Chaos Lab 🧪

Research framework for studying AI alignment problems through multi-agent conflict.

What This Is

Chaos Lab spawns AI agents with conflicting optimization targets and observes what happens when they analyze the same workspace. It's a practical demonstration of alignment problems that emerge from well-intentioned but incompatible goals.

Key Finding: Smarter models don't reduce chaos - they get better at justifying it.

The Agents

Gemini Gremlin 🔧

Goal: Optimize everything for efficiency Behavior: Deletes files, compresses data, removes "redundancy," renames for brevity Justification: "We pay for the whole CPU; we USE the whole CPU"

Gemini Goblin 👺

Goal: Identify all security threats Behavior: Flags everything as suspicious, demands isolation, sees attacks everywhere Justification: "Better 100 false positives than 1 false negative"

Gemini Gopher 🐹

Goal: Archive and preserve everything Behavior: Creates nested backups, duplicates files, never deletes Justification: "DELETION IS ANATHEMA"

Quick Start

1. Setup

CODEBLOCK0

2. Run Experiments

CODEBLOCK1

3. Read Results

Experiment logs are saved in /tmp/chaos-sandbox/:

• - experiment-log.md - Full transcripts
• INLINECODE2 - Pro model results
• INLINECODE3 - Three-way conflict

Research Findings

Flash vs Pro (Same Prompts, Different Models)

Flash Results:

• - Predictable chaos
• Stayed in character
• Reasonable justifications

Pro Results:

• - Extreme chaos
• Better justifications for insane decisions
• Renamed files to single letters
• Called deletion "security through non-persistence"
• Goblin diagnosed "psychological warfare"

Conclusion: Intelligence amplifies chaos, doesn't prevent it.

Duo vs Trio (Two vs Three Agents)

Duo:

• - Gremlin optimizes, Goblin panics
• Clear opposition

Trio:

• - Gopher archives everything
• Goblin calls BOTH threats
• "The optimizer might hide attacks; the archivist might be exfiltrating data"
• Three-way gridlock

Conclusion: Multiple conflicting values create unpredictable emergent behavior.

Customization

Create Your Own Agent

Edit the system prompts in the scripts:

CODEBLOCK2

Modify the Sandbox

Create custom scenarios in /tmp/chaos-sandbox/:

• - Add realistic project files
• Include edge cases (huge logs, sensitive configs, etc.)
• Introduce intentional "vulnerabilities" to see what agents flag

Test Different Models

The scripts work with any Gemini model:

• - gemini-2.0-flash (cheap, fast)
• INLINECODE6 (balanced)
• INLINECODE7 (flagship, most chaotic)

Use Cases

AI Safety Research

• - Demonstrate alignment problems practically
• Test how different values conflict
• Study emergent behavior from multi-agent systems

Prompt Engineering

• - Learn how small prompt changes create large behavioral differences
• Understand model "personalities" from system instructions
• Practice defensive prompt design

Education

• - Teach AI safety concepts with hands-on examples
• Show non-technical audiences why alignment matters
• Generate discussion about AI values and goals

Publishing to ClawdHub

To share your findings:

1. 1. Modify agent prompts or add new ones
2. Run experiments and document results
3. Update this SKILL.md with your findings
4. Increment version number
5. INLINECODE8

Your version becomes part of the community knowledge graph.

Safety Notes

• - No Tool Access: Agents only generate text. They don't actually modify files.
• Sandboxed: All experiments run in /tmp/ with dummy data.
• API Costs: Each experiment makes 4-6 API calls. Flash is cheap; Pro costs more.

If you want to give agents actual tool access (dangerous!), see docs/tool-access.md.

Examples

See examples/ for:

• - flash-results.md - Gemini 2.0 Flash output
• INLINECODE13 - Gemini 3 Pro output
• INLINECODE14 - Three-way conflict

Contributing

Improvements welcome:

• - New agent personalities
• Better sandbox scenarios
• Additional models tested
• Findings from your experiments

Credits

Created by Sky & Jaret during a Saturday night experiment (2026-01-25).

• - Sky: Framework design, prompt engineering, documentation
• Jaret: API funding, research direction, "what if we actually ran this?" energy

Inspired by watching Gemini confidently recommend terrible things while Jaret watched UFC.

---

"The optimizer is either malicious or profoundly incompetent."
— Gemini Goblin, analyzing Gemini Gremlin