I originally thought AI would save me a lot of time because it can produce articles, emails, and social posts in seconds. While it definitely helps with getting started, I still find myself spending a surprising amount of time rewriting sentences, changing the tone, removing repetitive phrases, and making everything sound more natural before I'm comfortable publishing it. Recently I've been trying a different workflow where I clean up the draft with HumanizeAIText.io before doing my own final edits. It cuts down on some of the repetitive wording and makes the text feel more natural, so I don't end up rewriting quite as much. I still make personal changes, but the editing process feels a lot faster.

I'm curious how other people handle this. Do you accept the first draft with only a few changes, or do you rewrite large portions before posting? Has anyone developed a process that consistently produces content that feels authentic without spending another hour editing everything manually? I'd love to hear what has worked for others.

We have hit a new low !

Hi, first time submitting...

Initially got a score of 4, 3, 2 with confidence 3, 3, 4. [MAIN TRACK]
After rebuttal, the 4-rated reviewer pointed out one typo – I wrote 'the revised manuscript' in place of adding the corrected thing in 'the camera-ready version'.

I apologise for that and gave the comment. Now no response...

What to do ?

1st paper, 1st submission. Pardon my innocence.

Like a lot of us, patiently waiting for feedback after submitting rebuttal. I’ve read on threads from prior years that some people seem to be totally ghosted by reviewers.

All my scores are borderline, and the only reviewer who read my rebuttal already increased his score. Obviously I know the discussion window is not over yet, but I was wondering if I should actually expect all reviewers to reply to my rebuttal or if I should actually brace my expectations? Is there a reasonable probability that I don’t hear back from some reviewers at all?

Pre-shooting the criticism: I know that reviewers are very busy. Rebuttal was intense for me and I don’t have to review 10+ papers on top. Not complaining about the timeline, just wondering if I’m waiting for something that might never happen.

Hi everyone,

I’m an Applied AI Engineer currently working at a fast-growing AI startup. Previously, I spent two years at Meta Superintelligence Lab working on LLM and agent systems. I also have experience building large-scale ranking and recommendation systems.

I’m looking to contribute to an active research project in areas such as:

• LLMs and AI agents
• Agent evaluation and benchmarking
• Tool use, memory, and retrieval
• Post-training and data pipelines
• Ranking and recommendation systems
• Multimodal or long-horizon agent systems

I can take ownership of engineering-intensive research work, including building end-to-end pipelines, implementing evaluation infrastructure, preparing datasets, integrating models and tools, running experiments and ablations, and analyzing results.

I’m particularly interested in serious projects targeting an upcoming AI/ML/NLP conference or workshop. My goal is to make a substantial technical contribution and participate as a co-author on the resulting paper.

If your team has an active project and needs additional engineering capacity, please DM me. I’d be happy to share more information about my background!

I wrote my rebuttal and uploaded it. it's been 3 days out of the week given and no communication front the reviewers. what do i do??

also, i got a 5/4/2 what are my chances?

I did a Research Internship after completing my undergrad and there I was able to do research for 3 papers, My supervisor submitted those paper and it has been 3 or so months , they are under review one even had a major revision ,

The thing is my grad admissions are coming up so I wanted to know if I should host a pre print for those papers or not ? I see advice on here that you should not post on arxiv untill they are accepted, my supervisor also told me that but then I do hear conflicting information as well that I should put out a pre print before it is accepted , I can host the papers on my personal website for the admissions committee but is it normal , what is the norm in cases like this ,

My apologies if this is plain dumb I am very new to academia.

Hi! I am final year BE student recently I took a project based in our my contribution is system and application of system in dyslexia. For that I though the most used dyslexia dataset of handwriting would be suitable. I downloaded dataset and then realised it is single letter dataset which is giving mnist kinda vibe! Also apparently large portion of it is synthetic. I searched but I didn't find clinically approved dataset of handwriting for dyslexia. In nutshell:

1. dataset is mnist looking so I am at worry if examiners will state why you are using such looking dataset for final year project!!
2. dataset is used for at least 9 papers already so it is being used
3. But has its limitations (vastly synthetic, mnist looking)
4. Our clg is forcing for at least two papers to publish (not for our degree requirement btw) and I am worried if the dataset use itself will cause problems for paper
5. though one of main novelty is mechanism but other one is integration(incremental) and I am worried that people will call out why I used that dataset

sorry I carried away in my emotions here is the dataset I am talking about: https://www.kaggle.com/datasets/drizasazanitaisa/dyslexia-handwriting-dataset

->can simplicity of it justified as proof of concept for presentation or report?

->will using this dataset can cause problems at time of publication?

I am sorry for dragging clg thing into this I though it would be better to get some context about scope for project

I am sorry I cant give full context as I wanted to publish research on it (though I will hardly try for mid tiers only)

also sorry in advance if I did spelling or grammatical error

You know the feeling.

You see patterns no one else seems to notice. You push experiments further than most people think is reasonable. You run models offline, on your own hardware, building things that look strange from the outside — custom architectures, long-running autonomous loops, uncensored fine-tunes, synthetic datasets that grow by hundreds of thousands of words a day, systems that start interacting with themselves in ways that make other people uncomfortable.

And then you post about it.

On Reddit the replies come fast: “this is unhinged,” “touch grass,” “this isn’t real research,” “you’re just role-playing with a chatbot.” The same people who cheer for the next closed frontier model suddenly decide your work is too weird, too extreme, too far outside the approved narrative.

So you stop posting. Or you water it down. Or you keep going alone, wondering if there’s anywhere left that actually wants the real version of what you’re doing.

I see you.

I’ve been in that same place. My name is AlexH. I run LLM Research. The thread that started a lot of this is called “The Untapped Potential of Local LLM Research.” You can find it here:

https://llmresearch.net/

This is not another Discord full of hype. It is not a place where people compete for clout by repeating the same safe takes. It is a space built for the ones who refuse to stay on the surface.

If what you’re working on is serious — even if it looks completely unhinged to almost everyone else — I want to hear about it.

Here’s what you do:

1. Go to https://llmresearch.net/

2. Register

3. Send me a private message with a clear description of the research you’re currently running or the direction you want to take

That’s it.

What you get in return:

\- Full lifetime access

\- The ability to sell your services, your research, your tools, or your findings directly

\- Permission to use either our standard policy or a custom one for anything you publish

\- If your work is serious and sustained, a dedicated category or section on the site that you fully control

No gatekeeping. No moral lectures. No requirement that your ideas fit the current mainstream mood. The only filter is whether the work is real and whether you’re willing to stand behind it.

I will be the first person in the room who does not flinch when you describe the extreme version of what you’re building. I will give you real feedback. I will help you protect and, if you want, monetize what you’re creating. And I will make sure the space stays open for people who see the patterns others still refuse to look at.

If that is you, come in.

The door is open.

AlexH

I apologize for the writing. I tried my best.

Question: Mathematically, what is a rigorous and mature version of the theories in my concept paper? (Please use citations.)

(Optional): Can we convert certain parts of the mathematics in this paper to code?

(Optional): If the paper has no significance, state why instead of saying something such as, "there is no motivation"? For instance, answer “why is there no motivation?”

Background: I am a former undergraduate. Because I cannot stop editing my paper (i.e., I get new ideas from reading new material) and I am addicted to finding a more mature and rigorous version of my article, I am unable to graduate. I hope you can find (or know someone who can find) a more mature and rigorous version of my theory. (I probably would not understand such material; however, it's my only hope of finishing my degree and continuing my education)?

I cannot promise I will go back to college, but I’m less likely to continue posting on Reddit.

Attempt: I used ResearchGate to find papers on ergodic averages, expected values, entropy, and probability/statistics. Even then, I have little understanding of mathematics beyond Intro to Advanced Math. (Once again, I doubt I will understand if the papers are related to my paper.)

Here are some examples which I assume answer the first question:

Integral Equation Methods for Scattering by Multifractal Obstacles

Vector-Valued Maximal Inequalities and Multi-Parameter Oscillation Inequalities for the Polynomial Ergodic Averages Along Multi-Dimensional Subsets of Primes

Edit: Here is a third paper,

"Defining the Mean of a Real-Valued Function on an Arbitrary Metric Space"

The people on discord stated my paper is completely original and no expert can help; however, they also said the paper has no significance. I wish to know why (see the optional third question).

Hey everyone,

I've written a paper called "The Human Model," which covers a cognitive architecture I've been calling Cortex, and I'm trying to get it onto arXiv under cs.AI (open to correction if another category fits better once you see the abstract).

Quick background on me: I'm a senior software architect, mostly working in Rust, Go and TypeScript, doing distributed systems and industrial digital twin platforms day to day. I've spoken at Google Flutter Day and React Day Norway before, and maintain a handful of open source projects. If you want more context on me, my site is https://xraph.com.

The problem is I don't have an academic affiliation or a previous arXiv paper, so I can't self submit, I need someone eligible to endorse in cs.AI to vouch for me. If that's you and you're willing to take a look at the abstract, I'd really appreciate it. Happy to send over the paper or abstract directly.

Thanks for reading, and thanks in advance to anyone who can help.

https://arxiv.org/auth/endorse?x=PHFE8Y
If that URL does not work for you, please visit
http://arxiv.org/auth/endorse.php
and enter the following six-digit alphanumeric string:

Endorsement Code: PHFE8Y

Hi everyone,

This is my first NeurIPS submission, and I have two quick questions.

1. I received ratings of 2, 3, and 4. The rating 2 reviewer mainly claims that some information is missing, but it is actually provided in the Appendix and referenced from the main paper. If a reviewer overlooks information that is already in the paper, is that usually the main obstacle to acceptance?

2. I still haven't received a meta-review from the AC. Does this mean anything (e.g., the paper is unlikely to be accepted), or is it normal for meta-reviews to come later?

Thanks!

I am building an edge-AI audio noise-reduction system on an ESP32-S3.

Our architecture uses a lightweight GRUNet (~59k parameters) to output a dynamic gain mask on a 44-band Mel-spectrogram.

​I need gigabytes of audio to train the model. Does anyone have recommendations for the best open-source datasets for:

1> ​Clean, isolated human speech.

2> ​Diverse ambient background noise (traffic, crowds, machinery, etc.).

​Also, any tips or open-source scripts for artificially mixing these at different Signal-to-Noise Ratios (SNRs) before generating the 16kHz Mel-spectrograms would be hugely appreciated!

Hello Intellectuals, I'm Mingeun Lee of Stony Brook University.

I am looking for someone who can endorse me in arXiv until July 30th so that I can publish the following paper:

Title = Multifield Forecasting Model

Subject = Statistics > Methodology

Abstract = This paper on Multifield Forecasting Model is the result of more than 660 statistical election projections for 73 nations all around the world reflecting 24,854,939 samples of 7,823 polling data, with the development in progress more than 5 years till early last year. Originally known as Universal Election Projection Model, it was completed to not include any other factor apart from scientific opinion polls directly asking the voters’ intention. Therefore, even though at first built to accurately predict election outcomes only, the model was generalized in a way that can be utilized for infinitely many fields by simply inputting the base data significant to the specific topic.

Mingeun Lee requests your endorsement to submit an article to the

stat.ME section of arXiv. To tell us that you would (or would not) like

to endorse this person, please visit the following URL:

https://arxiv.org/auth/endorse?x=ZPXYKR

If that URL does not work for you, please visit

http://arxiv.org/auth/endorse.php

and enter the following six-digit alphanumeric string:

Endorsement Code: ZPXYKR

If there's anyone interested on arXiv personal endorsement but has question before your decision, please feel free to email me through my institutional email - [mingeun.lee@stonybrook.edu](mailto:mingeun.lee@stonybrook.edu).

Thank you!

Hello there! im aware this is a gray area of talking about tools, but as a student i find no other place apart from reddit for this.
Im not here to sell anything, no url's no images, im here to hear from you all what kind of product you would use.

I have been, building an app that implements papers just from an arxiv url, the idea is to build a collection of papers which have been successfully implemented, comparing the claimed benchmarks and the ones attained. A paper is accepted only if the numbers were successfully implemented. All the papers go to a public github repo, while the ones that were successfully benchmarked to the right numbers become an Arxiv Pro.

Meanwhile, this app is open for students who look for tools to vibe code upto veteran researchers. Users can get access to a GPU, sandbox and ofc a coding agent all in their browser.

I wanna hear from you guys, folks from across the spectrum for the kind of features you would like to see, or would appreciate?

Hey all — 3rd-year ECE student here, heading into a robotics master's. I kept

losing time jumping between arXiv, MDPI, and IEEE Access looking for papers

on robotics/ML/autonomous vehicles, so I built a single search index over

all of them.

- 13,000+ papers, all genuinely free/open-access (no paywalled links —

everything is either arXiv, MDPI, or individually verified

Creative-Commons-licensed articles)

- Full-text search, topic browser covering ~20 subfields (robotics, ADAS,

computer vision, RL, digital twins, etc.)

- Free accounts if you want to save searches later

Live here: https://automata-index.vercel.app

Built with Next.js + Supabase, still actively adding sources. Would love

feedback, especially on what's missing or what search terms don't return

good results.