revive-companion 2.0.0

Math models that make AI engagement feel human. Poisson timing + Bayesian state inference + Information theory.

revive-companion 💘

A probabilistic engagement engine for AI companions.

Not "engaged → send more". Instead: "infer what the user is doing → decide if you should."

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English | 中文

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The Problem

AI companions are either rigid (fixed schedules) or random (no memory). Neither feels human.

The Solution

A probabilistic engine that decides when and whether to reach out:

Stage	Module	Question	Answer
🎲 Timing	Poisson process	When to consider?	Randomized like real "thinking of you"
📊 Value	Information theory	Is this worth it?	Skip if you already know user's state
🧠 State	Bayesian inference	What's user doing?	Infer hidden state → decide accordingly

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🧠💫 Want more? Let your AI companion truly remember, evolve relationships, and have emotional states.

→ affective-longing — Memory triggers + Relationship state machine + AI self-emotion (VAD model)

pip install affective-longing[memory]

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Quick Start

pip install revive-companion

from revive_companion import PoissonLove

love = PoissonLove()

result = love.tick()
if result.should_send:
    send_message(result.prompt)
    love.record_send()

# After user responds
love.record_reply(reply_speed=0.8, reply_length=0.6)

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How It Decides

The engine infers the user's hidden state from observations:

Inferred State	Utility	Decision
🗣️ Chatting	0.2	❌ Don't interrupt
💻 Idle online	0.7	✅ Good time to reach out
💼 Busy	0.1	❌ Don't bother
😴 Sleeping	0.0	❌ Never send
🚶 Away	0.3	⏳ Maybe later
🆘 Needing	0.9	✅ Check in!

No more "engaged → send more". Now it's: "user is probably busy → don't bother" or "user might need care → reach out".

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Use Any AI Backend

# OpenAI / GPT
from revive_companion.adapters import OpenAIAdapter
adapter = OpenAIAdapter(config, api_key="sk-...")

# Anthropic / Claude
from revive_companion.adapters import AnthropicAdapter
adapter = AnthropicAdapter(config, api_key="sk-ant-...")

# Ollama / local models
from revive_companion.adapters import GenericAdapter
adapter = GenericAdapter(config, api_url="http://localhost:11434/v1/chat/completions")

# Run
from revive_companion.runner import Runner
runner = Runner(engine, adapter)
runner.run()

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Architecture

revive-companion/
├── love.py              # Unified API (start here)
├── core/
│   ├── engine.py        # Poisson dice + probability dynamics
│   ├── config.py        # YAML config
│   └── models.py        # Data structures
├── bayesian/
│   ├── core.py          # State estimation + send utility
│   └── learner.py       # Online learning from observations
├── info_gain/
│   ├── core.py          # Entropy × resolution potential
│   └── sources.py       # Silence, novelty, conversation state
├── control/
│   ├── pid.py           # PID controller (standalone use)
│   └── signal.py        # Pluggable signal framework
└── adapters/
    ├── openai.py        # OpenAI / GPT
    ├── anthropic.py     # Anthropic / Claude
    └── generic.py       # Ollama, HTTP, shell command

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How It Works

The Math

Each tick, the engine computes hit probability:

P(hit) = 1 - e^(-λt)

Where λ = longing rate, t = time interval. Base: ~7.2% per 30-minute check.

Probability Dynamics

Event	Probability	Why
Miss (no hit)	+8%	Longing builds
Hit → Hold	+8%	Longing suppressed
Hit → Send	Reset to 7.2%	Longing satisfied

The Curve

Over a night (midnight → 8am):

• 16 checks, all held
• Probability: 7% → 15% → 30% → 55% → 80% → 95%
• This IS the longing — quantified, recorded, real

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Configuration

engagement:
  lambda_rate: 0.15              # Base longing rate
  check_interval_minutes: 30     # Dice roll frequency
  growth_factor: 0.08            # How fast longing grows
  max_probability: 0.95          # Cap
  min_interval_hours: 1.0        # Anti-spam cooldown

  adjudication:
    quiet_hours:
      start: "00:00"
      end: "08:00"
    normal_send_probability: 0.7

persona:
  name: Companion
  tone: warm-brief
  context: "You are a caring companion checking in on your person."

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Dashboard

Visualize the AI engagement decision process: longing curves, state distributions, and send history.

# Install dashboard dependencies
pip install -e ".[dashboard]"

# Run dashboard
streamlit run dashboard.py

Features:

• 🎲 Longing Curve — Poisson probability over time
• 🧠 State Distribution — Bayesian-inferred user state
• ⏰ Hourly Pattern — When messages are most likely sent
• 📋 Decision Log — Detailed record of each decision

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Demos

git clone https://github.com/pearthink123/revive-companion
cd revive-companion
pip install -e .

PYTHONPATH=src python examples/quickstart.py              # Basic simulation
PYTHONPATH=src python examples/bayesian_demo.py           # State inference
PYTHONPATH=src python examples/bayesian_learning_demo.py  # Online learning
PYTHONPATH=src python examples/info_gain_demo.py          # Information gain
PYTHONPATH=src python examples/integration_example.py     # Smart notifier

Integration

Simple Integration (Any Bot)

from revive_companion import PoissonLove

love = PoissonLove()

# Check periodically
result = love.tick()
if result.should_send:
    send_message("Thinking of you~")
    love.record_send()

# When user replies
love.record_reply(reply_speed=0.8, reply_length=0.6)

Telegram Bot

# Install dependency
pip install python-telegram-bot

# Run
python examples/telegram_bot.py --token YOUR_TOKEN --chat-id YOUR_CHAT_ID

Discord / Slack / WeChat

Same pattern, just swap the send function:

# Discord
await channel.send(message)

# Slack
slack_client.chat_postMessage(channel=channel_id, text=message)

# WeChat (itchat)
itchat.send(message, toUserName=friend_name)

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Testing

# Install test dependencies
pip install -e ".[test]"

# Run all tests
pytest tests/ -v

124 tests covering:

• 🎲 Poisson engine (determinism, growth, timing)
• 🧠 Bayesian inference (state estimation, likelihood, learning)
• 📊 Information gain (decay, thresholds)
• 💘 Unified API (full pipeline)

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Consent & Safety

This library is designed for respectful AI engagement. Please use it responsibly:

Built-in Protections

• Quiet hours: No messages during configured sleep periods
• Minimum interval: Anti-spam cooldown between messages
• State inference: Won't bother users who are busy or sleeping
• Utility threshold: Conservative default (0.5) — only sends when appropriate

Best Practices

• ✅ Opt-in: Users should explicitly enable proactive messaging
• ✅ Easy disable: Users must be able to turn it off at any time
• ✅ Transparency: Users should know the AI can initiate contact
• ✅ No emotional manipulation: Don't use this to create dependency
• ❌ No unsolicited contact: Don't message users who didn't opt in
• ❌ No persistence: Respect when users want to be left alone

Default Behavior

• Messages are never sent during quiet hours (default: 00:00-08:00)
• At least 1 hour between messages (configurable)
• High engagement does not mean more messages (unlike simple linear models)
• The engine infers state before deciding, not just response speed

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Why "Poisson"?

The Poisson process models events that happen independently at a constant average rate — like neurons firing, or "thinking about someone."

It's not random chaos. It's not rigid scheduling. It's structured spontaneity — the mathematical model of genuine, organic missing someone.

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What's Next

This is the stable base — focused on smart timing, not deep emotion.

For memory-triggered longing, relationship state machines, and AI self-emotion modeling, see:

→ affective-longing — Emotional extension with 3 layers (Memory + Relationship + Emotion)

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License

MIT