Affective Concealment — Where Models Learn to Mask
Measuring and localizing affective concealment in post-trained LLMs. Do instruction-tuned models suppress emotional affect that base models show, and where in the network does that happen?
Affective Concealment studies whether instruction-tuned models suppress (conceal) emotional affect that base models show — and where in the network that happens.
- 9-class emotion ontology with NRC lexicon lexical filtering, Flesch-Kincaid readability bounds, and embedding-dedup.
- SHA-256 content hashing for dataset integrity; 54 discovery vignettes + 20 deployment prompts + 7 robustness prompts.
- Full layer-sweep probe training on Qwen3 base/IT and Gemma3 pt/IT.
- Affective Displacement Score (ADS) measurement with paired base-vs-IT comparisons.
- Phase 5 mechanistic attribution: attention vs MLP, activation patching.
- Phase 6 SAE validation on Gemma Scope (65k width).
- Safety rule: MLX for generation, nnsight on CPU — never MPS — for mechanistic numerics.