Pyvene Interventions

This brings Stanford NLP's pyvene library to Claude, letting you run causal interventions on PyTorch models without writing boilerplate. If you're doing activation patching, causal tracing (ROME-style), or interchange intervention training, this handles the setup through a dict-based config system. The main win is reproducibility: experiments become shareable artifacts you can push to HuggingFace instead of scattered notebooks. It works with any PyTorch architecture, not just transformers, which matters if you're testing causal hypotheses beyond language models. The library has 840+ stars and a NAACL 2024 paper backing it, so the abstractions are research-tested rather than someone's weekend project.