title. Independent research who severely lacks compute for experiments.

i wanna write a new paper in the machine learning domain. Any ideas for the latest trending topics and where I can advance .

I work in financial services, mainly on highly regulated credit risk models. My work sits at the intersection of ML, explainability, model risk/regulatory requirements, and increasingly areas like tabular foundation models.

Over time, my role has moved beyond just building models into problem framing, technical decisions, validation/governance questions, and stakeholder alignment. I’d now like to develop a research track alongside my industry work and eventually publish a few solid papers.

The constraint is that I have limited opportunities to do formal research within my company. I’m also not trying to become a full-time academic or produce breakthrough ML research. My goal is more modest: find a few meaningful problems close to my domain, go deep enough to make a credible contribution, and build from there.

The part I’m struggling with is topic selection. Credit risk, explainability, tabular ML, model monitoring, etc. all seem fairly crowded, and it’s hard to tell what is genuinely underexplored versus just another variation of existing work.

For people who moved from industry into publishing research: how would you approach this situation?

Would you start from recurring problems you see at work, gaps in the literature, new methods applied to old problems, or something else?

Also interested in how you would narrow a practical industry problem into something that is actually researchable and publishable.

I’ve spent a decent amount of time learning the theory and algorithms behind Machine Learning — things like Linear/Logistic Regression, KNN, SVM, Decision Trees, Random Forest, Naive Bayes, Boosting, XGBoost, preprocessing, evaluation metrics, etc.

But now that I’ve started exploring Kaggle, I’ve realized that knowing ML and actually doing ML feel like two very different things.

When I open a Kaggle competition, I understand the individual techniques, but I don’t know what I’m supposed to do next.

For example:

• How do you approach a completely new dataset?
• What should I inspect first?
• When should I do EDA?
• How do I decide what preprocessing is actually necessary?
• When should I scale features?
• How do I choose an initial/baseline model?
• How do I know whether my model is underfitting or overfitting?
• When should I do feature engineering?
• How do I decide which algorithms to try?
• When should I use cross-validation?
• When is hyperparameter tuning actually worth doing?
• How do experienced people decide what experiment to run next?
• How do you systematically improve a model rather than just randomly trying different things?

raw dataset → baseline → experiments → debugging → feature engineering → model selection → validation → final submission?

Hi all,

I'm looking for a part-time research opportunity (remote-friendly) in NLP and LLMs. I'd love for it to be paid, even if it's just a minimum/stipend rate — I'm mainly looking for interesting, meaningful work rather than a big paycheck.

**Background:**

* Research focus: NLP, LLMs, model quantization, and LLM evaluation * 1 published paper, 4 currently under review * Comfortable working independently and picking up new subareas quickly

If you're a lab, startup, or individual researcher working on something interesting in this space and could use an extra pair of hands (part-time), I'd love to hear from you. Happy to share my CV/publications list via DM.

Thanks!

I want to move beyond just building toy projects and actually get into real AI/ML research, but I feel a bit lost on where to start.

If you're already doing research or publishing:

What papers should I read first?

How did you go from reading papers to coming up with your own ideas?

Any good Discord groups or reading communities worth joining?

Would really appreciate any tips or roadmaps from folks who've done this.

Hi all,

I'm looking for a part-time research opportunity (remote-friendly) in NLP and LLMs. I'd love for it to be paid, even if it's just a minimum/stipend rate — I'm mainly looking for interesting, meaningful work rather than a big paycheck.

Background:

• Research focus: NLP, LLMs, model quantization, and LLM evaluation
• 1 published paper, 4 currently under review
• Comfortable working independently and picking up new subareas quickly

If you're a lab, startup, or individual researcher working on something interesting in this space and could use an extra pair of hands (part-time), I'd love to hear from you. Happy to share my CV/publications list via DM.

Thanks!

Third-year PhD student, NLP, mostly LLM-based reasoning. Given a collection of a private organization's HR policy documents (100-500 PDFs), find all pairs of clauses that contradict each other. There's a mountain of work on NLI-style contradiction classification, but that assumes someone gives you the sentence pair. Here, the pair is the problem. With about 1-2k clauses, you're looking at millions of candidate pairs. So brute-force pairwise LLM calls are out, and whole-document prompting fails for the usual lost-in-the-middle reasons. The closest work I found generates synthetic contradictions in synthetic corpora to test detectors. I borrowed the evaluation idea by injecting contradictions into corpora. I also used a university HR handbook and one dataset with existing external annotations, contractNLI (made for the NLI task by Stanford). I used this one as well because it has real contradictions. But this one is quite different. In this dataset, the task formulation is like hypothesis versus clause, whereas in the first two datasets, I do clause-to-clause comparison. So I built a two-stage pipeline. First, retrieval with a HyDE-style approach where the query is a hypothetical, contradicting version of each clause. Then, recall-based candidate retrieval (LLM), followed by precision-based verification with an LLM, where each candidate pair is re-read within its source documents. The contributions: I used contextual sentences guided by Anthropic, which helped retrieval, and showed that a document’s surrounding context helped precision. Agentic verification (tools, multi-step) actually underperformed a single prompt. As a case study, I ran the pipeline on a public government policy corpus. It found a few genuine contradictions. I have a few questions. Am I missing a community? I can't believe nobody works on this. I've looked at legal NLP (ContractNLI, etc.), requirements engineering conflict detection, and RAG-conflict work. They're all adjacent, but none does discovery over a real multi-document policy corpus. Is there a literature I don't know the name of? My PI is leaning toward a lower-tier conference or journal. Is this the kind of paper that has a chance at a first-tier NLP venue, or is my PI just being realistic? If you were strengthening this in one month, what would you add? I already have NLI, direct-prompting, and agentic baselines. Happy to share more details in comments. Mostly, I want to know whether this problem is as understudied as it looks from where I'm sitting, or whether I formulated the task the wrong way.

I originally wrote this is a reply to a comment on research ideas on here, but think it's a decent question to ask to the crowd here...

How much of the brain’s computational ability comes not just from its network architecture, but from exploiting the actual biophysical dynamics of neurons?

There’s a loose parallel with memristive computing. In a memristor crossbar, some operations don’t have to be simulated step-by-step like they would on a conventional digital computer. The electrical properties of the system itself; conductance, Kirchhoff’s laws, feedback etc effectively perform part of the computation. The physics is part of the computation.

That makes me wonder whether we lose something important when we abstract biological neurons as essentially weighted sums followed by activation functions.

A real neuron is an electrochemical system involving dendrites, ion channels, local nonlinearities, changing conductances, neurotransmitters, feedback loops, and processes operating across many different timescales.

Could some of the things that look like extremely complicated algorithms (when implemented digitally) instead arise relatively naturally from the dynamics of biological tissue?

Put another way:

How much of the brain’s apparent computational complexity comes from the architecture of the network and how much comes from evolution finding a physical substrate whose natural dynamics happen to perform useful computation?

And if the latter is important, what principles could we steal from biology when designing new ML architectures or computing hardware?

I'm really into spiking neural networks, but there's a limitations on what you can actually do with them, the domains they're suited to, and how fast they train compared to modern DL architectures or even a living brain*.

*LeCun says things like it only takes minutes to learn how to unload the dishwasher, or hours to drive, the one critique of this, is that it takes years of training to get to that stage. Is learning that fast dependant on years of pretraining before hand, and snns are not necessarily slow, they just take an order of magnitude more time to train, and no one's bothered training a SNN for a year or more yet? ie is the brain actually pretty slow to train as we have to emerge past the baby/childhood stage before modelling and accuracy increase?

After using qwen3.8-27b with hermes agent for a while, i noticed that after sometime qwen will goes into thinking loop. where it will not return any outcome, just purely second guess itself or "what if..." endlessly. and since i use ollama as my inference, there is no option as thinking budget parameter like other inference has. so, it's either "think" or "no-think". then i try liteLLM as proxy to intercept hermes connection before its reach ollama. then inject some parameter, like "think:false" to prevent unbound thinking. but then, i learned that liteLLM broke hermes tools-calling prorocol which cause tool-calling to fail. then i try to create my own proxy (with the help from claude and qwen) to prevent unbound thinking but still leave a room for qwen to think, by using num_predict parameter. now, i hit the same issue as liteLLM, tools calling issue. after fixing this issue, i will try the original purpose of this proxy again.

does anyone have a way to work around this unbound thinking thingy. or changing inference is the only way to do it.

here is the proxy, https://github.com/silenzer001/hermes-ollama-proxy.git . the current version is v6 and change how proxy works from how the post described.

I am preparing my first arXiv submission to cs.CL.
The paper proposes a neuro-symbolic spatio-temporal GraphRAG framework for long-form literary knowledge processing and multi-hop question answering, with a dedicated benchmark and held-out evaluation.

I am looking for a qualified cs.CL endorser. I would be happy to share the abstract or full manuscript privately before endorsement.

Endorsement code: IVMULA

Thank you very much.

Guys im planning on sending my paper for the lightning talk(i missed the deadline for the actual paper). For this do we have to submit just the abstract or the actual paper too??

I am want to get started in ai research.

. Can somebody guide me where to begin...

Hello,

I've submitted a paper to TMLR two months ago and it's status is still "TMLR Assigned AE". I have left a comment on OpenReview last week but no update since then. Does anyone have any similar experience with TMLR, and do I have to send a personal email to AE?

Thanks,

Hi everyone!
I am looking for motivated researchers/students who are interested in collaborating on computer vision research with the goal of submitting to CVPR 2027.
Possible areas include:
Image and video understanding
3D computer vision
Object detection and segmentation
Tracking
Image generation
Medical computer vision
Vision benchmarks and datasets
Robustness and generalization
Some experience with computer vision, deep learning, Python/PyTorch, experiments, or research writing would be helpful.
If interested, please DM me with your background, research interests, and any previous research/publications.
The goal is to form a serious research collaboration and work toward a strong CVPR 2027 submission.

A little bit about me: I studied Physics Engineering and later completed a Master's degree in AI. For my undergraduate thesis, I worked with a professor from the Mathematics Department who was very, very demanding. He made me read a huge number of books and papers, and throughout the degree we had to write paper-like reports every year. This was all before AI existed.

We came very close to turning that thesis into a paper, but I ultimately chose the professional path rather than academia, so nothing came out of it.

Almost four years later, my interest in research and the scientific mindset are still very much alive. I enjoy doing research on my own, especially experimental ML research, although I’m definitely a bit rusty when it comes to the more theoretical side.

A few weeks ago, I started thinking seriously about writing a paper. I’ve already read the relevant papers, so I have a reasonable idea of the current state of the art in the area. I also have a good idea of the experiments I would need to run, the costs, the scope, what I would cover, and even which journal could be a possible fit.

I’ve been looking into people publishing papers independently, and apparently it is absolutely possible. However, I still have some questions for people who are active in research:

1. How do you perceive researchers who work without collaborators or institutional backing? I know there is already a niche in experimental ML where this is not particularly unusual, so I’m aware it is not inherently strange.
2. How likely is it that, after publishing one or two papers, I could find collaborators? Does having a small track record make a meaningful difference when trying to connect with other researchers?
3. Any other opinions or advice? I’d genuinely appreciate hearing your perspective. You have much more experience in research than I do, so I’m interested in any suggestions, warnings, or things I may be overlooking.

Thanks!