The first question that leapt into my mind, and I imagine the minds of other readers, is "what makes this an improvement over continuous chain of thought"?
I did manage to find one sentence in the paper that tries to address this question -- but it consists of nothing more than two unjustified assertions, both of which are dubious:
Unlike decoded text, continuous latent vectors (i) lack native pathways for tool invocation across multi-turn agentic reasoning (Chang et al., 2026), and (ii) do not readily expose human-interpretable semantics, leaving them difficult to supervise during training (Chen et al., 2026) and to audit or debug at inference (Korbak et al., 2025).
The latent space in a continuous chain of thought model is the same space the tokens are embedded into, just like an ordinary transformer. They make tool calls exactly the same way that an ordinary transformer does: by leaving thinking mode and emitting chat tokens (which get sampled as probability distributions in the image of the embedding function). The difference is quite simple: chat tokens are noncontinuous, and toolcall tokens are a subset of the chat tokens. Thinking tokens are the only ones which are continuous, and toolcalls don't happen in thinking mode in any model I've ever seen (maybe one exists; if it does it would be weird and interesting!).
As for human-interpretability, yeah, that's the tradeoff. Continuous chain of thought thinking tokens are not-obviously-interpretable, just like Hybrid Reasoning Agents "latent mode". I'm not seeing a win here.
I did manage to find one sentence in the paper that tries to address this question -- but it consists of nothing more than two unjustified assertions, both of which are dubious:
Unlike decoded text, continuous latent vectors (i) lack native pathways for tool invocation across multi-turn agentic reasoning (Chang et al., 2026), and (ii) do not readily expose human-interpretable semantics, leaving them difficult to supervise during training (Chen et al., 2026) and to audit or debug at inference (Korbak et al., 2025).
The latent space in a continuous chain of thought model is the same space the tokens are embedded into, just like an ordinary transformer. They make tool calls exactly the same way that an ordinary transformer does: by leaving thinking mode and emitting chat tokens (which get sampled as probability distributions in the image of the embedding function). The difference is quite simple: chat tokens are noncontinuous, and toolcall tokens are a subset of the chat tokens. Thinking tokens are the only ones which are continuous, and toolcalls don't happen in thinking mode in any model I've ever seen (maybe one exists; if it does it would be weird and interesting!).
As for human-interpretability, yeah, that's the tradeoff. Continuous chain of thought thinking tokens are not-obviously-interpretable, just like Hybrid Reasoning Agents "latent mode". I'm not seeing a win here.