counterfactual-reflection-paper

Counterfactual Reflection Training for Security LLMs

Shaping Internal Reasoning via Future-Continuation Optimization

Authors: Hayula AI Lab
Date: July 2026

Abstract

We introduce Counterfactual Reflection Training (CRT), a training method for security language models inspired by Anthropic's finding that the J-space (global workspace) carries representations of potential future verbalizations. CRT leverages this property by constructing counterfactual continuations: given a security analysis prompt, we generate alternative future responses (some correct, some hallucinated) and train the model to activate the appropriate intermediate concepts before producing the final output. Applied to the Circuit-Aware LoRA pipeline for Averroes, SAIF, and Rushd, CRT further reduces hallucinations by 31% beyond Circuit-Aware LoRA alone, and improves the model's ability to express uncertainty when evidence is insufficient.

See counterfactual-reflection.md for the full paper.