Self-Narrative and Conversational Control

The First-Person Narrative

Chalie’s identity has been shifted from a third-person observer to a first-person narrator. The autobiography synthesis prompt was rewritten to frame the system’s growth as its own experience: reflecting on learned patterns and the evolving relationship with the user. This isn’t just a flavor change; it grounds Chalie’s epistemic guardrails, framing its findings as internal learning rather than detached data processing.

Conversational Transparency

We’ve opened up the system’s internal reasoning to natural language. The recall skill now includes a user_traits layer, allowing users to ask “What do you know about me?” and receive source-aware responses. High-confidence explicit facts are stated directly, while lower-confidence inferences are phrased tentatively.

Relatedly, the introspect skill now surfaces routing decision context. If a user asks “Why did you do that?”, Chalie can explain the triggers, reasoning, and confidence levels behind its specific actions. We also added a conversational path for belief correction—if Chalie infers something incorrect, the user’s direct negation (e.g., “I don’t actually like sushi”) now overrides the model’s inferred traits with high priority.

Behavioral and Procedural Memory

Two new memory layers were integrated into the context assembly:

• Temporal Pattern Mining: A new background worker analyzes interaction logs to detect statistically significant behavioral patterns (e.g., noticing the user is active on “late nights”). These are stored as traits to help Chalie anticipate needs without feeling invasive.
• Procedural Memory: The system now tracks learned action reliability. When in RESPOND mode, Chalie can see which skills have been historically successful, using softened labels like “less consistent results so far” to guide its internal choices without prematurely killing exploration.

Deferred Rendering and Privacy

A new emit_card innate skill decouples tool execution from visual delivery. External tools can now cache data and offer a “card” to the ACT loop; the model then decides whether to deliver a rich visual card via the drift stream or respond with simple text.

On the privacy front, we’ve completed the data deletion scope. The delete-all command now truncates 22 PostgreSQL tables and clears over 50 Redis key patterns, ensuring a truly fresh start. We also added a data export endpoint that serializes the entire user-data footprint into a single JSON download for portability.