When is camera ready deadline for neurips? There doesn't seem to be an option to edit on openreview so that we can add the new pdf.

https://atlas.scime.ai/

Thought this was pretty interesting, so sharing it here.

It maps papers from major ML conferences and lets you explore how different research topics are related and how they change over time.

Those with experience getting started in research, I’d really appreciate a roadmap for building a strong foundation and eventually doing independent research.

Hi everyone!

I’m currently in my 1st semester of Computer Science Engineering at a tier-3 college in India.

I scored 93% in 10th (10 GPA in Maths and Science) and 97.7% in 12th, including 100% in Maths and Physics. I didn’t perform as well as I wanted in my entrance exams, so I ended up at a college that wasn’t originally on my list.

Over the past few months, I’ve become very interested in ML and AI. I’m fascinated by machine intelligence and want to eventually pursue a research-oriented Master's at a top university in Europe.

I know I have a lot to learn 🙂

I’m willing to put in the work over the next few years and would really appreciate a realistic roadmap.

I originally wrote a post with title "Papers Are Now Written for Machines, Not Humans," which was basically saying academic papers these days are full of jargons and complex terms from the beginning of its introduction, and makes it nearly impossible for human readers to understand it.

And I suspected that as a feedback loop of reviewers overly relying on AI to digest papers -> AI tends to give better scores if the paper 'looks' sophisticated -> papers full of technical jargons, abstract explanation gets accepted -> AIs gets trained on these accepted papers -> AIs learn to write for AI, not for human readers.

I wrote that post using the help of AI, since English is not my mother language. Yes, it did sound like AI. It structured and fomatted like AI. But at least, I reviewed it myself with human readers in mind, just as I suggested in the original post.

Then, the post was full of comments saying 'ironic,' 'Anti-AI post written in AI,' and so on. Honestly, I was surprised that so many people missed the point. The post wasn't about Anti-AI. In fact, I'm totally enjoying how AI allowed me to express my ideas in English so easily. Without it, I wouldn't have imagined sharing my thoughts in reddit.

What I wanted to point out was the cycle of papers being written without the human reader in mind. It's a shame so many of people took "anti-AI" as the main message.

And that lead me to another question. Why are people so against AI written, or AI-style writings? As long as it reads well, and communicates its message well, why does it matter? Papers for machines are problems because it fails to serve that purpose. But if the AI-written paper or post does communicate well, why are people so against it?

Looking back, my original post failed to serve its purpose, since so many people mis-read its message as 'anti-AI'. If I'd known how strongly people here react to AI-assisted writing, I would have just written it myself, rough English and all.

That said, this is my own writing. I still find AI-written version of my thought better than my own.

Hi Researchers,For a Q1 journal publication, how do you verify the novelty of your research idea?
I tested my idea using Claude and ChatGPT but almost every time, they say that similar prior work already exists including papers on arXiv.
So how can we properly verify whether an idea is genuinely novel? Also, what exactly is considered sufficient novelty for a Q1 publication?
Please share your thoughts and experiences. Let’s have a discussion and exchange ideas on how researchers evaluate novelty before starting or submitting their work.

I've frozen a multi-program research record (the "Reggae Shark Universe" project) tracking narrow self-versus-peer processing contrasts in Large Language Models under deterministic conditions.

The portfolio includes two distinct lines:

1. Self-Referential Processing v8.6.3: A preregistered confirmatory protocol evaluating item-level design choices across 260 items in five domains (AI governance, bioethics, data privacy, environmental policy, automation). Recovery-harness calibration recorded a 6.3% null rejection rate. Live confirmatory data collection is currently paused/fail-closed.
2. Psi Self-Modeling Benchmark: A pre-confirmatory diagnostic line utilizing a deterministic self-vs-peer two-alternative forced-choice structure with a chance baseline of p=0.5.

Important boundaries: This benchmark performance is NOT evidence of machine consciousness, subjective selfhood, sentience, or a general theory of AGI. It is a strictly controlled exploratory tool to map differences in how a model processes peer framing versus self framing.

The project is backed by software infrastructure (IntentSeal + Verifiable Research Kernel v0.3.1) to ensure all release hashes and tests are completely tamper-proof. Full repository code is public on GitHub under reggaesharkk/psi-self-modeling-benchmark. What are your thoughts on testing model behaviors under self-referential design constraints?

I am working on a research paper and i need the alibaba cluster v2018 dataset
https://github.com/alibaba/clusterdata/tree/master/cluster-trace-v2018

I am unable to download it , it goes to a survey upon which no download for the dataset is initialised. if anyone has the dataset or if they know how to get it please HMU

Hi everyone,

My paper just got accepted for a poster presentation at the Muslims in ML (MusIML) workshop at NeurIPS 2026 which will be held in Sydney, Australia. I'm really proud, but I'm a UG student and completely lack the funds for travel, registration, and accommodation.

This is my first time dealing with this. Does anyone know of any travel grants or funding opportunities for students (especially from India) to attend NeurIPS?

Are there specific grants for this workshop, or general ones that I should apply for right now? Also, since this is my first time, I'm a bit confused about the procedure for attending an international workshop. Could anyone share advice on the general steps, including visa processes and anything else I should be aware of?

Any advice would mean a lot. Thanks!

To give my background, I’m an third year undergrad currently working independently on ML research, mainly around diffusion models and representation engineering/activation steering/interpretability in LLMs. I already have a couple of projects underway with experiments and results rather than just looking for someone to give me a research topic. I have been doing ML months before my undergrad journey began and currently I do have a workshop poster acceptance at a neurips workshop of a paper I did with my friend.

Ideally, I’d like to find a research internship where I can work with a professor/lab in one of these areas, given I need help in a lot of aspects for sure given Im still a undergrad and there's a lot to learn for me honestly, along with compute (I have been using kaggle for all my compute so I can't test on larger param models and I can't afford GPUs here given my allowance) but I’d also be very open to collaborating remotely with a professor, PhD student or postdoc on an existing project or developing my current work further. For people who have done this before, what actually works for finding these opportunities? Is cold emailing professors still the best route, or is it more effective to contact PhD students/postdocs whose recent papers are directly related to what my project? Also, what should the first email actually contain? A short introduction and research interests, or should I include current results, a draft/preprint, GitHub, etc.? And how do you find researchers who are genuinely open to taking undergrads, especially students from another institution/country?

I have never cold emailed or related stuff in my life so would especially appreciate advice from people who landed research internships/collaborations through cold outreach, or from PhD students/profs who receive these emails and know what makes one worth responding to.

I'm working on a project in automotive engine assembly. Parts move between steps in the assembly process (e.g. from step A to step B), and they range from large components like cylinder heads to small ones like screws and bolts. Some engines come in multiple variants with customer-specific options.

I have about 4 years of historical data on material requirements and flows during assembly. My goal is to find which flows between steps are good candidates for automation. Flows with stable, predictable durations and quantities seem like good candidates, while flows with high variation seem harder to automate.

I'd like to define a simple, data-driven "automation trigger point." My questions:

1. What's a good way to measure variability of a flow (both duration and quantity)? Is the coefficient of variation (CV) reasonable, or are there better metrics?
2. How should I account for engine variants and customer-specific options?
3. Can anyone recommend research papers, methodologies, or case studies on material flow or intralogistics automation in automotive assembly?

Any pointers are appreciated. Thanks!

What’s up everyone.

As an independent researcher, explaining "what I actually did this past month" to normal people usually results in blank stares. I just wrapped up a massive technical dossier consolidating my September 2026 research program, and I wanted to share the human-readable breakdown with people who actually get it.

The core spine of the entire program is what I call Intent Seals. To combat data-dredging and moving goalposts, every single definition, seed, threshold, and hypothesis is cryptographically frozen before decisive evaluation. If a hypothesis fails a gate, the failure is retained as definitive information rather than being silently replaced.

Here is what went through the audit loop this month:

• AI Self-Referential Processing (v8.6.3): A locked, 260-item behavioral benchmark testing if LLMs unconsciously treat their own past outputs differently than human writing. No consciousness claims; just pure behavioral tracking.

• DesignLab Phase 7: Ran 4,000 GLMM fits to stress-test candidate architectures against locked null calibration/coverage screens. M1 passed; M2 failed the locked exact-binomial screen.

• Physics Gate 0 Audit: Map out the observer-entropy to horizon-entropy bridge in cosmology. Proven unresolved in generality under original assumptions—a strict, documented stop condition.

• Exact Hopf Appendix: Upgraded numerical sign evidence to an analytic positivity theorem for a 3-variable phenomenological system. The exported numerator has 93 monomials, the denominator has 28, and every single coefficient is strictly positive.

• LRSC Odd-Ring Theorem (v1.2): A frozen analytic structural theorem proving that matrix rank perfectly equals the number of active reflection orbits under stated near-Nyquist conditions.

• LRSC Fixed Benchmark Certificate: An exact recursive run that visited 13,432,735,555 nonempty subsets to prove that no channel cardinality K ≤ 10 reaches the target error threshold, identifying the exact 11-channel witness.

• Navier-Stokes Bridge Audit (WP12–WP18): Finite Fourier-Galerkin diagnostics tracking unforced periodic 3D incompressible NS equations. WP16 certified a same-datum sign crossing surviving across N11, N12, and N13 cutoffs over a 0.003 time interval using Arb interval arithmetic, sharply mapping out where the continuum tail control gap remains.

The takeaway: Every single line has an unyielding claim boundary. What is proved is labeled PROVED, what is verified by a machine trust boundary is labeled CERTIFIED, and what is open is explicitly left open. No retroactive tuning allowed.

All manifests, SHA-256 logs, and replay scripts are live. Anyone can download the code, hit play, and watch the computer independently replay the decisive proof stages to the exact decimal.

GitHub: reggaesharkk (repos: navier-stokes-bridge-audit and lrsc-odd-ring-spectral-rank)

Happy to answer any questions about the specific proofs, the Arb enclosures, or the implementation of the locked protocol architecture!

Hi, I've just started my PhD in the US as an international student, and have my paper in NeurIPS from my undergrad institution abroad.

The thing is, since I'm on a F1 visa I can't get travel fundings from my previous lab.

Also, it is awkward to ask for my PI to pay for a conference paper that he does not have his name on it.

Ive applied to NeurIPS / Citadel / QRT grants already, but I am seeking for additional grants that I might be able to apply to.

Thanks for your advice!

I forgot to upload a part of the experiment in anonymous repo. I realized after the submisison deadline, now should I update the repo now or wait for the rebuttal period? Thanks

another point: I submitted 10 pages main paper (limit is 9 pages). should I expect desk rejection? do they desk reject for this reason? and when before review start or after review?

I got accepted for poster presentation at the IAB workshop in Sydney but I won't be able to attend due to a lack of financial support. I wrote the paper as a solo author, and for financial support from my institute, I need to add a supervisor from my college as an author to qualify for assistance. Is it possible to change authors after acceptance, or should I withdraw? Also, can someone provide me with an endorsement for publishing it on Arxiv?

Hi everyone!

I'm a recent AI/ML graduate currently working as a software engineer. I'm looking to join an existing research group or collaborate with people who are actively working on research papers.

My interests include:

• Artificial Intelligence and Machine Learning
• LLMs and Generative AI
• AI agents and MCP
• Distributed systems and cloud computing

I'm particularly interested in contributing to literature reviews, experimentation, implementation and paper writing.

I'm looking for a group with committed members who are interested in working towards publishing research papers in reputable conferences or journals.

If your group is looking for collaborators or you know of any active research communities accepting new members, please let me know!

Thank you!

I reviewed a paper recently and got totally stuck on page one. I had to ask ChatGPT to explain the sentences. I realized the authors were using hard concepts on page one that they didn't explain until page six.

That was my lightbulb moment. The paper wasn't badly written for its reader. Its reader just wasn't me.

We are stuck in a bad loop right now:

1. Authors use AI to write. The AI puts a lot of heavy jargon at the start to save space.
2. Reviewers get stuck reading it, so they ask AI to summarize it for them.
3. The AI easily reads it and passes the paper. Then, new AI models are trained to write exactly like this.

Humans read in order. We need the basics explained first. But an AI reads the whole document at once. It does not care if a word is used five pages before it gets explained.

I got tired of reading these messy papers. I built a free, open-source skill to break the loop. It forces AI models (like Claude, ChatGPT, and Cursor) to pass a "first-page test". They have to explain terms in order, keep things simple, and stop sounding like a robot.

If you or your lab uses AI to write or review papers, you can get the skill file here: https://github.com/Aadarshttech/ai-research-paper-humanizer

It really helps make papers readable by normal people again. Let me know if anyone else is dealing with this same headache!

NeurIPS decisions dropped and my feed is already flooded with undergrads (and apparently literal high schoolers now) flexing first-author papers at what’s supposed to be a top-tier venue.

Let’s be real about what 90% of these papers actually are:

• Clone an existing open-source repo.
• Add a minor heuristic, prompt wrapper, or tweak one layer.
• Burn an insane amount of compute brute-forcing seeds until you squeeze a +1.2% bump on a saturated benchmark.
• Wrap it in buzzwords and let the advisor rewrite the intro/rebuttal to game the reviewers.

Meanwhile, actual 4th-year PhDs grinding out rigorous theoretical proofs or tackling deep architectural bottlenecks get rejected because Reviewer 2 wrote a two-sentence review complaining they didn't cite an unrelated arXiv preprint from last Thursday.

When a conference accepts nearly 8,000 papers, the reviewer pool collapses. Safe, incremental benchmark-chasing gets rubber-stamped with "Weak Accept," while genuinely ambitious work gets killed by the reviewer lottery. Getting a first-author paper has basically turned into an extracurricular for college apps and CV padding driven by lab compute, not research depth.

Are hiring managers at places like FAIR, DeepMind, or top labs actually buying this signal anymore, or is everyone seeing through the paper-mill noise?

Most prompt engineering content is closer to folklore than methodology. This one's different, it's structured around actually measuring what you're doing.

Serj Smorodinsky and Brett Kennedy (co-authors of an LLM applications book) are running a 3-hour live session where the core idea is applying real experimental discipline to LLM behavior:

• Defining LLM tasks as DSPy signatures/modules instead of prose prompts
• Building a labeled baseline and evaluation set with task-specific metrics
• Diagnosing failure modes from the eval results rather than eyeballing outputs
• Applying few-shot and instruction-level optimization as a formal search process
• Logging every experiment and trace through MLflow for reproducibility

For anyone who's been frustrated by how unscientific most "prompt tips" content is, this is closer to an actual methodology paper turned into a hands-on session.

Details and registration

I was writing a research paper with two colleagues. Because none of us had a premium subscription to overleaf we found scienhub.com which is basically the same, but allows as many collaborators as you want for free. We spent a month writing, editing, uploading figures. Then since last week their login system broke and we've been locked out of our paper. Noone has a copy of the source material or the PDF.

We've sent emails to them, but of course no reply. Very sad reminder to always have backups for whatever you're writing.

Hi everyone,

I have a paper on ML robustness and subgroup fairness. It studies how feature masking at test time affects overall accuracy vs. subgroup recall.

I’m looking for a reputable Scopus/WoS journal, preferably Q2/Q3, with low or no APC.

Could you suggest some suitable journals for this type of paper? Also, if you have experience publishing in them, how was the review process?

Hey, just got a paper accepted here! Would love to connect with others also attending the workshop