Monosemantic Feature Neurons: A Sparse Autoencoding Layer for Interpretable, Steerable Transformer Features
Bibliographic record
Abstract
Large Language Models achieve remarkable results but remain opaque due to dense, entangled activations. This paper introduces Monosemantic Feature Neurons (MFNs) — a sparse autoencoding layer embedded inside Transformer blocks. MFNs encode residual streams into $K$-sparse codes, reconstruct them, and blend them back into the model so that downstream computation depends on a sparse, interpretable basis. Five complementary loss terms (reconstruction, sparsity, competition, stability, and utility) bias features toward functional monosemanticity. Unlike post-hoc sparse autoencoders, MFNs embed a causally entangled bottleneck directly within Transformer computation, bridging the gap between interpretability and train-time transparency. The work outlines falsifiable predictions, evaluation metrics, and practical training heuristics for future empirical validation. Accepted for presentation at LLM 2025 (Springer CCIS).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".