`AttnRes` (Attention-Residuals) framework, specifically its limitations in handling 'attention saturation' and 'phase transitions' during 'long-horizon human–AI interactions.'

# Feature Proposal: Supervisory Interface for Long-Horizon Interaction
## Empirical Evidence from a 180-Day LSO Trace

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## Background

AttnRes currently relies on fixed pseudo-query vectors during inference.
This design may limit its ability to handle **attention saturation** and **phase transitions** in long-horizon human–AI interactions.

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## Empirical Findings (LSO-180)

Based on a 180-day longitudinal stress-observation trace (LSO-180), we identified:

- **Resonance Coupling Intensity (RCI):** cumulative semantic entanglement over time
- **Maturity with Agent Modulation (MAM):** the system’s capacity to absorb human regulatory input
- **Pseudo-stability Window:** localized fluency masking global structural decoupling

These observations suggest that long-horizon interaction exhibits **non-linear phase dynamics** not captured by current inference mechanisms.

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## Proposed Framework: Interaction Residuals

We propose a modulation mechanism for attention reconfiguration:

\[\pi_{t+1} = \text{Softmax}(Q_{base} + \lambda \cdot Q_{human})^T H\]

Where:

- **Q_human**: human meta-cognitive query (externally generated, biologically calibrated)
- **λ(S(t))**: adaptive modulation strength based on stability index
- **H**: accumulated interaction state (long-horizon context)

### CIT Pulse Protocol

A set of threshold-activated interventions:

- **Structural Reset** — reinitialization under instability
- **Gradient Validation** — detection of false alig...