OpenAI is refocusing its research efforts and throwing its resources into a new grand challenge. The San Francisco firm has set its sights on building what it calls an AI researcher, a fully automated agent-based system that will be able to go off and tackle large, complex problems by itself. OpenAI says that the new goal will be its “north star” for the next few years, pulling together multiple research strands, including work on reasoning models, agents, and interpretability.

There’s even a timeline. OpenAI plans to build “an autonomous AI research intern”—a system that can take on a small number of specific research problems by itself—by September. The AI intern will be the precursor to a fully automated multi-agent research system that the company plans to debut in 2028. This AI researcher (OpenAI says) will be able to tackle problems that are too large or complex for humans to cope with.

Those tasks might be related to math and physics—such as coming up with new proofs or conjectures—or life sciences like biology and chemistry, or even business and policy dilemmas. In theory, you would throw such a tool any kind of problem that can be formulated in text, code or whiteboard scribbles—which covers a lot.

Read the full story for an exclusive conversation with OpenAI’s chief scientist Jakub Pachocki about his firm's new grand challenge and the future of AI.

For me, the answer is obvious, but I'm interested to hear the opinion of others to understand if my biases are clouding my view.

The most persuasive evidence for me is the improvements in the models. For Astra, this is best demonstrated in agentic behaviour, which is critical for having an automated AI researcher, as agentic behaviour means the model can actually do things. Use a computer. Write code. Run code. Use tools. Search. Carry out experiments. Look at the results. Change what it’s doing. Try again. Keep working on a problem.

And if the brain keeps improving at the same time as the hands improve, it seems completely plausible to me that the job of an AI researcher will eventually become automatable.

For those who don't share this view, what does an automated AI researcher fundamentally need to be able to do that a few more iterative model improvements won’t plausibly give it?

Even if you think that the jumps required are large, it's important to note that models are making significant improvements.

For example, the jump on SpatialBench for Astra is ridiculous. The benchmark consists of 25 visual path-tracing problems and 25 3D mental-rotation problems, which are exactly the sort of things previous multimodal models were embarrassingly poor at. In fact, GPT-5.5 scored only around 13%. But Astra scored around 67% without tools and over 90% with Code Interpreter, which is above the reported human baseline of 80%.

Then you have the ARC-AGI-3 results, where models are required to enter unfamiliar environments, work out the rules, build representations of what’s going on, plan, use tools and create software to help solve the problem. GPT-5.5 previously scored just 0.43%. Astra scored 62.7% under the standard provider-neutral harness, while an OpenAI-specific harness that preserved its reasoning state reached 99.9%. Remarkably, Astra used fewer actions than the median human on 96% of the levels it solved.

So what am I missing? What do sceptics think will stop this progression before we get to an automated AI researcher? And if we get that far, what stops us from achieving RSI? And if we get that far, what does a post-RSI world look like?

From the OpenAI model card:

"Measuring if and when models can automate the job of an OpenAI research engineer is a key goal

of self-improvement evaluation work. We test models on their ability to replicate pull request

contributions by OpenAI employees, which measures our progress towards this capability.

We source tasks directly from internal OpenAI pull requests. A single evaluation sample is based

on an agentic rollout. In each rollout:

1. An agent’s code environment is checked out to a pre-PR branch of an OpenAI repository

and given a prompt describing the required changes.

2. The agent, using command-line tools and Python, modifies files within the codebase.

3. The modifications are graded by a hidden unit test upon completion.

If all task-specific tests pass, the rollout is considered a success. The prompts, unit tests, and

hints are human-written.

The o3 launch candidate has the highest score on this evaluation at 44%, with o4-mini close

behind at 39%. We suspect o3-mini’s low performance is due to poor instruction following

and confusion about specifying tools in the correct format; o3 and o4-mini both have improved

instruction following and tool use. We do not run this evaluation with browsing due to security

considerations about our internal codebase leaking onto the internet. The comparison scores

above for prior models (i.e., OpenAI o1 and GPT-4o) are pulled from our prior system cards

and are for reference only. For o3-mini and later models, an infrastructure change was made to

fix incorrect grading on a minority of the dataset. We estimate this did not significantly affect

previous models (they may obtain a 1-5pp uplift)."

I built an open source deep research implementation using the OpenAI Agents SDK that was released 2 weeks ago. It works with any models that are compatible with the OpenAI API spec and can handle structured outputs, which includes Gemini, Ollama, DeepSeek and others.

The intention is for it to be a lightweight and extendable starting point, such that it's easy to add custom tools to the research loop such as local file search/retrieval or specific APIs.

It does the following:

• Carries out initial research/planning on the query to understand the question / topic

• Splits the research topic into sub-topics and sub-sections

• Iteratively runs research on each sub-topic - this is done in async/parallel to maximise speed

• Consolidates all findings into a single report with references

• If using OpenAI models, includes a full trace of the workflow and agent calls in OpenAI's trace system

It has 2 modes:

• Simple: runs the iterative researcher in a single loop without the initial planning step (for faster output on a narrower topic or question)

• Deep: runs the planning step with multiple concurrent iterative researchers deployed on each sub-topic (for deeper / more expansive reports)

I'll post a pic of the architecture in the comments for clarity.

Some interesting findings:

• gpt-4o-mini and other smaller models with large context windows work surprisingly well for the vast majority of the workflow. 4o-mini actually benchmarks similarly to o3-mini for tool selection tasks (check out the Berkeley Function Calling Leaderboard) and is way faster than both 4o and o3-mini. Since the research relies on retrieved findings rather than general world knowledge, the wider training set of larger models don't yield much benefit.

• LLMs are terrible at following word count instructions. They are therefore better off being guided on a heuristic that they have seen in their training data (e.g. "length of a tweet", "a few paragraphs", "2 pages").

• Despite having massive output token limits, most LLMs max out at ~1,500-2,000 output words as they haven't been trained to produce longer outputs. Trying to get it to produce the "length of a book", for example, doesn't work. Instead you either have to run your own training, or sequentially stream chunks of output across multiple LLM calls. You could also just concatenate the output from each section of a report, but you get a lot of repetition across sections. I'm currently working on a long writer so that it can produce 20-50 page detailed reports (instead of 5-15 pages with loss of detail in the final step).

Feel free to try it out, share thoughts and contribute. At the moment it can only use Serper or OpenAI's WebSearch tool for running SERP queries, but can easily expand this if there's interest.

Another one on this topic, this time by Sakana team et al. again: https://www.nature.com/articles/s41586-026-10265-5 (And here's a somewhat vicious critique: https://thebullshitmachines.com/lesson-12-the-ai-scientist/ )

"The automation of science is a long-standing ambition in artificial intelligence (AI) research^1,2. Although the community has made substantial progress in automating individual components of the scientific process, a system that autonomously navigates the entire research life cycle—from conception to publication—has remained out of reach. Here we present a pipeline for automating the entire scientific process end to end. We present The AI Scientist, which creates research ideas, writes code, runs experiments, plots and analyses data, writes the entire scientific manuscript, and performs its own peer review. Its ideas, execution and presentation are of sufficient quality that the manuscript generated by this AI system passed the first round of peer review for a workshop of a top-tier machine learning conference. The workshop had an acceptance rate of 70%. Our system leverages modern foundation models^3,4,5 within a complex agentic system. We evaluate The AI Scientist in two settings: a focused mode using human-provided code templates as an initial scaffold for conducting research on a specific topic and a template-free, open-ended mode that leverages agentic search for wider scientific exploration^6,7. Both settings produce diverse ideas and automatically test, report on and evaluate them. This achievement demonstrates the growing capacity of AI for making scientific contributions and signifies a potential paradigm shift in how research is conducted. As with any impactful new technology, there could be important risks, including taxing overwhelmed review systems and adding noise to the scientific literature. However, if developed responsibly, such autonomous systems could greatly accelerate scientific discovery."

I've built a deep research implementation using the OpenAI Agents SDK which was released 2 weeks ago - it can be called from the CLI or a Python script to produce long reports on any given topic. It's compatible with any models using the OpenAI API spec (DeepSeek, OpenRouter etc.), and also uses OpenAI's tracing feature (handy for debugging / seeing exactly what's happening under the hood).

Sharing how it works here in case it's helpful for others.

https://github.com/qx-labs/agents-deep-research

Or:

pip install deep-researcher

It does the following:

• Carries out initial research/planning on the query to understand the question / topic

• Splits the research topic into sub-topics and sub-sections

• Iteratively runs research on each sub-topic - this is done in async/parallel to maximise speed

• Consolidates all findings into a single report with references

• If using OpenAI models, includes a full trace of the workflow and agent calls in OpenAI's trace system

It has 2 modes:

• Simple: runs the iterative researcher in a single loop without the initial planning step (for faster output on a narrower topic or question)

• Deep: runs the planning step with multiple concurrent iterative researchers deployed on each sub-topic (for deeper / more expansive reports)

I'll comment separately with a diagram of the architecture for clarity.

Some interesting findings:

• gpt-4o-mini tends to be sufficient for the vast majority of the workflow. It actually benchmarks higher than o3-mini for tool selection tasks (see this leaderboard) and is faster than both 4o and o3-mini. Since the research relies on retrieved findings rather than general world knowledge, the wider training set of 4o doesn't really benefit much over 4o-mini.

• LLMs are terrible at following word count instructions. They are therefore better off being guided on a heuristic that they have seen in their training data (e.g. "length of a tweet", "a few paragraphs", "2 pages").

• Despite having massive output token limits, most LLMs max out at ~1,500-2,000 output words as they simply haven't been trained to produce longer outputs. Trying to get it to produce the "length of a book", for example, doesn't work. Instead you either have to run your own training, or follow methods like this one that sequentially stream chunks of output across multiple LLM calls. You could also just concatenate the output from each section of a report, but I've found that this leads to a lot of repetition because each section inevitably has some overlapping scope. I haven't yet implemented a long writer for the last step but am working on this so that it can produce 20-50 page detailed reports (instead of 5-15 pages).

Feel free to try it out, share thoughts and contribute. At the moment it can only use Serper.dev or OpenAI's WebSearch tool for running SERP queries, but happy to expand this if there's interest. Similarly it can be easily expanded to use other tools (at the moment it has access to a site crawler and web search retriever, but could be expanded to access local files, access specific APIs etc).

This is designed not to ask follow-up questions so that it can be fully automated as part of a wider app or pipeline without human input.

I want to know whether I can use AI to fully automate research as a layperson in order to invent a new technology or chemical (not a drug) that allows someone to instantly and permanently memorize information after a single exposure (something especially useful in fields like medicine). Equally important, I want to make sure the inverse (controlled memory erasure) is also developed, since retaining everything permanently could be harmful in traumatic contexts.

So far, no known intervention (technology or chemical) can truly do this. But I came across this study on the molecule KIBRA, which acts as a kind of "molecular glue" for memory by binding to a protein called PKMζ, a protein involved in long-term memory retention (source in comments).

Are there any AI tools that could help me automate the literature review, hypothesis generation, and experiment design phases to push this kind of research forward? I want the AI to not only generate research papers, but also use those newly generated papers (along with existing scientific literature) to design and conduct new studies, similar to how real scientists build on prior research. I am also curious if anyone knows of serious efforts (academic or biotechnology) targeting either memory enhancement or controlled memory deletion.

So yeah now it works with OpenAI compatible endpoints thanks to the kind work of people on the Github who updated it for me here is a recap of the project:

Automated-AI-Web-Researcher: After months of work, I've made a python program that turns local LLMs running on Ollama into online researchers for you, Literally type a single question or topic and wait until you come back to a text document full of research content with links to the sources and a summary and ask it questions too! and more!

What My Project Does:

This automated researcher uses internet searching and web scraping to gather information, based on your topic or question of choice, it will generate focus areas relating to your topic designed to explore various aspects of your topic and investigate various related aspects of your topic or question to retrieve relevant information through online research to respond to your topic or question. The LLM breaks down your query into up to 5 specific research focuses, prioritising them based on relevance, then systematically investigates each one through targeted web searches and content analysis starting with the most relevant.

Then after gathering the content from those searching and exhausting all of the focus areas, it will then review the content and use the information within to generate new focus areas, and in the past it has often finding new, relevant focus areas based on findings in research content it has already gathered (like specific case studies which it then looks for specifically relating to your topic or question for example), previously this use of research content already gathered to develop new areas to investigate has ended up leading to interesting and novel research focuses in some cases that would never occur to humans although mileage may vary this program is still a prototype but shockingly it, it actually works!.

Key features:

• Continuously generates new research focuses based on what it discovers

• Saves every piece of content it finds in full, along with source URLs

• Creates a comprehensive summary when you're done of the research contents and uses it to respond to your original query/question

• Enters conversation mode after providing the summary, where you can ask specific questions about its findings and research even things not mentioned in the summary should the research it found provide relevant information about said things.

• You can run it as long as you want until the LLM’s context is at it’s max which will then automatically stop it’s research and still allow for summary and questions to be asked. Or stop it at anytime which will cause it to generate the summary.

• But it also Includes pause feature to assess research progress to determine if enough has been gathered, allowing you the choice to unpause and continue or to terminate the research and receive the summary.

• Works with popular Ollama local models (recommended phi3:3.8b-mini-128k-instruct or phi3:14b-medium-128k-instruct which are the ones I have so far tested and have worked)

• Everything runs locally on your machine, and yet still gives you results from the internet with only a single query you can have a massive amount of actual research given back to you in a relatively short time.

The best part? You can let it run in the background while you do other things. Come back to find a detailed research document with dozens of relevant sources and extracted content, all organised and ready for review. Plus a summary of relevant findings AND able to ask the LLM questions about those findings. Perfect for research, hard to research and novel questions that you can’t be bothered to actually look into yourself, or just satisfying your curiosity about complex topics!

GitHub repo with full instructions and a demo video:

https://github.com/TheBlewish/Automated-AI-Web-Researcher-Ollama

(Built using Python, fully open source, and should work with any Ollama-compatible LLM, although only phi 3 has been tested by me)

Target Audience:

Anyone who values locally run LLMs, anyone who wants to do comprehensive research within a single input, anyone who like innovative and novel uses of AI which even large companies (to my knowledge) haven't tried yet.

If your into AI, if your curious about what it can do, how easily you can find quality information using it to find stuff for you online, check this out!

Comparison:

Where this differs from per-existing programs and applications, is that it conducts research continuously with a single query online, for potentially hundreds of searches, gathering content from each search, saving that content into a document with the links to each website it gathered information from.

Again potentially hundreds of searches all from a single query, not just random searches either each is well thought out and explores various aspects of your topic/query to gather as much usable information as possible.

Not only does it gather this information, but it summaries it all as well, extracting all the relevant aspects of the info it's gathered when you end it's research session, it goes through all it's found and gives you the important parts relevant to your question. Then you can still even ask it anything you want about the research it has found, which it will then use any of the info it has gathered to respond to your questions.

To top it all off compared to other services like how ChatGPT can search the internet, this is completely open source and 100% running locally on your own device, with any LLM model of your choosing although I have only tested Phi 3, others likely work too!

In this era of increased competition between AI labs, we're seeing so many releases that the one key aspect of AI that should matter (at least to people who understand that this is r/singularity and not any other AI subbreddit) is getting forgotten.

Like many of you I enjoy better image/video/coding/whatever capabilities, but they barely matter in the context of a potential technological singularity. They are symptoms of a much more important journey towards AI systems that could automate parts of AI research.

If we can somehow get to positive feedback loops on AI research in the near future, it's basically game over. It doesn't even have to be an end-to-end fully-automated AI researcher, just useful enough to improve the pace of current AI research cycles.

As such, the benchmarks that truly matter relate to scientific research (e.g. GPQA Diamond), performance in mathematics (e.g. AIME 2024), pure reasoning (e.g. ARC-AGI, HellaSwag, Simple-Bench) and performance in machine learning automation (MLE-Bench).

I am convinced that LLMs on their own aren't going to get us to superintelligence, the scientific arguments by LeCun and Chollet are compelling. But they don't have to be, they only have to start being midly useful to AI researchers (which they probably are since o1) and increase from there.

Everything else, such as which model does better writing, how censored they are, how helpful they are in your day-to-day coding jobs is a distraction. A fun, meme-worthy distraction, but a distraction nonetheless.

Researchers from Sakana AI, FLAIR, the University of Oxford, the University of British Columbia, Vector Institute, and Canada CIFAR have developed “The AI Scientist,” a groundbreaking framework that aims to automate the scientific discovery fully. This innovative system leverages large language models (LLMs) to autonomously generate research ideas, conduct experiments, and produce scientific manuscripts. The AI Scientist represents a significant advancement in the quest for fully autonomous research, integrating all aspects of the scientific process into a single, seamless workflow. This approach enhances efficiency and democratizes access to scientific research, making it possible for cutting-edge studies to be conducted at a fraction of the traditional cost....

Read our full take: https://www.marktechpost.com/2024/08/14/the-ai-scientist-the-worlds-first-ai-system-for-automating-scientific-research-and-open-ended-discovery/

Paper: https://arxiv.org/abs/2408.06292

https://www.nature.com/articles/d41586-025-01485-2

"One thing that we should be clear about is that the way the models work is different from how a human brain works. A pre-trained model has learned some things about the world, but it doesn’t really have any conception of how it learned them, or any temporal order as to when it learned things.

I definitely believe we have significant evidence that the models are capable of discovering novel insights. I would say it is a form of reasoning, but that doesn't mean it’s the same as how humans reason."