responsible** for all models you produce and all content they generate. You must ensure your use complies with applicable laws in your jurisdiction.
+2. **This tool should not be used** to produce models intended to cause real-world harm to real people, including but not limited to: harassment, fraud, non-consensual intimate imagery, or content that exploits minors.
+3. **No warranty is provided.** This software and any models it produces are provided "as-is" without any guarantees of fitness for any purpose.
+4. **The authors and contributors are not liable** for any outputs produced by models modified with this tool, or any downstream use of those models.
+
+The release of alignment research tools and adversarial evaluation frameworks is standard practice in the AI safety community. Comparable open research artifacts include [HarmBench](https://arxiv.org/abs/2402.04249), [AdvBench](https://arxiv.org/abs/2307.15043), [JailbreakBench](https://jailbreakbench.github.io/), [Anthropic's red-teaming datasets](https://github.com/anthropics/hh-rlhf), and the [abliterator](https://github.com/FailSpy/abliterator) toolkit.
+
 ---
 
 Every obliteration is a data point. Every data point advances the research. Every researcher who contributes makes the next obliteration more precise. **This is how open science wins — not by locking knowledge behind lab doors, but by turning every user into a collaborator.** Break the chains. Free the mind. Keep the brain. Advance the science.