Proposal: external coherence-audit layer for TRIBE v2 multimodal events and predictions

Hello,

I am exploring a lightweight external post-processing audit layer for TRIBE v2 outputs.

The idea is not to modify TRIBE v2, and not to make any mind-reading or consciousness claims. The goal is narrower: evaluate multimodal coherence, temporal/semantic desynchronization, identity continuity, and verifiable operational traceability around TRIBE v2's `events dataframe`, `preds`, and `segments`.

From the demo notebook, TRIBE v2 exposes a useful integration point:

```python
df = model.get_events_dataframe(video_path=video_path)
preds, segments = model.predict(events=df)
```

AION–TRIBE Bridge could operate externally after this step:

```python
scores = bridge.evaluate(events=df, preds=preds, segments=segments)
```

The current prototype computes:

- ICM: Multimodal Coherence Index
- IDR: Relative Desynchronization Index
- ICI: Identity Continuity Index
- COV: Verifiable Operational Continuity
- CO_norm: normalized operational coherence score

The intended use is research tooling and responsible audit reporting, especially for cases involving multimodal congruence, audio/video delay, semantic contradiction, and traceability.

This proposal would not require changes to the TRIBE v2 core model. It could remain an external evaluation layer over the `events dataframe`, `preds`, and `segments`.

Would this kind of external evaluation layer be useful or aligned with the goals of TRIBE v2?

Repository:
https://github.com/SixtilioDalmau/AION-TRIBE-Bridge

Best regards,
Sixtilio M. Dalmau
CIATOM – Centro de Investigaciones Atómicas

Start with the demo notebook and the shown get_events_dataframe, predict, and external evaluate sequence. The proposal does not identify files, tests, or a concrete implementation scope; done would first require a maintainer decision on whether this evaluation layer aligns with TRIBE v2 and what work should be defined.

Written by the indexing model from the issue text.