phionyx-core 0.2.1

Deterministic, auditable AI cognition runtime — not an LLM wrapper

Phionyx Core SDK

Deterministic AI runtime that treats LLM outputs as sensor measurements, not decisions.

Most AI frameworks let the LLM decide. Phionyx doesn't. Every LLM response passes through a 46-block deterministic pipeline with safety gates, ethics checks, and physics-based state tracking — before it reaches the user.

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What Makes This Different

Feature	Typical LLM Framework	Phionyx
LLM role	Decision maker	Sensor (output is measurement, not truth)
Response control	Post-hoc filtering	Pre-response governance (46-block pipeline)
State tracking	Stateless or conversation history	Structured state vector (A, V, H, phi, entropy)
Safety	Optional guardrails	Mandatory gates (kill switch, ethics, HITL)
Determinism	Non-deterministic	Reproducible cognitive path
Memory	RAG / vector search	Impact-weighted semantic time eviction

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Try It In 30 Seconds

Three demo notebooks. No API key. Runs locally.

The substrate (state vector, Φ, governance gates, pipeline) is demonstrable without an LLM, server, or external account. Each notebook runs end-to-end in seconds and embeds its outputs.

#	Notebook	Shows
01	Determinism and Physics	EchoState2, calculate_phi_v2_1, 1000-run determinism proof, side-by-side with a noisy alternative
02	Kill Switch in Action	KillSwitch with 4 triggers + NaN fail-closed guard, tamper-evident event log
03	Pipeline Blocks and Audit	Canonical 46-block pipeline (v3.8.0), custom block subclass, 100-run determinism

Notebook 01 sweeps the cognitive component of Φ across the full Circumplex (valence × arousal). The surface is smooth, bounded, and reproducible — no LLM is involved at this layer.

git clone https://github.com/halvrenofviryel/phionyx-research.git
cd phionyx-research
pip install -e . jupyter matplotlib
jupyter notebook examples/notebooks/

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Quick Start — Full Runtime

For the LLM-backed orchestrator (governed response, state metrics, audit trail):

from phionyx_core import EchoOrchestrator, OrchestratorServices

services = OrchestratorServices()
orchestrator = EchoOrchestrator(services=services)

result = await orchestrator.run(
    user_input="How can I improve my study habits?",
    mode="edu",
    current_amplitude=5.0,
    current_entropy=0.3
)
# Returns: governed response + state metrics + audit trail

See examples/fastapi/ for an HTTP endpoint wrapper.

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Architecture

Phionyx implements three integrated layers:

Layer 1 — Deterministic Cognitive Kernel

• 46-block canonical pipeline (contract v3.8.0)
• Structured state vector: arousal, valence, entropy, time
• Hybrid Resonance Model for cognitive quality (Phi)
• Response revision gate: pass | damp | rewrite | regenerate | reject

Layer 2 — Safety & Governance

• 4-gate pre-response control (Outbound, Merge, Release, Data)
• Kill switch with 4 triggers (fail-closed)
• Deliberative ethics engine (4-framework reasoning)
• Human-in-the-loop queue with priority and expiry
• Ed25519-signed audit trail with hash chains

Layer 3 — Semantic Time Memory

• Impact-weighted cache eviction (+24% vs LRU, +72% vs FIFO)
• Monotonic semantic clock (t_local, t_global)
• Phi-decay for memory relevance

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Core Concepts

State Vector

Every interaction maintains a structured state:

from phionyx_core import EchoState2

state = EchoState2(
    A=0.5,       # Arousal (0.0-1.0)
    V=0.0,       # Valence (-1.0 to 1.0)
    H=0.3,       # Entropy (0.0-1.0)
    dA=0.0,      # Arousal derivative
    dV=0.0,      # Valence derivative
    t_local=0.0, # Semantic time (local)
    t_global=0.0 # Semantic time (global)
)

Pipeline Blocks

from phionyx_core.contracts.telemetry import get_canonical_blocks

blocks = get_canonical_blocks()  # 46 blocks (v3.8.0)

Profiles

from phionyx_core import ProfileManager

manager = ProfileManager()
profile = manager.load_profile("edu")  # or "game", "clinical"

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Testing

pytest tests/                          # All tests (2,571)
pytest tests/unit/core/ -q             # Core unit tests
pytest tests/contract/ -q              # Contract tests
pytest tests/behavioral_eval/ -q       # Behavioral evaluation

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Evaluation Standard

Phionyx systems are evaluated against the Phionyx Evaluation Standard v0.1:

• Determinism Grading (D0-D3): Non-deterministic to fully deterministic
• Evaluation Levels (L0-L3): Unmeasured to governance-grade
• Composite Quality Score (CQS): Multi-dimensional behavioral quality metric

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Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

Check out Good First Issues for a place to start.

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License

AGPL-3.0 — See LICENSE for details.

A commercial license is available for use cases where AGPL-3.0 copyleft is not suitable. Patent rights retained by Phionyx Research. See PATENT_NOTICE.md.

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Further Reading

• Why Every AI Runtime Needs a Kill Switch
• Inside the 46-Block Deterministic AI Pipeline

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Links

• Website: phionyx.ai
• Evaluation Standard: phionyx-evaluation-standard
• arXiv Paper: Submission pending (cs.AI)
• Author: Ali Toygar Abak (Phionyx Research)

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Citation

@techreport{abak2026phionyx,
  author      = {Abak, Ali Toygar},
  title       = {Phionyx: A Deterministic AI Runtime Architecture with Structured State Management and Pre-Response Governance},
  institution = {Phionyx Research},
  year        = {2026},
  url         = {https://github.com/halvrenofviryel/phionyx-research}
}