aiqbench analysis
This survey rigorously redefines the linear representation hypothesis (LRH) as a falsifiable scientific claim rather than an informal intuition, addressing inconsistencies in how it has been applied across AI and neuroscience. The authors propose a formal framework that explicitly ties LRH validity to specific model architectures, feature definitions, and evaluation datasets, thereby clarifying the theoretical boundaries of current research. By identifying critical dependencies previously overlooked, this work sets a necessary standard for interpreting linear probing results and advancing interpretability theory.
A Survey on the Linear Representation Hypothesis
arXiv:2609.22695v1 Announce Type: new Abstract: The term "linear representation hypothesis" (LRH) has appeared across diverse subfields of artificial intelligence, neuroscience, and cognitive science. But previous works have not consistently treated the LRH as a falsifiable scientific hypothesis; we analyze these inconsistencies and examine their implications for how prior theoretical and methodological results should be interpreted. Based on this analysis, we argue that claims regarding linear representations become well-defined only through careful examination of the model, representation location, feature definition, and evaluation dataset. We therefore propose a more rigorous formalization of the LRH that makes these dependencies explicit and allows the hypothesis to be evaluated as a falsifiable scientific claim. Finally, we identify some non-trivial open problems that warrant further attention from the research community.
Abstract:The term "linear representation hypothesis" (LRH) has appeared across diverse subfields of artificial intelligence, neuroscience, and cognitive science. But previous works have not consistently treated the LRH as a falsifiable scientific hypothesis; we analyze these inconsistencies and examine their implications for how prior theoretical and methodological results should be interpreted. Based on this analysis, we argue that claims regarding linear representations become well-defined only through careful examination of the model, representation location, feature definition, and evaluation dataset. We therefore propose a more rigorous formalization of the LRH that makes these dependencies explicit and allows the hypothesis to be evaluated as a falsifiable scientific claim. Finally, we identify some non-trivial open problems that warrant further attention from the research community.
Artificial Intelligence (cs.AI)
arXiv:2609.22695 [cs.AI]
(or arXiv:2609.22695v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.22695 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 213 words
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