gavagai 0.1.3

Quantify translation indeterminacy between sparse autoencoder feature dictionaries (Quine × Mechanistic Interpretability).

gavagai

“The very fact of the indeterminacy of translation is a finding about meaning, not a failure of method.” — paraphrased after W. V. O. Quine, Ontological Relativity (1968)

gavagai quantifies translation indeterminacy between two Sparse Autoencoder (SAE) feature dictionaries — how many empirically valid feature-to-feature alignments exist, not which single alignment is "the right one". It is a Mechanistic Interpretability tool grounded in Quine's philosophy of language.

🇯🇵 日本語の説明は docs/README.ja.md にあります。

Why this exists

Cross-SAE alignment tools (Universal SAE, SPARC, Sparse Crosscoders) ask: what is the correct mapping between two SAEs' features? Quine's gavagai thought experiment suggests this question is structurally underdetermined: the observational data fixes an equivalence class of translations, not a single one. gavagai does not solve that underdetermination — it measures it.

Concretely:

• Train two SAEs (different seed, different model checkpoint, different layer) on aligned activations.
• Run gavagai_score(sae_a, sae_b).
• Get a number in [0, 1]: 0 = deterministic alignment exists; 1 = radical indeterminacy (many empirically valid alignments).

The score drops into CI as a regression gate: refuse model pushes whose indeterminacy with the baseline exceeds a threshold.

Install

pip install gavagai

Optional extras:

pip install "gavagai[saelens]"    # SAELens SAE objects
pip install "gavagai[behavior]"   # downstream-KL behavior equivalence
pip install "gavagai[holism]"     # circuit-tracer integration (v0.2)

Quick start

import numpy as np
from gavagai import gavagai_score

# Decoder matrices: shape (n_features, d_model). gavagai also accepts
# SAELens SAE instances and {"W_dec": ndarray} dicts.
sae_a = np.random.default_rng(0).standard_normal((1024, 768))
sae_b = np.random.default_rng(1).standard_normal((1024, 768))

score = gavagai_score(sae_a, sae_b)
print(f"indeterminacy: {score:.4f}")

# With diagnostics
score, details = gavagai_score(sae_a, sae_b, return_details=True)
print(f"  candidates : {details.n_equivalent_translations}")
print(f"  95% CI     : [{details.ci_low:.4f}, {details.ci_high:.4f}]")

CI gate (gavagai-lint)

The kill-app. Drop into your pre-push hook or GitHub Action:

gavagai-lint \
    --before sae_baseline.npz \
    --after  sae_after_abliteration.npz \
    --threshold 0.3

Exit 0 if the indeterminacy is below threshold (acceptable drift), 1 otherwise. Designed for gating Hugging Face uploads, abliteration patches, and post-train fine-tuning steps where the model's feature semantics may silently re-arrange.

GitHub Action (composite):

- uses: hinanohart/gavagai/.github/actions/gavagai@v0.1.0
  with:
    before: artifacts/baseline.npz
    after:  artifacts/candidate.npz
    threshold: 0.3

Equivalence relations

The score is relative to a choice of equivalence relation — this is the Ontological Relativity commitment, made explicit:

equivalence=	What "two features are equivalent" means	Needs
"cosine"	decoder directions within ε cosine distance	decoder matrices
"activation"	overlapping token-firing patterns (Jaccard ≥ 1−ε)	activations_* arrays
"behavior"	similar downstream KL when ablated (1/(1+kl) ≥ 1−ε)	ablation_kl matrix

score = gavagai_score(sae_a, sae_b, equivalence="cosine", epsilon=0.1)

Different relations yield different scores. That is the point: there is no relation-independent "true" indeterminacy.

Caveat (v0.1): the same epsilon is applied across all three relations despite their differing scales (cosine ∈ [−1,1], Jaccard ∈ [0,1], KL-derived ∈ (0,1]). Comparison across relations should be qualitative, not threshold-equal. v0.1.x will add per-relation epsilon normalization.

How it works

1. Extract decoder matrices W_A, W_B.
2. Compute similarity matrix S under the chosen relation.
3. Threshold by ε to get a candidate adjacency A_ε.
4. Count valid bipartite matchings of A_ε (DFS with backtracking, capped at cap=1000). Empty adjacency ⇒ cap (radical indeterminacy).
5. Compress matching count to [0, 1] via 1 − 1 / (1 + log(n)).
6. Bootstrap a 95% CI over feature-row resamples.

Step 4 is the Quinean heart: we never collapse the candidate space to a single bijection.

Roadmap

version	adds
v0.1.0	scalar gavagai_score, CLI gate, 3 equivalence relations
v0.1.x	per-relation ε normalization, coverage diagnostic for sparse adjacency
v0.2.0	holism propagator (Duhem-Quine) via circuit-tracer
v0.3.0	ontological commitment detector (AlignSAE binding)
v1.0.0	cross-paradigm translator (probe ↔ SAE ↔ patching)

Anti-goals

• Not a SAE trainer. Use SAELens.
• Not a circuit visualizer. Use circuit-tracer.
• Not a universal feature library. We measure indeterminacy; we do not pretend to eliminate it.

Reading

• W. V. O. Quine, Word and Object (1960), ch. 2.
• W. V. O. Quine, Ontological Relativity and Other Essays (1968).
• Marks et al., Sparse Feature Circuits, ICLR 2025 (arXiv:2403.19647).
• Bricken et al., Towards Monosemanticity, Anthropic 2023.
• Arditi et al., Refusal direction is mediated by a single direction, NeurIPS 2024 (arXiv:2406.11717).
• Mechanistic Interpretability Needs Philosophy (arXiv:2506.18852).

License

MIT. See LICENSE.

The name draws on Quine's philosophical work as a scholarly reference. No endorsement or affiliation is claimed or implied.