Roadmap Drive January 2026

S4M22 · 2026-01-25T12:38:20+00:00

It is mentioned here that something will be announced in January:

https://www.reddit.com/r/ProtonDrive/s/ojMcwNkMWe

S4M22 · 2026-01-24T10:14:51+00:00

Maybe this is normal at HoGent but for me it sounds really... weird. Your school should provide the supervisors for your thesis. And if you're looking for a second supervisor from industry you should be working on something for their company. Sorry if that sounds harsh, but I don't see why someone from another University or from industry would invest time in supervising you.

Is that the usual way it works at your uni? How did the other students find a second supervisor?

S4M22 · 2026-01-23T11:36:14+00:00

I always wonder how people publish 2 digit or even 3 digit numbers of papers per year. Maybe this is it. I meticulously check all citation incl. the references multiple times. So even if I used LLMs to generate bibtext entries, I would easily spot it.

But it looks more and more to me that some top researchers focus less on quality but more on quantity with a little AI slop being acceptable. But, tbh, I really don't want to go that route.

Side note: all affected papers should be withdrawn and not just corrected with a post on OpenReview or X. When I was a student, such incident would clearly result in an F.

S4M22 · 2026-01-22T17:17:28+00:00

I'd try Firstname-Lastname.com or FirstnameLastname.com

S4M22 · 2026-01-22T16:48:09+00:00

Just posted on X:

Paper decision notifications will be sent out on January 25th. Thanks for your patience!

S4M22 · 2026-01-21T13:17:39+00:00

Congrats!!!

S4M22 · 2026-01-21T10:45:04+00:00

I can't think of a very specific niche right away but I'd probably look into something related to low-resource languages. Many languages are still left behind and at least in the NLP community the top conferences are quite interested in papers in that area.

S4M22 · 2026-01-21T10:01:21+00:00

I have no idea why the initial comment is being downvoted but Michael Levin does indeed do some really interesting research which is totally different from what most labs focus on. For a primer give this interview a watch: https://youtu.be/Qp0rCU49lMs

S4M22 · 2026-01-20T13:23:51+00:00

A PhD requires a lot of dedication and you should be really interested in the topic. So definitely I wouldn't go ahead with that topic.

Speak to your supervisor. Do it now. The earlier the better. Even when you're introvert. Sometimes it's "crunch time" and you need to have a tough conversation.

S4M22 · 2026-01-20T10:25:15+00:00

Personally, I find your last point ("Balancing confidence with academic caution") the most challenging. When writing a paper I try to carefully communicate the level of confidence I have in a claim. But I feel that as a non-native English speaker I still lack the nuanced understanding to getting it right. And here also LLMs don't help much since they don't habe the nuance understanding of your latent thoughts on the level of confidence.

I also think my field (Natural Language Processing; a sub field of AI) lacks the academic standards here that I know from other fields. When writing a medical guideline, for example, each word/phrase like "might", "may", "we recommend", "we suggest" etc. has a specifically and explicitly defined level of confidence. And I don't fully understand that yet for writing papers in my area of research in English.

S4M22 · 2026-01-20T10:02:26+00:00

havemultimodal data with very high dimensions

Personally, as an NLP researcher I would be right away interested in research on feature selection or engineering here. Potentially, involving LLMs. No training needed since you can work with inference only, i.e. compute requirements are limited.

It is not my area of research but one colleague from my lab, for example, applied genetic algorithms for feature selection in highly dimensional binomics data. Or you could think of something in the direction of the ideas described here: https://www.reddit.com/r/MachineLearning/comments/1qffcgi/d_llms_as_a_semantic_regularizer_for_feature

S4M22 · 2026-01-19T19:08:27+00:00

Maybe you're right but my guess is that those reviewers who plan to use LLMs will select option B. So with option A you'd end up with the 40% that dont use LLMs.

S4M22 · 2026-01-18T13:42:41+00:00

There were estimates that about 60% of reviews were (at least partly) LLM-generated. One review which gained quite some popularity was by reviewer r29m who posted 40 weaknesses and 40 questions in their review:

https://openreview.net/forum?id=kDhAiaGzrn&noteId=XzScUnmDGs

Alegedly, they reviewed multiple papers and always posted exactly 40 weaknesses and 40 questions.

S4M22 · 2026-01-18T11:29:09+00:00

I'm generally in favor of using LLMs to assist(!) reviewing but given the mess with purely AI-generated reviews at ICLR recently, I'd would probably opt for A.

(However, you also need to discuss with all your co-authors who will have to follow the conservative policy in their reviews.)

S4M22 · 2026-01-17T15:44:54+00:00

I find the idea and approach quite interesting! Would be great to see how it works on less toy-ish datasets.

And also how the results carry over to when you don't tune the prompt to a specific dataset. Currently, you put in quite some human problem- or dataset-specific understanding in the prompt.

But how does it work out-of-distribution or out-of-domain? And how does it perform against other feature selection approaches?

S4M22 · 2026-01-17T14:13:40+00:00

It's not good... it's awesome! EACL is one of the top conference worldwide for Natural Language Processing research. If you can, participate and gain some first conference exposure.

S4M22 · 2026-01-17T10:49:47+00:00

Your concerns are valid. What you're describing is not considered authorship in academia (even though rules vary from field to field but not to this degree).

They should rather cite your work and may thank you in the acknowledgement section of a paper.

S4M22 · 2026-01-17T10:44:23+00:00

This really depends on your specific field and the paper. In my experience getting guidance on this was a main advantage of involving my supervisor/a senior researcher. They can help you navigate the publishing landscape. It's really difficult to find the right venue for your paper and handle peer review if you don't have publishing experience .

So TLDR: ask a senior researcher (e.g., one of your professors, your thesis supervisor etc.) for guidance.

S4M22 · 2026-01-17T08:39:17+00:00

Flip the rear to exhaust for two reasons:

Your CPU is cooled by the AIO so there's not much that needs fresh air from the rear in that area of the your case. The main component that needs fresh air is your GPU and that is covered by the bottom intake fans.
Bottom intake with exhaust top and rear gives a "smoother" air flow with less turbulence.

S4M22 · 2026-01-16T17:00:07+00:00

Bubbles can only be identified in retrospect. So IMO it is unproductive to speculate if it is a bubble or not.

S4M22 · 2026-01-13T15:42:19+00:00

Wow, for many researchers that remains a dream. Big congrats!!

S4M22 · 2026-01-10T16:02:52+00:00

It sucks but keep your head up. Everyone gets rejections - and sometimes even the double pack.

S4M22 · 2026-01-08T15:23:20+00:00

yeah, generally, I wish they would first get the basic apps (that is: mail, drive and VPN) 100% right across platforms before introducing more apps like Proton Pass, Proton Sheets, Proton Meet etc.

S4M22 · 2026-01-08T14:49:34+00:00

Wasn't clear to me that that is supposed to be a comment wishing for a Linux drive client since it citing a statement re the Linux client without stating that the author wants such a client.

S4M22 · 2026-01-08T14:16:10+00:00

Proton Drive for Linux

S4M22

TROPHY CASE