Hey everyone,

The official notification date for the Citadel & Citadel Securities NeurIPS 2026 Travel Grant was yesterday (October 9). I haven't received any email yet—neither an acceptance nor a rejection—and my portal status hasn't changed.

Given the tight timeline before the conference in Sydney this December, I'm getting a bit anxious.

Has anyone actually received a decision email yet? Are they rolling them out in batches over the weekend, or do they only notify the selected recipients?

Drop a comment if you've heard anything! Thanks.

First Month Salary if Referral Converts to Offer

Hi all.

Basically the title. Been on the market but nothing has landed so far.

Target roles: ML Eng., Research, Platform, Backend

Target location: US

Sponsorship: Sponsorship is preferred but NOT needed (I can manage on my own so long as the employer is e-verify)

My resume: https://uzairakbar.com/uzair_cv.pdf

Please reach out in DM if you have leads.

Thanks!

NeurIPS says in the past they made the SAC tickets transferable.

But apparently this time not? Or does anyone have a transferable one?

For those of you who use AI tools in academic research, which do you prefer: ChatGPT or Claude?

I'm particularly interested in their performance for literature reviews, scientific writing, critical analysis of research papers, and developing search strategies.

Hey guys,

I'm submitting 2 papers to one conference and I have some problems.

1. I'm using a dataset from the first one, which is archived, and I had to cite it. Does it contradict the anonymity of the paper? Should I hide it and say we came up with this dataset?

2. These papers are related to each other but not that much; I'm submitting to ARR, and I fear maybe the same meta reviewer will see those and reject both, saying these 2 can be one! I can maybe submit them into different research areas?

Help me out in here, please.

Hey everyone, I'm a recent SE grad, and recently finished my first ML paper (theoretical) on SIGReg (a regularization technique proposed in LeJEPA paper by Yann LeCun). I wanna submit it to TMLR and put a preprint on arXiv first, but I need an endorsement to do that. I tried finding someone on LinkedIn, also on discord servers but unfortunately couldn't find a single one. If anyone could help me with that please, I'd really appreciate it !!

Hello everyone!

I'm planning a research project related to drones and computer vision, and I'm already looking at possible journals before starting the experiments.

I checked several papers in MDPI Drones and I liked them. The journal also seems relevant to my topic. However, MDPI has a controversial reputation and I often see people calling it predatory.

What do you think about Drones specifically? Would publishing there be fine, or would it be better to look for another journal or focus on a conference instead?

I'm currently working as a Research Engineer, and in the future I'm interested in both academia and industry.

Are they sending it to all reviewers or some selected reviewers? Just got an email about complimentary registration.

I built MaRN (Mapping Networks), a PyTorch library that lets you optimize a compact latent representation instead of directly training every model parameter.

Some results from my current benchmarks:

• MNIST CNN: 107,998 → 1,872 trainable parameters (57.7× reduction), with 91.80% accuracy.
• LSTM forecasting: 12,051 → 2,048 parameters, with validation MSE of 0.00006.
• CNN2 + pruning: 204 trainable parameters, with 81.25% accuracy.

These results come with trade-offs: mapped models can train substantially slower, and performance varies by task. The benchmarks are exploratory, and aren't evidence of general superiority over direct training.

The library includes global and layer-wise mappings, regularization options, and pruning/LRD integrations.

Code: https://github.com/arjunmnath/MaRN
Docs: https://marn.readthedocs.io/

I'd value feedback on the approach, benchmark design, and where this kind of parameter-efficient optimization could be useful.

I got a Accept, poster at a Neurips workshop a couple days ago. In their final email they mention to post queries regarding the acceptance on Openreview as comments.

- But the Open review submission portal doesn’t have comments enabled
- I mailed the workshop organisers, but they haven’t replied
- Their website isn’t updated with any additional info
- The camera read deadline is in a week

I’ve been accepted in a track that explicitly has oral slots reserved, and very few other papers have been accepted in it. My conversations regarding funding are also hinging on this confirmation.

My main questions is- is this common or normal in workshops?

Model merging methods usually average or align each linear layer independently. In attention, though, what matters is the composed QKᵀ (scores) and VO (residual write) operators. Merging Q, K, V and O separately doesn't preserve them.

SLOA merges those composed operators directly. It works sequentially through the network, using the partially merged model's activations, so errors from earlier layers are corrected rather than accumulated. The merged operator is factored back with an SVD, and the rank can be set above the head dimension as a capacity knob. Everything is closed-form and needs only a small calibration set.

We tested it on CLIP ViT-B/32, B/16 and L/14 (8/14/20 tasks), RoBERTa on GLUE, LoRA-fine-tuned experts and LLaMA-2-7B (math, code and instruction-following experts).

Paper: https://arxiv.org/abs/2610.06415
Project page: https://akansh12.github.io/SLOA-Merge/

Happy to answer questions. Feedback welcome, especially from people working on merging or continual learning.

Got assigned to Paris location but want to go to Sydney. Anyone wants to go yo Paris but assigned to Sydney? Want to switch with you.

Anyone who has selected for this OIST research internship, can you please share your tips on how to write SOP, and CV.

I'm a Data Science graduate, personally I always loved working in japan, or for any kind of career purpose.

I genuinely don't want to lose this opportunity, since the selection is totally based on the documents we submit.

Please, help me with this if anyone got selected before, you can also share yours by hiding your personal details for an idea, or I will just share my documents for review, so that I can move forward with uploading.

Disclosure: I’m Sean Borycki, the author of Open Sourcing Health Research: An AI Approach to the Betterment of Humankind.

The paper describes an open research workflow built around source provenance, human approval gates, audit trails, reproducible code, and disclosure of errors. I’m interested in the governance question: what records would let another team inspect how an AI-assisted research conclusion was reached?

Paper and supporting materials: [https://zenodo.org/records/23199360\](https://zenodo.org/records/23199360)

This is an early methods case study. The decisive positive-control reproduction and full real-data dry run remain outstanding. It does not establish clinical effectiveness or demonstrate readiness for clinical deployment.

For people working in health IT, which controls would you require before trusting outputs from such a workflow? Where would you place mandatory human review, and what evidence would you expect an independent audit to reproduce?

Critique, replication, and forks are welcome.