MechInterp Overview

Get a comprehensive first-look overview of an SAE feature before deep investigation. This skill provides a fast summary of key characteristics to help you decide what hypotheses to test.

⚠️ CRITICAL: Overview is NOT Findings

The overview shows CORRELATIONS, not CAUSATION. It is a starting point for generating hypotheses, NOT a source of conclusions.

You CANNOT conclude from overview alone:

• That a token "drives" or "causes" activation
• That the feature "detects" a specific ability
• That correlations are meaningful vs spurious

To make conclusions, you MUST run experiments (see mechinterp-investigator for deep dive basics).

Purpose

The overview skill:

• Computes PageRank-weighted top tokens for a feature
• Shows activation statistics (mean, std, median, sparsity)
• Aggregates tokens by ability family
• Lists top weapons associated with the feature
• Provides sample high-activation contexts
• Checks for existing labels and ReLU floor issues

When to Use

Use this skill when:

1. Starting to investigate a new feature
2. You want a quick summary before running experiments
3. Deciding which feature to label next
4. Checking if a feature has already been labeled

DO NOT use overview results as final findings. Always follow up with experiments.

Output Information

Sparsity Definition

Sparsity = % of examples where feature activation is ZERO

A high sparsity percentage means the feature fires RARELY (is selective):

Common confusion: "89% sparsity" means "fires on 11% of examples" NOT "fires often."

Think of it as: Sparsity = how empty/silent the feature usually is.

CRITICAL: Always check the Bottom Tokens section! Tokens that rarely appear in high-activation examples reveal what the feature avoids, which is often more informative than what it detects.

Usage

Command Line

cd /root/dev/SplatNLP

# Basic overview (markdown output)
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 18712 \
    --model ultra

# JSON output for programmatic use
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 18712 \
    --model ultra \
    --format json

# More top tokens
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 18712 \
    --model ultra \
    --top-k 25

# Full model
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 5432 \
    --model full

# Verbose logging
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 18712 \
    --model ultra \
    --verbose

Extended Analyses

Additional analysis flags provide deeper insights:

# Token enrichment (enhancers/suppressors)
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 6235 --model ultra --enrichment

# Activation region breakdown (anti-flanderization)
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 6235 --model ultra --regions

# Binary ability enrichment (main-only abilities)
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 6235 --model ultra --binary

# Sub/special weapon breakdown (kit analysis)
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 6235 --model ultra --kit

# All extended analyses at once
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 6235 --model ultra --all

# Customize high-activation threshold (default: 0.90 = top 10%)
poetry run python -m splatnlp.mechinterp.cli.overview_cli \
    --feature-id 6235 --model ultra --enrichment --high-percentile 0.95

Extended Analysis Reference

--enrichment

--regions

--binary

--kit

--all

--kit-region

core

high

all

--high-percentile

Programmatic

from splatnlp.mechinterp.labeling import FeatureOverview, compute_overview
from splatnlp.mechinterp.skill_helpers import load_context

# Load context
ctx = load_context("ultra")

# Compute overview
overview = compute_overview(
    feature_id=18712,
    ctx=ctx,
    top_k_tokens=15,
    n_sample_contexts=5,
)

# Display markdown
print(overview.to_markdown())

# Access fields directly
print(f"Mean: {overview.activation_mean}")
print(f"Top token: {overview.top_tokens[0]}")
print(f"Main family: {max(overview.family_breakdown.items(), key=lambda x: x[1])}")

Sample Output

## Feature 18712 Overview (ultra)

### Activation Stats
- Mean: 0.5056
- Std: 0.5163
- Median: 0.3835
- Sparsity: 97.1%
- Examples: 108,163

### Top Tokens (PageRank)
1. `special_charge_up` (0.274)
2. `swim_speed_up` (0.099)
3. `ink_saver_sub` (0.084)
4. `stealth_jump` (0.049)
5. `run_speed_up` (0.048)

### Family Breakdown
- special_charge_up: 31.2%
- swim_speed_up: 11.2%
- ink_saver_sub: 9.6%

### Top Weapons
- weapon_id_5021: 28
- weapon_id_220: 28

### Bottom Tokens (Suppressors)
Tokens rarely present in high-activation examples:
1. `respawn_punisher` (high_rate_ratio=0.00) - Never in high activation
2. `special_saver` (high_rate_ratio=0.16) - 6x less common than baseline
3. `quick_respawn` (high_rate_ratio=0.47) - 2x less common than baseline

### Sample Contexts (High Activation)
1. [weapon_id_1111] special_charge_up_6, special_charge_up_57 (act=0.731)
2. [weapon_id_1111] special_charge_up_6, special_charge_up_51... (act=0.724)

FeatureOverview Dataclass

@dataclass
class FeatureOverview:
    feature_id: int
    model_type: str

    # Activation statistics
    activation_mean: float
    activation_std: float
    activation_median: float
    sparsity: float  # Percentage (0-100)
    n_examples: int

    # PageRank-weighted top tokens
    top_tokens: list[tuple[str, float]]

    # Bottom tokens (suppressors) - tokens excluded from high activation
    bottom_tokens: list[tuple[str, float]]  # (token, high_rate_ratio)

    # Detailed token influence statistics
    token_influences: list[TokenInfluence]

    # Aggregated by family
    family_breakdown: dict[str, float]

    # Weapon breakdown
    top_weapons: list[tuple[str, int]]

    # Sample high-activation contexts
    sample_contexts: list[SampleContext]

    # Diagnostic flags
    relu_floor_rate: float
    existing_label: str | None

Performance

• Typical runtime: 30-60 seconds (dominated by PageRank computation)
• Loads activation data lazily from efficient database
• Caches context between calls in the same session

Interpretation Tips

1. High sparsity (>90%): Most inputs don't activate this feature. Look at what's special about the ones that do.

2. ReLU floor warning: If >50% of examples hit the ReLU floor, the feature may be hard to interpret or require special handling.

3. Single dominant family: If one family has >50% of the breakdown, the feature likely responds to that ability family.

4. Multiple families: If breakdown is spread across families, look for interactions or common contexts.

5. Weapon concentration: If a few weapons dominate, the feature may be weapon-specific rather than ability-specific.

⚠️ CRITICAL: Super-Stimuli Detection

Don't only examine high activations - they may be "super-stimuli"!

High activation examples can be exaggerated, "flanderized" versions of the true concept. The core region (25-75% of effective max) often reveals the actual feature meaning better than the flanderization zone (90%+ of effective max).

Why "effective max"? Activation distributions are heavy-tailed. Use

effective_max = 99.5th percentile of nonzero activations

Warning Signs of Super-Stimuli

Activation Region Bins

Use these standard bins (as % of effective max = 99.5th percentile) to analyze feature behavior:

Example: Feature 9971

Initial analysis (looking only at 90%+ activations):

• Top weapons: Bloblobber, Glooga Deco, Range Blaster, Octobrush
• Conclusion: "SCU stacker on special-dependent weapons"

After region analysis (examining core 25-75%):

• Core region: Splattershot (115), Wellstring (65), Sploosh (57)
• Splattershot appears in EVERY region (29→125→83→115→61→19)
• True concept: "General offensive investment (death-averse)"
• Flanderization zone (90%+): "Super-stimuli" version on niche special-dependent weapons

Key insight: Label the core-region concept, not the flanderized extreme!

Coverage Threshold Rule

When overview shows a dominant token or weapon, CHECK CORE-REGION COVERAGE before treating it as the concept.

A token can have high enrichment in the tail but be a tail marker, not the true concept.

Example (Feature 13934):

Overview showed: respawn_punisher with 8.57x tail enrichment
BUT: RP only present in 12% of core-region examples

⚠️ Flag in overview: "respawn_punisher: high enrichment (8.57x) but <30% core coverage - may be tail marker, not core concept"

When to flag: If any token in top-10 has enrichment >3x but core coverage <30%, add a warning note.

6. Weapon Outlier Detection: If a single weapon has >2x the examples of the second weapon, this is a weapon-dominated feature:

   • Use splatoon3-meta skill to look up the weapon's kit (sub + special)
   • Check if other high-activation weapons share the same sub OR special
   • If they share kit components, the feature may encode kit behavior, not weapon behavior
   • Run kit_sweep experiment to analyze activation by sub/special

7. Check suppressors: Always examine bottom tokens! If death-mitigation abilities (QR, SS, CB) are suppressed, the feature encodes "death-averse" builds. See mechinterp-ability-semantics for semantic groupings.

8. Enhancers + Suppressors together: The combination tells the full story. A feature with SCU enhanced AND death-perks suppressed isn't just "SCU detector" - it's "death-averse special builds".

9. "Weak activation" ≠ "unimportant feature": If all scaling effects are weak (max_delta < 0.03), don't immediately label as "weak feature". Check the feature's decoder weights to output tokens. Net influence = activation × decoder weight. A feature with low activation effects but high decoder weights may still strongly influence predictions.

⚠️ WARNING: Correlation ≠ Causation

PageRank scores show correlation, NOT causation. Tokens appearing in the overview may be:

• True drivers: Actually cause activation changes
• Spurious correlations: Just happen to co-occur with the true driver

How to Distinguish

1. Run 1D sweep for top token (likely primary driver)
2. If confirmed, run 2D heatmaps for other tokens:

   • PRIMARY × SECONDARY

     reveals if secondary has conditional effect
   • If secondary shows effect only at high primary → true interaction
   • If secondary shows NO effect at any primary level → spurious

Example: Feature 18712

Overview showed: SCU (24%), Opening Gambit (17%), SSU (12%)

1D sweeps:
- SCU: strong effect (0.03→0.58) ✅ PRIMARY
- OG: delta ≈ 0 → appears to have no effect
- SSU: delta ≈ 0 → appears to have no effect

BUT WAIT! 1D sweeps for secondary abilities are MISLEADING.

2D heatmaps (SCU × OG, SCU × SSU):
- Both show NO conditional effect at any SCU level
- Conclusion: OG and SSU were SPURIOUS correlations

2D heatmaps (SCU × QR, SCU × SS):
- QR_12+ SUPPRESSES activation by 70-99% at high SCU!
- SS_12+ SUPPRESSES activation by 40-60%!
- Conclusion: Feature is DEATH-AVERSE (not visible in 1D)

Always verify top overview tokens with conditional 2D testing!

See mechinterp-investigator for the full Iterative Conditional Testing Protocol.

See Also

• mechinterp-labeler: Manage labeling workflow and save labels
• mechinterp-runner: Run experiments to test hypotheses
• mechinterp-next-step-planner: Generate experiment specs based on overview
• mechinterp-glossary-and-constraints: Reference for token families and AP rungs
• mechinterp-ability-semantics: Ability semantic groupings (check AFTER forming hypotheses)
• mechinterp-investigator: Full investigation workflow