cognize 0.1.0

Symbolic cognition engine for epistemic drift, rupture detection, and realignment.

Cognize

Give any Python system cognition.

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Cognize is a symbolic cognition layer for Python systems — from LLMs to agents to simulations.
It enables programmable epistemic control by modeling belief (V), reality (R), misalignment (∆), memory (E), and rupture (Θ).

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Features

• Drift-aware cognition engine (EpistemicState)
• Programmable rupture thresholds and realignment logic
• Symbolic rupture and collapse modeling
• Supports high-dimensional reality inputs (e.g., embeddings)
• Export cognition logs (.json, .csv) for external audits
• Control layer for hallucination detection in LLMs or symbolic gating in agents
• Minimal, extensible, domain-agnostic
• Built with symbolic state logic — extensible for memory, attention, or projection systems

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Installation

pip install cognize

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Core Concepts

Symbol	Meaning
V	Projection (belief)
R	Reality (signal)
∆	Distortion
Θ	Tolerance threshold
E	Misalignment memory
⊙	Stable
⚠	Rupture
∅	No signal yet

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Example Usage

from cognize import EpistemicState

# Scalar-based epistemic drift tracking
e = EpistemicState(V0=0.0, threshold=0.4)

for R in [0.1, 0.3, 0.6, 0.8]:
    e.receive(R)
    print(e.symbol(), e.summary())

# Access rolling drift statistics
print(e.drift_stats(window=3))

# Trigger fallback if cognitive rupture risk is too high
e.intervene_if_ruptured(lambda: print("⚠ Intervention triggered!"))

# Manually realign belief to current signal
e.realign(R=0.7)

# Export logs
e.export_json("cognition.json")
e.export_csv("cognition.csv")

Expected Output:

⊙ {'V': 0.03, 'E': 0.01, 'Θ': 0.4, ...}
⊙ {...}
⚠ {'V': 0.0, 'E': 0.0, 'Θ': 0.4, ...}
{'mean_drift': 0.24, 'std_drift': 0.13, 'max_drift': 0.3, 'min_drift': 0.1}
⚠ Intervention triggered!

Sample cognition.json output:

[
  {
    "t": 0,
    "V": 0.03,
    "R": 0.1,
    "delta": 0.1,
    "Θ": 0.4,
    "ruptured": false,
    "symbol": "⊙",
    "source": "default"
  },
  ...
]

Cognize also supports vector input (e.g. NumPy arrays) for multi-dimensional drift modeling — useful for embeddings or continuous signals.

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License

Cognize is released under the Apache 2.0 License.

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© 2025 Pulikanti Sashi Bharadwaj
Original work licensed under Apache 2.0