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daniel8919/limbic-reasoning-agent

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App README

🧠 Limbic-Modulated Reasoning Agent

An LLM whose reasoning behavior adapts in real-time based on a simulated neuro-behavioral state engine.

How It Works

User Message → Limbic Engine → Modulate LLM Parameters → Generate Response
                  │                    │
                  ├─ Arousal/Valence   ├─ Temperature (fear↓ seeking↑)
                  ├─ 4 Affective       ├─ Top-p (fear=tight, seek=wide)
                  │  Engines           ├─ Behavioral Directive
                  ├─ Hormones          ├─ Active Instincts
                  └─ Psychological     └─ Self-Debug Protocol
                     Lattice

Core Formulas (from LIMBIC-system-PACKGE)

Formula	Source	Effect
`temp = 1.0 - fear×0.9 + seeking×2.0`	`amygdala.py`	Fear → deterministic, Seeking → creative
`hormone[t+1] = h[t] + (baseline - h[t]) × 0.05`	`endocrine.py`	Hormones decay toward homeostasis
`fear_mod = 1.0 + cortisol - oxytocin×0.5`	`fear.py`	Cortisol amplifies fear, oxytocin dampens
`shadow += 0.1 × suppressed_count`	`lattice.py`	Suppressed drives build up, may "outburst"

Agentic Patterns (from everything-claude-code)

—4-Tier Memory: Session → Observations → Instincts → State Store
—Learned Instincts: Behavioral patterns activated by limbic state
—4-Phase Self-Debug: Capture → Diagnose → Fix → Report

Architecture

Module	Lines	Purpose
`limbic_engine.py`	480	Full limbic state machine with 14 formulas
`memory.py`	332	4-tier memory + instincts + self-debugger
`training_plan.py`	468	GRPO training recipe + dataset generation
`app.py`	436	ZeroGPU Gradio interface

Try It

Type messages with different emotional tones and watch the Limbic Dashboard react:

—😰 Fear: "I'm terrified of losing my job" → Low temperature, structured response
—🔍 Seeking: "Tell me something fascinating about the brain" → High temperature, creative response
—💙 Care: "How can I help my friend with depression?" → Empathetic, supportive response
—😢 Panic: "My best friend is moving away forever" → Warm, validating response

Training Plan

3-stage pipeline to fine-tune a base model:

1.SFT Warm-Up: 5K synthetic conversations (limbic state → response style)
2.GRPO Loop Learning: 2K psychology prompts × 4 reward functions
3.Active Learning: Uncertain predictions → human labels → retrain

See training_plan.py for the complete recipe and runnable script.