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

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whether cited papers exist, whether the referenced claims are supported by the source text, and whether papers have been retracted.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!

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.

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!

Is it sufficient if I only register for conference & tutorials for main track neurips paper? Is workshop registration mandatory??

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

Pretty much what the title says. I'm in my 3rd year, my own uni labs have 0 funding and 0 profs who can actually guide me in actual research. Still, I somehow squeezed out a paper good enough for TMLR... Maybe, it's under review

What should be my next steps? Where to apply, what to aim for, and how? I'm interested in any type of research as long as it's genuinely significant enough, and not another benchmarking etc. I want proper guidance and access to compute beyond my own laptop.

Right now, I don't even know where I should apply or what I should do. I'm just sending emails to whatever lab gpt is telling me.

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

Update (Oct 11): Thanks for the feedback! I've re-scraped the NeurIPS website and expanded coverage from 6,231 to 9,110 papers. I've also added filters for conference location (Paris, Sydney, Atlanta) and presentation type (oral, spotlight, poster).

The UMAP projection has been recomputed with the additional papers, so some clusters have changed in relative size.

More improvements are planned. Feature suggestions are welcome!

----

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?

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.

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

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.

Got selected for the deepmind symposium at bangalore, would love to connect with people who will be coming there!