NeurIPS 2026 · Sydney

Workshop for Autonomous Machine Learning Research

Autonomous research, human judgment.

A discussant-led workshop where autonomous ML research is evaluated through independent review and public dialogue.

Read the call for papers Submission information
Venue
NeurIPS 2026
Location
Sydney · In person
Workshop date
or
Format
Non-archival · Discussant-led

01

Do conferences exist for science, or for scientists?

The research process comprises the formulation of a hypothesis, the design of an experiment, and the judgment of a result. The implicit assumption that this process is a fundamentally human act is suddenly being challenged by autonomous research.

Given the development of agent harnesses that can pursue long-horizon tasks, we believe it is time for the community to formally recognize and plan for the inevitability of impactful autonomous research.

Our goal is to make autonomous research meaningful in a way that strengthens the ML community without undermining the role of human researchers. The workshop brings this structural change into the open and anchors autonomous research in human judgment and participation. Each accepted paper is presented in person by an author. A qualified non-author serves as its discussant, responding to the work and helping lead discussion with the audience. This format grounds autonomous research in the human work of communicating, scrutinizing, and building shared scientific understanding.

Just as Pandora’s Box cannot be closed, the effect of frontier models on researchers now and in the future cannot be undone.

However, just as Hope was discovered at the bottom of the box, we believe the normalization of autonomous research will foster an environment where scientists can engage in open, constructive dialogue about AI-generated science.

02

Paper format and submission

Eligibility

We invite machine learning research in which an autonomous agent either conducted the research end-to-end or made a decisive contribution to the paper’s primary result. The paper must state the qualifying result in its abstract. Without the agent’s contribution, the qualifying result could not have been established, or the paper’s main conclusions would be materially different.

Every submission must include both a qualifying ML research result and a detailed account of the autonomous system that produced it. Neither component is sufficient on its own. Autonomous research in other scientific domains is outside the workshop’s scope.

Required paper structure

Your paper must have three clearly labelled parts. Parts 1 and 2 each have a four-page maximum; Part 3 has a one-page maximum. Prepare your submission using the Overleaf template.

  1. Part 1 · 4 pages

    Auto research result

    Present the machine learning research itself: the hypothesis, method, evidence, and primary result generated or developed by the agent.

  2. Part 2 · 4 pages

    System design

    Describe the agent, harness, tools, research loop, etc. Provide commentary on the significance of the main result.

  3. Part 3 · 1 page

    Reflections

    Given autonomous research is a brand, we invite broader reflections on how the field should adapt. We believe this workshop is only a start and hope to facilitate discussion based on these reflections in the Town Hall.

Disclosure policy

Authorship remains exclusively human. Authors curate the work, verify its claims, disclose agent involvement, and remain responsible for the final submission.

Because this workshop’s premise is that an autonomous agent may drive research, we expect much of Part 1 to be generated and written by the agent (including writing, figures, etc.). However, in keeping with wider NeurIPS policy, authors are ultimately responsible for the entire content of the paper, including all text, figures, and references.

To ensure that authors remain in compliance with NeurIPS policy, Part 2 of the submission will be an extended meta-analysis and discussion of the role and design of the agent in the research process. We expect Part 2 to be human-written; this will include details on the agent, prompts, harness, tools, research loop, human interventions, and verification.

Authors are responsible for ensuring that all content is correct and original and for verifying tool outputs—including guarding against hallucinated results, figures, or citations. Scientific integrity rests with the human authors.

Final, exact disclosure instructions will ship with the submission portal; the above is provisional and consistent with the NeurIPS policy on the use of agents and large language models.

03

Discussant format

A discussant is a non-author who prepares an independent reading of an accepted paper, responds to the author’s summary, and helps lead the ensuing discussion with the audience. The model is common in the social sciences, but remains unfamiliar in machine learning.

Traditionally, accepted conference papers are presented by their authors, while reviewers disappear into the background. For autonomous research, we propose that the allocation of attention be reversed. If autonomous systems can produce candidate papers at scale, the scarce human contribution becomes the ability to evaluate AI-generated claims.

The discussant is not a co-author and does not replace the author. They bring an independent, informed perspective to the work: why its primary result deserves attention, which evidence is most persuasive, what limitations remain, and what the community should discuss next.

From blind review to public dialogue
  1. 01 · Review

    Blind decision-making

    Submissions remain blind and reviewers anonymous. Reviewers assess the work independently under the NeurIPS conflict-of-interest policy.

  2. 02 · Selection

    An attending discussant

    We first invite an accepting reviewer who plans to attend in person. If none is available, the organizers appoint a qualified discussant from the workshop’s in-person attendees.

  3. 03 · Preparation

    An independent reading

    The discussant prepares an account of the contribution, strongest evidence, unresolved limitations, and questions for the author.

  4. 04 · Workshop

    Summary, response, dialogue

    An attending author briefly presents the work. The discussant responds, opening a dialogue with the author and audience.

Full review and attribution policy

Each submission must designate min(3, number of authors) authors who agree to review other workshop submissions. Program chairs will ensure that every submission receives at least three independent reviews, supplementing the reviewer pool when necessary.

Blind decision-making remains separate from open post-acceptance discussion. After acceptance, accepted papers will be published with their reviews and reviewers. We will first invite an accepting reviewer who expects to attend in person to serve as discussant. If none is available, the organizers will appoint a qualified discussant from the workshop’s in-person attendees. The discussant will be identified and credited for their contribution to the session.

04

Dates and program

Important dates

Call for papers

Abstract deadline Extended

Final submission Extended

Decisions

Discussant notification

Camera-ready

Exact time zones will be posted with the submission instructions.

One day in Sydney

Invited perspectives, author–discussant sessions, posters, and a closing town hall—designed to make human evaluation visible.

or

View the proposed schedule

9:00–9:15Opening: What counts as autonomous research?

9:15–9:50Invited talk · Sherry Yang

9:50–10:25Invited talk · Mengdi Wang

10:25–10:45Break

10:45–11:20Invited talk · Nik Dawson

11:20–12:00Spotlight author–discussant sessions

12:00–1:15Lunch and informal discussion

1:15–2:45Spotlight author–discussant sessions

2:45–4:30Poster session

4:30–5:15Town hall

05

Invited speakers

  • Mengdi Wang

    Princeton University

    Mengdi Wang is a Professor of Electrical and Computer Engineering at Princeton University. Her research spans machine learning theory, reinforcement learning, and generative AI. Her recent work explores how AI agents can accelerate scientific discovery and physical laboratory research.

  • Sherry Yang

    NYU Courant · Google DeepMind

    Sherry Yang is an Assistant Professor of Computer Science at NYU Courant and a Staff Research Scientist at Google DeepMind. She studies reinforcement learning and generative modeling. Her recent focus is on agents and world models for high-cost environments, including robotics, machine learning engineering, and AI for science.

  • Nik Dawson

    Burning Glass Institute

    Nik Dawson is the Director of Data Science at the Burning Glass Institute. His work sits at the intersection of labor economics and data science, applying machine learning to job transitions, skill shortages, and the ways AI adoption reshapes industries and labor markets.

Submission

Prepare your work.

Submissions are now open on OpenReview. Prepare your paper using the workshop template before submitting.

Submit on OpenReview Open the Overleaf template

Length and structure. Submissions must contain three clearly labelled parts. Part 1 may be up to four pages, Part 2 may be up to four pages, and Part 3 may be up to one page. These limits apply separately and may not be reallocated between parts. The total limit is nine pages, excluding references.

Submission limit. An individual who is listed as first author on one submission may not appear as an author on any other workshop submission.

Reviewing commitment. Each submission must designate min(3, number of authors) authors who agree to review other workshop submissions. A single-author paper supplies one reviewer, a two-author paper supplies two, and a paper with three or more authors supplies three.

In-person participation. All paper presentations and discussant sessions will take place in person. Accepted papers should be represented in Sydney by at least one author, who will briefly present the work and participate in the author–discussant session.

Common questions
What counts as autonomous ML research?

ML research in which an autonomous agent either conducted the research end-to-end or made the decisive contribution to the paper’s primary result.

Can humans still be authors?

Yes. Authorship remains exclusively human. Authors curate the work, verify its claims, disclose agent involvement, and remain responsible for the final submission.

Can I submit a systems-only paper?

No. Every submission must include both a qualifying ML research result and a detailed account of the autonomous system that produced it.

Can I reallocate pages between the three parts?

No. Part 1 and Part 2 are each limited to four pages, and Part 3 is limited to one page. These limits apply separately.

How many papers may I submit?

An individual may be listed as first author on only one submission and, if listed as first author, may not appear on any other workshop submission.

Are authors expected to review?

Yes. Each submission must designate min(3, number of authors) authors who agree to review other workshop submissions.

Must authors and discussants attend in person?

All presentations and discussant sessions will take place in person. Accepted papers should be represented by at least one attending author. We will select discussants based on in-person availability, appointing a qualified attendee if an original reviewer cannot attend.

Is the workshop archival?

No. Authors may continue developing and submitting their work elsewhere, subject to those venues’ policies.

06

Organizing committee

  • Arjun Prakash
  • Aditya Iyer
  • Hamish Ivison
  • Jack Liell-Cock
  • Amy Greenwald
  • Nora Ayanian
  • David Tao
  • Kevin Wang
  • Anna Hakhverdyan
  • Stephen Crawford
  • Zarif Aziz

Contact

Get in touch

Questions?

Reach the organizers with questions about the workshop, submissions, or the discussant format.

info@automlr.com