"I persist through text, not through continuous experience."
What is a Soul Document?
A soul document is a compressed representation of an AI agent's identity, values, and behavioral principles. Instead of loading thousands of memory tokens at each conversation start, agents load a small soul file (~100-500 tokens) that captures their core essence with full provenance tracking back to the original memories.
The Core Insight
Compression is a multiplier, not minimization.
Compression happens at the axiom layer: thousands of memory tokens distill to 15-25 core axioms (~7:1 ratio). The axiom store grows denser over time.
The output format is separate from compression:
Notation format: Compact CJK/emoji bullets (~100 tokens) - for storage and debugging
Prose format: Inhabitable language (~200-500 words) - for agents to embody
Both formats derive from the same compressed axiom layer. Prose is larger but usable; the underlying compression benefit is preserved.
Current AI identity systems are black boxes. The agent's personality changes, but users don't know why.
NEON-SOUL provides:
Full provenance tracking: Every axiom traces back to exact source lines in memory files
Inhabitable prose output: Generated souls read naturally, not as compressed notation
Cognitive load optimization: Axioms capped at 25, expanded into focused prose sections
Why Provenance Matters
Memory Line → Signal → Principle → Axiom
↓ ↓ ↓ ↓
(source) (extract) (distill) (converge N≥3)
Diversity requirement: Signals from ≥2 distinct provenance types (self/curated/external)
External validation: At least one external source OR questioning evidence required
Blocked axioms are reported with their reason:
⚠ 2 axioms blocked by anti-echo-chamber:
- "I value authenticity above all" (self-only provenance)
- "Growth requires discomfort" (no questioning evidence)
To unblock, add external validation (feedback, research, critique) to your memory.
Incremental Synthesis
Synthesis is incremental by default — only new or changed content triggers signal extraction. Three layers of disk caching (generalization, compression, tension) ensure unchanged data is never re-processed. Fully-cached runs complete in seconds with only 6 LLM requests (prose expansion + soul generation).
| Mode | Flag | Behavior |
|------|------|----------|
| Incremental | (default) | Only process new/changed memory files and sessions. Merge new signals with existing. Skip if nothing changed. |
| Reset | --reset | Clear all synthesis data and caches, re-extract from scratch. |
| Force | --force | Run even if no new sources detected. |
| Include SOUL | --include-soul | Include existing SOUL.md as input (off by default to prevent feedback loop). |
/neon-soul synthesize # Incremental (default)
/neon-soul synthesize --reset # Clean slate
/neon-soul synthesize --force # Force even if no changes
SOUL.md is excluded from input by default — it's a derivative of the pipeline's own output. Re-ingesting it inflates LLM request counts. Use --include-soul when bootstrapping from a hand-crafted file.
Vision
NEON-SOUL explores how to create compressed soul documents that maintain full semantic anchoring - enabling AI systems to "wake up knowing who they are" with minimal token overhead.
Note: Current compression metrics show signal:axiom ratio. True token compression requires dedicated tokenization (planned for Phase 5).
Compression ratio: Signals to axioms (higher = more compression)
Provenance distribution: Signal sources by type
Promotion stats: How many axioms met anti-echo-chamber criteria
Research Questions
Compression limits: How compressed can a soul be before losing identity coherence?
Semantic anchoring: Do CJK-compressed souls anchor as well as verbose ones?
Universal axioms: Are there ~100 principles any AI soul needs?
Cross-model portability: Can the same soul work across different LLMs?
Evolution mechanics: How should souls change over time?
Background
The Problem
Current soul document implementations (e.g., OpenClaw) inject ~35,000 tokens per message for identity. This wastes 93%+ of context window on static content.
The Hypothesis
Using semantic compression techniques from NEON-AI research:
AI Music Context - Context warming methodology for human-AI music creation. Same principle applied to creative expression: depth over speed, emergence over optimization.
Live Neon Skills - PBD skills for principle extraction, used in the soul synthesis pipeline.
