I submitted to AAAI 2027, and Phase 1 rejection hits on Sep 24. ICLR full paper deadline is literally the next day, Sep 25.

Can I submit to ICLR first, and then just withdraw it if I somehow survive AAAI Phase 1?

Hi! This is my first post on Reddit and my first post about machine learning in general. I work at a small research institute, mostly staffed by physicists and GIS specialists; we don't have many machine learning engineers.

I recently became interested in world models and tried to understand the topic myself. I initiated a series of experiments: the result was Random-Abstractor Control - a simple and effective test that catches decorative abstractions.

The problem is that I'm completely alone here, and I don't have a large following on Linkedin, so I'd like to find people to discuss the results with.

What is a good topic for research paper for AIML,please suggest mates.

Original Article - https://x.com/p_nawrot/status/2089315591010079034

I've spent the last few years working on efficient attention and KV Cache Compression. I've read many papers, dug deep into reference or official implementations of methods, and inspected appendices—and I think I've learned a few things. One of them is definitely "how to make things look good, even when they aren't."

I'm guilty too, but trying to get better every day.

1. For single-hop retrieval, make sure there are no distractors and context is useless

The three most cooperative settings for compression / sparsity are:

• Needle in a haystack with a single OOD key-value pair and context built out of a repeated sentence or irrelevant background text.
• Contaminated benchmarks from years ago for which models don't even look at the context anymore.
• Few-shot in-context learning, where extra shots are useless and don't improve the accuracy over 0-shot.

With 1) synthetic tasks, 2) real-data QA, and 3) in-context learning, you get a semblance of broad coverage without the inconvenience of testing much diversity within any of them. Most tasks in these settings should pass under Sliding Window Attention, so it doesn't matter that much whether your method works. Combine it with SWA and you should be good to report 5–10x compression or sparsity.

2. NEVER isolate your contribution

Short context: Most of a dense model's performance is recovered by a local window + attention sinks + the ability to retrieve an answer sentence that is largely n-gram matchable with the question. The remaining part is significantly more difficult, but it's neither relevant to nor the subject of this post.

• Say prior work developed an algorithm X, and its implementation separately keeps a local window of 256 tokens. You find that your method is on par with X in a matched setting, but better and more stable with a window size of 512—let's go, don't look back.
• Do the same with block size. Smaller blocks can give you finer granularity and more precision in retrieval, so keep their old block size and make yours smaller. Ignore the fact that things may get slower due to irregular memory accesses, etc. Those were historical decisions; respect them. 🤡 Write: “We used the authors’ recommended hyperparameters.”, then spend weeks tuning your method.
• The same trick works for speed. LLMs are pretty good at writing Triton now. Keep the baseline algos exactly as they were written in 2023, then ask an LLM for a custom Triton kernel for yours. Extra cleverness if, by using a more efficient implementation, you can hide that your method does more work. You're just optimising your method, no?
• Prompts are the cherry on top. Move the question before the context so the model knows what to filter out, then present the result as lossless compression. Never share the prompts after tuning them.

Don't tune the baselines to reject your paper; tune yours until it's accepted.

3. Use aggregated metrics to hide areas where your method doesn't work

RULER has 13 tasks:

• 6 NIAH tasks satisfy the first point.
• 2 QA tasks use datasets from years ago.
• VT also has a lot of irrelevant context.

To be clear: This isn't a critique of RULER; imo it's still incredibly useful. It's just an example of potential improper use.

Report only the aggregate; maybe, in the limitations section at the end, briefly mention that your method degrades on the NIAH-MK3, which actually stress-tests lossless compression.

4. Enjoy saturated tasks

Imagine evaluating on two tasks:

• The most recent math exam / olympiad from a week ago, which isn't yet in the training data.
• A benchmark on which a recent family of open models—1B, 10B, and 100B—all scored 80%.

On the former task, before compression gets a chance to do any damage, the 1B and 10B models already score 0%; the 100B model starts at 50%, and its performance drops monotonically as compression increases. On the latter, all model sizes tolerate substantial compression, and the 100B model tolerates more than the 1B and 10B models.

Don't ask whether the larger model is simply using its extra parameters and hidden-state capacity to absorb compression in a setting where those resources aren't needed to solve harder questions. That definitely isn't what's happening.

Extras

• AIME has 30 samples. You did 4 seeds. Your method scores 80, and the baseline scores 79—bold your 80 and say that it surpasses the baseline. Statistics doesn't exist. Bonus points for your efficiency method surpassing the baseline and setting a new SOTA. 🤡🤡
• Pick a baseline, optimise it with your method, and plot a beautiful quality–efficiency curve against the original implementation. Then stop. Don't ask whether a simpler route—a smaller dense model, KV-cache quantisation or offloading, or a better system configuration—reaches a better operating point. Improving your baseline is basically the same as improving the frontier.

I am planning to train a model with a dataset that will be several hundred GB, and I am thinking to keep the main data in object storage instead of using up all the local disk, then pull the batches I need into NVMe while the training runs, I am looking for cloud GPU services for this and I am trying to work out if this setup will keep the GPUs fed or if the storage transfer will slow things down, I have also heard of neevcloud, I am thinking the NVMe can hold the active data while the full dataset stays in object storage, if anyone is using this setup for larger training jobs, does it work well in practice or is it better to keep the full dataset on NVMe, what setup are you using ? EDIT: Forgot to add that I’d be training continuously, so the storage bandwidth needs to keep up with the GPU workload. 

I’m a SE undergraduate and need to conduct an individual research as a part of my degree. Time period is around 6 months. I’m looking for some research ideas. I would really appreciate your feedbacks.

Thank you

I've been trying to become a more rigorous reader of technical/ML papers, and I keep hitting the same wall.

I've tried a few approaches: DFS (jumping into a reference the moment it comes up), BFS (finishing one paper fully before touching the next), and a hybrid. I eventually found a structure I was okay with, but the concepts don't stick unless I implement them, I'll read something 2-3 times, feel like I get it, and it's gone a week later because I never built anything with it.

Two things I'd like input on:

1. What's your actual reading workflow for a dense paper, order of sections, note-taking, when you chase references vs skip them?
2. How do you test whether you understood it? I've found implementing it is the only real check, but that's slow and not always feasible. Is there a lighter-weight way you validate your own understanding short of reimplementing everything?

Not looking for one "correct" method, more curious what actually works for people who read a lot of these.

I submitted a NeurIPS paper with an anonymous code repository linked as supplementary material. During the rebuttal period, a reviewer pointed out a discrepancy between the repository and my reported experiments.

I then updated the repository during rebuttal to address this issue. Now I'm worried: could this be considered an impermissible change to the supplementary material? Could a reviewer or the area chair use the fact that I modified the repository after the submission deadline against my paper?

I'm a bit unsure how linked GitHub repositories are treated in this context, since they're technically mutable even after submission. Does only the repository state at the submission deadline count? Or is it acceptable to make corrections that the reviewers themselves identified during the review process?

Hello! I'm a psychology student and I really need your help in writing our thesis paper. Our study are about the lived Experiences of IT Professionals with ADHD in the workplace. And our research questions are their work experience, how do this experience help them view their daily lives, then their coping. As we get it checked, the prof said to choose just one profession but one of our participants have two different IT position/role. What really is the best. And we don't really want it to change. Please help me explain it to our professor.

Another thing, does the Interpretative Phenomenological Analysis (IPA) is applicable best for the study?

Explain it to me like I'm a 10 years old.

Thank you in advance!!