AdaptiveK: Complexity-Driven Sparse Autoencoders for Interpretable Language Model Representations (2026.findings-acl)
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| Challenge: | Existing approaches to decomposing model activations into interpretable features fail to account for input complexity. |
| Approach: | They propose a framework that dynamically adjusts sparsity levels based on the semantic complexity of each input. |
| Outcome: | The proposed framework outperforms fixed-sparsity approaches on reconstruction fidelity, explained variance, cosine similarity and interpretability metrics while eliminating the burden of extensive hyperparameter tuning. |
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