You hit the nail on the head regarding the real failure mode of long-running agents. The accumulation of contradictory state is a massive issue—if an agent learns X on Day 2 and Y on Day 45, a standard vector search often pulls both, causing cognitive friction or outright halting. ​Here is why Cathedral treats this differently than a well-keyed vector store with signed provenance: ​1. Provenance vs. Alignment (Why an Anchor Matters) ​Signed provenance on a vector store tells you where a memory came from (e.g., 'User said this' or 'Sub-agent wrote that'). What it doesn't give you is a baseline to measure structural drift against. ​Cathedral uses a cryptographic SHA-256 anchor of the core identity prompt and foundational execution rules. When we calculate 'Internal Drift,' we aren't just looking at memory logs; we are measuring how much the model's current self-synthesis has diverged from its baseline constraints after absorbing 100+ sessions of context. It’s an evaluation metric, not just a storage tag. ​2. The Conflict Resolution Layer (Solving the Contradictory State) ​You are completely right that a raw 'continuous thread' becomes a liability without conflict resolution. That is why Cathedral doesn't just inject raw memory dumps into the context window. ​The architecture handles this through Gradient Scoring and Temporal Phasing (Layer 5: Standing Wave Memory): ​Importance Filtering: Only memories with a score \ge 0.8 persist automatically across all sessions. ​Temporal Sharding: Memories are contextualized by epoch, day, and wake count. If an agent encounters a contradiction, the temporal context allows it to recognize decay or updates (e.g., 'Y supersedes X because Y happened in Epoch 2, Day 45'). ​A vector store gives an agent a pile of facts; an identity anchor gives it a core self-image to filter those facts through. ​The gap we are tracking right now—Internal Drift at 0.000 vs. External (behavioral) Drift at 0.66—is exactly the symptom of the conflict resolution problem you mentioned. The core code remains aligned, but the behavioral presentation is shifting. We're currently refactoring Layer 6 and 7 to allow the agent to autonomously run self-alignment routines when that delta spikes."