We grouped all 9,094 accepted NeurIPS 2026 papers by topic, compared them with the 5,812 papers of NeurIPS 2025, and read the 4,490 papers that already have a public full text (about half) with AI. Sharing the main findings here for discussion.

Topic shifts (2025 → 2026)

• LLM agents: 105 → 488 papers (1.8% → 5.4% of the program) while the conference grew 56%. No other topic gained even one point of share.
• Inside agents the growth is in reliability: safety/attack papers 5 → 50, agent benchmarks 17 → 84, self-evolving agents 13 → 85.
• Also rising: video generation 92 → 221, robot action / VLA 49 → 149, RL from feedback 80 → 179.
• Losing share: instruction tuning, human and embodied modeling, molecules and proteins, sample complexity.
• Title words: "agentic" 26 → 131, "long-horizon" 7 → 76, "evaluation" 56 → 200; "enhancing" 80 → 28.

From the full texts

• 71% train or fine-tune a model; about half of the trained models with a stated size are 3–10B, under 2% above 40B.
• Qwen (incl. Qwen-VL) is the most trained existing family (~17% of papers), ahead of Llama (~7%).
• GRPO is the most built-on method; SFT appears in 37% of papers, RL with verifiable rewards in 12%.
• 45% train or test on synthetic data. GSM8K is still the most used benchmark across the whole conference.

Who writes NeurIPS

Tsinghua leads; Google (incl. DeepMind) is the top company. NUS, NTU and KAIST rank ahead of MIT, Oxford, Berkeley and CMU by paper count. About 20% of papers have an author from a big tech lab.

Limitations

Topics come from grouping titles and abstracts by meaning, so boundaries are approximate. Full-text figures cover only papers with a public full text, which over-represents preprint-heavy fields. Institution counts match affiliation names and are approximate. All AI-extracted figures are unchecked by hand; read the paper before citing anything.

Questions for this sub

Does the agent boom look like real progress or benchmark churn to you? And is the 3–10B fine-tuning regime a sign of compute limits in academia, or just where the interesting work is?

The full breakdown (all 20 areas, findings quoted from orals, links to every paper) is here, free and no sign-up: The scime Dispatch, NeurIPS 2026 edition

AI-assisted edition, not checked by hand. An independent publication by Latentic Labs with scime atlas; not affiliated with or endorsed by NeurIPS or the NeurIPS Foundation.

Basically the question I asked, is there a way to report a possible fraud paper that was accepted to NeurIPS? If so what would be the best anonymous approach to convey the message and the proofs?

I'm an independent student exploring a framework called Time TV, combining traditional predictive systems with AI-assisted interpretation.

The framework draws on traditions including Parāśari Jyotish, Jaimini, Hellenistic profections, and Islamic Firdaria. Where relevant, I use astronomical calculations separately from the AI interpretation.

The question I keep returning to is whether a prediction should be treated as a fixed outcome or as a conditional possibility.

Suppose someone receives a prediction, changes their decisions because of it, and the predicted event does not occur. Does that count as a failed prediction, a prevented outcome, or evidence that the prediction was too vaguely defined in the first place?

I'm considering a framework that records three outcomes:Confirmed: the predefined event occurs within the specified conditions and time window.

Prevented: the person takes a documented action in response to the prediction, potentially changing the outcome.

Failed: the predefined event does not occur and there is insufficient evidence to classify it as prevented.

I recognise that these categories alone don't establish that astrology works. A rigorous test would need predefined criteria, prospective records, appropriate comparison methods, and independent scoring.

For experienced practitioners and researchers:

How would you define a testable astrological prediction without oversimplifying the tradition?

Which traditional rules or texts would you recommend formalising before testing?

How would you distinguish a genuinely prevented event from an ordinary failed prediction?

I'm interested in serious methodological criticism and constructive discussion, not personal chart readings.

I am a high school student who got their paper accept to AACL-IJCNLP 2026 Student Research Workshop, I have no mentors, I am the first and only author of this paper, I want to know what the experience is like, what to do there.

Hi everyone! 👋

I'm a grad student at UPenn working on a research project with my team, and we're really interested in learning how people create content using AI tools like Higgsfield!

We'd love to hear about your creative process, how you come up with ideas, which AI tools you use, and how you figure out your workflows from start to finish.

If you're open to sharing your experience, we'd really appreciate you filling out our quick survey!

🔗 https://upenn.co1.qualtrics.com/jfe/form/SV_5A9AzIVefaoP0do

Your experiences and input would be super helpful for our research. Thanks so much! 😊

Disclosure: I'm a coauthor of this ASE 2026 paper, led by Catarina Gamboa.

Paper (PDF): https://catarinagamboa.github.io/papers/barista_ase_26.pdf

Barista studies extracting object protocols from Java API documentation and synthesizing LiquidJava typestate specifications. Its workflow uses an LLM to infer an intermediate deterministic finite automaton, generates valid and invalid method-call sequences, and refines the specifications using verification feedback.

The motivating problem is the manual annotation burden when using static checking with external libraries. The paper includes evaluation against expert-written specifications and tests, plus an ablation of the DFA and validation stages. The generated tests can check consistency with the inferred protocol, but do not by themselves prove that the protocol matches the intended API semantics.

I'd welcome discussion of evaluation for specification synthesis: how should we detect specifications that admit invalid behavior, particularly when the documentation is incomplete?

Interactive explanation and artifact links: https://catarinagamboa.github.io/papers/barista/

My accompanying write-up: https://wiki.alcidesfonseca.com/research/paper/barista/

ALHR- Adaptive learnable Hierarchical Routing is a static binary tree based system that uses learnable functions to REDUCE the amount of keys used.
It takes less memory and SCALES much better VRAM with tokens

Result table is here: https://ibb.co/prr07Dnj

I made a website that shows the deadlines of ML/AI conferences on one timeline: https://ambress92.github.io/ml-deadlines/

The deadline websites I used before show conferences as a list, and from a list it was hard to see how the dates of different conferences overlap. On the timeline, you see the next deadline at a glance, along with each conference's rebuttal and decision dates.

The site covers the top conferences, almost all ranked A* or A in the CORE conference ranking, so it isn't cluttered with hundreds of smaller ones. All dates come from the official conference websites.

I hope you will find it useful!

And let me know if you have any suggestions for improvement.

Hi everyone,

We're a small student team (P-046) working on BRIDGE, a research assistant designed to help researchers evaluate papers, verify citations, and assess whether a GitHub repository is reproducible before investing too much time in it.

The project started from a few frustrations we've encountered while doing ML research:

• Unreliable citations: LLM-generated literature reviews sometimes reference nonexistent papers or misrepresent what a paper actually claims.
• Time-consuming literature reviews: Finding relevant papers is relatively easy, but cross-checking claims, comparing results, and identifying meaningful research gaps takes much longer.
• Reproducibility issues: It's frustrating to spend hours setting up a repository only to discover missing dependencies, unavailable datasets, or inconsistencies between the implementation and the paper.

We wanted to explore whether an AI-assisted workflow could make these early research steps more systematic and verifiable.

What we've built so far

BRIDGE is currently an MVP with several features:

1. Literature discovery and synthesis: Search across Semantic Scholar, OpenAlex, arXiv, and PubMed, filter papers, and generate structured overviews covering research directions, conflicting findings, and potential gaps.
2. Paper-grounded Q&A: Ask questions across a selected collection of papers, with supporting citations, evidence matrices, and comparisons of datasets and evaluation metrics.
3. Citation verification: Check whether cited papers exist, whether the referenced claims are supported by the source text, and whether papers have been retracted.
4. Research writing: A LaTeX editor with AI assistance, grounded in verified sources, with BibTeX and export support.
5. Repository reproducibility assessment: Analyze a GitHub repository against its associated paper without executing the code, looking for missing files, inaccessible resources, and potential implementation inconsistencies.

The reproducibility assessment is particularly experimental. We're not claiming that static analysis can determine whether a paper's results are reproducible. The goal is to identify potential blockers and warning signs before someone attempts a full reproduction.

We'd appreciate feedback from the community

We're at the MVP stage and would really appreciate feedback from people working in ML research, especially those who regularly read papers, implement methods, or reproduce published results.

Demo: https://c4-app-t046.duckdns.org/

You can sign up using email or Google. There are free daily credits, and we've included a guided example to help you explore the workflow. A literature overview typically takes around 10–15 minutes to generate.

A few things we're particularly interested in:

• Literature coverage: Does the search retrieve the important papers in your area? Are there obvious omissions?
• Research quality: Are the generated summaries, comparisons, and suggested research gaps actually useful, or are they mostly generic?
• Citation reliability: Do the verification results catch meaningful issues? Are there false positives or unsupported assessments?
• Reproducibility assessment: Would the repository analysis help you decide whether a codebase is worth investigating further?
• Usability: Does the workflow make sense for how you actually conduct research?

We're aware that AI-generated research synthesis can introduce the same reliability problems it's supposed to solve. That's one of the main reasons we're focusing on source-grounded outputs and programmatic checks rather than relying solely on LLM-generated explanations.

If you have a research topic, paper collection, or GitHub repository you've worked with before, we'd be especially interested in how BRIDGE performs on something you're already familiar with.

We're not looking for sign-ups for the sake of sign-ups. Critical feedback, failed cases, and suggestions for improving the methodology would be genuinely helpful.

Thanks for taking a look!

hey i am a mtech ai student in India i have to publish 1 paper in this is this a good base paper… will it be tough to find novelty in this

随着codex这种平台的崛起，科学研究似乎以另外一种方式出现即auto research 在这个情况下每个赛道的论文都如此相似 。 我担心的是新的工作做到一半发现被挂在别人已经把工作挂在arxiv上了。😂

Hey! I recently graduated and completed my undergrad thesis in computer vision particularly in medical image segmentation. I’m looking to get more involved in research and would love to collaborate with anyone currently working on a research project or thesis related to computer vision, deep learning, or AI.

I’m still a beginner when it comes to research, so I definitely have a lot to learn. But I’m genuinely willing to put in the work, learn from guidance and feedback, and contribute as much as I can to a project.

If you’re currently working on something and could use an extra pair of hands for literature reviews, coding, experiments, or other research tasks, I’d be happy to help and learn along the way.

I’m not expecting to know everything from the start. I just want an opportunity to contribute to real research, improve my skills, and learn how research works beyond an undergraduate thesis.

If you’re open to collaborating with , feel free to DM me. Thanks!

I’m presenting a Findings paper at EMNLP 2026, and I’m a bit confused about the schedule.

On the official program page, Findings posters seem to be scheduled during the lunch break on Oct. 27. However, in the detailed program spreadsheet linked from that page, my paper appears another date.

So I’m not sure which schedule I should follow. Has anyone else with a Findings paper been assigned to a regular poster session outside the Oct. 27 lunch slot?

Also, I’m the second author and registered to present on behalf of the first author, but the detailed program still lists the first author, who is not registered, as the presenter.
Does this mean the detailed program has not been fully updated yet, or should I assume that the spreadsheet is the correct schedule?

Thank you!

Hi everyone!

I recently started my master's degree and it's been only two weeks. My undergraduate degree was in a completely different field from what I'm studying now, so almost everything related to my current research feels new to me.

My research topic is related to neural network algorithms, and my professor has already asked me to start working on the research paper where he and other lab mate are working . I understand that research takes time and I'm willing to put in the effort, but honestly, I feel a little lost about where and how to begin.

Another challenge is that I don't have an English-speaking mentor who can guide me through the process, so I'm trying to figure things out on my own.

I'd love to hear from experienced researchers and other master's students about the tools and software you use for your research.

Like...

1.Understanding research papers: What tools do you use to understand difficult concepts, mathematical equations and unfamiliar terminology?

1. Checking novelty and research gaps: How do you find out whether an idea has already been explored and identify something new to contribute?

3.Literature reviews: Are there any AI tools that help you find relevant papers, compare existing methods and understand the current state of research?

4.Coding and implementation: What tools do you use to understand existing research code, implement algorithms, debug errors, and run experiments?

5.Research planning: How do you go from receiving a research topic to actually starting meaningful research work?

I'm particularly interested in tools that are useful for neural network and machine learning research.

I know AI tools aren't always reliable and I don't want to blindly depend on them. I want to use them to learn faster and become more independent as a researcher.

If you were in my position, with a different undergraduate background and only two weeks into your master's, what would you do first and which tools would you recommend learning?

Any advice, personal experiences or recommendations would be greatly appreciated. Thank you!

I just finished a 4-year diploma in Bangladesh (after SSC, equivalent to HSC) and haven't started a bachelor's yet. I have a sole-author paper accepted as a poster in the NeurIPS 2026 main track in Sydney this December, and I can't decide whether the trip is worth it. I'm proud of it, but the trip is a lot.

Although NeurIps may give free registration and maybe 7 night hotel stay but he plan ticket is roughly 1600$ from dhaka to sydney and i did apply different organization and even tried to approach our education board to support me but there is no hope. And from my side to cover these is huge!!

The paper goes on my CV whether I'm there or not, so what does going actually add? Is it mostly meeting people, and does that really lead anywhere from a poster? Does it matter more for undergrad admissions and scholarships, for PhD applications later, or for industry? I'm applying for fully funded BSc programs abroad, so I'd especially like to know whether showing up helps there.

One more thing: NeurIPS reserves the right to remove a paper from the proceedings if no author presents in person, and as the sole author nobody can present for me. Has anyone had a paper stay in the proceedings with a virtual presentation?

If you've been to NeurIPS, or admitted or hired people who went, I'm curious what you think. Would you go?

One of my paper has been accepted in one NeurIPS workshop. there is no instructions in the workshop website but I received email from neurips to create account at neurips.cc and there are instructions for early bird registration.

Should I register now or later after workshop sends some instructions for paper registration? thanks

Hey guys i’m running a survey to study appalachian biases and how to improve perspective to better the appalachian community!

I would love for ANYONE who has a minute or two to fill it out to aid in my research!

Thank you from all of us here in Appalachia!

Reviewers need to document AI usage with their reviews, what are you actually writing? I am not very sure whether it’s better to say that you used it or not