Current Large Language Models (LLMs) like ChatGPT, while impressive at mimicking language, are hitting a fundamental wall in their ability to perform multi-step reasoning. Their transformer architecture is designed to predict the next word based on statistical patterns, not to think, plan, or deliberate. This core limitation leads to hallucinations, contradictions, and failures in complex problem-solving, a problem that simply making models bigger cannot solve.
The Limits of ‘Thinking Out Loud’
An early attempt to fix this was “Chain of Thought” reasoning, where models are prompted to explain their process step-by-step. While this offered some improvement, it’s an inefficient and brittle workaround. A single mistake early in the chain can invalidate the entire result, and it doesn’t reflect how humans think, which is mostly a silent, internal process of exploring and discarding ideas.
The Next Wave: Latent Space Computing
The proposed solution is a new paradigm called Latent Space Computing. A model’s “latent space” is its internal, mathematical map of how concepts relate to one another. Instead of forcing the AI to translate every thought into human language, this approach allows it to reason directly within this abstract space. This enables a form of silent, internal dialogue where the model can explore multiple solution paths in parallel, weigh different possibilities, and only present a final answer once it has reached a confident conclusion.
Conclusion and Future Direction
Latent Space Computing represents a crucial architectural shift from creating models that sound smart to creating models that think smarter. It moves beyond the limitations of next-word prediction to enable more robust, efficient, and human-like reasoning. Early experiments like the Hierarchical Reasoning Model (HRM) show promise, and major AI labs are actively researching this direction. This shift is seen as a necessary step for developing AI that can move beyond mimicking language to performing genuine, complex reasoning about the world.
Mentoring question
The video raises the question of trusting an AI that reasons silently. In your professional life, how do you approach the trade-off between a system’s transparency (showing its work) and its performance (getting the right answer efficiently)?
Source: https://youtube.com/watch?v=klkOdh4l0Eo&si=hutXo8_ZNSza3geJ
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