Cognitive architecture AI weighted memory, and a falsifiable continuity metric
The article introduces PHI // DRIFT, a cognitive middleware architecture aimed at improving AI companion behavior. It emphasizes the importance of contextually coherent interactions over traditional model scaling. The architecture includes innovative components like the Decision Memory Unit and a Persistence-Embodiment-Drift Index to enhance memory retrieval and behavioral continuity.
- ▪PHI // DRIFT focuses on continuous, contextually coherent behavior in AI companions rather than just scaling models.
- ▪The Decision Memory Unit (DMU) enhances memory retrieval by weighting memories based on time-decay and contextual relevance.
- ▪The architecture includes a security defense layer that successfully passed tests against various adversarial attack classes.
Opening excerpt (first ~120 words) tap to expand
Published May 23, 2026 | Version v1 Working paper Open The dominant paradigm in AI development is scale. Bigger models, more parameters, more compute. PHI // DRIFT is a different bet. It's a cognitive middleware architecture built on a single thesis: that distinct, continuous, contextually coherent behavior in an AI companion emerges not from model weights alone — but from what is assembled into the prompt, what is retrieved from memory, and what structured state is updated between turns. Five architectural contributions: DMU — Decision Memory Unit. Replaces cosine similarity retrieval with exp(-t/τ) × reinforcement × contextual × extra. Memories are scored by what mattered to the system's ongoing state — not just what was semantically adjacent.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Zenodo.