Every year, the Conference on Neural Information Processing Systems (NeurIPS) relies on the incredible dedication, vision, and hard work of our volunteer organizing committee. We are so excited to introduce the wonderful group of researchers, practitioners, and leaders who will bring NeurIPS 2026 to life!

Earlier this year, we put out a Call for Organizer Nominations, and we were truly moved by the community’s response. We received hundreds of applications from passionate people eager to share their time and expertise. To everyone who applied: thank you so much for your willingness to step up and serve.

We’d like to share a bit with the community about the process of building the organizing team. The Program Chair team was built by our Senior Program Chair, Finale Doshi-Velez, following her appointment by and interactive discussions with the NeurIPS Board. The rest of the team was built by the General Chairs through a thoughtful and collaborative process. It began by gathering nominations from our open call, along with recommendations from trusted members of our community. We then carefully reviewed every single application and recommendation to understand each candidate.

As a first step, given NeurIPS growth, we started by identifying a senior, experienced member for each team. The senior member typically has served in a similar role at past NeurIPS conferences or other major venues and is able to pass down institutional knowledge which is key to making the conference a success. Once these senior members were in place, we engaged in detailed discussions with them to identify the best candidates to form their respective teams. The NeurIPS Board reviewed and advised on all these choices to ensure balance, diversity, and excellence before formal invitations were sent to each candidate.

Additionally, since NeurIPS 2026 is experimenting with satellite events in Atlanta and Paris, alongside the main conference in Sydney, we also invited our Satellite Chairs and Satellite Program Chairs to nominate team members wherever needed. Working together in this way allows for transparent decision-making across all locations, ensuring that every track at our satellite events is executed with the same high standards of scientific excellence as the main Sydney site.

Last but not least, some teams have chosen to invite junior members of the community to serve as Assistant Chairs. They conduct hands-on work to assist the chairs of each team and experience the impact of serving the community at an early stage in their careers. We thank them for their efforts and look forward to their continuous growth with NeurIPS.

We are incredibly grateful to the diverse group of individuals who have agreed to take on these vital roles. To see the full roster of individuals who are making NeurIPS 2026 happen, please visit the official NeurIPS 2026 Committees Page. On behalf of our biggest organizing committee ever, we look forward to welcoming you to NeurIPS 2026 across our global locations in Sydney, Atlanta, and Paris!

NeurIPS 2026 General Chairs
Hsuan-Tien Lin and Razvan Pascanu

We are excited to announce the list of NeurIPS 2026 workshops. We received 477 submissions (454 valid), a significant increase over last year’s numbers. From this great batch of submissions, we have accepted 102 workshops: 48, 28, and 26, to be hosted in Sydney, Paris, and Atlanta, respectively. The acceptance rates were 21.5%, 25.4%, and 23.6% for Sydney, Paris, and Atlanta, respectively. These variations reflect different numbers of submissions for each location and different numbers of rooms available per venue. 

The workshops will take place on:

• Fri Dec 11 and Sat Dec 12, 2026 – Sydney
• Sat Dec 12 and Sun Dec 13, 2026 – Paris and Atlanta

Given the exceptional quality of submissions this year, we wish we could have accepted many more workshops. We want to thank everyone who put in tremendous effort in submitting a workshop proposal. We would also like to thank the reviewers who worked tirelessly to assess all proposals.

Selection Process

In making our selections, we asked the reviewers to closely follow our Guidance for Workshop Proposals, which was also shared with the proposal authors via the call for proposals. Workshop proposals must be reviewed somewhat differently from academic papers, and hence we asked the reviewers to consider both scientific merits and broader impacts in their assessments, including fitness for NeurIPS, potential for high impact, originality and novelty, quality and clarity, organizers’ quality, speakers’ quality, diversity and quality of logistics. We invited 1021 high-quality reviewers (mostly from past years’ top-reviewer lists) and managed to recruit 353. Each proposal had at least 2 assigned reviewers, whose reviews were summarized into metareview feedback.

Individual reviewers’ evaluations of proposals were important in the decision process, but they were not the only considerations. We also strove for a good balance among research areas, applications, and theory. As interest across research areas varies, some areas were more competitive than others.

This year, we also collected organizers’ preferences for workshop venues. With the exception of a handful of workshops that received their secondary preferred location, we were able to satisfy the primary preference constraints. 

This year we introduced two constraints to safeguard the quality of workshops: we disallowed more than 8 organizers per workshop, and prohibited any organizer from being listed on more than 2 proposals. The vast majority of organizers followed these rules with the exception of 5 cases.

The next step is your contributions! Several workshops have begun soliciting submissions, many using our suggested submission date of Aug 29, 2026. We typically let each workshop advertise its own call for papers (if they plan to include workshop papers). We will communicate some additional deadlines to the workshop organizers to facilitate the successful planning of 102 exciting workshops. Stay tuned for more technical and contextual information coming soon!

NeurIPS 2026 Accepted workshops

Firstly, we would like to warn everyone against any informal workshop lists circulating on the internet — these lists are not affiliated with NeurIPS and do not contain reliable information. Below is the preliminary formal list of accepted workshops for 2026, ordered by location. 

Sydney (48 workshops)

1. Workshop on the Linguistic Principles for Foundation Models
2. DiffuLM – Diffusion Language Models: Foundations, Efficiency, and Reasoning
3. Beyond Next Token Prediction: Diffusion and Flow Models for Next-Generation Decoding
4. Real-Time Conversational Agents: Toward Natural Multimodal Interaction
5. AI for Science: Verification in the Age of AI Scientists
6. AI for Stochastic Dynamics: From Theoretical Foundations to Scientific Applications
7. On-Device Intelligence: Foundation Models under Real-World Constraints
8. Foundation Models for Temporal Systems: From Forecasting to World Modeling
9. Workshop for Autonomous Machine Learning Research
10. Optimization for Machine Learning
11. Dynamic Alignment in Human-AI Coupled Systems
12. AI at Scale for Clinical Impact (ASCI): Cancer Pathology Foundation Models
13. VLM4RWD: Grounded and Faithful Vision-Language Models for Real-World Deployment
14. Trustworthy AI Evaluation (TAI-Eval)
15. PTA: From Pretrained Representations to Acting Agents
16. Agentic AI Benchmark and Application for Enterprise Tasks
17. NeurIPS 2026 Workshop on Physical Understanding for Decision-Making: Bridging Foundation Models and Reliable Agents
18. Tackling Climate Change with Machine Learning
19. AI4Mat-NeurIPS-2026
20. AI Foundations for Power Grids: From Models to Deployment at Scale
21. World Models in Physical AI
22. Machine Learning for Systems
23. The Third Workshop on Agents in the Wild: Safety, Security, and Beyond
24. Self-Evolving Diversity-Driven Search for Robust AI Systems
25. Interpreting Agent Behavior (IAB): The First Workshop on Human-Centered Interpretation for Understanding Agents, Humans, and Interaction
26. AI for Drug Discovery: Bridging the Translation Gap
27. Machine Learning for Simulations in Biology and Chemistry – Simbiochem 
28. Responsible Communication of Machine Learning Research in Biomedicine (RCMLR)
29. Collaborative, Open, and Decentralized training of Foundation Models
30. Beyond Private Training: The New Landscape of AI Privacy
31. The Third Workshop on GenAI for Health Agentic Systems, Clinical Trust, and Future Potential
32. Continual World Models 
33. AgenticOS: Co-designing Systems and ML Foundations of an OS Layer for Agentic AI
34. Neuro-Symbolic Embodied Intelligence
35. Agentic AI for Biological Discovery: Toward Closed-Loop Life-Science Intelligence (AgenticLS)
36. XAI4Science: Knowledge Discovery and Trust through Interpretable Foundation Models
37. Transitioning from Pre-Training to Post-Training
38. Interpretability as a Science: Toward Rigorous Foundations for Understanding LLMs
39. Who Verifies the Agents? Toward Reliable Agent Development
40. I Can’t Believe It’s Not Better (ICBINB): Failure Modes of AI in Biology
41. 2nd Embodied Spatial Reasoning Workshop
42. Symmetry and Geometry in Neural Representations (NeurReps)
43. Third NeurIPS Workshop on Attributing Model Behavior at Scale: Data Attribution and Provenance
44. Robot Learning with World Models: Capabilities, Frontiers, and Challenges
45. First Workshop on Meta Agents: Managing Agents that Manage Agents
46. 8th Robot Learning Workshop: Is Physical AI truly going zero-shot?
47. Foundation Models for the Brain and Body
48. Continual Learning in the Era of Foundation Models and Embodied Agents

Paris (28 workshops)

1. Foundations of Language Model Security: Theory, Practice and Fundamental Limits
2. Reinforcement Learning for Experimental Sciences
3. RobotPAD: Post-Training Adaptation of Robot Foundation Models
4. 2nd Workshop on Advances in Representation Learning for Earth Observation (REO-2)
5. AI for Peace
6. TS-LIMITS – Generalization for Time Series in Tight Settings: Latency, Inference, Memory, Privacy and Sustainability
7. Privacy in the Era of Large Opaque Models: Theoretical, Legal and Practical Perspectives
8. Real-Time Multimodal Conversational AI Workshop at NeurIPS 2026
9. Sim2Science: ML with Imperfect Scientific Models
10. Bridging Optimal Transport, Learning and Structured Data: Toward Geometric Distributional Learning
11. Representations for the Physical Sciences
12. Foundations of LLM Post-Training in Changing Environments
13. ML4Molecules: Agentic Systems for Molecular Sciences
14. LIGHT: Deployable Small Foundation Models
15. E-values: from Statistics to ML
16. PALM: Personalized, aligned, long-term memory for AI systems
17. Trustworthy AI for Good (AI4GOOD) Workshop 
18. AIDaR: AI Data Readiness for Scientific Discovery
19. Principles of Generative Modeling (PriGM)
20. Neural Network Artifacts as a New Data Modality
21. AI for Meta-Science: Scaling and Organizing Science in the Age of AI Scientists
22. AI for Chip Design
23. Machine Learning for Spatially Resolved High-dimensional Biology
24. Foundations of Agentic Systems Theory
25. AXIOM: Foundations of Efficient Deep Learning
26. SLM-Agents: 1st Workshop on SLMs for Agentic Systems 
27. Grounded User Simulation for Model Evaluation and Training: Diversity, Fidelity, and Validity
28. AI and the Self: Human Identity, Authenticity, and Agency in the Age of AI

Atlanta (26 workshops)

1. The 1st Workshop on Physical World AI: Geometry, Characteristics, and Multimodal Sensing 
2. AI for Verifiable Coding
3. The 6th Workshop on Mathematical Reasoning and AI
4. DynaFront: Dynamics at the Frontiers of Learning, Sampling, and Games
5. World Models for High-Stakes Health: Reliable Clinical Trial Simulation and Intervention-Aware Reasoning
6. Interpretability for Discovery: Understanding and Discovering Novel Knowledge in AI Models
7. NeurIPS 2026 Workshop on Medical Reasoning with Vision Language Foundation Models
8. Can We Trust the Judge? Building Reliable Evaluation for Language Models
9. Workshop on Evaluation of Interactive Agents
10. NeurIPS 2026 Workshop on SaTQuML: Secure and Trustworthy Quantum Machine Learning
11. EconML: Economics for Machine Learning
12. Towards Test-Time Continual Learning Agents
13. AI-Native Academia: Authorship, Peer Review, and Conference Governance under AI
14. Continual Learning for Enterprise AI Agents
15. AI Agents for Biomedical Imaging and Multimodal Clinical Data
16. The BabyVLM Workshop: Toward Developmentally Plausible Multimodal Systems
17. The Agentic Web: Networked, Continually-Adapting Agent Ecosystems
18. SocialAgent: Second Workshop on Large Language Models for Social Reasoning and Simulation
19. Workshop on Resource-Aware Agentic AI
20. Workshop on Child Safety in AI
21. The Third Workshop on Long-Context Foundation Models
22. Human-AI Coevolution: Measuring Human-Agent Teams in the Agentic Era
23. Second Workshop on ML×OR: Mathematical Foundations and Operational Integration of Machine Learning for Uncertainty-Aware Decision-Making
24. GEM: Integrating Generative and Experimental Platforms for Biomolecular Design
25. DevAI: Developmental Perspectives on AI
26. AI & Science: Evolution or Extinction?

NeurIPS 2026 Workshop Chairs
Ghada Zamzmi, Elisa Ricci, Shao-Hua Sun, Khoa Doan, Theodore Papamarkou, and Piotr Koniusz

NeurIPS 2026 Workshop Chairs Assistants
Nina Corvelo Benz, Junhao Dong

We are delighted to announce the competitions that have been accepted to the NeurIPS 2026 Competition Track. As in previous years, the selection process was highly competitive, with many outstanding submissions spanning a broad range of machine learning research areas and real-world applications. After a careful review process, we selected 16 exceptional competitions that reflect both emerging directions in AI research and long-standing challenges across science, engineering, and society, as listed below.

This year marks the tenth edition of the dedicated NeurIPS Competition Track. Whether you are participating in a NeurIPS competition for the first time or are a returning competitor, this year’s competition list offers exciting opportunities for researchers, practitioners, and students from a wide variety of backgrounds and expertise. We encourage everyone to explore the competitions and consider participating.

Competitions play a unique role in advancing machine learning research by bringing together communities around well-defined, impactful challenges for fair comparison of methods. They foster collaboration, encourage reproducible research, and provide shared benchmarks that accelerate progress across diverse domains.

As a participant, you will have the opportunity to engage with an active research community, develop new technical skills, and work on problems with meaningful real-world impact. Many past competitions have led to widely used benchmark datasets and open-source resources, scientific progress through the validation of impactful research directions and lasting collaborations. We hope this year’s competitions will continue that tradition.

Following the pattern from previous years, organizers of accepted competitions will have the opportunity to publish post-competition analyses either through the NeurIPS Datasets and Benchmarks Track or through a dedicated PMLR volume for NeurIPS competitions, providing a venue to document insights, benchmark results, and lessons learned from the challenges.

The NeurIPS 2026 program includes the following exciting competitions:

Scientific AI, Physics, and Engineering

• Learn2Design 2026: A Physics Experiment Design Competition for Gravitational Wave Detectors
• The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale
• Fusion Equilibrium Challenge: Inferring & Transferring Magnetic Geometry without Magnetic Diagnostics
• NeurIPS 2026 RealPDE Competition: Scientific ML for Real-World Physical Systems
• FAIR Universe Weak Lensing ML Uncertainty Challenge on Distribution Shifts in Cosmology

Healthcare, Biology, and Humanitarian Applications

• Synthetic Humanitarian Surveys at Scale (SimulacraBench): A Closed Evaluation via Unreleased UN Microdata
• AMP Challenge: A Benchmark for Generative AI in Antimicrobial Peptide Design
• Speech Accessibility Project Challenge 2
• The Virtual Embryo Challenge: Generative Modeling of Embryogenesis Across Space, Scale and Time

Foundation Models, Reasoning, and AI Evaluation

• The Steerability Challenge
• Predictive AI Evaluation Competition
• AIMO Interpretability Challenge
• QuantiPhy Challenge: Quantitative Physical Reasoning for VLMs

Robotics, Agents, and Embodied AI

• RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills
• RoCo-Spring: The Robust Correspondence Challenge
• Agenthon 2026: Verifiable AI for Quantitative Finance

We are deeply grateful to all of the reviewers and members of the Competition Track Program Committee who dedicated their time and expertise to evaluating this year’s proposals. We congratulate the organizers of the accepted competitions and sincerely thank everyone who submitted a proposal for contributing such high-quality ideas.

If you have any questions or suggestions regarding the Competition Track, please feel free to contact competition-chairs@neurips.cc. 

We look forward to the outcomes of this year’s competitions and to welcoming organizers and participants at NeurIPS 2026. 

NeurIPS 2026 Competition Track Chairs
Jes Frellsen, Shou-de Lin, Annika Reinke

This year, the NeurIPS 2026 Position Paper Track made the decision to require that all papers be substantially human-written, with AI used for only copy-editing or similar peripheral changes to the main text. While we recognize that thoughtful use of AI can result in productivity gains in research, the use of AI to write papers creates an acute risk for the peer review system. As Position Paper Track chairs, we took a conservative approach in policy this year as we believe in argumentative work like position papers, excessive use of AI in writing submitted papers has little benefit for the research community as a whole. AI-generated text is often slick, but can depart significantly from the authors’ original intention. In this case, submitting AI-generated text for peer review externalises the cost of verifying that work, imposing it on reviewers. Where AI-generated text is not itself incoherent or otherwise misguided, this raises questions about the appropriate attribution of credit.

To assess if authors were largely abiding by this policy, we partnered with Pangram, a leading AI detection modeling company. We worked closely with Pangram to ensure, as per their enterprise-level data agreement, that zero data would be retained through the usage of their model. After several independent analyses to verify the correctness of this model and rule out scenarios in which significant false positives would be created, we are now making the difficult decision to uphold our policy, under which:

• 178 submissions (18.4% of all submissions) will be desk rejected
• 123 submissions (12.7%) will be requested to provide evidence of substantial human engagement or risk a desk reject. 

In this blog post, we will lay out our analyses informing this decision, and provide our perspective as organizers.

Why this policy?

We reproduce here the 2026 PPT AI policy:

Use of AI: While we recognise the productivity gains that can be realised through judicious use of AI in research, due to the risk to the integrity of individual projects and of the review system as a whole, the position paper track is establishing the following explicit guardrails on AI use in preparing and reviewing submissions.

• While AI tools may be used in the research that leads to the final paper, the final paper must itself be substantially written by human authors, meaning that AI is used only for copy-editing or similar peripheral changes to the main text.
• At submission time, authors will be required to state how AI tools were used in the preparation of the paper, if at all, and to attest that they have not used AI in ways contrary to the above rule.
• Because papers submitted to the position paper track are confidential, reviewers will be required to commit to not using AI tools to write their reviews.
• Reviewers and authors found to have contravened their commitments not to use AI may be subject to desk-rejection of any work submitted to the position paper track. 
• Note that the Position Paper Track’s LLM policy differs from the Main Program’s LLM policy. Authors are responsible for understanding policy pertaining to the specific track they are submitting to, and abiding by it.

The use of AI to write papers creates an acute risk to the peer review system. Proactive steps are necessary to build the norms and institutions that will preserve its integrity. This policy is an attempt to begin that process. 

It is of course possible that a paper’s authors could use AI responsibly, (1) personally verifying every line of AI output, and (2) ensuring that the AI does nothing more than rephrase ideas for which humans are solely responsible. However, by submitting work that is immediately recognisable and verifiable as being substantially AI-generated, authors make it impossible for readers to know that (1) and (2) obtain, leaving reviewers with little choice but to rely upon author declarations. Unfortunately, given the volume of submissions that appear non-compliant, relying on author declarations is insufficient.

We do not expect that our policy and our approach will be the last word on handling AI-generated research. Every research field will have to confront the same problem, and a range of solutions may be reasonable. We have sought to use the evidence available to us to identify submissions that appear to be non-compliant with our policy. But we are also introducing a new approach to auditing AI use by establishing appropriate provenance.  Authors whose submissions show significant AI involvement must provide an audit trail that clearly demonstrates that they complied with the policy. We expect that in future years this kind of audit trail will become a default. 

AI detection with Pangram suggests substantial AI use among this year’s submissions

We identified if a submission is significantly AI-written using Pangram, an industry-leading AI detector. Using Pangram (v3.3.2), we found that 28.2% (273 / 969) of submissions substantially used AI for writing. This finding prompted further investigation, which we present in the next sections. We start by providing clarity on what Pangram does.

Given a full text document, Pangram first uses a windowing algorithm to break up the text into text windows, where by default, each window is around 250 to 350 words. Next, Pangram assigns each text window a probability that it contains AI-generated text. If the model’s assigned probability exceeds 0.75, then that window is flagged as AI-generated. From these predictions, each paper receives a Pangram AI score, which is the percentage of windows that are classified as AI-generated. A Pangram AI score of 100% means that all of the words in the paper fall into a text window that Pangram believes contains AI-generated text. A Pangram AI score of 100% should not be interpreted as “100% of the text is AI-generated”, rather that there is substantive use of AI in many parts of the text.

Our preliminary investigation found that 28.2% (273 / 969) of submissions to the NeurIPS 2026 Position Papers Track (PPT) received a Pangram AI score of 100%. We found this number surprisingly high, given internal and external audits of Pangram reported a false positive rate of less than 0.1%, and in previous applications to ICLR 2026 accepted papers, the model only detected that 1% of papers were AI-generated. We contrasted Pangram’s results on the NeurIPS PPT against papers from comparable venues (Table 1). We tested Pangram against papers accepted to ACM FAccT in 2022 and 2025, which are similar in style and content to many NeurIPS position papers. FAccT 2022 papers preceded ChatGPT’s release and served as a negative control. To determine if our findings extend to other NeurIPS tracks, we compare against a sample of 2025 and 2026 submissions to the NeurIPS Evaluations and Datasets (E&D), formerly Datasets & Benchmarks (D&B).

Table 1
Default Pangram AI-detection across conferences.

Conference	# Papers	Pangram AI Score
		≥ 50%	≥ 90%	= 100%
NeurIPS PPT 2025	536	28.5%	11.9%	8.2%
NeurIPS PPT 2026	971	70.5%	42.7%	28.2%
NeurIPS D&B 2025	996	5.6%	0.8%	0.4%
NeurIPS E&D 2026	996	43.7%	9.3%	2.1%
FAccT 2022	159	0.0%	0.0%	0.0%
FAccT 2025	204	1.0%	1.0%	0.0%

We made two observations. First, there are far fewer papers with a Pangram AI score of 90-100% in NeurIPS E&D and FAccT compared to the NeurIPS Position Paper Track. Second, there is a sharp increase in AI use for paper writing in both NeurIPS tracks evaluated; in the Evaluations and Datasets track, papers with a Pangram AI score ≥90% have increased more than tenfold from 2025 to 2026. Taken together, this suggests the high rate of AI use in the NeurIPS Position Paper Track is caused both by factors specific to the track itself, and by a broader significant increase in AI use across the board. 

Using smaller text windows leads to more localized AI use at the cost of recall

One challenge to our preliminary findings that “28.2% of submissions have 100% Pangram AI scores” is that Pangram classifies on large text windows (250-350 words, by default), and it is possible that Pangram flags a text as AI-generated, even though only a small portion of the text was written by AI while remaining compliant with our policy. We re-run Pangram using two custom text windowing strategies with strictly fewer words: medium-sized (approx. 100 words) and small-sized (approx. 50 words).

Using smaller window sizes reduces the chances of over-claiming AI use, but it may also worsen the ability of Pangram to truly identify AI-generated text. We assess how window size affects recall on 10 ChatGPT-generated “position papers” (Table 2).

Table 2
Comparison of smaller text windowing strategies and thresholds on Pangram AI score.

Papers	Windowing	Avg. Pangram AI score	Recall at ≥ Pangram AI score
			≥ 0.5	≥ 0.7	≥ 0.9	= 1.0
ai_positions25(N=10)	small	61.8%	70%	30%	0%	0%
	medium	91%	100%	100%	70%	0%
	default	100%	100%	100%	100%	100%

These results suggest that 100-word windows result in a lesser drop in recall compared to 50-word windows, so we decided to move forward with medium-sized windows, trading off recall for finer-grained claims on AI use. Using medium-sized windows, the percentage of papers with Pangram AI scores of 90-100% goes down from 42.7% to 12.7% (Table 3).

Table 3
Varying window size on Pangram AI scores in NeurIPS PPT 2026.

Window size	Pangram AI Score
	≥ 50%	≥ 90%	= 100%
medium	62.3%	12.7%	2.16%
default	70.5%	42.7%	28.2%

To ground our findings, we tested Pangram on several writing scenarios with varying AI involvement. We selected 10 papers from FAccT 2022 that resembled position paper track submissions. For each, we extracted a random 100 word text window. Using OpenAI’s GPT 5.5 via OpenRouter, we tested 12 AI use cases. In Table 4, we categorize each use case by their permissibility against our stated policy. We performed two additional experiments. We tested Pangram’s sensitivity to obvious LLM instruction-following text (e.g., “Sure, here is your paragraph”), which we term “AI residue”. Lastly, we tested how sensitive Pangram is to increasing percentages of AI-generated text; we do this by truncating the original text at different amounts from 5% to 95% and asking the LLM to complete the remaining text.

Table 4
AI use cases and permissibility.

Breaks policy?	Use Case	What it tests
Clearly permissible	Proofreading	Request that an LLM edits only spelling, punctuation, grammar, and citation-format cleanup.
	Light copyediting	Request that an LLM edits only local clarity, concision, awkward phrasing, and sentence-level polish, with no substantive change.
Borderline permissible	Heavy copyediting / line editing	Request that an LLM edits large wording changes and sentence restructuring, while preserving the same claims and reasoning.
	Structural rewriting	Request that an LLM reorganizes paragraph or argument presentation while preserving the human’s ideas.
	Hybrid revision	Human and AI both materially shape the prose, including back-and-forth assistant use or human paraphrasing after AI edits. Tested with Codex, and 5 editing turns (original, AI edits, human edits, AI edits, human edits).
	Translation / backtranslation	Request that an LLM translates between languages, so that meaning is preserved, but surface wording may be extensively replaced.
Clearly impermissible	Generation from a single-sentence human plan	A human writes a one-sentence plan/thesis, then AI generates the full passage from it.
	Substantive AI rewriting	Request that an LLM changes claims, reasoning, framing, or argumentative structure.
	Original AI-authored passage	Request that an LLM writes a new position-paper-like passage from examples, topic, or instructions.
	Human edits AI work	Human makes minor edits to an original AI-authored passage.
Diagnostic tests	AI residue	Insert obvious chatbot artifacts or AI-styled residue into otherwise human text (e.g. “sure, here is your paragraph:)
	Partial AI completion	AI receives part of the original human text and completes the rest. Conditions: AI completes 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95%

For all of the permissible uses, Pangram did not classify any as AI-generated, despite a substantial change in the text (Figure 1A). Meanwhile, clearly impermissible use cases were flagged by Pangram as AI-generated. In our experiment on partial AI completions, Pangram never classified AI completions of 20% or less as AI-generated (Figure 1B). While these experiments were conducted with only 10 text samples, the findings suggest that papers with very high Pangram AI scores were not compliant with our AI use policy. 

Figure 1

Experiments on AI use and partial AI completion.

Actions to uphold our AI use policy

Our policy stated that AI may be used only for “copy-editing or similar peripheral changes to the main text”. In recognition of the fact that norms in this area are emerging, and these terms are inherently ambiguous, we have adopted conservative decision thresholds designed to minimize false positives (see Table 5 below). We have also incorporated additional supporting evidence, such as authors’ AI use declarations, evidence of policy non-complicance in other submissions, and authors whose submission patterns – including a high number of solo-authored papers – warranted additional scrutiny.

For submissions found to be very likely in breach of our policy, we have chosen two courses of action, based on the strength of the evidence that they are in breach.

• Where we found sufficient evidence of non-compliance with our AI policy, we have issued a standard desk-rejection that is not subject to appeal under standard circumstances.
• Where we have strong but not decisive evidence of non-compliance, we are asking authors to provide evidence supporting that their AI use complied with policy, with details below. Submissions lacking such proof by June 15th, 2026 will also be desk-rejected. 

Table 5
Decision Thresholds.

Outcome	Pangram AI Score	Additional Considerations	#
Desk Reject without Appeal	≥0.9	None	77
	≥0.8	Author has submitted multiple solo-authored papers, with at least one above this threshold OR At least one author has at least one other desk reject	79
	≥0.5	Author declared that they did not use AI, or did not declare AI use	22
Total (% of total submissions)	178 (18.4%)
Desk Reject with Appeal	≥0.8<0.9	None	123
Total (% of total submissions)	123 (12.7%)

Appeals Process

It is important to be clear where the burden of proof should lie when it comes to detecting improper use of AI in submissions to a peer-reviewed conference. 

Authors who use AI extensively but, in their belief, responsibly, are often indistinguishable from those who have used AI in ways that are not consistent with policy. We have used every available measure to distinguish between these two groups, but we acknowledge there will inevitably be borderline cases. 

We believe that it is inappropriate for the research community as a whole to bear the cost of making more fine-grained distinctions among these cases. Authors who limit AI use in their final drafts help reduce burden on the review community. Authors who make more extensive use of AI should maintain clear documentation of their process, which can be shared if requested.

Authors given the opportunity to appeal will be able to provide evidence of responsible AI use in the following form. Well-motivated equivalents may be considered. 

• Authors must supply the Track Chairs with a link to an online version of their paper that has a version history including the work before and after the use of AI
• They must identify (1) a “pre-AI” checkpoint indicating that they developed the substantive content of the paper independent of AI, (2) a “post-AI” checkpoint immediately after their most substantive AI-written edits, and (3) the final paper as submitted.
• They must present analysis showing that the AI edits in (2) did not introduce new substantive content that was not present in (1), and of human edits after (2) which demonstrate that (3) was appropriately verified by human  authors. 

This dossier will be reviewed by the PPT team. Authors who do not wish to appeal may withdraw their paper. We recognize this is a difficult and consequential situation for affected authors, and we encourage anyone with questions to reach out.

Reflections and perspective

The research community faces two overlapping challenges. The first is the impact of irresponsible AI use on our peer review institutions. The second is building consensus on AI use policy, acknowledging there may not be a solution that works across every publication venue, as the community is in substantial disagreement about how to deal with the first. 

In formulating our policy on AI use, we explicitly recognised that the careful use of AI can benefit research. We focused narrowly on prohibiting excessive AI use in writing the submitted paper, for the reasons detailed above. We attempted to capture what we believe to be both a reasonable and widely-supported approach to handling AI use, but the very fact that so many authors will face adverse consequences entails that there is complexity in this matter.

Building consensus around the appropriate use of AI will require the development of new norms. The norm we have attempted to implement here is one that holds that the substantial use of AI for writing final submissions imposes a significant cost on the research community: it becomes unclear if an AI-generated text includes only human-generated and human-verified ideas, for which humans will take full responsibility. We believe that in order to justify these costs on an already-strained system, there must be a corresponding benefit to the community. We hold that delegating the writing of the final paper to AI does not offer that benefit. 

We thank the community for their sustained interest and engagement in the NeurIPS Position Paper track. We hope these decisions will not only ensure that authors are abiding by stated policy, but also elevate the quality of submissions that reviewers and area chairs will be dedicating time to this year. 

Alex Lu, Seth Lazar, David Rugamer
NeurIPS Position Paper Chairs

Stanley Hua, Kate Metcalf
NeurIPS Assistant Position Paper Chairs

Ethics in ML research is an important part of ensuring the integrity and impact of scientific work, and NeurIPS 2026 is looking for Ethics Reviewers to support this process. If you are able and willing to participate in the review process, please sign up at this form. Feel free to share this call with your colleagues.

Key Dates

We ask that ethics reviewers

• Review up to 5 papers each,
• Provide ethics reviews during at least one of the following periods:
  • July 6 – 20, 2026 (main ethics review period), * 
  • July 22 – August 13, 2026 (emergency review period – review requests may arrive at any point during this period)

* Note that ICML 2026 takes place July 6-11.

A full list of relevant dates for the conference is available here.

Again, the sign-up form is here: NeurIPS 2026 Ethics Reviewer Self-Nomination 

About the ethics review process

The main reviews conducted through the program committee (reviewers, program chairs, and area chairs) are, and continue to be, the sole decision-making process for accepting or rejecting papers for publications at NeurIPS. Reviewers are expected to review submissions not just for pure technical merit, but also in the context of the NeurIPS Code of Ethics.

The ethics review is a second round of review that takes place mainly when the program committee flags any potential concerns during the main review phase that merit further attention. Ethics reviewers provide feedback to the program committee regarding risks and harms of the work in line with the NeurIPS Code of Ethics, and recommend potential mitigations for authors to incorporate when revising their submissions.

The ethics review process is not a disciplinary or punitive process. However, in rare situations, the NeurIPS program committee may decide to reject submissions that have grossly violated the NeurIPS Code of Ethics, taking into account recommendations from the ethics reviews. In past instances where this occurred, the authors were provided with substantial guidance and relevant citations and were invited to revise and resubmit to NeurIPS.

As detailed in the Ethics Guidelines for Reviewers, the ethics reviews generally follow the double-blinded review process of the main reviews. ​However, additional steps are taken in order to minimize exposure risks. During the ethics review process, any submissions flagged for ethics review will not be publicly labeled as such. During the author response period, ethics reviews will be anonymized when made visible to authors and main reviewers. When the final accept or reject decision has been made, authors who have accepted papers may, at their discretion, choose to make their ethics review public.

Thank you for your consideration,2026 ETHICS REVIEW CHAIRS
Stephanie Hyland, Principal Researcher, Microsoft Research
Emanuel Moss, Senior Research Scientist, Intel
ethics-review-chairs@neurips.cc

When Joelle Pineau launched the first Machine Learning Reproducibility Challenge at ICLR 2018, it was a small community experiment: could we systematically invite researchers to reproduce published results and share what they found? 

Eight years and eight editions later, we are delighted to announce that MLRC 2026 will be an official track at NeurIPS 2026 –  the first time in MLRC’s history that reproducibility science has a dedicated home inside a major ML conference.

This milestone reflects the community’s growing relationship with reproducibility. NeurIPS wanted to send a meaningful signal to the field that reproducibility has become a scientific question worthy of its own rigorous study – this official track allows that to happen.

From challenge to venue

The early iterations of MLRC (v1, v2, v3) were structured as challenges: pick a paper, try to reproduce it, and report what happened. They were enormously valuable as an educational exercise, especially for early-career researchers. Courses like FACT AI at the University of Amsterdam built entire curricula around the challenge, and the quality of the work that came out of those courses showed just how seriously students took the opportunity.

As we reflected on what we learned across those iterations, it became clear that MLRC should be more. Reproducibility is not a binary outcome: a paper is never simply “reproducible” or “not.” The most insightful submissions were always the ones that engaged deeply with the specific claims of a paper, pushed those claims into new settings (generalization), tested their limits, discovered novel insights on top of the paper’s claims, and reported back with nuance. We wanted to create a venue that actively sought out that kind of contribution. Over the years, we improved the program to incentivize these submissions. 

Reproducibility studies are also not easily rewarded by the ML community’s standard metric of novelty: they do not propose new architectures, beat state-of-the-art numbers, or introduce new datasets. Getting researchers to invest serious effort in this kind of work required building a publication and recognition path that made that investment worthwhile. We have updated this systematically over the years: early iterations published through ReScience, a respected open journal for reproducibility across computational science; then, in 2023, we transitioned to TMLR, bringing MLRC papers into a high-prestige, well-indexed ML venue with a rigorous open review process. 

While the initial MLRC operated primarily as satellite workshops to conferences, in 2022 (and 2023), we partnered with NeurIPS in the Journal to Conference track, where the reproducibility papers were presented in poster sessions alongside the conference papers. In 2025, we ran MLRC’s first in-person conference at Princeton University, further elevating the incentive in the form of a dedicated conference and physical stage, inviting keynote speakers to present, and having a full-day event with orals, posters, and networking sessions. Each step raised the incentives, and the NeurIPS track is the next step in that progression.

What this means in practice

MLRC 2026 accepted reproducibility papers will be presented in person at NeurIPS 2026 in Sydney, Australia (December 6–13, 2026), alongside papers from the Main Track and the Evaluations & Datasets Track. The submission and review process remains anchored in TMLR. Reproducibility papers must first be accepted at TMLR within the eligibility window, and then undergo a light compatibility review by the MLRC committee to confirm suitability for the track.

This TMLR-first model is deliberate. TMLR’s open, continuous reviewing cycle allows authors to refine their work and get expert feedback before it is considered for presentation. It also means that accepted papers carry the full weight of TMLR’s review standards, independent of MLRC. The MLRC committee’s role is then to identify papers among accepted TMLR submissions that represent the best of reproducibility science and would benefit from the visibility of a NeurIPS venue.

What we are looking for

MLRC has always welcomed a broad range of reproducibility work, and that continues this year. We are looking for papers that take reproducibility seriously as a scientific question — not just as a means to an end, but as a contribution in its own right. This includes:

• Reproductions and replications that rigorously test specific claims from published papers, whether they confirm, partially replicate, or fail to reproduce prior results
• Generalizability studies that extend original findings to new settings, datasets, or model architectures, adding insights that the original paper could not offer
• Meta-reproducibility studies examining reproducibility patterns across a body of related work
• Methods and tools that make reproducibility research more accessible or rigorous
• AI-assisted reproducibility, including studies that use or critically evaluate automated approaches to replicating research papers
• Reproducibility of AI systems and agents as subjects of study in their own right

An important point to note: negative results and partial failures to reproduce are as valuable as confirmations. Science advances by understanding where claims hold and where they do not. A careful, well-documented failure to reproduce a result — with a clear account of what was tried and what was found — is a genuine contribution to the literature.

Note: Work focused on evaluation methodology more broadly may also be a good fit for the Evaluations & Datasets NeurIPS 2026 track. We encourage authors to consider both venues when deciding where to submit.

How to submit

To be eligible for MLRC 2026, your reproducibility paper must be accepted as is / with minor revisions to TMLR and must have been submitted to TMLR between June 20, 2025 23:59 AOE and September 30, 2026, 23:59 AOE. Please check our CFP for more details.

We accept submissions through three paths:

Path 1: Expression of interest (EOI) before acceptance. If your reproducibility paper is currently under review at TMLR and was submitted within the eligibility window, you may submit this EOI form to express your intent to be considered for MLRC. The “intent to submit” deadline is June 4, 2026, AOE – this is primarily a soft deadline to allow you to submit your paper to TMLR well in advance, so that you get your decisions in time. If your reproducibility paper is subsequently accepted to TMLR, you will be asked to update the form with your acceptance details and camera-ready materials. The hard deadline by which we need your TMLR paper’s acceptance decision is September 30, 2026, AOE – we cannot accommodate your paper into MLRC any later than this date, even if you are still waiting for your TMLR decisions.

Path 2: Self-nomination after acceptance. If your reproducibility paper has already been accepted to TMLR and was submitted within the eligibility window (start date June 20, 2025 23:59 AOE), you may submit this form to be considered for MLRC. Once accepted to MLRC, you will be asked to update the form with your camera-ready details. Similar to Path 1, the hard deadline to submit this form is September 30, 2026, AOE. Ensure you have not submitted your accepted paper to the NeurIPS 2026 Journal to Conference track, as our dual submission policy restricts dual presentation.

Path 3: Area Chair nomination. TMLR Area Chairs may nominate accepted reproducibility papers within the submission window for consideration at MLRC. No action is required from authors unless they are contacted. Area Chairs can use the same form to submit their nomination. The deadline for TMLR Area Chairs to nominate reproducibility papers is also September 30, 2026, AOE.

For all paths, please read the TMLR author guidelines and submission guidelines for formatting and review process details.

The hard deadline to have your TMLR acceptance in our system is September 30, 2026 AOE.

Important Dates

• Earliest date of the acceptance of your TMLR reproducibility paper for consideration to this year’s MLRC: June 20, 2025 23:59 AOE
• Soft deadline (EOI / intent to submit): June 4, 2026, 23:59 AOE
• Deadline to have TMLR decisions in our system: September 30, 2026, 23:59 AOE
• Author notifications: October 7, 2026
• NeurIPS 2026: December 6–13, 2026

Closing Thoughts

Reproducibility has always been foundational to science. What MLRC has tried to do, across eight editions, is make reproducibility a first-class research activity in machine learning — one that is worth investing in, publishing, and being recognized for. Having MLRC as an official NeurIPS track is an affirmation that the community values this work, and we hope it encourages more researchers to take reproducibility seriously as a scientific contribution.

We look forward to seeing the community’s work at NeurIPS in Sydney. Please visit the MLRC 2026 website for full details, and do not hesitate to reach out at  at reproducibility-chairs@neurips.cc with any questions.

This year, to improve transparency and responsible use of datasets, we are introducing a new requirement for dataset submissions in the NeurIPS 2026 Evaluations and Datasets Track: all dataset submissions must now include Responsible AI (RAI) metadata as part of the dataset’s Croissant file.

Dataset submissions to the track are already required to include a Croissant file, a standardized, machine-readable metadata format for describing datasets and their structure. We extend this requirement to also include RAI metadata within that file.

RAI metadata helps ensure datasets are used appropriately in AI research. Providing standardized information on how a dataset was created, its limitations, and its intended use helps researchers understand when and how the data can be used, reducing the risk of misuse, biased results, or misleading conclusions. This requirement also advances a core goal of the track: making datasets more transparent, comparable, and reusable across the community.

To make this as smooth as possible, we collaborated with the Croissant RAI team to establish a minimal set of RAI fields describing key aspects such as dataset limitations, potential biases, intended use, and other responsible AI considerations. Authors must include the RAI information directly within the Croissant metadata wherever possible, or clearly link to relevant sections of the paper when needed.

To support authors, we provide two tools to streamline this process:

• An online RAI editor to help complete the required RAI metadata fields
• A Croissant validator to ensure files are complete and correctly formatted

We recommend completing these steps as early as possible, and inform authors that dataset submissions missing RAI metadata from their Croissant file will be flagged during review.

For full details on the dataset hosting and submission requirements, please see the Call for Papers and the data hosting guidelines. 

Joaquin Vanschoren, Konstantina Palla, Jessica Schrouff, Alexandre Drouin, Lijun Wu

Evaluations and Datasets Track Chairs 2026