aiqbench analysis
This paper introduces "feature recall," a mechanism in deep learning where linear projections retrieve stored information scaled by input activations, extending beyond simple factual memory to a broader operational paradigm. By contrasting this with feature combination across various architectures, the work offers philosophers and researchers a new conceptual framework for mechanistic interpretability. It highlights specific empirical directions for understanding how models internally access and scale stored representations during inference.
Deep Learning Models Also Recall Features
arXiv:2608.20970v1 Announce Type: new Abstract: Recent work in mechanistic interpretability has studied how large language models recall facts stored in their weights. This paper argues that factual recall points to something broader: a general kind of operation in deep learning models, which I call feature recall. The core observation is that a linear projection can be read as retrieving stored information scaled by input activations. I define feature recall, show it applies across architectures, and contrast it with the established paradigm of feature combination. I also consider how cases of feature recall might be mechanistically identified. The account gives philosophers a new conceptual tool for understanding deep learning, and points to empirical directions for mechanistic interpretability research.
Abstract:Recent work in mechanistic interpretability has studied how large language models recall facts stored in their weights. This paper argues that factual recall points to something broader: a general kind of operation in deep learning models, which I call feature recall. The core observation is that a linear projection can be read as retrieving stored information scaled by input activations. I define feature recall, show it applies across architectures, and contrast it with the established paradigm of feature combination. I also consider how cases of feature recall might be mechanistically identified. The account gives philosophers a new conceptual tool for understanding deep learning, and points to empirical directions for mechanistic interpretability research.
Artificial Intelligence (cs.AI)
arXiv:2608.20970 [cs.AI]
(or arXiv:2608.20970v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.20970 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 206 words
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