Dimensions overall score 2.0

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• Latent space is rapidly emerging as a native substrate for language-based models.technical 90%
• Many critical internal processes in language models are more naturally carried out in continuous latent space than in human-readable verbal traces.result 85%
• Explicit-space computation in language models suffers from structural limitations including linguistic redundancy, discretization bottlenecks, sequential inefficiency, and semantic loss.limitation 88%
• Latent space in language-based models is distinct from both explicit/verbal space and from latent spaces in generative visual models.technical 82%
• Latent space supports a broad capability spectrum spanning Reasoning, Planning, Modeling, Perception, Memory, Collaboration, and Embodiment in language models.result 80%
• The survey organizes technical developments in latent space through four major lines: Architecture, Representation, Computation, and Optimization.method 85%
• Latent space represents a general computational and systems paradigm for next-generation intelligence.technical 75%

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