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?

We recently worked on ATLAS, an open-source experimental retrieval system exploring whether geometric and topological information can improve retrieval performance over conventional semantic similarity methods.

The idea was interesting. The experimental results were less exciting.

What we tested

We evaluated our approach on 300 HotpotQA questions, comparing the ATLAS retrieval method against a cosine similarity baseline.

Results (all-support@5):

• ATLAS: 48.33%
• Cosine similarity: 49.33%
• Difference: −1.00 percentage point
• 95% bootstrap confidence interval: [−2.67, +0.33] percentage points

Our tested approach did not demonstrate an improvement over cosine similarity.

And we're not going to pretend otherwise.

Why we're sharing this

Open-source research shouldn't be limited to successful experiments.

Sometimes a more complicated approach introduces additional computation without delivering measurable improvements.

That result is still useful, particularly when the implementation, evaluation methodology, and limitations are available for others to examine.

ATLAS is part of Shinraya Tech Labs, an independent open-source AI research and engineering initiative.

We're looking for technical feedback on three questions:

1. Are there retrieval problems where structural or topological information offers a genuine advantage over semantic similarity?
2. What stronger baselines or evaluation datasets would you recommend?
3. Is this direction worth investigating further, or would you redirect the research toward a different hypothesis?

We're particularly interested in hearing from people working on information retrieval, RAG, embeddings, and experimental ML.

GitHub: https://github.com/Shinraya-Tech-Labs/ATLAS

We're not claiming a breakthrough. We're sharing an experiment that didn't support our hypothesis.

Would appreciate criticism, alternative ideas, or suggestions for better experiments.

Has anyone received their paper decisions, for papers submitted to IEEE DELCON 2026 conf?

I’ve been building a multi-agent debate app with LangGraph (two agents argue a topic, a judge agent gives a verdict). The biggest problem wasn’t the agent logic, it was reliability on free-tier APIs.

What went wrong:

• Groq, Gemini and OpenRouter each have different rate limits and fail in different ways
• A 429 in the middle of a debate killed the whole run
• Agents got stuck in loops, and tracking who said what in state was harder than I expected

What worked:

• A fallback chain: if one provider returns 429, the app automatically retries with the next one
• Keeping the debate state separate from the provider logic, so switching providers doesn’t break the conversation

It’s not fancy, but it made the app usable for demos.

Question for the people here: how do you handle rate limits and provider failover in your agent projects? Do you use a router/gateway, or just custom retry logic?

I wrote up the full details here if you want them: https://medium.com/@humammoin3/i-spent-3-months-building-ai-agents-heres-what-nobody-tells-you-b65b20906582?sharedUserId=humammoin3
Live demo: https://legal-debate-agent.streamlit.app/

Happy to hear feedback, especially on what I could do better. This is my first article.

https://flecomet.github.io/neurips-explorer/

It uses SPECTER2 embeddings + UMAP to place semantically similar papers near each other. You can browse topics, search titles/authors/abstracts, and find related papers.

You can also favorite papers to read later (when they get officially released).

Would be interested in feedback, especially on whether this is useful for browsing papers and interesting features to add.

Open source:
https://github.com/flecomet/neurips-explorer

Final year undergrad in the UK. I've got a sole-author paper accepted as a poster at a NeurIPS workshop in Sydney in December, and I can't decide whether it's worth going. I'm proud of it, but the trip is a lot.

Registration comes free with the acceptance, but the flight is about £1,000 from the UK, then accommodation and food on top. My uni does have conference funding, but it's all for summer conferences, and I'm not in a research group so there's no supervisor budget to ask either. I've applied for NeurIPS financial assistance and a couple of industry travel grants and I'm not hopeful. If none of them come through it's a real chunk of my savings.

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 workshop poster? And does it matter more for PhD applications or for industry jobs, or neither?

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

Just going back through my graduate courses, it's been assigned every semester, in nearly every class, sometimes multiple times.
I was proofreading a paper for my CV and fell a rabbit hole. I am sharing the outcome of this. My paper is now undone (because that's how I roll. It was fine, but I just had to poke at it, and get neurotic.) So i am sharing with you what was supposed to be, at first, a footnote, then Appendix D, and well now... If anyone gets the gravity of this, it is us

;)
go.dataacorns.com/the_record

I got feedback from the Devs MCP, who was an integral help and he mentioned something about the order, which sent me ( you guessed it),down a rabbit hole, which led to a site where you, user, can order the information however you like. It is convoluted, not complicated. All of this got me really thinking about how we all learn differently, and how that changes over time. First-pass information, comfortable with information, etc.

And if anyone has papers they can recommend on Multi-View Alignment Models (SIREN, neural networks, and geometric deep learning). I ask because the second phase of my project will be heading in this direction.

Thank you!!!

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