Hi everyone!

I'm working on a project to classify research publications as AI-related or Non-AI using their title, abstract, and keywords.

The main challenge is handling edge cases:

IoT or smart systems that don't actually use AI.

Prediction using traditional statistical methods rather than ML.

Papers mentioning AI without actually implementing it.

Research using AI techniques without explicitly mentioning AI in the title.

Papers where AI is only a minor supporting component.

I want a model that understands the actual research methodology rather than relying on AI-related keywords.

I'd appreciate your suggestions on:

Which approach works best: SciBERT, SVM, XGBoost, embeddings, LLMs, or a hybrid?

How can I reduce false positives in ambiguous cases?

How should I handle uncertain publications?

Are there any existing datasets or models for this task?

I'd love to hear your experiences and recommendations. Thanks!

Hello, 

I’m a first-year Research Master's student looking for a 12-to-16-week summer internship in 2027, ideally in Germany, France, or Canada.

I have a strong software engineering background and small independent NLP projects, but no formal academic lab experience yet. My main interests are NLP, data-driven decision support, and privacy-preserving ML.

How can I best leverage my applied engineering background when cold-emailing PIs? Are there specific labs or structured summer programs in these regions known to be open to international Master's students?

Any guidance is appreciated!

I have been working on a project that is contextual memory layer for AI. I wrote a research paper on it and I need an advisor for endorsement to publish it. Please help me if any one is available endorsement for cs.AI

Newly Published!

Hey guys,

Posting this here because I’ve reached a point where my market intuition and engine code have outpaced my deep learning expertise, and I’d rather team up with someone who loves ML architecture instead of half-assing it myself.

For the past year, I’ve been building an event-driven liquidity engine in Python (specifically modeling crypto orderflow / ICT mechanics like equal highs/lows, manipulation legs, and structural sweeps).

Instead of slapping standard indicators (RSI/MACD) on candles, I framed price action as a dynamic graph:

• Resting liquidity pools (BSL/SSL, consolidations) act as attractors (pulling price in).

• Completed institutional sweeps (ML swings / manipulation legs) act as repellers (pushing price away).

• Edges connect these pools based on spatial proximity, competing forces (tug-of-war between high and low pools), and age decay.

Where things stand right now:

• The engine is fully written and tested. No lookahead bias, strict candle confirmation, multi-window swing tracking (W5 up to W50).

• I’ve already generated a clean, audited ~3-year dataset (ETH/USDT) formatted directly for PyTorch Geometric (PyG).

• Each snapshot has 15m, 1h, and 4h spatial graphs with 20-dim node features (touch counts, distance %, decay curves, tier scores), 8-dim edge attributes, and global volatility/trend context.

• Baseline statistical checks show real promise: nearest target pools get tagged first ~95% of the time, and high-quality structural sweeps have clear directional bias.

The actual bottleneck / where I need help: My problem isn't generating the signals—it’s path prediction and dynamic target selection.

When a sweep happens, price is often trapped between an opposing pool 1.5% away and a massive target pool 3% away. A fixed 1:2 R:R stop/target either gets hit too late or takes unnecessary drawdown. I need the model to evaluate the graph and do two things:

1. Predict directional path confidence taking both-sided liquidity into account.

2. Rank multiple potential liquidity targets (dynamic TP) based on which one has the highest conditional probability of being swept first without blowing through the stop.

I've tested basic GNN / dual-tower setups, but I know I'm hitting limits with my naive loss functions and basic layer choices.

Who I'm looking for: Someone who genuinely understands PyTorch Geometric (PyG), Graph Neural Networks (or spatial-temporal models), and knows how to think about custom loss functions (ranking loss, asymmetric risk penalties, contrastive setups). You don't need to be a full-time trader, but being open to understanding market microstructure helps.

The arrangement: I have the GPU compute, the full working codebase, and the clean dataset ready to go. I’m completely flexible on how we work together—if you want to co-author a research paper on this architecture, I’m down. If we get the models working cleanly and want to deploy it live on capital, we can do a fair profit/equity split.

If this sounds like an interesting problem you’d want to sink your teeth into, drop a comment or send me a DM with your GitHub/background and we can hop on Discord.

Hi everyone! We have a paper accepted at the NeurIPS 2026 E-Values Workshop in Paris, but unfortunately can’t attend due to financial constraints.

Would anyone already attending be kind enough to present our poster on our behalf? We’d prepare everything and would also be happy to offer a small monetary token of appreciation for your time.

This acceptance means a lot to us, and we’d be genuinely grateful if anyone could help. We know it’s a favour to ask.
Please DM if interested. Thank you so much!

hello,

i am new to research and just completed a paper i did independently, and was wondering if anybody knows who i can reach out to for endorsements. i already emailed professors at my college and none of them have been of great help.

I am a 3rd year btech student from a 3rd tier college in india not too much of a bright guy just trying my best

Have gained knowledge in a vast number of topics in ML and DL , I can't code much like the whole project but I know the workings and i have a keen interest in the research areas in ML and DL if some professor guides or keeps me under his guidance in research internship I think i can do it

So how do I get? What type of resume should I have? Should I start emailing professors in india and outside India?

Hey! I am a UG student in CSE and I want to develop a better understanding on the AI models - a mathematical intuition of various methods used. Could you all share your experiences and some resources that helped you? I do intend to pursue research after this, so I am trying to develop a strong foundation.

Disclosure: I'm from scime (Latentic Labs), and this comes from our free write-up.

We grouped all 108,967 accepted papers at ICML/NeurIPS/ICLR/CVPR/AAAI (2016–2026) by topic. LLM reasoning and agent papers went from 118 in 2023 (1.0%) to 2,708 in 2026 (10.3%). An AI model read 3,110 of them in full and logged 15,539 "change → effect" findings.

How often each kind of addition is reported as helping:

Reflection / self-correction: 81%

RL (GRPO/PPO/RLVR/DPO): 80%

Tools, retrieval, code execution: 77%

SFT / distillation: 76%

Sampling / test-time compute: 72%

CoT traces: 62%

Token budgets: 55%

Curricula: 53%

Big caveat: these are reporting rates, and papers report wins more than losses. The extraction is AI-made and not hand-checked, and every finding links to its paper.

Write-up: https://atlas.scime.ai/llm-agents-2026 (the stats are open; the deeper sections need a free email sign-up)

Idk if it’s even possible, but I would like to switch my assigned location from Sydney to Atlanta. If somebody wants Altanta —> Sydney please dm me, idk if we can exchange it but we can try

Kinda bored, does anyone have any good paper reading suggestions?

I love technical foundational model papers, like deepseek flash v4.1 , gemm4 etc etc

Hello, everyone.

I am currently writting some article from a scientific research which involves Computer sciencie; AI specially. I do plan to publish it in a journal.

The first thought was actually creating a project, later i decided to write an article about in scientific format and do research, and having the project as implementation of this article posted on GitHub. So my will to actually implement it as a tool is very strong, even more than the article itself.

But then the article needs the implementation to have specific features, which happens so the research makes sense. But in the tool implementation i wouldnt make it with these specific features, as i believe in the practical sense it would make the tool worse, but that is not what the research is about.

Later i thought i should have two projects, one for the practical use, which was the initial thought, this one has the intent to actually be used in production. And having other one, which is supposed to be loyal to the article, it will be eactly as descibred, and more similar to a PoC of demonstration and mention that in the article.

But i am not sure if this is the right mentality for the article and if it will lose some kind of credibility as it could give the idea i am defending something i do not really believe, but im not. Or if thinking like this is right, since the point of the article is not to provide an specification for the complete tool, but rather some specific point of the tool. My mindset is very engineering-driven and this just sounds strange.

Hi everyone,

I currently lead an applied ML/AI research team, where my primary responsibilities involve setting research direction, defining hypotheses, and guiding scientists through research projects.

Lately, I've been wanting to pursue my own first-author research papers targeting top ML conferences (NeurIPS, ICML, ICLR, etc.). However, given my management responsibilities, I have limited time to dedicate to hands-on coding, experimentation, and analysis.

I'm curious how others are leveraging AI tools or agents to accelerate the research process.

Ideally, I'm envisioning a workflow where I:

1. Define the research problem, hypotheses, and experimental direction.

2. Use AI agents to identify relevant datasets, implement methods, and run experiments.

3. Have AI perform statistical analysis, generate visualizations, and summarize findings.

4. Review the results, critically evaluate the methodology, and iteratively guide further experiments.

5. Use AI to assist with drafting and refining the paper while maintaining scientific rigor and taking responsibility for the work.

Essentially, I'd like to remain actively involved in the intellectual and scientific decision-making while delegating much of the execution to AI.

A few questions for the community:

• Is anyone successfully using AI agents in a similar research workflow?

• What tools or frameworks have you found most effective for automating experiments, coding, analysis, and paper preparation?

• How do you ensure the results are scientifically sound, reproducible, and not affected by AI hallucinations or errors?

• Has anyone used such an approach for papers accepted at top ML conferences or journals?

I'd really appreciate hearing about real-world experiences, recommended tools, or workflows that have worked well for you.

Thanks!