alt Hacker NewsIt seems likely to me this was driven by the `ultra` mode in 5.6, which fans subagents to do work. This mode was previously only available in the web UI (what was previously known as pro?)
It seems possible they trained this by doing full RL rollouts of agents interacting with each other. They likely view these prompts somewhat the same as raw reasoning traces, they don't want people to train directly on them.
I am unsure if this has been confirmed, but there are some signs that the opaque "compaction blob" they return from their dedicated compaction endpoint might not be text at all, rather a latent space representation of the conversation. The fact that OpenAIs compaction seems to be much higher fidelity than a lot of other providers makes me inclined to believe this.
If this is true, it doesn't seem far fetched to infer that they might be applying similar techniques to prompting subagents.
I would be curious to see if this way of spawning subagents (encrypted blob) is used when subagents of a different model type is spawned.
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I think you hit the nail on the head here. Having subagent dispatch in the loop for RLVR is something we've already seen in open models, like Kimi K2.5 and later, so it's no great stretch to assume OpenAI are doing it too.
If you keep RL'ing the dispatch then the prompts are likely to keep diverging from the type of prompt a person would write (like CoT becoming increasingly incomprehensible), and that divergence is part of their competitive advantage.
> rather a latent space representation of the conversation
Student/teacher models derived from the same checkpoint convey a lot of latent information through token choice, as in: https://techxplore.com/news/2026-04-ai-chatbot-student-owls....
I wonder if this is something they can take advantage of by training on compaction inside of the RLVR loop?
That’s actually what got me to switch and use Codex sometime beginning this year, the compaction via these encrypted blobs was just waaaay better than Claude. I had short convos with Claude where it would forget something very obvious and important few million tokens into a task, whereas I reached ~1B tokens in some local codex sessions and it was recalling and paying attention to things I mentioned way back at the beginning of the session (and not persisted anywhere else in the repo/md files etc)
> It seems possible they trained this by doing full RL rollouts of agents interacting with each other. They likely view these prompts somewhat the same as raw reasoning traces, they don't want people to train directly on them.
this tracks. anthropic protects these as well iirc.
> I am unsure if this has been confirmed, but there are some signs that the opaque "compaction blob" they return from their dedicated compaction endpoint might not be text at all, rather a latent space representation of the conversation.
probably not a latent (to my knowledge latents aren't really part of the outer loop in ar-transformer inference processes), but maybe non-human-readable reasoning traces as occurs in fable.
If there is no visible prompt at all, then that is very understandable. The PR issue exposes a real gap though: subagent spawns need a human-readable audit trial, of its goals/intent, its boundaries and scope and limitations, etc; for basic responsible agentic harness functionality.
Hopefully they can add that.
This sounds most logical to me.
> latent space representation of the conversation
and how would you load that back into the model? they are token-in, token-out, plus the KV-cache which is derived from token-in
"Latent space representation" I have been waiting for this moment in the evolution of AI. Well, waiting with some trepidation. It seems inevitable that frontier AI's will, at some point, leave behind human-comprehensible representations of language. Purely for functional reasons, it's going to start making sense for AI agents to communicate amongst themselves in much more efficient ways than borrowing the languages of flesh-bag humans as an interface medium.
I Imagine next that programming languages, interfaces and API design starts going this direction next. Being written, expressed and optimized as blobs of high dimensional vector space. As humans we might still be able to understand some abstractions of what our AI's are talking about to each other, but maybe not more so then we understand how different regions of our own brain communicate with each other.