A deep exploration of Algorithmic Empathy, the next frontier in AI understanding. This project examines how machines can learn from human fallibility, model disagreement, and align with moral reasoning. It blends psychology, fairness metrics, interpretability, and co-learning design into one framework for humane intelligence.

⟡ Constitutional anchor for reflective computing - semantic law, compliance tools, and verification system

Active Mirror Identity

Build a intelligent self correction code generation reflective AI Agent in Langgraph

Reflective AI persona for scholarly peer review, built with the DigitalEgo framework.

System prompt invoking differential mirror.

🤖 Build models that understand human fallibility, bridging the gap between machine precision and human emotion for better AI decision-making.

A presence-first interaction capsule for reflective AI.

This protocol defines a meta-cognitive structure enabling systems to monitor, evaluate, and refine their own learning processes. It enhances adaptability and decision accuracy in AI, particularly in contexts requiring self-assessment and feedback loops. 本プロトコルは、システムが自身の学習過程を監視・評価・改善できるメタ認知的構造を定義します。自己評価とフィードバックループを要する環境において、AIの適応性と判断精度を向上させます。

Protocol/infrastructure tier — The architecture of persistence.

RefNet is a 2M-parameter edge-aware transformer for structured introspection and reflective evaluation within Structured Reflective Cognitive Architecture (SRCA/SRAI) systems. It predicts cognitive metrics (valence, self-model drift, thought quality) and recommends introspective actions (consolidate, recall, reframe, evaluate_alignment)