GitHub Project Structure for Emergence Engine

Proposed organization for Future Directions development

Project Overview

Name: Emergence Engine Evolution Roadmap
Type: GitHub Project Board (Table/Board view)
Purpose: Track development from proof-of-concept to sophisticated emergence platform

Milestones

Milestone 1: Foundation (Q1 2026)

Goal: Establish architectural foundation for advanced features
Key Deliverables:

Architectural refactoring (Environment/Species/Individual layers)
Basic memory system
Configuration management improvements

Milestone 2: Intelligence (Q2 2026)

Goal: Make agents feel genuinely alive
Key Deliverables:

Personality & trait system
Reinforcement learning
Memory-based decision making

Milestone 3: Usability (Q2-Q3 2026)

Goal: Lower barrier to exploration
Key Deliverables:

Conversational LLM interface
Parameter translation system
Analysis & interpretation tools

Milestone 4: Ecosystem (Q3-Q4 2026)

Goal: Create rich multi-species dynamics
Key Deliverables:

Multi-species support
Evolutionary mechanics
Co-evolution systems

Milestone 5: Research Platform (2027+)

Goal: Enable serious scientific investigation
Key Deliverables:

Research protocols
Data export & analysis
Consciousness experiments

Epic Labels

epic:architecture - Foundational structural changes
epic:intelligence - Personality, memory, learning systems
epic:llm-interface - Conversational AI integration
epic:multi-species - Species diversity and ecosystems
epic:evolution - Genetic and evolutionary systems
epic:self-modifying - Meta-optimization and code evolution
epic:research - Scientific investigation directions
epic:applications - Practical use cases
epic:technical - Infrastructure and tooling
epic:visualization - UI/UX improvements

Priority Labels

priority:critical - Blocking or foundational
priority:high - Major impact, near-term
priority:medium - Valuable but can wait
priority:low - Nice-to-have
priority:research - Exploratory, no timeline

Complexity Labels

complexity:small - Few hours to 1-2 days
complexity:medium - 3-7 days
complexity:large - 1-3 weeks
complexity:epic - Weeks to months

Type Labels

type:feature - New functionality
type:refactor - Code improvement
type:research - Investigative work
type:documentation - Docs and guides
type:optimization - Performance improvement
type:experiment - Prototype or test

Status Columns

Backlog

Ideas and planned work, prioritized

Ready

Refined, unblocked, ready to start

In Progress

Active development

Review/Testing

Implemented, needs validation

Done

Completed and merged

Blocked/Deferred

Can't proceed or deprioritized

Issues Breakdown

🎯 Quick Wins (10 Issues)

Issue #1: Export simulation state to CSV

Epic: epic:technical
Priority: priority:high
Complexity: complexity:small
Type: type:feature

Description: Add ability to export current simulation state (agent positions, energy, links, etc.) to CSV format for external analysis.

Acceptance Criteria:

Export button in UI
Exports: tick, agent ID, position (x,y), chi, link count, state
Filename includes timestamp
Works with 100+ agents without lag

Implementation Notes:

Add export function to trainingUI.js
Format: tick,agentId,x,y,chi,linkCount,state
Consider streaming for large datasets

Issue #2: Save/load named configuration presets

Epic: epic:technical
Priority: priority:high
Complexity: complexity:small
Type: type:feature

Description: Allow users to save current config with a custom name and load it later from a preset list.

Acceptance Criteria:

Save button with name input
Load dropdown showing saved presets
Delete preset option
Stores in localStorage or JSON files
Includes metadata (date saved, description)

Implementation Notes:

Extend config.js
Format: {name, timestamp, description, config}
Consider preset categories (training, exploration, stress-test)

Issue #3: Add "reset to baseline" button

Epic: epic:technical
Priority: priority:medium
Complexity: complexity:small
Type: type:feature

Description: Quick button to restore default/baseline configuration without manual parameter adjustment.

Acceptance Criteria:

Button in UI
Restores to documented baseline config
Confirms before reset if current config is modified
Works without page reload

Issue #4: Parameter bounds checking

Epic: epic:technical
Priority: priority:high
Complexity: complexity:small
Type: type:feature

Description: Validate config parameters to prevent crashes from invalid values (negative chi, division by zero, etc.).

Acceptance Criteria:

Define valid ranges for all parameters
Validate on config load/change
Show clear error messages
Suggest corrections for common mistakes
Document valid ranges

Implementation Notes:

Add validation to config.js
Consider JSON schema validation
Log warnings for questionable but valid values

Issue #5: Keyboard shortcuts

Epic: epic:technical
Priority: priority:medium
Complexity: complexity:small
Type: type:feature

Description: Add keyboard shortcuts for common actions (pause, reset, speed control).

Acceptance Criteria:

Implementation Notes:

Add key listener to index.html
Show keyboard shortcuts in help overlay
Ensure shortcuts don't conflict with browser

Issue #6: Agent color coding by state

Epic: epic:visualization
Priority: priority:medium
Complexity: complexity:small
Type: type:feature

Description: Visual feedback: agents change color based on chi level, state, or behavior.

Acceptance Criteria:

Color gradient: red (low chi) → yellow → green (high chi)
Optional modes: by state, by link count, by age
Toggle in UI
Color legend displayed

Implementation Notes:

Modify rendering in app.js
Consider HSL color space for smooth gradients
Make color scheme configurable

Issue #7: FPS counter and performance metrics

Epic: epic:technical
Priority: priority:medium
Complexity: complexity:small
Type: type:feature

Description: Display real-time performance metrics to identify bottlenecks.

Acceptance Criteria:

Implementation Notes:

Add performance monitoring to app.js
Use performance.now() for accurate timing
Display as overlay, not intrusive

Issue #8: Screenshot capture with metadata

Epic: epic:technical
Priority: priority:low
Complexity: complexity:small
Type: type:feature

Description: Capture current simulation view as image with config metadata embedded.

Acceptance Criteria:

Screenshot button or keyboard shortcut
Saves as PNG
Includes tick count, agent count in filename
Optional: embed config as JSON metadata
Optional: include metrics overlay

Implementation Notes:

Use canvas.toDataURL()
Consider adding watermark with key params

Issue #9: Comparison mode (side-by-side configs)

Epic: epic:technical
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: Run two different configurations side-by-side for direct comparison.

Acceptance Criteria:

Implementation Notes:

May require architectural changes
Consider using two canvas elements
Share common code but separate state

Issue #10: Parameter randomization

Epic: epic:technical
Priority: priority:low
Complexity: complexity:small
Type: type:feature

Description: Generate random valid configurations to explore parameter space.

Acceptance Criteria:

Implementation Notes:

Use validated bounds from Issue #4
Consider weighted randomization (avoid extremes)

🏗️ Architectural Refactoring (5 Issues)

Issue #11: Design layered architecture specification

Epic: epic:architecture
Priority: priority:critical
Complexity: complexity:medium
Type: type:documentation

Description: Document the three-layer architecture (Environment/Species/Individual) before implementation.

Acceptance Criteria:

Clear separation of concerns documented
API boundaries defined
Data flow diagrams
Migration strategy from current flat structure
Example configurations for each layer

Blocks: Issues #12-15

Implementation Notes:

Reference Future Directions section
Consider existing config.js structure
Plan for backward compatibility

Issue #12: Implement Environment Layer

Epic: epic:architecture
Priority: priority:critical
Complexity: complexity:large
Type: type:refactor

Description: Create Environment layer containing world physics, resources, computation substrate, field dynamics.

Acceptance Criteria:

Environment config separate from species/individual
Contains: grid size, CA rules, field parameters, chi distribution
Can be swapped without affecting species definitions
Backward compatible with current configs

Blocked By: Issue #11

Implementation Notes:

Refactor config.js
Update app.js to use layered config
Maintain existing functionality

Issue #13: Implement Species Layer

Epic: epic:architecture
Priority: priority:critical
Complexity: complexity:large
Type: type:refactor

Description: Create Species layer with behavioral templates (genetic traits, movement patterns, psychology).

Acceptance Criteria:

Species template structure defined
Multiple species can coexist
Agents instantiate from species templates
Species can have inheritable traits
Can define species-specific behaviors

Blocked By: Issue #11, #12

Implementation Notes:

May require agent structure changes
Consider species registry/factory pattern

Issue #14: Implement Individual Layer

Epic: epic:architecture
Priority: priority:critical
Complexity: complexity:large
Type: type:refactor

Description: Create Individual layer for personal identity, memory, learned behaviors, personality development.

Acceptance Criteria:

Blocked By: Issue #11, #12, #13

Implementation Notes:

Foundation for personality system (Issue #16)
Consider memory limits for performance

Issue #15: Migration and testing

Epic: epic:architecture
Priority: priority:critical
Complexity: complexity:medium
Type: type:refactor

Description: Migrate existing configurations to new architecture and validate no regression.

Acceptance Criteria:

Blocked By: Issues #12-14

Implementation Notes:

Test with all profiles/* configs
Compare behavior against baseline recordings

🧠 Personality & Memory Systems (8 Issues)

Issue #16: Design memory system architecture

Epic: epic:intelligence
Priority: priority:high
Complexity: complexity:medium
Type: type:research + type:documentation

Description: Design memory system supporting episodic, semantic, and social memory.

Acceptance Criteria:

Memory types defined (episodic, semantic, social, trauma/success)
Storage format specified
Retrieval mechanism designed
Decay/forgetting strategy
Performance impact analyzed

Blocks: Issues #17-20

Implementation Notes:

Balance detail vs. performance
Consider circular buffers for episodic memory
Reference cognitive science literature

Issue #17: Implement episodic memory

Epic: epic:intelligence
Priority: priority:high
Complexity: complexity:medium
Type: type:feature

Description: Agents remember specific events: "I found food at location X at tick T."

Acceptance Criteria:

Blocked By: Issue #16

Implementation Notes:

Event types: food_found, near_death, link_formed, etc.
Store last N events per agent
Consider spatial indexing for location queries

Issue #18: Implement semantic memory

Epic: epic:intelligence
Priority: priority:high
Complexity: complexity:medium
Type: type:feature

Description: Agents learn general patterns: "Trail-following usually works."

Acceptance Criteria:

Strategy effectiveness tracking
Generalized patterns (not specific events)
Confidence levels based on experience
Updates over time
Influences decision making

Blocked By: Issue #16

Implementation Notes:

Track: trail_following_success_rate, cooperation_payoff, etc.
Exponential moving average for adaptive learning

Issue #19: Implement social memory

Epic: epic:intelligence
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: Agents remember interactions with other agents: "Agent Y is trustworthy."

Acceptance Criteria:

Track relationships with other agents
Trust/reputation scores
Interaction history
Social learning (learn from others' success)
Preferential bonding based on history

Blocked By: Issue #16

Implementation Notes:

Store relationships by agent ID
Handle agent death (memory cleanup)
Consider social network analysis

Issue #20: Implement trauma/success memory

Epic: epic:intelligence
Priority: priority:medium
Complexity: complexity:small
Type: type:feature

Description: Memorable events (near-death, windfall) have stronger influence on behavior.

Acceptance Criteria:

Detect memorable events (chi < threshold, sudden gain, etc.)
Higher weight in memory
Affects personality traits
Persists longer than normal memories

Blocked By: Issue #16, #17

Implementation Notes:

Threshold-based detection
Emotional valence (positive/negative)
Influences risk tolerance, exploration bias

Issue #21: Design personality trait system

Epic: epic:intelligence
Priority: priority:high
Complexity: complexity:medium
Type: type:documentation

Description: Define personality trait dimensions and how they blend nature (inherited) and nurture (learned).

Acceptance Criteria:

Trait dimensions defined (exploration bias, cooperation, risk tolerance, patience)
Nature/nurture blend formula (50/50)
How traits influence decisions
Trait evolution over time
Limits and constraints

Blocks: Issues #22-24

Implementation Notes:

Reference personality psychology (Big Five, etc.)
Keep simple initially, can expand later

Issue #22: Implement personality traits

Epic: epic:intelligence
Priority: priority:high
Complexity: complexity:medium
Type: type:feature

Description: Agents have personality traits that affect decision-making.

Acceptance Criteria:

Traits: exploration_bias, cooperation_tendency, risk_tolerance, patience
Values range [-1, 1] or [0, 1]
Initialized from species template (nature)
Modified by experience (nurture)
Influences behavior decisions

Blocked By: Issue #21

Implementation Notes:

Add to agent structure
Modify decision functions in controllers.js
Visualize in agent inspector

Issue #23: Implement reinforcement learning

Epic: epic:intelligence
Priority: priority:high
Complexity: complexity:large
Type: type:feature

Description: Agents adjust personality traits based on outcomes of their actions.

Acceptance Criteria:

Track action → outcome correlations
Update traits based on success/failure
Learning rate configurable
Successful strategies amplified
Failed strategies dampened
Doesn't override core species traits completely

Blocked By: Issues #21, #22

Implementation Notes:

Credit assignment problem (which action caused outcome?)
Use temporal discounting
Consider actor-critic approach

Issue #24: Implement memory-based decision making

Epic: epic:intelligence
Priority: priority:high
Complexity: complexity:large
Type: type:feature

Description: Agents use memories to inform current decisions (not just reactive).

Acceptance Criteria:

Query memory before decisions
"Have I been here before? What happened?"
Use semantic memory for strategy selection
Use social memory for partner selection
Balances memory with current sensory input

Blocked By: Issues #17-20, #22

Implementation Notes:

Modify controllers.js decision functions
Add memory weight vs. sensory weight parameter
Avoid analysis paralysis (fast decisions)

🤖 Conversational LLM Interface (10 Issues)

Issue #25: Research LLM integration options

Epic: epic:llm-interface
Priority: priority:high
Complexity: complexity:small
Type: type:research

Description: Evaluate options for LLM integration (local via Ollama, API via Claude/GPT, both).

Acceptance Criteria:

Blocks: Issues #26-34

Implementation Notes:

Consider hybrid: local for fast ops, API for complex reasoning
Test with simple parameter translation task

Issue #26: Design conversational interface architecture

Epic: epic:llm-interface
Priority: priority:high
Complexity: complexity:medium
Type: type:documentation

Description: Design how conversation state, context, and simulation state interact.

Acceptance Criteria:

Conversation flow documented
Context management strategy
How LLM accesses simulation state
How LLM modifies parameters
Safety boundaries defined

Blocked By: Issue #25
Blocks: Issues #27-34

Implementation Notes:

Message format
System prompts
Function calling for parameter changes

Issue #27: Set up Electron/Tauri app structure

Epic: epic:llm-interface
Priority: priority:high
Complexity: complexity:medium
Type: type:refactor

Description: Convert from web app to standalone app with filesystem access (needed for LLM integration).

Acceptance Criteria:

Blocked By: Issue #26

Implementation Notes:

Tauri is lighter, Electron more mature
Consider development vs. production builds

Issue #28: Implement Phase 1 - Parameter translation

Epic: epic:llm-interface
Priority: priority:high
Complexity: complexity:large
Type: type:feature

Description: Natural language → parameter changes. "Make them more cooperative" → adjust link.guidanceGain.

Acceptance Criteria:

LLM understands parameter names and effects
Translates intent to parameter changes
Shows user what will change before applying
Validates changes are reasonable
Can undo changes

Blocked By: Issues #26, #27

Implementation Notes:

Create parameter dictionary for LLM
Use function calling to structure changes
Prompt engineering critical here

Issue #29: Implement Phase 2 - Analysis & interpretation

Epic: epic:llm-interface
Priority: priority:high
Complexity: complexity:large
Type: type:feature

Description: LLM analyzes current simulation state and explains emergent behavior.

Acceptance Criteria:

"Why are they clustering?" → explains in natural language
Can query specific metrics
Identifies unusual patterns
Suggests explanations for behavior
References parameter settings

Blocked By: Issue #28

Implementation Notes:

Provide LLM with: current params, metrics, recent events
May need to generate summaries (not full state)
Consider vision API for visual analysis

Issue #30: Implement Phase 3 - Training objectives

Epic: epic:llm-interface
Priority: priority:medium
Complexity: complexity:large
Type: type:feature

Description: Natural language training goals → objective function → CEM optimization.

Acceptance Criteria:

"Optimize for long-term survival" → defines fitness function
Configures training parameters
Runs CEM or other optimizer
Reports results with interpretation
Can refine based on feedback

Blocked By: Issue #29

Implementation Notes:

Reuse existing learner.js infrastructure
LLM translates goal → reward function code
Safety: validate objective functions

Issue #31: Implement Phase 4 - Autonomous experimentation

Epic: epic:llm-interface
Priority: priority:medium
Complexity: complexity:large
Type: type:feature

Description: LLM designs and runs experiments overnight, generates reports.

Acceptance Criteria:

"Run experiments on energy metabolism" → designs protocol
Runs multiple variations
Collects data systematically
Generates summary report
Highlights interesting findings

Blocked By: Issue #30

Implementation Notes:

Experiment templates
Results database
Report generation
Human review before major changes

Issue #32: Context management system

Epic: epic:llm-interface
Priority: priority:high
Complexity: complexity:medium
Type: type:feature

Description: Manage conversation history, simulation context, and relevant documentation.

Acceptance Criteria:

Stores conversation history
Retrieves relevant past context
Includes current simulation state
References documentation when needed
Manages token limits

Blocked By: Issue #26

Implementation Notes:

Vector database for semantic search over docs/history?
Sliding window for conversation
Summarization for old context

Issue #33: LLM integration tests

Epic: epic:llm-interface
Priority: priority:high
Complexity: complexity:medium
Type: type:feature

Description: Test suite for LLM integration (parameter translation accuracy, etc.).

Acceptance Criteria:

Blocked By: Issues #28-31

Implementation Notes:

Use known good examples
Check LLM doesn't suggest dangerous params
Evaluate interpretation against human assessment

Issue #34: LLM interface documentation

Epic: epic:llm-interface
Priority: priority:medium
Complexity: complexity:small
Type: type:documentation

Description: User guide for conversational interface.

Acceptance Criteria:

Blocked By: Issues #28-31

🌍 Multi-Species Ecosystems (6 Issues)

Issue #35: Design species template system

Epic: epic:multi-species
Priority: priority:high
Complexity: complexity:medium
Type: type:documentation

Description: Define how species templates work and how agents inherit from them.

Acceptance Criteria:

Species template structure
Trait inheritance mechanism
How multiple species interact in shared environment
Species-specific behaviors
Visual differentiation (colors, shapes)

Blocked By: Issue #13 (Species Layer)
Blocks: Issues #36-40

Implementation Notes:

Build on Species Layer from architecture refactor
Consider species as config presets

Issue #36: Create example species: Foragers

Epic: epic:multi-species
Priority: priority:high
Complexity: complexity:small
Type: type:feature

Description: Implement Foragers: fast movement, high trail following, strong cooperation.

Acceptance Criteria:

Blocked By: Issue #35

Issue #37: Create example species: Explorers

Epic: epic:multi-species
Priority: priority:high
Complexity: complexity:small
Type: type:feature

Description: Implement Explorers: slow methodical, low trail following, independent, impatient.

Acceptance Criteria:

Blocked By: Issue #35

Issue #38: Create example species: Coordinators

Epic: epic:multi-species
Priority: priority:medium
Complexity: complexity:small
Type: type:feature

Description: Implement Coordinators: medium speed, extreme cooperation, facilitate group formation.

Acceptance Criteria:

Blocked By: Issue #35

Issue #39: Create example species: Scouts

Epic: epic:multi-species
Priority: priority:medium
Complexity: complexity:small
Type: type:feature

Description: Implement Scouts: very fast, low energy cost, high sensing range, short lifespan.

Acceptance Criteria:

Blocked By: Issue #35

Issue #40: Multi-species analysis tools

Epic: epic:multi-species
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: Tools to analyze multi-species dynamics (population ratios, niche partitioning, symbiosis detection).

Acceptance Criteria:

Track population per species over time
Detect cooperation between species
Identify resource partitioning
Analyze species interactions
Export multi-species data

Blocked By: Issues #36-39

Implementation Notes:

Extend policyAnalyzer.js
Species-specific metrics
Interaction matrix visualization

🧬 Evolutionary Systems (7 Issues)

Issue #41: Design evolutionary mechanics

Epic: epic:evolution
Priority: priority:medium
Complexity: complexity:medium
Type: type:documentation

Description: Design reproduction, inheritance, mutation, and selection mechanisms.

Acceptance Criteria:

Blocks: Issues #42-47

Implementation Notes:

Reference evolutionary algorithms literature
Balance exploration vs. exploitation
Consider sexual vs. asexual reproduction

Issue #42: Implement reproduction mechanics

Epic: epic:evolution
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: Agents can reproduce when conditions met (chi threshold, maturity, etc.).

Acceptance Criteria:

Reproduction costs chi
Creates offspring agent
Reproduction conditions configurable
Can be disabled for non-evolutionary runs
Population control (death when overcrowded)

Blocked By: Issue #41

Implementation Notes:

Add reproduction trigger to controllers.js
Chi cost should be significant
Consider cooldown period

Issue #43: Implement genetic inheritance

Epic: epic:evolution
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: Offspring inherit traits from parent(s) with variation.

Acceptance Criteria:

Parameter inheritance (copy parent traits)
Genetic crossover (if two parents)
Small mutations (5-10% variation)
Personality inheritance (nature component)
Species membership inherited

Blocked By: Issues #41, #42

Implementation Notes:

Which parameters are genetic vs. environmental?
Mutation rate per parameter
Bounds checking after mutation

Issue #44: Implement fitness tracking

Epic: epic:evolution
Priority: priority:medium
Complexity: complexity:small
Type: type:feature

Description: Track fitness metrics to identify successful strategies.

Acceptance Criteria:

Blocked By: Issue #41

Implementation Notes:

Add fitness tracking to agent
Log fitness at death
Export for analysis

Issue #45: Implement speciation mechanics

Epic: epic:evolution
Priority: priority:low
Complexity: complexity:large
Type: type:feature

Description: Populations diverge into distinct species over time.

Acceptance Criteria:

Detect trait divergence
Geographic isolation (agents in different regions)
Behavioral isolation (different strategies)
Species boundary emergence
Track species lineage

Blocked By: Issues #42-44

Implementation Notes:

Clustering algorithm on trait space?
Define species distance metric
Visualize phylogenetic tree

Issue #46: Long-term evolution experiments

Epic: epic:evolution
Priority: priority:low
Complexity: complexity:medium
Type: type:experiment

Description: Run 1000+ generation experiments to observe evolutionary dynamics.

Acceptance Criteria:

Blocked By: Issues #42-45

Implementation Notes:

Headless mode (no rendering)
Automatic data export
Consider cloud computing

Issue #47: Evolutionary analysis tools

Epic: epic:evolution
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: Analyze evolutionary trajectories (fitness over time, trait distributions, phylogeny).

Acceptance Criteria:

Fitness landscape visualization
Trait distribution over generations
Lineage tracking
Identify evolutionary innovations
Compare to theoretical predictions

Blocked By: Issue #46

Implementation Notes:

Extend policyAnalyzer.js
Phylogenetic visualization
Statistical analysis of evolution

🔧 Self-Modifying Code (8 Issues)

Issue #48: Design self-modification architecture

Epic: epic:self-modifying
Priority: priority:medium
Complexity: complexity:large
Type: type:documentation

Description: Design safe architecture for LLM-proposed code changes.

Acceptance Criteria:

Safety architecture documented
Sandbox strategy defined
Validation criteria (tests, invariants, benchmarks)
Human review process
Rollback mechanisms

Blocks: Issues #49-55

Implementation Notes:

Reference Future Directions safety section
Inspired by genetic programming, AutoML
Start conservative, expand gradually

Issue #49: Implement Phase 1 - Read-only analysis

Epic: epic:self-modifying
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: LLM reads codebase, proposes changes as diffs, human applies manually.

Acceptance Criteria:

LLM can read project files
Proposes changes in diff format
Explains rationale for changes
Human reviews and applies
No automatic code modification

Blocked By: Issue #48

Implementation Notes:

Build on LLM interface (Issues #28-31)
Git integration for diff display
Clear change proposals

Issue #50: Set up sandboxed execution environment

Epic: epic:self-modifying
Priority: priority:medium
Complexity: complexity:large
Type: type:feature

Description: Isolated environment to test code changes safely.

Acceptance Criteria:

Blocked By: Issue #48

Implementation Notes:

Docker is probably easiest
WebAssembly sandbox for browser?
Copy codebase to sandbox, test, destroy

Issue #51: Define behavioral invariants

Epic: epic:self-modifying
Priority: priority:high
Complexity: complexity:medium
Type: type:documentation

Description: Define properties that must hold regardless of code changes.

Acceptance Criteria:

Agents must survive (no immediate death)
No crashes or infinite loops
Bounded memory usage
Bounded execution time
Chi conservation (if applicable)
Documented as testable assertions

Blocked By: Issue #48
Blocks: Issues #52-55

Implementation Notes:

Inspired by formal verification
Write as test suite
Run before/after changes

Issue #52: Implement Phase 2 - Sandboxed testing

Epic: epic:self-modifying
Priority: priority:medium
Complexity: complexity:large
Type: type:feature

Description: LLM proposes changes, tests in sandbox, reports results.

Acceptance Criteria:

Blocked By: Issues #49-51

Implementation Notes:

Automated testing pipeline
Comparison metrics
Clear go/no-go decision

Issue #53: Implement Phase 3 - Automated testing

Epic: epic:self-modifying
Priority: priority:low
Complexity: complexity:large
Type: type:feature

Description: Comprehensive test suite allows auto-apply if tests pass.

Acceptance Criteria:

Blocked By: Issue #52

Implementation Notes:

Requires high confidence in test suite
Still needs human oversight
Version control integration

Issue #54: Shadow testing system

Epic: epic:self-modifying
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: Run stable and modified versions in parallel to detect subtle differences.

Acceptance Criteria:

Blocked By: Issue #52

Implementation Notes:

Similar to Issue #9 (comparison mode)
Need deterministic simulations
Compare metrics, not exact state

Issue #55: Implement Phase 4 - Autonomous evolution

Epic: epic:self-modifying
Priority: priority:low
Complexity: complexity:epic
Type: type:experiment

Description: System runs overnight experiments, tries variations, keeps what works.

Acceptance Criteria:

Blocked By: Issues #52-54

Implementation Notes:

Meta-optimization of code, not just parameters
Genetic programming inspired
High risk, high reward
Requires extensive safety infrastructure

🔬 Research Directions (10 Issues)

Issue #56: Research: Minimal conditions for consciousness

Epic: epic:research
Priority: priority:research
Complexity: complexity:epic
Type: type:research

Description: Investigate: At what threshold does subjective experience emerge?

Approach:

Implement memory, temporal integration, self-reference
Scale up network complexity
Add meta-awareness layer
Test behavioral markers

Metrics:

Self-recognition
Anticipation
Theory of mind
Surprise/curiosity

Dependencies: Issues #16-24 (memory/personality systems)

Issue #57: Research: Computational ecology

Epic: epic:research
Priority: priority:research
Complexity: complexity:large
Type: type:research

Description: How do different CA rules shape evolutionary outcomes?

Approach:

Test Rule 110, Rule 30, Rule 90, etc.
Vary computational complexity
Measure adaptation strategies
Map fitness landscapes

Hypothesis: More computationally rich environments → more diverse strategies

Implementation Notes:

Extend tc/ system
Systematic comparison framework

Issue #58: Research: Thermodynamic bounds on intelligence

Epic: epic:research
Priority: priority:research
Complexity: complexity:medium
Type: type:research

Description: What's the minimal energy budget for adaptive behavior?

Approach:

Systematically reduce chi availability
Measure intelligence metrics
Find phase transitions
Compare to biological systems

Implementation Notes:

Automated chi scaling experiments
Intelligence benchmarks needed

Issue #59: Research: Constraint design principles

Epic: epic:research
Priority: priority:research
Complexity: complexity:large
Type: type:research

Description: Can we derive principles for designing constraints that produce desired behaviors?

Approach:

Catalog constraint configurations → outcomes
Use meta-optimization
Build design heuristics
Test generalization

Implementation Notes:

Large-scale parameter sweep
Pattern recognition in results
"If you want X, constrain Y" rules

Issue #60: Research: Collective cognition

Epic: epic:research
Priority: priority:research
Complexity: complexity:large
Type: type:research

Description: When does a swarm become "smart" as a collective?

Approach:

Implement problem-solving challenges
Measure individual vs. collective performance
Identify coordination patterns
Compare to biological collectives

Implementation Notes:

Need benchmark tasks (mazes, resource allocation)
Measure emergence of collective intelligence

Issue #61: Implement cognitive test battery

Epic: epic:research
Priority: priority:medium
Complexity: complexity:large
Type: type:feature

Description: Suite of tests to measure agent/swarm intelligence.

Tests:

Navigation (mazes, obstacle avoidance)
Resource allocation (optimal distribution)
Problem solving (multi-step challenges)
Social coordination (group tasks)
Learning rate (adaptation speed)

Blocked By: None (can implement anytime)

Issue #62: Implement self-recognition tests

Epic: epic:research
Priority: priority:low
Complexity: complexity:medium
Type: type:feature

Description: Test if agents can distinguish self from others.

Approaches:

Mirror test analogue
Self vs. other agent identification
Own-state awareness

Blocked By: Issues #16-24 (memory/personality)

Issue #63: Implement theory of mind tests

Epic: epic:research
Priority: priority:low
Complexity: complexity:large
Type: type:feature

Description: Test if agents model other agents' states/intentions.

Approaches:

Predict other agents' behavior
Cooperation requires modeling partners
Deception detection

Blocked By: Issue #62

Issue #64: Data export for academic publication

Epic: epic:research
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: Export data in formats suitable for academic papers.

Acceptance Criteria:

Issue #65: Write research protocols document

Epic: epic:research
Priority: priority:medium
Complexity: complexity:small
Type: type:documentation

Description: Standard protocols for running experiments and collecting data.

Contents:

Experimental design guidelines
Control group requirements
Statistical significance testing
Replication procedures
Data collection standards

🎮 Practical Applications (5 Issues)

Issue #66: Educational platform prototype

Epic: epic:applications
Priority: priority:low
Complexity: complexity:large
Type: type:feature

Description: Interactive demos and guided explorations for teaching emergence.

Features:

Guided tutorials
Explanation overlays
Interactive parameter exploration
"What if?" scenarios
Student-friendly interface

Issue #67: Game AI integration example

Epic: epic:applications
Priority: priority:low
Complexity: complexity:large
Type: type:experiment

Description: Demonstrate using Emergence Engine for game NPC behavior.

Features:

Export agent behaviors to game engine
Personality-driven NPCs
Emergent storytelling
Integration guide

Issue #68: Swarm robotics export

Epic: epic:applications
Priority: priority:low
Complexity: complexity:large
Type: type:feature

Description: Export coordination algorithms for physical robot swarms.

Features:

Real physics constraints
Communication limits
Hardware-compatible control
ROS integration?

Issue #69: Organizational dynamics abstraction

Epic: epic:applications
Priority: priority:low
Complexity: complexity:medium
Type: type:experiment

Description: Map simulation concepts to organizational dynamics (teams, information flow, etc.).

Features:

Abstract away biology
Team coordination metrics
Communication patterns
Decision-making processes

Issue #70: Research platform packaging

Epic: epic:applications
Priority: priority:low
Complexity: complexity:medium
Type: type:feature

Description: Package for academic researchers (systematic experiments, data export, hypothesis testing).

Features:

Experiment templates
Data export utilities
Statistical analysis tools
Documentation for researchers

🔧 Technical Infrastructure (10 Issues)

Issue #71: Performance profiling

Epic: epic:technical
Priority: priority:high
Complexity: complexity:small
Type: type:optimization

Description: Identify performance bottlenecks in simulation loop.

Acceptance Criteria:

Profile major functions
Identify hotspots
Document performance characteristics
Target optimization areas

Issue #72: Spatial indexing optimization

Epic: epic:technical
Priority: priority:medium
Complexity: complexity:large
Type: type:optimization

Description: Optimize agent lookups using spatial data structures (grid, quadtree, etc.).

Acceptance Criteria:

Faster nearest-neighbor queries
Reduced O(n²) comparisons
Scales to 1000+ agents
No behavior changes

Implementation Notes:

Grid-based spatial hashing
Update when agents move

Issue #73: GPU acceleration research

Epic: epic:technical
Priority: priority:low
Complexity: complexity:large
Type: type:research

Description: Investigate GPU compute shaders for field updates and agent processing.

Acceptance Criteria:

Prototype GPU implementation
Performance comparison
Feasibility assessment
Implementation strategy if viable

Implementation Notes:

WebGPU API
Data transfer overhead critical
May not be worth complexity

Issue #74: Web Workers for parallel processing

Epic: epic:technical
Priority: priority:medium
Complexity: complexity:medium
Type: type:optimization

Description: Use Web Workers to parallelize agent updates.

Acceptance Criteria:

Agents processed in parallel
Synchronization handled correctly
Performance improvement measured
Falls back gracefully

Implementation Notes:

Partition agents across workers
Handle shared state (fields)

Issue #75: Time-series data export

Epic: epic:technical
Priority: priority:high
Complexity: complexity:small
Type: type:feature

Description: Export metrics over time for analysis (population, energy, coherence, etc.).

Acceptance Criteria:

CSV format
Configurable metrics
Configurable sample rate
Works with long runs

Implementation Notes:

Extend Issue #1
Circular buffer for memory efficiency

Issue #76: Network topology export

Epic: epic:technical
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: Export link structure as graph for network analysis.

Acceptance Criteria:

Graph format (edge list, adjacency matrix)
Compatible with NetworkX, igraph
Timestamped snapshots
Includes node attributes (chi, position, etc.)

Issue #77: 3D visualization option

Epic: epic:visualization
Priority: priority:low
Complexity: complexity:large
Type: type:feature

Description: 3D rendering option using Three.js or WebGL.

Features:

Agents as spheres
Fields as volumetric fog
Camera controls
Optional for users who want it

Issue #78: Trail history visualization

Epic: epic:visualization
Priority: priority:medium
Complexity: complexity:small
Type: type:feature

Description: Show agent paths over time (fading trails).

Acceptance Criteria:

Store recent positions per agent
Render as fading lines
Configurable trail length
Performance impact acceptable

Issue #79: Energy flow visualization

Epic: epic:visualization
Priority: priority:medium
Complexity: complexity:medium
Type: type:feature

Description: Visualize chi transfer between agents and environment.

Acceptance Criteria:

Show chi absorption from fields
Show chi sharing via links
Animated flow
Toggle on/off

Issue #80: Agent inspector tool

Epic: epic:visualization
Priority: priority:high
Complexity: complexity:medium
Type: type:feature

Description: Click agent to view detailed internal state.

Acceptance Criteria:

Shows: chi, position, velocity, links, state
Shows: personality traits (when implemented)
Shows: memories (when implemented)
Real-time updates
Can follow agent

Implementation Notes:

Side panel or modal
Useful for debugging and understanding

📚 Documentation (10 Issues)

Issue #81: Theoretical foundation document

Epic: epic:documentation
Priority: priority:high
Complexity: complexity:large
Type: type:documentation

Description: Comprehensive explanation of E², C-F-A, Zone 3, theoretical underpinnings.

Contents:

E² (Emergence from Entanglement)
Constraint-Field-Agent framework
Zone 3 theory
Thermodynamic foundations
Why this approach matters

Issue #82: Architecture guide

Epic: epic:documentation
Priority: priority:high
Complexity: complexity:medium
Type: type:documentation

Description: How the system is structured and how components interact.

Contents:

Layered architecture (after refactor)
Module responsibilities
Data flow
Extension points

Blocked By: Issues #11-15 (architectural refactor)

Issue #83: API reference

Epic: epic:documentation
Priority: priority:medium
Complexity: complexity:medium
Type: type:documentation

Description: If we make this a library, document the public API.

Contents:

Function signatures
Parameter descriptions
Return values
Usage examples

Issue #84: Tutorial series

Epic: epic:documentation
Priority: priority:high
Complexity: complexity:large
Type: type:documentation

Description: Step-by-step tutorials from basics to advanced.

Tutorials:

Getting started
Understanding emergence
Parameter exploration
Training with CEM
Multi-species simulations
Custom behaviors
Running experiments
Data analysis

Issue #85: Contributor guide

Epic: epic:documentation
Priority: priority:medium
Complexity: complexity:small
Type: type:documentation

Description: How to contribute to the project (if open-sourced).

Contents:

Setup instructions
Code style
Testing requirements
PR process
Issue templates

Issue #86: Philosophy & motivation document

Epic: epic:documentation
Priority: priority:medium
Complexity: complexity:medium
Type: type:documentation

Description: Why this project exists and what it's trying to accomplish.

Contents:

Core philosophy
Research goals
Design principles
Trajectory so far
Future vision

Issue #87: Parameter reference

Epic: epic:documentation
Priority: priority:high
Complexity: complexity:medium
Type: type:documentation

Description: Complete reference of all configuration parameters.

For Each Parameter:

Name and path
Valid range
Default value
What it controls
How it affects behavior
Related parameters

Issue #88: Troubleshooting guide

Epic: epic:documentation
Priority: priority:medium
Complexity: complexity:small
Type: type:documentation

Description: Common problems and solutions.

Sections:

Performance issues
Unexpected behavior
Configuration errors
Browser compatibility

Issue #89: Case studies

Epic: epic:documentation
Priority: priority:low
Complexity: complexity:medium
Type: type:documentation

Description: Documented examples of interesting emergent behaviors.

Examples:

Emergence of cooperation
Multi-cellular formation
Resource competition
Evolutionary adaptation

Issue #90: Video tutorials

Epic: epic:documentation
Priority: priority:low
Complexity: complexity:large
Type: type:documentation

Description: Screencast tutorials for visual learners.

Topics:

Quick start
Parameter exploration
Running experiments
Interpreting results

Summary Statistics

Total Issues: 90

By Epic:

Architecture: 5
Intelligence: 8
LLM Interface: 10
Multi-Species: 6
Evolution: 7
Self-Modifying: 8
Research: 10
Applications: 5
Technical: 10
Visualization: 5
Documentation: 10
Quick Wins: 10

By Priority:

Critical: 5
High: 25
Medium: 35
Low: 15
Research: 10

By Complexity:

Small: 20
Medium: 40
Large: 25
Epic: 5

By Type:

Feature: 45
Refactor: 5
Research: 12
Documentation: 18
Optimization: 5
Experiment: 5

Dependency Graph (Major Chains)

Architecture Refactor (#11-15)
    ↓
Species Layer (#13)
    ↓
Multi-Species System (#35-40)

Memory System Design (#16)
    ↓
Memory Types (#17-20)
    ↓
Personality System (#21-24)

LLM Research (#25-26)
    ↓
App Structure (#27)
    ↓
LLM Phases (#28-31)
    ↓
Autonomous Experimentation (#31)

Evolution Design (#41)
    ↓
Reproduction (#42-44)
    ↓
Speciation (#45)
    ↓
Long-term Experiments (#46)

Self-Mod Design (#48)
    ↓
Sandboxing (#50) + Invariants (#51)
    ↓
Automated Testing (#52-53)
    ↓
Autonomous Evolution (#55)

Recommended Roadmap

Phase 1: Foundation (Months 1-3)

Complete Quick Wins (#1-10)
Begin Architecture Refactor (#11-15)
Start Documentation (#81, #87)

Phase 2: Intelligence (Months 4-6)

Memory System (#16-20)
Personality System (#21-24)
Agent Inspector (#80)

Phase 3: Usability (Months 7-9)

LLM Interface (#25-34)
Tutorial Series (#84)

Phase 4: Expansion (Months 10-12)

Multi-Species (#35-40)
Evolution Basics (#41-44)
Research Tools (#61, #64-65)

Phase 5: Advanced (Year 2+)

Self-Modifying Code (#48-55)
Research Directions (#56-60)
Applications (#66-70)

Project Board Views

View 1: Kanban (Status)

View 2: Priority Matrix

Axes: Priority (High/Med/Low) vs Complexity (Small/Large)

View 3: Epic Timeline

Timeline view grouped by epic, showing milestone dependencies

View 4: Team View

If multiple contributors, assign issues and show workload

Issue Templates

Feature Request Template

**Epic:** 
**Priority:** 
**Complexity:** 
**Type:** feature

**Description:**
What feature should be added?

**Acceptance Criteria:**
- [ ] Criterion 1
- [ ] Criterion 2

**Implementation Notes:**
Technical considerations

**Blocked By:**
Dependencies

**Blocks:**
What depends on this

Bug Report Template

**Description:**
What's broken?

**Steps to Reproduce:**
1. 
2. 
3. 

**Expected Behavior:**

**Actual Behavior:**

**Environment:**
Browser/OS/Version

**Screenshots:**

Research Question Template

**Epic:** epic:research
**Type:** type:research

**Question:**
What are we investigating?

**Hypothesis:**

**Approach:**

**Success Metrics:**

**Dependencies:**

Notes

Start Small: Begin with Quick Wins to build momentum
Prioritize Foundation: Architecture refactor unlocks many features
Document as You Go: Don't defer all documentation to the end
Iterate: Don't need to complete one epic before starting another
Community Input: If open-sourced, let community help prioritize
Stay Flexible: This is a living roadmap, adapt as needed

Generated: November 2025
Based on: Future Directions.md
Total Estimated Effort: ~2-3 person-years for full roadmap

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GitHub Project Structure for Emergence Engine

Project Overview

Milestones

Milestone 1: Foundation (Q1 2026)

Milestone 2: Intelligence (Q2 2026)

Milestone 3: Usability (Q2-Q3 2026)

Milestone 4: Ecosystem (Q3-Q4 2026)

Milestone 5: Research Platform (2027+)

Epic Labels

Priority Labels

Complexity Labels

Type Labels

Status Columns

Backlog

Ready

In Progress

Review/Testing

Done

Blocked/Deferred

Issues Breakdown

🎯 Quick Wins (10 Issues)

Issue #1: Export simulation state to CSV

Issue #2: Save/load named configuration presets

Issue #3: Add "reset to baseline" button

Issue #4: Parameter bounds checking

Issue #5: Keyboard shortcuts

Issue #6: Agent color coding by state

Issue #7: FPS counter and performance metrics

Issue #8: Screenshot capture with metadata

Issue #9: Comparison mode (side-by-side configs)

Issue #10: Parameter randomization

🏗️ Architectural Refactoring (5 Issues)

Issue #11: Design layered architecture specification

Issue #12: Implement Environment Layer

Issue #13: Implement Species Layer

Issue #14: Implement Individual Layer

Issue #15: Migration and testing

🧠 Personality & Memory Systems (8 Issues)

Issue #16: Design memory system architecture

Issue #17: Implement episodic memory

Issue #18: Implement semantic memory

Issue #19: Implement social memory

Issue #20: Implement trauma/success memory

Issue #21: Design personality trait system

Issue #22: Implement personality traits

Issue #23: Implement reinforcement learning

Issue #24: Implement memory-based decision making

🤖 Conversational LLM Interface (10 Issues)

Issue #25: Research LLM integration options

Issue #26: Design conversational interface architecture

Issue #27: Set up Electron/Tauri app structure

Issue #28: Implement Phase 1 - Parameter translation

Issue #29: Implement Phase 2 - Analysis & interpretation

Issue #30: Implement Phase 3 - Training objectives

Issue #31: Implement Phase 4 - Autonomous experimentation

Issue #32: Context management system

Issue #33: LLM integration tests

Issue #34: LLM interface documentation

🌍 Multi-Species Ecosystems (6 Issues)

Issue #35: Design species template system

Issue #36: Create example species: Foragers

Issue #37: Create example species: Explorers

Issue #38: Create example species: Coordinators

Issue #39: Create example species: Scouts

Issue #40: Multi-species analysis tools

🧬 Evolutionary Systems (7 Issues)

Issue #41: Design evolutionary mechanics

Issue #42: Implement reproduction mechanics

Issue #43: Implement genetic inheritance

Issue #44: Implement fitness tracking

Issue #45: Implement speciation mechanics

Issue #46: Long-term evolution experiments

Issue #47: Evolutionary analysis tools