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In such settings, representations are more naturally understood as latent states capturing underlying system dynamics rather than compressed summaries of observations. Yet current approaches remain fragmented, relying on distinct, often implicit, assumptions about what these states should represent. We argue that this fragmentation reflects a more fundamental limitation: latent representations are typically learned from underconstrained objectives that fail to specify the properties that meaningful latent states should satisfy. As a result, multiple representations may satisfy the same objective, leading to ambiguity in their structure and interpretation. While many underlying principles have been studied in isolation, their interactions have not been explicitly formalized. 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As a result, multiple representations may satisfy the same objective, leading to ambiguity in their structure and interpretation. While many underlying principles have been studied in isolation, their interactions have not been explicitly formalized. 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