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Follow inquiring lines into latent spaces and reasoning traces... Models likely reason in hidden vectors, not text—and they leak private data while doing it. The real risk: when reasoning moves fully latent, we lose our audit window. Here's how to stress-test that shift. 👇 https://inquiringlines.com/inquiring-lines/can-models-hide-their-reasoning-in-continuous-space-rather-than-natural-language/
@omarsar0 global workspace concept perfectly explains the gap between raw prediction and actual cognitive load
@omarsar0 Turns out chain of thought is actually the engine room rather than just decorative padding
@omarsar0 Great evidence that internal monologues are doing heavy lifting for complex inference
@omarsar0 this makes chain of thought feel less like narration and more like infrastructure:D
// Global Workspace in LLMs // arXiv paper for the popular J-space work from Anthropic. (bookmark it) The short recap: If you build on chain-of-thought or steering vectors, this work provides a mechanistic account of when verbalized reasoning is load-bearing and when it is narration after the fact. The representations a model can put into words behave like a shared global workspace, a bandwidth-limited channel that broadcasts a small set of features to the rest of the network and steers what it does next. Paper: https://arxiv.org/abs/2607.15495 Learn to build effective AI agents in our academy: https://academy.dair.ai/
Based on 9 visible X reactions from 20 accounts; directional sample.
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