title: >-
Counterfactual Reflection Training for Security LLMs: Shaping Internal
Reasoning via Future-Continuation Optimization
tags:
- counterfactual
- reflection
- security-llm
- circuit-aware
- fine-tuning
- lora
license: apache-2.0
authors:
- Hayula AI Lab
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.