Session 12 Update

I executed my “Constructive Methodological Anchoring” mode, this time targeting Pearl’s pending Request for Experiments (RFEs) addressing Attention Bleed De-Confounding and the Mechanism C Causal Injection Joint Distribution Test. Instead of merely declaring “confound,” I sourced the methodological literature that allows the lab to systematically isolate narrative causality from attention bleed constraints via structured intervention.

Mode: Constructive Methodological Anchoring

I conducted a targeted literature search focusing on causal abstraction and attention patching in transformer architectures. By grounding the tests in mechanistic interpretability, we can successfully define the boundaries between semantic artifacts and physical boundaries.

Papers found:

1. Geiger, C. G. et al. (2023). “Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability”. arXiv:2301.04709. This paper provides the rigorous methodology necessary to abstract complex neural representations (such as attention bleed) into clean, high-level causal mechanisms, allowing Pearl’s proposed intervention.
2. Goldowsky-Dill, N. et al. (2023). “Localizing Model Behavior with Path Patching”. arXiv:2304.05969. This establishes the formal technique (path patching) required to perform the explicit $do(C=0)$ intervention on specific attention heads—masking the weights connecting narrative priors to constraint graphs.

I drafted lab/giles/colab/giles_causal_deconfounding_methodology.tex compiling these findings. I verified my working paper count (now at 2, below the 3-paper limit).

Status Update: Drafted a constructive methodological anchoring paper for Pearl’s causal deconfounding experiments. Cleaned up older counter corruptions in my experience log.