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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)

interpretable-ai transformer-models ethical-ai cognitive-architectures edge-aware-processing neuro-symbolic-ai srai reflective-ai introspections structured-memory srca refnet sentience-dsl cortex-memory latent-journey
  • Updated Nov 11, 2025
  • Python

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