Should LLMs Have their Own Language?

LLMs are incredible, revolutionary tools, but they are not perfect. This is not news to regular readers of this AI Atlas; I have previously discussed problems around how these models require exponential effort at scale, with reference to proposed alternative architectures such as Hyena, Mamba, and SAMBA. However, there is also an even simpler underlying challenge: LLMs “reason” by writing everything out in English words. They use a chain-of-thought approach to mimic human problem solving by explaining each step in natural language. This makes the reasoning process visible and interpretable, but it is not necessarily the most efficient way for machines to think.

The main issue is that much of this text is filler. Many tokens (or pieces of words) are produced simply to keep sentences readable to users, not to actually move reasoning forward. At the same time, truly complex decision points, where multiple strategies or calculations must be weighed, receive no special attention from the model. In other words, today’s LLM systems spend equal effort on both easy filler words and hard reasoning steps. That imbalance slows it down and limits how well it can solve more complex business problems.

To address these limitations, engineers at Meta developed an extremely interesting approach that unlocks an entirely new level of reasoning for LLMs, as always with a uniquely weird name: Coconut. For today’s AI Atlas, I will be focusing on this approach and what it could mean in the next few years.

🗺️ What is Coconut?

Coconut (short for Chain of Continuous Thought) is a new technical approach designed to overcome inefficiencies in LLM-based reasoning. Instead of forcing all reasoning into written words, Coconut allows an AI model to reason silently in a continuous “latent space.” Think of it as letting the model sketch ideas on an internal whiteboard before deciding which ones are worth putting into words. However, the ideas sketched by the model are not in English, nor any other language recognizable by humans. Instead, they stay within the pure mathematical format best understood by the neural network.

This means the AI can hold onto multiple possible solutions at once, exploring them in parallel before committing to a final answer. It is like a management team brainstorming multiple strategies at the same time rather than having to articulate and argue step by step. By freeing the reasoning process from the constraints of language, Coconut enables faster, more flexible, and more sophisticated problem solving.

🤔 What is the significance of Coconut, and what are its limitations?

The real breakthrough of Coconut lies in how it redefines the very process of AI reasoning. Traditional chain of thought forces models to “think in words,” which is intuitive for humans but inefficient for machines. Coconut breaks free from that constraint by allowing reasoning to happen in the flexible, internal format that neural networks are built for. This shift is almost comparable to moving from a typewriter to a digital spreadsheet. Suddenly, the system can track multiple paths, make quick adjustments, and focus on the hard problems instead of wasting effort on filler. For businesses, this represents not just an incremental improvement but a fundamental leap in how AI could soon support decision-making and problem-solving.

• Efficiency: Coconut can reason with fewer wasted steps, reducing processing time and costs.
• Parallel processing: Coconut enables the model to consider multiple possibilities simultaneously, improving its ability to handle complex decisions.
• Accuracy: By not having to "round" intermediate steps to the nearest English equivalent, research suggests that Coconut is able to produce more accurate outputs.

As always, these benefits currently come with tradeoffs. In particular, the shift to a liminal reasoning space makes it harder for human users to understand the AI's reasoning process:

• Interpretability: Because Coconut’s reasoning happens in a continuous, non-linguistic space, it becomes much harder to trace how the AI arrived at a decision. This would pose challenges for regulated industries where explainability is essential.
• Generalization: While Coconut has demonstrated promising results on math and logic benchmarks, it remains to be tested how the system would perform with messier, more ambiguous real-world objectives.
• Training cost: It is possible that continuous latent reasoning requires a certain extent of retraining or fine-tuning. Enterprises relying on off-the-shelf LLMs would then face barriers in adapting existing infrastructure to leverage Coconut effectively.

🛠️ Use cases of Coconut

Coconut is especially valuable in solving problems where exploration, backtracking, and multi-path reasoning are needed, such as:

• Low latency chatbots: Perhaps the most obvious use case; Coconut's increased processing time would substantially lower the time-to-response of AI systems such as ChatGPT and Claude, as well as make real-time customer service agents even more useful.
• Supply chain optimization: By exploring several planning paths simultaneously, Coconut-based AI could recommend more resilient logistics strategies that account for disruptions.
• Risk and compliance: Coconut could weigh multiple regulatory interpretations or risk scenarios at once, offering more nuanced guidance to compliance teams.