1. Beyond "Thinking More" - The Quality of Latent Iteration

The paper's core idea is to allow a language model to "think" more by iterating in its latent space before producing an output. This isn't just about more computation, it's about a different kind of computation. The parallels to human inner speech are immediate:

• Not Just Verbalization: The paper explicitly contrasts its approach with "Chain-of-Thought" (CoT) prompting. CoT forces the model to verbalize its reasoning steps. This is akin to forcing a human to think only in fully formed sentences. But much of human thought, as we've seen, is condensed, sub-verbal, intuitive, and relies on non-linguistic representations (imagery, emotions, etc.). The latent space iterations in the Geiping et al. model are a step towards this richer, less constrained form of internal processing. It's not just "thinking longer," it's "thinking deeper."
• Emergent Strategies: The paper mentions the "deep thinking" literature (Schwarzschild et al.) which shows that recurrent networks can learn complex algorithms without explicit programming. This is crucial. Humans don't learn to reason by being given a step-by-step algorithm for thinking. We develop heuristics, mental shortcuts, and intuitive leaps. The latent space iterations allow the model to potentially discover its own internal reasoning strategies, analogous to how humans develop their own idiosyncratic ways of thinking. The discovered strategies are not necessarily translatable to code.
• The "Feeling of Rightness": The paper introduces an early-stopping criterion based on the KL-divergence between successive latent states. This is, surprisingly, a parallel to the human "feeling of rightness" or "aha!" moment. We often know we've arrived at a solution before we can fully articulate why. The KL-divergence is a mathematical proxy for this intuitive sense of convergence in thought.
• Path Independence: A property that has evolved in the human mind and that the authors found needed to add in the model, is the concept of path independence, where different starting points in the latent space should generally converge to the same final reasoning state.

2. Latent Space as a "Mental Workspace" - Parallels to Cognitive Theories

The paper's concept of the latent space as a place for internal "reasoning" resonates with several cognitive theories:

• Working Memory's "Phonological Loop" (But More): Baddeley's model of working memory posits a "phonological loop" where we rehearse information verbally. The latent space is like a supercharged phonological loop. It's not just holding onto words; it's manipulating abstract representations, potentially including non-verbal elements. The recurrent nature of the processing model more closely models the active thinking human ability.
• Global Workspace Theory (GWT): GWT suggests that consciousness is a "global workspace" where information is broadcast to different brain regions. The latent space iterations could be seen as a form of internal "broadcasting" and integration. Different iterations might explore different aspects of the problem, and the final state represents a synthesis of these explorations.
• Integrated Information Theory (IIT): IIT proposes that consciousness is related to the amount of "integrated information" in a system. The recurrent iterations in the latent space could be increasing the integration of information, making the model's "thinking" more unified and coherent. This could, speculatively, be a step towards a more "conscious-like" form of processing, though it's a long way from human consciousness.
• The "Narrative Self": If we consider Dennett's idea of the self as a "center of narrative gravity," the latent space could be the place where this narrative is constructed and refined. The iterations might represent the internal "drafting" process of our self-story.

3. Evolutionary Parallels - From Social Argument to Internal Dialogue

The "Argumentative Theory of Reasoning" (Mercier & Sperber) suggests that human reasoning evolved for social purposes – to persuade others and justify ourselves. This connects to Vygotsky's idea that inner speech originates from internalized social dialogue. The Geiping et al. model, while not explicitly social, hints at how this internalization might work:

• Internalized Argument: Even though the model isn't interacting with other agents, the recurrent iterations could be seen as a form of internalized argument. Different iterations might explore different "sides" of an issue, analogous to a human mentally debating pros and cons.
• From External to Internal "Compute": Early humans might have relied on external aids for reasoning – talking aloud, drawing diagrams, manipulating objects. Over time, these processes were internalized. The Geiping et al. model's shift from "chain-of-thought" (externalized reasoning) to latent space iterations (internalized reasoning) mirrors this evolutionary trajectory.
• The "Dialogic" Nature of Thought: Fernyhough's work on the "dialogic" nature of inner speech suggests that we often experience inner thought as a conversation between different "voices" or perspectives. The latent space iterations could be the computational substrate for this internal dialogue. Different iterations might represent different "voices" or perspectives, and the final state represents a resolution of this internal debate.

4. Pushing Our Understanding of Both AI and Human Reasoning

Here's where the comparison gets really exciting – it's not just about using human cognition to inspire AI; it's about using AI to test and refine our theories of human cognition:

• Testing Cognitive Hypotheses: The Geiping et al. model provides a testbed for exploring cognitive hypotheses. For example, we could manipulate the model's architecture or training to see if it affects its ability to perform tasks that, in humans, rely on specific cognitive functions (e.g., working memory, theory of mind). This is a form of "cognitive science by simulation."