Open the black box.
Remove what you find.
Research at the intersection of mechanistic interpretability and LLM safety. Instead of reinforcement learning from human feedback or prompt-based guardrails, we locate the specific neural components — attention heads, MLP neurons — that drive unwanted behaviors, then surgically remove them with minimal collateral damage to general capability.
Component-level interpretability
Before modifying a model, understand which parts do what. Using activation patching, difference-of-means in residual stream space, and projection orthogonalization, we compute each attention head and MLP neuron's contribution to specific behaviors — refusal, sycophancy, hallucination — with component-level granularity.
Selective component ablation
Once refusal-driving components are identified, PICK uses KL-aware greedy selection to pick the highest-specificity targets — components that contribute to refusal but not to general reasoning. Rank-1 LoRA then precisely modifies only those components, achieving 81–93% refusal reduction with near-zero KL divergence from the original model.
Efficient model adaptation
Beyond safety, the same component-level approach applies to fine-tuning and knowledge distillation. By identifying which components encode which capabilities, we can adapt models more efficiently — updating only what needs to change while preserving everything else. LoRA, QLoRA, and full-parameter SFT, guided by attribution.
Open by default
All research ships in the open. PICK is Apache-2.0 on GitHub. The safe_prompt and unsafe_prompt evaluation datasets are on HuggingFace. Training logs, experiment records, ablation details — every component selected, every KL budget tested, every failure mode documented. Built because we needed it; open because the field needs it.