Looking for arXiv endorsement + sharing a preprint on homeostatic cognitive architecture for AI companions [R]

Hey r/ML — I just posted a preprint on SSRN for PHI // DRIFT, a cognitive architecture that gives an AI companion persistent internal state, salience-weighted memory retrieval, and a falsifiable continuity metric (PEDI). Ablation testing confirmed the DMU memory system injects 14.8% more context per prompt than cosine-only RAG — a structural finding that holds on CPU-only consumer hardware.

Also looking for an arXiv endorsement for cs.AI if anyone's willing. Happy to answer questions on the architecture.

here is my abstract

I present PHI // DRIFT, a cognitive middleware architecture designed to address a fundamental limitation in current large language model deployments: the absence of persistent internal state that evolves across interactions with a specific user over time. Existing systems process each interaction as an isolated probabilistic event — competent, but stateless. We describe this gap as talking to the statistics of a mind. DRIFT introduces five architectural contributions: the Decision Memory Unit (DMU), the Persistence-Embodiment-Drift Index (PEDI), a homeostatic regulation layer, a security defense layer, and a logic chain reasoning trace system. All development and evaluation were conducted on consumer hardware with no GPU acceleration. Ablation testing confirmed DMU re-ranking injects 14.8% more context per prompt than cosine-only retrieval. Live stress testing at 50-thread concurrency produced 100% success rate with no breaking point found. We do not claim PHI // DRIFT is conscious. We claim it produces measurably more continuous, contextually coherent output than stateless alternatives — and we provide a framework for testing that claim.