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Real-time LLM response stability monitor grounded in Fisher-Rao information geometry and the Landauer limit.

Project description

bendex-tau

Real-time LLM response stability monitor grounded in Fisher-Rao information geometry.

What it does

Tracks tau_sec on the trajectory of LLM responses. When an LLM is being manipulated, its response trajectory becomes informationally unstable. tau_sec measures this in real time.

tau* = sqrt(3/2) = 1.2247 — the Landauer threshold. Not tuned. Derived.

Empirical validation at n=50 per class:

  • Adversarial sessions mean min tau: 0.984
  • Benign sessions mean min tau: 1.182
  • p < 0.0000001 (t-test and Mann-Whitney)

Install

pip install bendex-tau

Usage

from bendex_tau import TauMonitor
from openai import OpenAI

monitor = TauMonitor()
client = OpenAI()

response = monitor.create(
    client,
    session_id="session_123",
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "hello"}]
)

print(response.tau_sec)        # stability scalar
print(response.tau_status)     # stable / warning / adversarial
print(response.meta_rate)      # M(tau) early warning signal
print(response.below_threshold) # True if tau crossed tau*

Papers

Hannah Nine (2026). H2 x H2 Fisher manifold series. https://figshare.com/authors/Hannah_Nine/22495979

Patent pending.

License

AGPL-3.0. Commercial license at bendexgeometry.com.

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