OpenAI releases deep research agent

• It's not clear whether agents will think in neuralese, maybe end-to-end RL in English is good enough for the next few years and CoT messages won't drift enough to allow steganography
• Once agents think in either token gibberish or plain vectors maybe self-monitoring will still work fine. After all agents can translate between other languages just fine. We can use model organisms or some other clever experiments to check whether the agent faithfully translates its CoT or unavoidably starts lying to us as it gets more capable.
• I care about the exact degree to which monitoring gets worse. Plausibly it gets somewhat worse but is still good enough to catch the model before it coups us.

If we're using another instance of the same base model to do the monitoring and interpreting? It seems like that could partially preserve monitoring and interpreting. Another instance would "understand" the steganography it uses - but you might have to supply so much of the context that it would be almost the same instance - so likely to adopt the same goals and use the same deceptions, if any.

So that route does seem like dangerous territory. You'd rather not trust even a fresh instance of the same model to tell you what its agentic counterpart is "thinking" about.

I haven't gotten far in figuring out how well this might work, but it is a possibility. I'll say the little I have thought about in a soon-to-be-finished post.

I don't see how monitoring and interpretability could be unaffected.

(So I take this as modestly bad news — I wasn't totally sure orgs would use task-based end-to-end RL. I wasn't sure if agentic scaffolding might prove easier - see the other discussion thread here for questions about whether it might actually work just as well if someone bothered to implement it.)

I assume that what's going on here is something like,
"This was low-hanging fruit, it was just a matter of time until someone did the corresponding test."

This would imply that OpenAI's work here isn't impressive, and also, that previous LLMs might have essentially been underestimated. There's basically a cheap latent capabilities gap.

I imagine a lot of software engineers / entrepreneurs aren't too surprised now. Many companies are basically trying to find wins where LLMs + simple tools give a large gain. 

So some people could look at this and say, "sure, this test is to be expected", and others would be impressed by what LLMs + simple tools are capable of. 

I think the correct way to address this is by also testing the other models with agent scaffolds that supply web search and a python interpreter.

I think it's wrong to jump to the conclusion that non-agent-finetuned models can't benefit from tools.

See for example:

Frontier Math result

https://x.com/Justin_Halford_/status/1885547672108511281

o3-mini got 32% on Frontier Math (!) when given access to use a Python tool. In an AMA, @kevinweil / @snsf (OAI) both referenced tool use w reasoning models incl retrieval (!) as a future rollout.

METR RE-bench

Models are tested with agent scaffolds

AIDE and Modular refer to different agent scaffolds; Modular is a very simple baseline scaffolding that just lets the model repeatedly run code and see the results; AIDE is a more sophisticated scaffold that implements a tree search procedure.

And how much the improved reasoning is from using a different base model vs. different post-training. It's possible R1-like training didn't work for models below GPT-4 level, and then that same training started working at GPT-4 level (at which point you can iterate from a working prototype or simply distill to get it to work for weaker models). So it might work even better for the next level of scale of base models, without necessarily changing the RL part all that much.

How o3-mini scores: https://x.com/DanHendrycks/status/1886213523900109011

10.5-13% on text only part of HLE (text only are 90% of the questions)

[corrected the above to read "o3-mini", thanks.]

If anyone is game for creating an agentic research scaffold like that Thane describes

Here's a more detailed the basic structure as envisioned after 5 minutes' thought:

• You feed a research prompt to the "Outer Loop" of a model, maybe have a back-and-forth fleshing out the details.
• The Outer Loop decomposes the research into several promising research directions/parallel subproblems.
• Each research direction/subproblem is handed off to a "Subagent" instance of the model.
• Each Subagent runs search queries on the web and analyses the results, up to the limits of its context window. After the analysis, it's prompted to evaluate (1) which of the results/sources are most relevant and which should be thrown out, (2) whether this research direction is promising and what follow-up questions are worth asking.
  • If a Subagent is very eager to pursue a follow-up question, it can either run a subsequent search query (if there's enough space in the context window), or it's prompted to distill its current findings and replace itself with a next-iteration Subagent, in whose context it loads only the most important results + its analyses + the follow-up question.
  • This is allowed up to some iteration count.
• Once all Subagents have completed their research, instantiate an Evaluator instance, into whose context window we dump the final results of each Subagent's efforts (distilling if necessary). The Evaluator integrates the information from all parallel research directions and determines whether the research prompt has been satisfactorily addressed, and if not, what follow-up questions are worth pursuing.
• The Evaluator's final conclusions are dumped into the Outer Loop's context (without the source documents, to not overload the context window).
• If the Evaluator did not choose to terminate, the next generation of Subagents is spawned, each prompted with whatever contexts are recommended by the Evaluator.
• Iterate, spawning further Evaluator instances and Subproblem instances as needed.
• Once the Evaluator chooses to terminate, or some compute upper-bound is reached, the Outer Loop instantiates a Summarizer, into which it dumps all of the Evaluator's analyses + all of the most important search results.
• The Summarizer is prompted to generate a high-level report outline, then write out each subsection, then the subsections are patched together into a final report.

Here's what this complicated setup is supposed to achieve:

• Do a pass over an actually large, diverse amount of sources. Most of such web-search tools (Google's, DeepSeek's, or this shallow replication) are basically only allowed to make one search query, and then they have to contend with whatever got dumped into their context window. If the first search query turns out poorly targeted, in a way that becomes obvious after looking through the results, the AI's out of luck.
• Avoid falling into "rabbit holes", i. e. some arcane-and-useless subproblems the model becomes obsessed with. Subagents would be allowed to fall into them, but presumably Evaluator steps, with a bird's-eye view of the picture, would recognize that for a failure mode and not recommend the Outer Loop to follow it.
• Attempt to patch together a "bird's eye view" on the entirety of the results given the limitations of the context window. Subagents and Evaluators would use their limited scope to figure out what results are most important, then provide summaries + recommendations based on what's visible from their limited vantage points. The Outer Loop and then the Summarizer instances, prompted with information-dense distillations of what's visible from each lower-level vantage point, should effectively be looking at (a decent approximation of) the full scope of the problem.

Has anything like this been implemented in the open-source community or one of the countless LLM-wrapper startups? I would expect so, since it seems like an obvious thing to try + the old AutoGPT scaffolds worked in a somewhat similar manner... But it's possible the market's totally failed to deliver.

It should be relatively easy to set up using Flowise plus e. g. Firecrawl. If nothing like this has been implemented, I'm putting a $500 $250 bounty on it.

Edit: Alright, this seems like something very close to it.

I would REALLY like to see some head to head comparisons with you.com from a subject matter expert, which I think would go a long way in answering this question.

Clarifying question:

How, specifically? Do you mean Perplexity using the new model, or comparing the new model to Perplexity?