aiqbench analysis
Persistent Sparse Autoencoders introduce a novel mechanism for language models to capture temporal dependencies in feature representations by learning distinct timescales from fast detectors to slow semantic states. This advancement significantly enhances interpretability and monitoring capabilities, particularly for detecting persistent threats like prompt injections across long contexts without sacrificing reconstruction quality. The work represents a meaningful step forward in understanding the internal dynamics of large language model activations through structured sparsity over time.
Persistent Sparse Autoencoders: Learning Feature Timescales in Language Models
arXiv:2607.17117v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose language model activations into sparse features, but standard SAEs encode each token independently and do not expose information that persists across a sequence. We introduce Persistent Sparse Autoencoders (Persistent SAEs), which extend standard SAEs by learning a persistence coefficient for each feature, allowing the model to learn which features should persist and for how long. Our experiments show that they retain competitive reconstruction quality while learning a spectrum of feature timescales: fast features behave as locally interpretable detectors, whereas slow features concentrate topic-level information in a persistent state. Moreover, as shown in a prompt-injection monitoring case study, slow features preserve detection signals and remain causally effective over long contexts. These results suggest that Persistent SAEs open up new opportunities for interpreting and monitoring language models through persistent semantic representations.
Abstract:Sparse autoencoders (SAEs) decompose language model activations into sparse features, but standard SAEs encode each token independently and do not expose information that persists across a sequence. We introduce Persistent Sparse Autoencoders (Persistent SAEs), which extend standard SAEs by learning a persistence coefficient for each feature, allowing the model to learn which features should persist and for how long. Our experiments show that they retain competitive reconstruction quality while learning a spectrum of feature timescales: fast features behave as locally interpretable detectors, whereas slow features concentrate topic-level information in a persistent state. Moreover, as shown in a prompt-injection monitoring case study, slow features preserve detection signals and remain causally effective over long contexts. These results suggest that Persistent SAEs open up new opportunities for interpreting and monitoring language models through persistent semantic representations.
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2607.17117 [cs.LG]
(or arXiv:2607.17117v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.17117 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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