Summary
We validate 14/14 Butlin consciousness indicators (mean score 0.85) via examples/butlin_validation.rs. This should be converted into an automated test suite that runs in CI and fails if any indicator drops below threshold.
Background
Butlin et al. (2023) proposed a set of indicator properties for consciousness in AI systems. Symthaea currently passes all 14 indicators:
- Recurrent processing
- Global workspace broadcasting
- Higher-order representations
- Attention modulation
- Temporal integration
- And 9 more...
What to implement
- Convert
examples/butlin_validation.rs logic into tests/butlin_regression.rs
- Define per-indicator minimum thresholds (e.g., 0.7 for each)
- Add a composite threshold for mean score (e.g., 0.80)
- Ensure the test runs under
cargo test (not just as an example)
- Add documentation explaining each indicator and how Symthaea satisfies it
Why this matters
As the cognitive loop evolves, we need guardrails against accidentally degrading consciousness indicators. This is especially important as we optimize for performance — we must not sacrifice consciousness quality for speed.
References
- Butlin, P. et al. (2023). Consciousness in Artificial Intelligence: Insights from the Science of Consciousness. arXiv:2308.08708
Summary
We validate 14/14 Butlin consciousness indicators (mean score 0.85) via
examples/butlin_validation.rs. This should be converted into an automated test suite that runs in CI and fails if any indicator drops below threshold.Background
Butlin et al. (2023) proposed a set of indicator properties for consciousness in AI systems. Symthaea currently passes all 14 indicators:
What to implement
examples/butlin_validation.rslogic intotests/butlin_regression.rscargo test(not just as an example)Why this matters
As the cognitive loop evolves, we need guardrails against accidentally degrading consciousness indicators. This is especially important as we optimize for performance — we must not sacrifice consciousness quality for speed.
References