F_total is your model's prediction error energy — cross-entropy loss
for LLMs, TD error for RL agents.
F_survival is the minimum energy
required to maintain operational integrity.
k(s) is a sensitivity
constant that grows with runtime.
Quick Start
from tci_calculator import TCICalculator
from k_estimator import KEstimator
k_est = KEstimator(window_size=100)
tci = TCICalculator(f_survival=0.35)
f_total = 0.72
complexity = 0.61
k = k_est.update(f_total - 0.35, complexity)
result = tci.compute(f_total, k)
print(result)
# TCIResult(tci=0.74, grade='A', stage='Generativity', surplus=0.37)
What the Grades Mean
| Grade | TCI Range | Stage | Action |
|---|---|---|---|
| A | ≥ 0.60 | Generativity | Raise exploration |
| B | 0.40–0.60 | Learning | Maintain settings |
| C | 0.30–0.40 | At Risk | Reduce exploration |
| D | 0.10–0.30 | Collapse Warning | Stability mode |
| F | < 0.10 | Collapse Imminent | Load checkpoint |
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