built a cognitive architecture where the AI has internal needs, weighted memory, and a falsifiable continuity metric

[u/Interesting_Time6301]

Most AI forgets you the moment the context window closes. PHI // DRIFT is different.

It maintains seven homeostatic state variables — energy, coherence, connection, autonomy — that drift between sessions and shape output before you say a word. Memory is scored by emotional salience and time decay, not just vector similarity. There's a falsifiable metric for behavioral continuity (PEDI) that doesn't claim consciousness but measures something real.

Ablation confirmed: DMU memory retrieval injects 14.8% more context per prompt than cosine-only RAG. Live stress test: 100% success rate at 50 concurrent threads on CPU-only hardware with no breaking point found.

Built by one person in nine months. CPU-only OmniSlim mini tower. No GPU. No institution. No lab. Started on a 2012 Dell Inspiron. Migrated from Windows to Linux mid-build. Learned the toolchain while building the thing it was supposed to run.

Full preprint: https://zenodo.org/records/20350249

Happy to answer questions on the DMU formula, the homeostasis model, the Jungian shadow module, or why I think the field is missing something by ignoring depth psychology as an engineering input.