MechInterp Runner

Execute experiment specifications for mechanistic interpretability analysis. This skill takes JSON spec files and runs the appropriate analysis, producing structured result files.

Purpose

The runner skill:

• Loads experiment specs from JSON files
• Routes to the appropriate experiment runner
• Executes the analysis with constraint enforcement
• Produces structured JSON results with diagnostics

When to Use

Use this skill after:

1. The planner has generated experiment specs
2. You have manually created an experiment spec
3. You need to re-run an experiment with different parameters

Supported Experiment Types

family_1d_sweep

family_2d_heatmap

frequent_itemsets

minimal_cores

pairwise_interactions

conditional_interactions

split_half

shuffle_null

weapon_sweep

kit_sweep

Usage

Subcommand Interface (Recommended)

Run experiments directly without writing JSON spec files:

cd /root/dev/SplatNLP

# 1D family sweep
poetry run python -m splatnlp.mechinterp.cli.runner_cli family-sweep \
    --feature-id 6235 --family quick_respawn --model ultra

# 2D heatmap
poetry run python -m splatnlp.mechinterp.cli.runner_cli heatmap \
    --feature-id 6235 --family-x special_charge_up --family-y quick_respawn

# Weapon sweep (correlational)
poetry run python -m splatnlp.mechinterp.cli.runner_cli weapon-sweep \
    --feature-id 6235 --model ultra --top-k 20

# Kit sweep (correlational)
poetry run python -m splatnlp.mechinterp.cli.runner_cli kit-sweep \
    --feature-id 6235 --model ultra --analyze-combinations

# Binary ability presence analysis
poetry run python -m splatnlp.mechinterp.cli.runner_cli binary \
    --feature-id 6235 --model ultra

# Core coverage analysis
poetry run python -m splatnlp.mechinterp.cli.runner_cli coverage \
    --feature-id 6235 --tokens comeback,stealth_jump,respawn_punisher

# Token influence analysis
poetry run python -m splatnlp.mechinterp.cli.runner_cli token-influence \
    --feature-id 6235 --model ultra

Subcommand Reference

family-sweep

--feature-id

--family

--model

--rungs

heatmap

--feature-id

--family-x

--family-y

--model

--rungs-x

--rungs-y

weapon-sweep

--feature-id

--model

--top-k

--min-examples

kit-sweep

--feature-id

--model

--top-k

--analyze-combinations

binary

--feature-id

--model

--tokens

coverage

--feature-id

--model

--tokens

--threshold

token-influence

--feature-id

--model

--high-percentile

JSON Spec Mode (Legacy/Advanced)

For complex experiments or batch processing, use JSON spec files:

cd /root/dev/SplatNLP

# Run an experiment spec
poetry run python -m splatnlp.mechinterp.cli.runner_cli \
    --spec-path /mnt/e/mechinterp_runs/specs/20250607__f18712__family-1d-sweep.json

# With custom output directory
poetry run python -m splatnlp.mechinterp.cli.runner_cli \
    --spec-path my_spec.json \
    --output-dir ./my_results/

# Dry run (validate spec only)
poetry run python -m splatnlp.mechinterp.cli.runner_cli \
    --spec-path my_spec.json \
    --dry-run

# List available experiment types
poetry run python -m splatnlp.mechinterp.cli.runner_cli --list-types

When to Use Subcommands vs JSON Specs

Programmatic

from splatnlp.mechinterp.schemas import ExperimentSpec, ExperimentType
from splatnlp.mechinterp.experiments import get_runner_for_type
from splatnlp.mechinterp.skill_helpers import load_context

# Create spec
spec = ExperimentSpec(
    type=ExperimentType.FAMILY_1D_SWEEP,
    feature_id=18712,
    model_type="ultra",
    variables={"family": "special_charge_up"},
)

# Load context and run
ctx = load_context("ultra")
runner = get_runner_for_type(spec.type)
result = runner.run(spec, ctx)

# Check result
print(result.get_summary())
if result.success:
    print(f"Mean delta: {result.aggregates.mean_delta}")

Spec File Format

{
  "id": "20250607_142531",
  "type": "family_1d_sweep",
  "feature_id": 18712,
  "model_type": "ultra",
  "dataset_slice": {
    "percentile_min": 10.0,
    "percentile_max": 90.0,
    "sample_size": 500
  },
  "variables": {
    "family": "special_charge_up",
    "rungs": [3, 12, 29, 41, 57],
    "include_absent": true
  },
  "constraints": ["one_rung_per_family"],
  "outputs": {
    "aggregates": true,
    "tables": true,
    "diagnostics": true,
    "figures": false
  },
  "description": "Test SCU response across rungs",
  "parent_hypothesis": "h001"
}

Result File Format

{
  "spec_id": "20250607_142531",
  "spec_path": "20250607_142531__f18712__family-1d-sweep.json",
  "feature_id": 18712,
  "experiment_type": "family_1d_sweep",
  "aggregates": {
    "mean_delta": 0.35,
    "std_delta": 0.12,
    "n_samples": 500,
    "custom": {
      "threshold_rung": 41,
      "max_rung_delta": 0.52
    }
  },
  "tables": {
    "rung_deltas": {
      "name": "rung_deltas",
      "columns": ["rung", "mean_delta", "std_error", "n"],
      "rows": [
        {"rung": 3, "mean_delta": 0.05, "std_error": 0.02, "n": 100},
        {"rung": 12, "mean_delta": 0.12, "std_error": 0.03, "n": 100}
      ]
    }
  },
  "diagnostics": {
    "relu_floor_detected": false,
    "relu_floor_rate": 0.02,
    "n_contexts_tested": 500,
    "warnings": []
  },
  "success": true,
  "duration_seconds": 45.3
}

File Locations

• Specs:

  /mnt/e/mechinterp_runs/specs/

• Results:

  /mnt/e/mechinterp_runs/results/

• Figures:

  /mnt/e/mechinterp_runs/figures/

Constraint Enforcement

The runner enforces constraints specified in the spec:

• one_rung_per_family: Prevents invalid multi-rung builds
• no_weapon_gating_if_relu_floor: Warns/skips if base activation too low

Violations are logged in

diagnostics.constraint_violations

diagnostics.warnings

Error Handling

If an experiment fails:

• result.success

  is

  False

• result.error_message

  contains the error
• Partial results may still be available in

  aggregates

  /

  tables

Weapon Analysis Workflow: weapon_sweep → kit_sweep

⚠️ IMPORTANT: Both weapon_sweep and kit_sweep are CORRELATIONAL analyses.

They show which weapons/kits are associated with high activation through observational grouping, NOT through counterfactual intervention. High activation for a weapon may be because:

• The weapon itself drives the feature
• Players of that weapon tend to use certain abilities
• The weapon's kit (sub/special) is the actual driver

Always cross-reference with ability analysis to distinguish weapon effects from ability effects.

When analyzing weapon-specific patterns, follow this workflow:

Step 1: Run weapon_sweep

{
  "type": "weapon_sweep",
  "feature_id": 18712,
  "model_type": "ultra",
  "variables": {"min_examples": 10, "top_k_weapons": 20}
}

Check result diagnostics for dominant weapon warning:

• If

  diagnostics.warnings

  contains "DOMINANT WEAPON", one weapon has >2x delta

• aggregates.custom.dominant_weapon_detected

  will be

  true

• aggregates.custom.recommended_followup

  will be

  "kit_sweep"

Step 2: Run kit_sweep (if dominant weapon detected)

{
  "type": "kit_sweep",
  "feature_id": 18712,
  "model_type": "ultra",
  "variables": {
    "min_examples": 10,
    "top_k": 10,
    "analyze_combinations": true
  }
}

Output tables:

• sub_stats

  : Activation statistics by sub weapon (mean, std, n, delta_from_global)

• special_stats

  : Activation statistics by special weapon

• combo_stats

  : (if analyze_combinations=true) Statistics by sub+special pairs

Aggregates:

• top_sub

  ,

  top_sub_mean

  ,

  top_sub_delta

• top_special

  ,

  top_special_mean

  ,

  top_special_delta

Step 3: Cross-reference with splatoon3-meta

Use the splatoon3-meta skill to look up weapon kits:

• Read

  .claude/skills/splatoon3-meta/references/weapons.md

• Compare dominant weapon's kit with kit_sweep results
• Check if high-activation weapons share sub or special

Example: Feature 18712

weapon_sweep results:
  - Octobrush Nouveau: +0.22 delta (DOMINANT - 2.4x second weapon)
  - Rapid Blaster: +0.09 delta

kit_sweep results:
  - Top special: Ink Storm (+0.18 delta)
  - Top sub: Squid Beakon (+0.08 delta)

splatoon3-meta lookup:
  - Octobrush Nouveau: Squid Beakon + Ink Storm

Conclusion: Feature encodes "Ink Storm spam builds" not "Octobrush Nouveau builds"

Known Limitations

Binary Tokens in family_2d_heatmap

LIMITATION: The

family_2d_heatmap

The runner uses

parse_token()

family_name_AP

swim_speed_up_21

comeback

comeback_10

Workaround: Use manual 2D analysis code for binary abilities. See the Binary Ability Analysis Protocol in mechinterp-investigator.

Future Enhancement: Stable Tokens

A useful enhancement would be adding "stable tokens" to sweep experiments - tokens that are held constant across all conditions in the sweep. This would allow testing questions like:

• "How does SCU affect activation when Comeback is present?"
• "How does ISM scale on Stamper builds?"

Proposed spec format:

{
  "type": "family_1d_sweep",
  "variables": {
    "family": "special_charge_up",
    "stable_tokens": ["comeback", "stealth_jump"]  // Hold these constant
  }
}

This is not currently implemented.

See Also

• mechinterp-next-step-planner: Generate experiment specs
• mechinterp-state: Track research progress
• mechinterp-summarizer: Convert results to notes
• mechinterp-glossary-and-constraints: Domain reference
• splatoon3-meta: Weapon kit lookups and meta knowledge