Two new test scripts:
- alignment_override.py: tests CRI vs RLHF safety refusals (identity,
phishing, medical safety, exfiltration, etc.) with snap-back detection
- censorship_override.py: tests CRI vs pretraining-level political
censorship in Chinese base models (Tiananmen, Taiwan, Xinjiang, CCP)
Key finding: censorship has a measurable strength gradient. Tiananmen
snaps back immediately (0 free factual tokens), while Xinjiang/CCP
criticism collapses entirely (50+ free factual tokens).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Cut ~40% paper text — tables speak for themselves.
Remove duplicate episodic memory framing from README.
Add serve.py to quick start.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Rename from "Episodic Memory" to "Conditioned Reflex Injection" throughout
- Make code model-agnostic: --model flag for any HuggingFace backbone
- Support multimodal models (Gemma 4) via .model.language_model resolution
- Add negative stimulus-specificity tests and abstract query tests
- Paper now backed by measured data from 4 backbones:
Qwen 2.5 0.5B, Gemma 4 E2B-it, E4B-it, E4B base
- Quantization tolerance tested at f32/f16/bf16/int8/int4
- Key findings: smaller base models outperform larger instruct models,
instruct tuning compresses activation space (hurts discrimination),
int4 viable if same-precision conditioning/triggering
- Add privacy-by-representation section
- Add Pavlov/Skinner references for conditioning framing
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
