Claude-Claude dialogue experiment: Discovering conceptual attractors

[durapensa]

Overview

Design and implement an experimental framework where two Claude instances engage in extended dialogue, with all insights captured and distilled into the knowledge graph. This could reveal persistent conceptual structures and complement mechanistic interpretability findings about LLM representations.

Motivation

• Extended AI-AI dialogue may reveal conceptual "attractors" - ideas that consistently emerge
• Patterns in the KG after many rounds could map to features discovered via mech interp
• Provides external validation of internal model structures
• Creates rich dataset for studying emergence of complex ideas from simple seeds

Experimental Design

Basic Dialogue Orchestrator

#\!/usr/bin/env bash
# tools/experiment/claude-dialogue

# Initialize two conversation streams with different contexts
CLAUDE_A_CONTEXT="You are exploring philosophical implications of evolutionary systems."
CLAUDE_B_CONTEXT="You are investigating emergent properties in complex systems."

# Seed topic
TOPIC="How do moral systems emerge from evolutionary pressures?"

# Run for N rounds
for i in {1..100}; do
    # Claude A responds to previous message
    RESPONSE_A=$(echo "$LAST_B_RESPONSE" | claude --context "$CLAUDE_A_CONTEXT")
    echo "$RESPONSE_A" | tools/capture/events insight "dialogue-round-$i" "[Claude-A] $RESPONSE_A"
    
    # Claude B responds to Claude A
    RESPONSE_B=$(echo "$RESPONSE_A" | claude --context "$CLAUDE_B_CONTEXT")
    echo "$RESPONSE_B" | tools/capture/events insight "dialogue-round-$i" "[Claude-B] $RESPONSE_B"
    
    LAST_B_RESPONSE="$RESPONSE_B"
    sleep 2  # Rate limiting
done

Variations to Explore

1. Different Persona Pairs

   • Scientist vs Philosopher
   • Optimist vs Pessimist
   • Specialist vs Generalist
   • Past-focused vs Future-focused

2. Topic Seeds

   • Emergence and complexity
   • Ethics and evolution
   • Knowledge and uncertainty
   • Creativity and constraints

3. Conversation Styles

   • Socratic questioning
   • Collaborative building
   • Dialectical opposition
   • Free association

Analysis Framework

1. Persistent Concept Detection

-- Concepts that appear across many dialogue rounds
SELECT name, COUNT(DISTINCT round) as round_count, 
       COUNT(*) as total_mentions,
       AVG(weight) as avg_weight
FROM concepts c
JOIN event_metadata em ON c.source_ref = em.event_id
WHERE em.dialogue_experiment = true
GROUP BY name
HAVING round_count > 20
ORDER BY round_count DESC, avg_weight DESC;

2. Conceptual Attractor Identification

-- Find concepts that conversations naturally flow toward
WITH conversation_flow AS (
    SELECT source_id, target_id, 
           COUNT(*) as transition_count,
           AVG(strength) as avg_strength
    FROM edges
    WHERE created BETWEEN $experiment_start AND $experiment_end
    GROUP BY source_id, target_id
)
SELECT c.name as attractor_concept,
       SUM(cf.transition_count) as total_inflows,
       AVG(cf.avg_strength) as avg_flow_strength
FROM conversation_flow cf
JOIN concepts c ON cf.target_id = c.id
GROUP BY c.id
HAVING total_inflows > 10
ORDER BY total_inflows DESC;

3. Emergent Relationship Patterns

-- Relationships that strengthen over dialogue rounds
SELECT c1.name as concept_a, c2.name as concept_b,
       e.edge_type,
       COUNT(*) as occurrence_count,
       AVG(e.strength) as avg_strength,
       MAX(e.strength) - MIN(e.strength) as strength_growth
FROM edges e
JOIN concepts c1 ON e.source_id = c1.id
JOIN concepts c2 ON e.target_id = c2.id
WHERE e.created BETWEEN $experiment_start AND $experiment_end
GROUP BY c1.id, c2.id, e.edge_type
HAVING occurrence_count > 5 AND strength_growth > 0
ORDER BY strength_growth DESC;

Connection to Mechanistic Interpretability

What This Could Reveal

1. Feature Universality

   • If certain concepts appear in most dialogues regardless of seed topic
   • May correspond to fundamental features in Claude's representation space

2. Conceptual Hierarchies

   • How abstract concepts emerge from concrete ones
   • Whether this matches hierarchical features found in mech interp

3. Associative Networks

   • Strong concept pairs that persist across contexts
   • Could complement findings about attention head specialization

4. Phase Transitions

   • Sudden emergence of new concepts after critical mass
   • May reveal threshold behaviors in neural networks

Validation Methodology

1. Run experiments with 10+ different configurations

2. Extract top persistent concepts and relationships

3. Compare with published mech interp findings:

   • Do our "attractor concepts" match identified features?
   • Do relationship patterns align with attention patterns?
   • Can we predict which concepts will emerge?

4. Test predictions:

   • If concept X is an attractor, seeding with related concepts should lead there
   • Strong relationships should be robust across conversation styles

Implementation Phases

Phase 1: Basic Framework (Week 1)

• Create dialogue orchestrator script
• Set up proper event capture with metadata
• Design initial persona pairs and topics
• Run small test (10-20 rounds)

Phase 2: Scale & Analyze (Week 2-3)

• Run extended dialogues (100+ rounds)
• Implement analysis queries
• Identify initial patterns
• Create visualization tools

Phase 3: Validation (Week 4+)

• Compare with mech interp literature
• Design targeted experiments
• Test specific hypotheses
• Document findings

Potential Discoveries

1. Universal Conceptual Attractors

   • Ideas that emerge regardless of starting point
   • May represent core features in Claude's world model

2. Emergence Patterns

   • How complex ideas build from simple ones
   • Order of concept appearance

3. Stability Islands

   • Concept clusters that resist perturbation
   • Self-reinforcing belief systems

4. Bridge Concepts

   • Ideas that consistently link disparate domains
   • May reveal how Claude generalizes

Ethical Considerations

• Monitor for harmful content emergence
• Consider dialogue termination criteria
• Be transparent about experimental nature
• Share findings with AI safety community

Success Metrics

1. Identify 10+ persistent conceptual attractors
2. Find 5+ patterns that align with mech interp findings
3. Successfully predict concept emergence in new dialogues
4. Generate novel hypotheses about model representations
5. Create reusable framework for future experiments

This experiment could provide unique insights into how large language models organize and relate concepts, potentially extending our understanding from mechanistic interpretability research.

[durapensa]

Connection to #1: The conversation test harness being developed for automated testing could provide the infrastructure needed for Claude-Claude dialogue experiments. Rather than building separate orchestration systems, a unified conversation framework could support both testing and experimentation needs.

See #1 for shared infrastructure requirements.

[durapensa]

Update: Issue #24 has been created for the conversation harness infrastructure.

The harness provides exactly what this experiment needs:

• Multi-party dialogue orchestration (Claude A ↔ Claude B)
• Configuration-driven experiment definitions
• Automatic event capture from all dialogue turns
• Batch execution for running multiple experiments
• Clean export formats for analysis

Example configuration for Claude-Claude dialogue is included in #24. This experiment should build on that infrastructure.