The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
Xinlei Yu, Zhangquan Chen, Yongbo He, Tianyu Fu, Cheng Yang, Chengming Xu, Yue Ma, Xiaobin Hu, Zhe Cao, Jie Xu, Guibin Zhang, Jiale Tao, Jiayi Zhang, Siyuan Ma, Kaituo Feng, Haojie Huang, Youxing Li, Ronghao Chen, Huacan Wang, Chenglin Wu, Zikun Su, Xiaogang Xu
2026-04-03
Summary
This paper is a comprehensive overview of how language models are increasingly using a hidden, internal 'space' – called latent space – to process information, rather than just working with words directly.
What's the problem?
Traditional language models work by manipulating words and sentences, which can be inefficient and lose meaning. Dealing with language directly has limitations because of things like repeated information, the need to break down language into discrete pieces (like tokens), processing things one step at a time, and losing the overall meaning during these steps. Essentially, it's hard for computers to truly *understand* language when they're stuck dealing with it as just strings of text.
What's the solution?
The paper surveys the current state of using 'latent space' in language models. Think of latent space as a more abstract, continuous representation of meaning. Instead of processing words, the model works with points in this space, which capture the underlying concepts. The paper breaks down how this latent space is being developed, looking at the architecture of models that use it, how information is represented within it, how computations are performed there, and how the models are optimized. It also examines what kinds of abilities – like reasoning, planning, and remembering – are enabled by using latent space.
Why it matters?
This research is important because it suggests that latent space is a key to building more powerful and intelligent language models. It's not just about summarizing existing work, but also about laying the groundwork for future advancements in artificial intelligence, potentially leading to systems that can understand and interact with the world in a more human-like way.
Abstract
Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an increasing body of work shows that many critical internal processes are more naturally carried out in continuous latent space than in human-readable verbal traces. This shift is driven by the structural limitations of explicit-space computation, including linguistic redundancy, discretization bottlenecks, sequential inefficiency, and semantic loss. This survey aims to provide a unified and up-to-date landscape of latent space in language-based models. We organize the survey into five sequential perspectives: Foundation, Evolution, Mechanism, Ability, and Outlook. We begin by delineating the scope of latent space, distinguishing it from explicit or verbal space and from the latent spaces commonly studied in generative visual models. We then trace the field's evolution from early exploratory efforts to the current large-scale expansion. To organize the technical landscape, we examine existing work through the complementary lenses of mechanism and ability. From the perspective of Mechanism, we identify four major lines of development: Architecture, Representation, Computation, and Optimization. From the perspective of Ability, we show how latent space supports a broad capability spectrum spanning Reasoning, Planning, Modeling, Perception, Memory, Collaboration, and Embodiment. Beyond consolidation, we discuss the key open challenges, and outline promising directions for future research. We hope this survey serves not only as a reference for existing work, but also as a foundation for understanding latent space as a general computational and systems paradigm for next-generation intelligence.