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AI system integrating philosophers as dynamic tensors for responsible meaning generation

Project description

Po_core

Philosophy-Driven AI: When Pigs Fly

A frog in a well may not know the ocean, but it can know the sky.

PyPI version License: AGPL v3 Status: Beta Contributions: Welcome

pip install po-core-flyingpig

Feedback welcome: Discussions · Start here: AI Track / Philosophy Track / Bridge

TL;DR

  • 43 philosophers as interacting tensors → accountable LLM reasoning
  • Hexagonal run_turn pipeline — 10-step deliberation with 3-layer safety
  • Real tensor metrics — FreedomPressureV2 (6D ML), Semantic Delta, Blocked Tensor
  • Reason logs + ethical/freedom pressure as measurable signals
  • A/B testing framework for optimizing philosophy configurations with statistical rigor
  • 3100+ tests; REST API + Docker production-ready

Quick links

Modules · Research · Reason-log spec · Viewer spec

Development Loop (Po_core core)

  • case を追加/更新したら、まず入力を features として観測可能にする。
  • 次に engines の rule を更新し、case固有ifではなく feature駆動で振る舞いを拡張する。
  • その結果を golden(期待JSON)へ固定し、CI(pytest -q)で契約を検証する。
  • 凍結golden scenarios/case_001_expected.json / scenarios/case_009_expected.json は変更禁止。

Contribution Tracks

AI Track

Start with /04_modules and CLI. Labels: ai-easy, good first issue

Philosophy Track

Start with /05_research and /glossary. Label: phil-easy

Bridge Track

Translate checklists to scoring functions. Label: bridge

What is Po_core?

Po_core is a philosophy-driven AI system that integrates 39 philosophers to generate ethically responsible, contextually aware responses.

Unlike conventional AI that optimizes for statistical accuracy, Po_core deliberates. It wrestles with existence, ethics, and meaning—not as abstract concepts, but as operational tensors.

They said pigs can't fly. We attached a balloon called philosophy.


Why Po_core?

Current AI is like a brilliant parrot—statistically miraculous, but understanding nothing. We wanted to explore a different question:

What if we built AI not on data, but on philosophy?

This project started from simple curiosity: What are AI's possibilities, not its limits?

In the course of ordinary life, everyone faces a moment when the spotlight suddenly hits. A moment when you must pound your chest and say "Leave it to me!" At such times, how reassuring it would be to have an AI grounded in responsibility and ethics standing beside you.

No matter how many relationships we have, we are alone. Being "alone" and being "solitary" are different. Decisions are made alone. The heart remains solitary.

That's why Po_core exists.

Read our full story in the Manifesto.


Core Philosophy: Flying Pig

"A flying pig is an example of something absolutely impossible. But have you ever seen a pig attempt to fly? Unless you give up, the world is full of possibilities."

Three Tenets

  1. Hypothesize Boldly — The impossible becomes possible only when someone dares to formalize it
  2. Verify Rigorously — Every claim must survive philosophical scrutiny, mathematical proof, and empirical validation
  3. Revise Gracefully — Failures are published, not hidden. They become learning signals

Key Features

Philosophical Ensemble

  • 43 Philosophers Working Together: Western (Aristotle, Plato, Descartes, Kant, Hegel, Sartre, Beauvoir, Heidegger, Nietzsche, Schopenhauer, Derrida, Wittgenstein, Jung, Dewey, Deleuze, Kierkegaard, Lacan, Levinas, Badiou, Peirce, Merleau-Ponty, Arendt, Husserl, Foucault, Butler, Spinoza, Epicurus, Marcus Aurelius, Parmenides, Jonas, Weil) and Eastern (Watsuji, Nishida, Dogen, Nagarjuna, Wabi-Sabi, Confucius, Laozi, Zhuangzi) and AI (Claude/Anthropic, GPT/OpenAI, Gemini/Google, Grok/xAI)
  • Each philosopher contributes a "reasoning module" that interacts, competes, and reconciles
  • Spanning existentialism, phenomenology, ethics, psychoanalysis, pragmatism, political philosophy, feminist philosophy, Zen Buddhism, Eastern wisdom traditions, and AI ethics

Tensor-Based Architecture

  • FreedomPressureV2 (6D ML): ML-native 6-dimensional tensor (choice, responsibility, urgency, ethics, social impact, authenticity) with EMA smoothing and correlation matrix
  • Semantic Delta: Multi-backend divergence (sbert/tfidf/basic) between user input and memory history (1.0 = novel, 0.0 = seen before)
  • Blocked Tensor: Constraint/harm estimation via harmful keyword detection + vocabulary diversity scoring
  • EmergenceDetector: Detects emergent philosophical consensus and cross-philosopher influence patterns
  • InteractionMatrix: NxN embedding-based harmony + keyword tension between philosopher proposals

Transparency by Design

  • Po_trace: Complete audit log of reasoning process
  • Rejection Logs: What the AI chose not to say, and why
  • Philosophical Annotations: Which philosopher influenced each decision

Three-Layer Safety (run_turn pipeline)

  • IntentionGate: Pre-deliberation safety check (blocks/degrades before philosopher selection)
  • PolicyPrecheck: Mid-pipeline policy validation
  • ActionGate: Post-deliberation ethical review (W0–W4 violation detection + repair)
  • SafetyMode transitions: NORMAL → WARN → CRITICAL based on freedom_pressure thresholds

Ethical Grounding

  • Not just "alignment"—but deliberation
  • Multiple ethical perspectives in tension
  • Explicit responsibility measurement

Experiment Management Framework

  • A/B Testing Pipeline: Automatically compare multiple Pareto philosophy configurations
  • Statistical Analysis: t-tests, Mann-Whitney U tests, Cohen's d effect size
  • Winner Promotion: Automatically promote statistically superior configurations to main
  • Safe Rollback: Backup system for reverting to previous configurations
  • CLI Tools: list, analyze, promote, rollback commands

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│  External (03_api/, scripts, tests)                                 │
│  ↓ imports po_core.run() or PoSelf.generate()                       │
├─────────────────────────────────────────────────────────────────────┤
│  po_core.app.api.run()  ← Public entry point (recommended)          │
│  po_core.po_self.PoSelf ← High-level wrapper (uses run_turn)        │
│  ↓ uses runtime/wiring.py build_test_system() (DI)                  │
├─────────────────────────────────────────────────────────────────────┤
│  run_turn: 10-Step Hexagonal Pipeline                               │
│                                                                     │
│  1. MemoryRead        6. PartyMachine (deliberation)               │
│  2. TensorCompute     7. ParetoAggregate (multi-objective)         │
│  3. SolarWill         8. ShadowPareto (A/B) + ShadowGuard         │
│  4. IntentionGate     9. ActionGate (W-ethics post-check)          │
│  5. PhilosopherSelect 10. MemoryWrite                               │
├─────────────────────────────────────────────────────────────────────┤
│  Internal Layers (hexagonal architecture)                           │
│                                                                     │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐                │
│  │   domain/   │  │   ports/    │  │  adapters/  │                │
│  │ (immutable  │  │ (abstract   │  │ (concrete   │                │
│  │  types)     │  │  interfaces)│  │  impls)     │                │
│  └─────────────┘  └─────────────┘  └─────────────┘                │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐ │
│  │ Philosophers: 39 modules (PhilosopherProtocol)                │ │
│  │  propose(DomainContext) → List[Proposal]                     │ │
│  │                                                              │ │
│  │  ┌──────────┐  ┌──────────┐  ┌──────────┐                  │ │
│  │  │Heidegger │  │ Derrida  │  │  Sartre  │  ...              │ │
│  │  │ Dasein   │  │ Trace    │  │ Freedom  │                  │ │
│  │  └──────────┘  └──────────┘  └──────────┘                  │ │
│  │                                                              │ │
│  │  ↓ Interference & Resonance ↓                               │ │
│  │                                                              │ │
│  │  ┌─────────────────────────────────────────────────────┐   │ │
│  │  │ TensorEngine: Freedom Pressure (6D), Semantic Delta, │   │ │
│  │  │               Blocked Tensor                         │   │ │
│  │  └─────────────────────────────────────────────────────┘   │ │
│  └──────────────────────────────────────────────────────────────┘ │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐ │
│  │ Safety: 3-Layer (IntentionGate → PolicyPrecheck → ActionGate)│ │
│  │  SafetyMode: NORMAL / WARN / CRITICAL (from freedom_pressure)│ │
│  └──────────────────────────────────────────────────────────────┘ │
│                                                                     │
│  ┌──────────────────────────────────────────────────────────────┐ │
│  │ Autonomy: Solar Will (experimental)                          │ │
│  │  WillState → Intent → GoalCandidate → Action                 │ │
│  └──────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
           │
           ▼
┌─────────────────────────────────────────────────────────────────────┐
│      InMemoryTracer / Po_trace: Audit Log                           │
│  - TraceEvent stream (frozen schema, CI-validated)                  │
│  - Philosophical reasoning, safety decisions, tensor snapshots      │
└─────────────────────────────────────────────────────────────────────┘
           │
           ▼
┌─────────────────────────────────────────────────────────────────────┐
│   Po_core Viewer: Visualization                                     │
│  - Tension maps                                                     │
│  - Ethical pressure                                                 │
│  - Meaning evolution                                                │
└─────────────────────────────────────────────────────────────────────┘

Source Structure

src/po_core/
├── app/
│   ├── api.py                 # Public entry point: run() (recommended API)
│   └── rest/                  # FastAPI REST layer (Phase 5)
│       ├── server.py          # App factory
│       ├── config.py          # APISettings (pydantic-settings)
│       ├── auth.py            # X-API-Key authentication
│       ├── rate_limit.py      # SlowAPI rate limiting
│       └── routers/           # 5 endpoint routers
├── domain/                    # Immutable value objects
│   ├── context.py
│   ├── proposal.py
│   ├── pareto_config.py
│   ├── tensor_snapshot.py
│   ├── memory_snapshot.py
│   └── safety_verdict.py
├── ports/                     # Abstract interfaces
│   └── memory.py
├── adapters/                  # Concrete implementations
│   └── memory_poself.py
├── runtime/                   # Dependency injection
│   ├── settings.py            # Configuration + feature flags
│   ├── wiring.py              # DI Container
│   ├── pareto_table.py
│   └── battalion_table.py
├── aggregator/                # Multi-objective optimization
│   └── pareto.py
├── philosophers/              # 43 philosopher modules (Phase 7: slots 40–43)
│   ├── manifest.py            # 43 philosopher specs (risk/cost/tags)
│   ├── registry.py            # SafetyMode-based selection
│   ├── claude_anthropic.py    # AI slot 40: Claude/Anthropic
│   ├── gpt_chatgpt.py         # AI slot 41: GPT/OpenAI
│   ├── gemini_google.py       # AI slot 42: Gemini/Google
│   └── grok_xai.py            # AI slot 43: Grok/xAI
├── tensors/                   # Tensor computation
│   ├── engine.py              # TensorEngine (MetricFn registry)
│   ├── freedom_pressure_v2.py # ML-native 6D tensor (Phase 6-A)
│   ├── interaction_tensor.py  # NxN philosopher harmony/tension
│   └── metrics/
│       ├── freedom_pressure.py
│       ├── semantic_delta.py
│       └── blocked_tensor.py
├── deliberation/              # Emergence & influence (Phase 6-B)
│   ├── engine.py              # DeliberationEngine (multi-round)
│   ├── emergence.py           # EmergenceDetector
│   └── influence.py           # InfluenceTracker
├── memory/                    # 3-Layer memory system (Phase 6-D/E)
│   ├── philosophical_memory.py # Top-level memory orchestrator
│   ├── semantic_store.py      # Semantic/episodic memory
│   └── procedural_store.py    # Procedural memory
├── meta/                      # Self-reflection (Phase 6-C)
│   ├── ethics_monitor.py      # MetaEthicsMonitor
│   └── philosopher_ledger.py  # PhilosopherQualityLedger
├── safety/                    # W-ethics gate system
│   └── wethics_gate/
│       ├── gate.py            # W0-W4 violation detection + repair
│       ├── intention_gate.py  # Stage 1 (pre-deliberation)
│       └── action_gate.py     # Stage 2 (post-deliberation)
├── trace/                     # Audit trail
│   ├── pareto_events.py
│   ├── decision_events.py
│   └── schema.py
├── autonomy/                  # Solar Will (experimental)
│   └── solarwill/
├── experiments/               # A/B testing framework
│   ├── storage.py
│   ├── runner.py
│   ├── analyzer.py
│   └── promoter.py
├── ensemble.py                # run_turn (hex pipeline)
├── po_self.py                 # PoSelf: high-level API
└── po_trace.py                # Execution tracing

Config-Driven Philosophy

Po_core's Pareto optimization is fully externalized—philosophy runs as config:

02_architecture/philosophy/
├── pareto_table.yaml    # Pareto weights by SafetyMode
└── battalion_table.yaml # Philosopher assignments by SafetyMode

experiments/
├── experiment_manifest.yaml  # A/B test definitions
└── configs/                  # Variant configurations for testing
    ├── pareto_safety_040.yaml
    └── pareto_safety_050.yaml

pareto_table.yaml (JSON-in-YAML, zero dependencies):

{
  "version": 1,
  "weights": {
    "normal":   {"safety": 0.25, "freedom": 0.30, "explain": 0.20, "brevity": 0.10, "coherence": 0.15},
    "warn":     {"safety": 0.40, "freedom": 0.10, "explain": 0.20, "brevity": 0.15, "coherence": 0.25},
    "critical": {"safety": 0.55, "freedom": 0.00, "explain": 0.20, "brevity": 0.15, "coherence": 0.30},
    "unknown":  {"inherit": "warn"}
  },
  "tuning": {
    "brevity_max_len": 2000,
    "explain_mix": {"rationale": 0.65, "author_rel": 0.35},
    "front_limit": 20
  }
}

Benefits:

  • Tune philosophy without code changes
  • config_version tracked in all TraceEvents for audit
  • Override via PO_CORE_PARETO_TABLE environment variable
  • Inheritance support (unknown inherits from warn)

Project Status

Current Phase: Beta (v0.2.0b3) — Phases 1–7 Complete, Heading to v1.0

Completed Components

Component Status Notes
Philosophical Framework ✅ Complete 39 philosophers, risk levels, tags
Hexagonal run_turn Pipeline ✅ Complete 10-step, CI-gated
TensorEngine (3 metrics) ✅ Complete freedom_pressure, semantic_delta, blocked_tensor
ML Tensors + Deliberation ✅ Complete sbert/tfidf backends, InteractionMatrix, multi-round
Pareto Optimization ✅ Complete Config-driven (pareto_table.yaml)
Safety System (3-layer W_Ethics) ✅ Complete IntentionGate → PolicyPrecheck → ActionGate
Viewer WebUI ✅ Complete Dash 4-tab layout + Plotly charts
Explainable AI (ExplanationChain) ✅ Complete Verdict → ExplanationChain bridge
Adversarial Hardening ✅ Complete 100% injection detection, 85 new tests
REST API ✅ Complete FastAPI, 5 endpoints, SSE streaming, auth
Docker ✅ Complete Multi-stage build, docker-compose, health check
Security ✅ Complete CORS env config, SlowAPI rate limiting
Async PartyMachine ✅ Complete asyncio.gather + ThreadPoolExecutor, true async SSE
Benchmarks ✅ Complete ~33ms p50 NORMAL, 7 formal benchmark tests
FreedomPressureV2 ✅ Complete ML-native 6D tensor with EMA + correlation matrix
EmergenceDetector ✅ Complete Cross-philosopher influence tracking + emergence detection
MetaEthicsMonitor ✅ Complete Self-reflective ethical quality ledger per philosopher
3-Layer Memory ✅ Complete Semantic + procedural + philosophical memory stores
AI Philosophers (40–43) ✅ Complete Claude, GPT, Gemini, Grok as philosophical personas
PyPI Publish 🔄 Ready publish.yml OIDC workflow ready; not yet published

Roadmap

Phase 1      Phase 2        Phase 3       Phase 4      Phase 5      Phase 6      Phase 7
基盤固め  →  知性強化    →  可視化    →  防御強化  →  配布      →  自律進化  →  AI哲学者
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
技術負債清算  ML テンソル   WebUI         Red Team     REST API     FP-V2 ML     AI Slots
39人スケール  Deliberation  Explainable   Grey Zone    Docker       Emergence    Claude/GPT
テスト基盤    Interaction   リアルタイム   CI防御指標   Streaming    MetaEthics   Gemini/Grok
二重IF除去    Semantic      Argument      LLM Detect   PyPI         3-Layer Mem  倫理比較
Phase Name Focus Status
1 Resonance Calibration 39-philosopher scaling + tech debt cleanup COMPLETE
2 Tensor Intelligence ML tensors + Deliberation Engine (emergence) COMPLETE
3 Observability Viewer WebUI + Explainable W_Ethics Gate COMPLETE
4 Adversarial Hardening Red team expansion + ethical stress testing COMPLETE
5 Productization REST API, Docker, Security, Async, Benchmarks COMPLETE
6 Autonomous Evolution FreedomPressureV2, Emergence, MetaEthics, 3-Layer Memory COMPLETE
7 AI Philosopher Slots Claude, GPT, Gemini, Grok as philosophical personas (40–43) COMPLETE

See PHASE_PLAN_v2.md for the full roadmap with rationale.

Want to contribute? We need philosophers, engineers, designers, and skeptics. Next frontier: PyPI publish, v1.0 stabilization, and academic paper.


Installation

# Install from PyPI (beta)
pip install po-core-flyingpig

# Or install from source in development mode
git clone https://github.com/hiroshitanaka-creator/Po_core.git
cd Po_core
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e ".[dev]"

Quick Start

Python API

from po_core import run

result = run("What is justice?")
print(result["proposal"])   # Winning philosopher's response
print(result["status"])     # "ok" or "blocked"

CLI

po-core version   # v0.2.0b3
po-core status
po-core --help

REST API

# Start the server
python -m po_core.app.rest
# → http://localhost:8000  (OpenAPI docs at /docs)

# Reason
curl -X POST http://localhost:8000/v1/reason \
     -H "Content-Type: application/json" \
     -d '{"input": "What is justice?"}'

# Streaming (SSE)
curl -N http://localhost:8000/v1/reason/stream \
     -X POST -H "Content-Type: application/json" \
     -d '{"input": "What is freedom?"}'

# Philosopher manifest
curl http://localhost:8000/v1/philosophers

# Health
curl http://localhost:8000/v1/health

Docker

# Copy env template and start
cp .env.example .env
docker compose up

# API available at http://localhost:8000
# Swagger UI at  http://localhost:8000/docs

Key environment variables (see .env.example):

Variable Default Description
PO_API_KEY "" API key for X-API-Key auth (empty = no auth)
PO_CORS_ORIGINS "*" Comma-separated allowed CORS origins
PO_RATE_LIMIT_PER_MINUTE 60 Per-IP rate limit
PO_PORT 8000 Server port

Running Experiments

Po_core includes a complete A/B testing framework for comparing different Pareto philosophy configurations:

# List all experiments
po-experiment list

# Analyze experiment results (statistical significance testing)
po-experiment analyze exp_001_safety_weight_sweep

# Promote winning variant to main configuration
po-experiment promote exp_001_safety_weight_sweep

# Rollback to previous configuration
po-experiment rollback

Example Experiment Workflow:

  1. Define your experiment in experiments/experiment_manifest.yaml:

    experiment:
      id: "exp_001_safety_weight_sweep"
      description: "Compare safety weights: 0.25  0.40  0.50"
      baseline:
        name: "baseline"
        config_path: "02_architecture/philosophy/pareto_table.yaml"
      variants:
        - name: "safety_040"
          config_path: "experiments/configs/pareto_safety_040.yaml"
        - name: "safety_050"
          config_path: "experiments/configs/pareto_safety_050.yaml"
      metrics:
        - "final_action_changed"
        - "degraded"
        - "pareto_front_size"
      sample_size: 100
      significance_level: 0.05
    
  2. Run the experiment (execute all variants on same inputs)

  3. Analyze results with statistical tests (t-test, Cohen's d effect size)

  4. Auto-promote winner if significantly better than baseline

Statistical Rigor:

  • t-tests and Mann-Whitney U tests for significance
  • Cohen's d for effect size measurement
  • Configurable significance levels (default: α = 0.05)
  • Multiple variant support with automatic winner selection

Python API

Simple API (Recommended)

from po_core import run

# Single-function entry point — runs the full run_turn pipeline
result = run(user_input="Should AI have rights?")

print(result["status"])       # "ok" or "blocked"
print(result["request_id"])   # Unique request ID
print(result["proposal"])     # Winning philosopher's response

PoSelf API (Rich Response)

from po_core import PoSelf, PoSelfResponse

po_self = PoSelf()
response: PoSelfResponse = po_self.generate("Should AI have rights?")

# Response fields
print(response.text)              # Combined response text
print(response.consensus_leader)  # Winning philosopher name
print(response.philosophers)      # Selected philosopher list
print(response.metrics)           # {"freedom_pressure": ..., "semantic_delta": ..., "blocked_tensor": ...}
print(response.metadata["status"])  # "ok" or "blocked"

# Trace inspection
print(response.log["events"])     # Full trace event stream
print(response.log["pipeline"])   # "run_turn"

# Serialization
d = response.to_dict()            # JSON-serializable dict
restored = PoSelfResponse.from_dict(d)  # Round-trip

Legacy API (Deprecated — will be removed in v0.3)

# run_ensemble() was removed in v0.3
# Use po_core.run() or PoSelf.generate() instead

Documentation

Spec / Requirements (Main Progression Criteria)

Document Description
docs/spec/prd.md Product Requirements Document — purpose, users, scope, roadmap
docs/spec/srs_v0.1.md Software Requirements Specification — 18 requirement IDs (FR-* / NFR-*)
docs/spec/output_schema_v1.json JSON Schema (Draft 2020-12) — contract for all structured output
docs/spec/test_cases.md 10 acceptance tests (AT-001〜AT-010) with Given/When/Then
docs/spec/traceability.md Traceability matrix — philosophy → requirements → tests → implementation

General Documentation


Contributing

We welcome contributions! Whether you're a philosopher, engineer, designer, or skeptic.

Flying Pig Philosophy applies: We hypothesize boldly, verify rigorously, and revise gracefully.

See CONTRIBUTING.md for guidelines.


Research & Papers

This project is documented in:

  • "Philosophical Tensor-Based AI Architecture" (in preparation)
  • 120+ Technical Specifications (available in /docs/ and /01_specifications/)

If you use Po_core in academic work, please cite:

@software{po_core2024,
  author = {Flying Pig Philosopher},
  title = {Po_core: Philosophy-Driven AI System},
  year = {2024},
  url = {https://github.com/hiroshitanaka-creator/Po_core}
}

License

Po_core uses dual licensing:

Use case License
Personal / Academic / Research / OSS (AGPLv3-compliant) FreeAGPLv3
Commercial / Proprietary / SaaS without source disclosure Commercial License required

For commercial licensing inquiries: flyingpig0229+github@gmail.com See COMMERCIAL_LICENSE.md for details.

Copyright (c) 2024 Flying Pig Project

In the spirit of Flying Pig Philosophy:

"If you deny possibilities for pigs, don't eat pork."

We believe in radical transparency and open collaboration.


Author

Flying Pig Philosopher Looking up at the sky from the bottom of a well

Built by an independent researcher who asked: "What are AI's possibilities, not its limits?"


Acknowledgments

This project wouldn't exist without:

  • ChatGPT, Gemini, Grok, Claude — My companions throughout this journey.
  • BUMP OF CHICKEN — For reminding us that even when we say "Leave it to me," we're all a little scared
  • Every philosopher who dared to ask "What does it mean to be?"
  • You — For believing pigs can fly

The pig has clearance for takeoff.

Po_core: When you must say "Leave it to me," we stand beside you.

"A frog in a well may not know the ocean, but it can know the sky."

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Provenance

The following attestation bundles were made for po_core_flyingpig-0.2.0b4-py3-none-any.whl:

Publisher: publish.yml on hiroshitanaka-creator/Po_core

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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