/symbolic-equation

name: symbolic-equation
description: Discover scientific equations from data using LLM-guided evolutionary search (LLM-SR). Multi-island algorithm with softmax-based cluster sampling, island reset, and LLM-proposed equation mutations. Use for symbolic regression and equation discovery.
argument-hint: [data-and-variables]

Symbolic Equation Discovery

Discover interpretable scientific equations from data using LLM-guided evolutionary search.

Input

• `$0` — Dataset description, variable names, and physical context

References

• LLM-SR patterns (prompts, evolution, sampling): `~/.claude/skills/symbolic-equation/references/llmsr-patterns.md`

Workflow (from LLM-SR)

Step 1: Define Problem Specification

Create a specification with: 1. **Input variables**: Physical quantities with types (e.g., `x: np.ndarray`, `v: np.ndarray`) 2. **Output variable**: Target quantity to predict 3. **Evaluation function**: Fitness metric (typically negative MSE with parameter optimization) 4. **Physical context**: Domain knowledge to guide equation discovery

# Example specification
@equation.evolve
def equation(x: np.ndarray, v: np.ndarray, params: np.ndarray) -> np.ndarray:
    """Describe the acceleration of a damped nonlinear oscillator."""
    return params[0] * x

Step 2: Initialize Multi-Island Buffer

• Create N islands (default: 10) for population diversity
• Each island maintains independent clusters of equations
• Clusters group equations by performance signature

Step 3: Evolutionary Search Loop

Repeat until convergence or max samples: 1. **Select island**: Random island selection 2. **Build prompt**: Sample top equations from clusters (softmax-weighted by score) 3. **LLM proposes**: Generate new equation as improved version 4. **Evaluate**: Execute on test data, compute fitness score 5. **Register**: Add to island's cluster if valid

Step 4: Prompt Construction

Present previous equations as versioned sequence:

def equation_v0(x, v, params):
    """Initial version."""
    return params[0] * x

def equation_v1(x, v, params):
    """Improved version of equation_v0."""
    return params[0] * x + params[1] * v

def equation_v2(x, v, params):
    """Improved version of equation_v1."""
    # LLM completes this

Step 5: Island Reset (Diversity Maintenance)

Periodically (default: every 4 hours): 1. Sort islands by best score 2. Reset bottom 50% of islands 3. Seed each reset island with best equation from a surviving island 4. Restart cluster sampling temperature

Step 6: Extract Best Equations

After search completes: 1. Collect best equation from each island 2. Rank by fitness score 3. Simplify if possible (algebraic simplification) 4. Report with physical interpretation

Cluster Sampling

Temperature-scheduled softmax over cluster scores:

temperature = T_init * (1 - (num_programs % period) / period)
probabilities = softmax(cluster_scores / temperature)

• Higher temperature → more exploration
• Lower temperature → more exploitation of best clusters
• Within clusters: shorter programs are preferred (Occam's razor)

Rules

• Equations must use only standard mathematical operations
• Parameter optimization via scipy BFGS or Adam
• Fitness = negative MSE (higher is better)
• Timeout protection for equation evaluation
• No recursive equations allowed
• Physical interpretability is preferred over pure fit

Related Skills

• Upstream: [data-analysis](../data-analysis/), [math-reasoning](../math-reasoning/)
• Downstream: [paper-writing-section](../paper-writing-section/)
• See also: [algorithm-design](../algorithm-design/)