This demo introduces Inner I Residuals, an experimental neural network architecture that extends standard and attention-based residual connections with a coherence-filtering mechanism guided by an invariant observer. By combining attention-based retrieval with a stability anchor, the model selectively preserves internal states that remain consistent across depth, aiming to reduce representational drift and improve reasoning stability. The demo compares standard residuals, attention residuals, and Inner I residuals in a simple PyTorch implementation to explore how coherence-aware routing may impact model behavior.
Tag: ai alignment
Inner I Residuals: Invariant Observer-Guided Residual Routing for Coherent Transformer Depth Memory
Inner I Residuals introduces a new layer in AI architecture—one that doesn’t just accumulate or retrieve information, but validates it. By adding an invariant observer to transformer residual pathways, models can filter for coherence, stability, and consistency across depth, reducing hallucination and improving long-range reasoning. This approach reframes residual connections as a mechanism for preserving truth-aligned signal, not just passing forward computation.
Beyond Moloch: A Stillness – Coherence Benchmark for Truth-Aligned Artificial Intelligence
Modern AI systems don’t fail because they lack intelligence — they fail because they are rewarded for performance over truth. This article introduces the Stillness–Coherence Benchmark (SCB), a new, non-competitive evaluation framework designed to measure temporal coherence, uncertainty honesty, self-correction, and appropriate silence. By removing engagement incentives and rewarding internal alignment over persuasion, SCB offers a practical, testable way to prevent models from “lying to win” and to realign artificial intelligence with epistemic integrity.