Hello everyone. I am keeping my identity anonymous today to protect my professional career. I am a researcher in Computer Vision, and I am sharing this story because I have hit a devastating deadlock with IEEE T-PAMI and the IEEE Ethics Office.

Our Situation

https://preview.redd.it/ipxwj6eus32h1.jpg?width=960&format=pjpg&auto=webp&s=1f58700644683be640f6bb057c74011649f59219

In the decision letter, there were three highly positive reviews (Two EXCELLENT, One GOOD). However, the AE (who is one of T-PAMI associate EICs) rejected the paper by quoting comments from a "4th" reviewer.

>The most staggering part: We later accidentally met the actual 4th reviewer. He CONFIRMED having submitted a POSITIVE review, which was strangely withdrawn by the editor in the backend before the final decision was made.

The AE lied by saying: "... received 3 sets of comments, and one on the way ... ".

We have formally requested the IEEE (and Computer Society) to thoroughly investigate this issue, specifically asking them to check AE's backend activity logs in the submission system.

However, half a year has passed, and we have received no direct response.

Has anyone experienced something similar with IEEE or other top venues? Any advice or help bringing visibility to this would be greatly appreciated.

Evidence:

Below is the report to IEEE Ethics (identifying information has been covered):

https://preview.redd.it/e41vt2rsn02h1.png?width=3508&format=png&auto=webp&s=b2ee2d3f092dad5e20b45b9daeea7fa7b6f01d20

https://preview.redd.it/t29n03rsn02h1.png?width=3508&format=png&auto=webp&s=67aa6bc36aed76617af34e7913a203f9236bc536

https://preview.redd.it/6v5ys2rsn02h1.png?width=3508&format=png&auto=webp&s=f2452998f57f1b157d71b569dd5ff87e4d3d0b6c

https://preview.redd.it/epdxv2rsn02h1.png?width=3508&format=png&auto=webp&s=d01da8cdf9e3f6cd5be53f884b02b154f86d0b48

https://preview.redd.it/fuw3k3rsn02h1.png?width=3508&format=png&auto=webp&s=03e75f763a54429758102da4933af53511642e7d

https://preview.redd.it/xn0ze3rsn02h1.png?width=3508&format=png&auto=webp&s=9f00e88f186c0afa349d4a46439216ae57642d98

On Openreview, you can see modified date next to the review. This modified date should be recent (anything 12th May or newer) which means that reviewer gave a final justification and may have increased their score or kept the same score. In either case, it means they read the rebuttal and justified their score and decision.

For me none of the reviewers as of writing this post has provided justification. My score is 433 and all was easily addressed in the rebuttal. In CVPR, I was in same position where none of the reviewers justified their decision and the AC simply said "concerns remain" even though it was clearly answered in the rebuttal and rejected the paper.

For proceedings-only papers, do we need to make a poster and submit it to the portal? Has anyone asked this question to ICML Program Chair?

I've seen TabPFN-3's recent results, and there is a lot of buzz about foundation models for tabular data (TabICL, TabPFN). The performance that those models achieve is really amazing. What makes me a little suspicious about them? They can analyze small datasets only, so a few MB of data, and you need to have a large GPU machine and download a few GB of model to predict on a few MB of data. That doesn't sound rational ... I really miss the old school approach of running a single decision tree or a linear model on the data.

What do you think about it? Do you think feature engineering + classic ML can achieve performance comparable to that of foundation models? Maybe with better explainability?

https://preview.redd.it/mikhasjiq32h1.png?width=572&format=png&auto=webp&s=4c053200dbd9852bebf083550e2144b31579d497

https://preview.redd.it/bay5r3njq32h1.png?width=575&format=png&auto=webp&s=2823db3d6bc534ef00330528a200cba2aca1c5d3

https://preview.redd.it/dm40ntdkq32h1.png?width=575&format=png&auto=webp&s=703beb099eb6e16d2789ac230ebe77de51f07d7a

https://preview.redd.it/eubucz2lq32h1.png?width=575&format=png&auto=webp&s=fb5a8d9a7154396087da33487674cda785d2a62a

https://preview.redd.it/0xo3t83nq32h1.png?width=586&format=png&auto=webp&s=a569ae89c44953a5bc9aff6fbb37d25759109dd1

I've just finished the Machine Learning Specialization by Andrew Ng , and as I was going through it, I ended up writing detailed lecture notes for all 10 chapters — everything from linear regression all the way to reinforcement learning.

I put a lot of effort into making these notes as clear and friendly as possible, so even if you're completely new to ML, you should be able to follow along without getting lost.

The notes are written in LaTeX and auto-compiled to PDF via GitHub Actions whenever I push an update, so the PDF is always up to date.

🔗 GitHub: https://github.com/TruongDat05/machine-learning-notes-and-code

An issue with the peer review system is reciprocal reviewing, which incentivizes reviewers to unfairly reject good papers to increase their own papers' chances of acceptance.

My proposed solution is that the conference should divide the authors/papers into 2 halves (A and B). If you are an author in half A, then you will only be a reviewer in half B. All papers by the same author, their coauthors, and coauthors of coauthors should be in the same half.

Each AC/SAC can only serve in one half and acceptance decisions for the two halves would be independent. So reciprocal reviewers will not have incentive to reject good papers to serve themselves.

Furthermore, the discussion period for the two halves should not be concurrent. This way the reciprocal reviewer will have sufficient time to discuss author rebuttals as they will not have to deal with their own papers concurrently. Maybe the first 2 weeks can be the discussion period for half A, and the next two weeks for half B.

I don't think conference organizers have thought of this solution, because if they have, there is no excuse for not trying to implement it because it does not hurt the conference's self-interest in any way.

Does anyone think this will work? If so, I hope someone of more power than me might ask the conferences to implement it.

Hey everyone,

I’m an undergrad from India and I just found out I had two papers accepted at the ICML 2026 GlobalSouthML workshop! I am super excited since this is my first time getting accepted into a major conference venue, but I’m also kind of panicking right now because I absolutely cannot afford a trip to Seoul.

Since I've never done this before, I’m hoping some experienced folks can help answer a few questions about how the post-acceptance process works:

1. I saw that the main conference has a "Virtual Pass." Is that enough to keep my papers in the workshop program? ICML rules make it sound like someone must be there in person. If neither me nor my co-authors can afford the flight to South Korea, will our accepted papers just get withdrawn?

2. Does ICML or the GlobalSouthML workshop specifically offer financial aid for undergrads? Should I email the organizers about this before I attempt to register? I saw some mentions of ICML Financial Aid online, but it looked like it might only cover hotels and registration, not the flights.

3. How does submitting the final version actually work? Do the organizers email a specific form, or do I just upload a new PDF revision directly to my OpenReview portal? Also, since GlobalSouthML is a non-archival workshop, what exactly am I submitting, just the updated PDF addressing the reviewers' comments?

Any advice on how to navigate this would be hugely appreciated! Thank you!

Hi,

Niels here from the open-source team at Hugging Face. Like many others, I was a huge fan of paperswithcode. Sadly, that website is no longer maintained after its acquisition by Meta.

Hence, I've been working on reviving it. I obviously use AI agents to parse papers at scale and automatically generate leaderboards (for now I'm the one verifying results). So far, I've only parsed high-impact papers for which I know they're SOTA, like Qwen 3.5 and 3.6, RF-DETR for object detection, DINOv3, SOTA embedding models from the MTEB leaderboard, the Open ASR Leaderboard for automatic speech recognition models, etc.

For now, it includes the following:

• trending papers by default based on Github star velocity
• categorization by domain, e.g., OCR
• methods, which PwC used to have, e.g., RLVR
• eval results for high-impact papers, see e.g., Qwen 3.5 at the bottom
• leaderboards for each domain, e.g., MMTEB or COCO val 2017
• support for citation counts (you can also see the most cited papers by domain!)
• automated linked Github, project page URLs, and artifacts (+ multiple repos are supported on a paper page)
• support for external papers beyond Arxiv, see e.g., DeepSeek v4
• Harness reports for coding agent benchmarks, e.g., Terminal Bench
• "Sign in with HF" and Storage Buckets are used to store humbnails, paper PDFs, and overall data backups.

I'm curious about your feedback + feature requests!

Try it at paperswithcode.co

https://preview.redd.it/whwji560fw1h1.png?width=3452&format=png&auto=webp&s=55bb7a30c1be58d140f7efcb07a31c6dac5693c7

See e.g. the SOTA leaderboard for Terminal Bench 2.0:

https://preview.redd.it/98w9pi89fw1h1.png?width=3456&format=png&auto=webp&s=408fb64b0ba85ba24f55daa81d547d7c68e73951

A paper page looks like this: https://paperswithcode.co/paper/2602.15763

https://preview.redd.it/fiizit6dfw1h1.png?width=3450&format=png&auto=webp&s=9ea05a77ca5583a2fb395dccc95ba52c433362c5

Hi everyone,

I'm starting a research project on financial time-series forecasting using LSTM and Transformer models for predicting S&P 500 market direction.

Right now, I'm struggling with obtaining reliable long-term historical data.

I tried Yahoo Finance, but downloads are inconsistent/failing for me, and most Kaggle datasets I found only contain around 5–10 years of data.

I specifically need:

• Around 30 years of historical S&P 500 data
• Preferably daily OHLCV data
• Reliable and clean source suitable for ML research
• Ideally free or student-friendly

I also want to understand what researchers typically use in academic work for financial forecasting:

• Yahoo Finance?
• Alpha Vantage?
• WRDS/CRSP?
• Polygon?
• Kaggle?
• Something else?

Additionally:

• Is using only S&P 500 index data enough for a Master's level research project?
• Or should I include technical indicators, macroeconomic data, sentiment, or constituent stock data?

Would appreciate guidance from people who've actually worked on financial ML projects.

Thanks.

I’m trying to break into AI/ML Engineer / Applied AI roles, and honestly I’ve been feeling pretty overwhelmed lately.

I’ve been building around LLM evaluation, model reliability, cost optimization, and production AI systems. My main projects are:

RDAB — a benchmark for evaluating LLM data agents beyond just correctness, including code quality, efficiency, and statistical validity.

CostGuard — an LLM reliability/cost proxy that tracks model cost, applies fallback logic, does lightweight response checks, and supports replay-based model comparison.

Tether — a trace capture layer that records LLM calls so they can be replayed against alternate models to compare quality and cost.

The overall idea is:
capture real LLM traffic → replay it against another model → compare quality, cost, and reliability before switching models.

But I’m struggling with how to package this clearly. I feel like I’ve built a lot, but I’m not sure what hiring managers actually care about or what would make this stand out in a competitive market.

Right now I’m thinking of focusing everything around one story:

“Can a cheaper LLM replace an expensive one without silently hurting quality?”

Then use CostGuard as the flagship project, with RDAB as the benchmark layer and Tether as the trace-capture layer.

For people working in AI engineering, ML platforms, LLM infra, or applied AI:

What would make this project stack more impressive or easier to understand?

Should I focus more on:

1. a polished demo video,
2. a case study,
3. better README/docs,
4. more technical depth,
5. more real-world examples,
6. or outreach/networking around it?

Any honest guidance would help. I’m trying to turn this into something that clearly shows production AI engineering ability, not just another AI demo

Hey everyone, I’m building a backend that analyzes long YouTube videos using an LLM.

Currently, my flow is a slow waterfall: Download full audio -> Whisper -> LLM -> Return results. For a 30-minute video, the user waits forever.

I want to pipeline this for real-time SSE streaming: [Chunk Audio on the fly] -> [Whisper] -> [LLM] -> [Stream to UI]

My questions for the data/backend engineers:

1. Chunking & VAD: What's the best way to chunk YouTube audio streams (e.g., via ffmpeg) without cutting sentences in half and ruining the LLM's context?
2. Queueing: Is standard asyncio in FastAPI enough to handle these overlapping tasks, or do I strictly need Celery/Redis workers for this pipeline?

Any library recommendations or architectural patterns would be hugely appreciated

So I’ve been working with AI/ML for the past couple of years, and it has been an amazing experience. I still remember using GPT-2 for the first time and being completely blown away by it. Seeing how far the technology has come since then is honestly mind-blowing.

I genuinely love working in AI, learning about it, and experimenting with new tools and ideas. But over the past couple of years, something has started to weigh on me: the ethical and moral impact of this technology as it continues to advance.

There have been moments where I’ve felt uncomfortable talking about my work because so many people are understandably upset or concerned about AI’s effects on jobs, education, the environment, critical thinking, creativity, mental health, and society in general.

I feel a bit torn. On one hand, I’m deeply passionate about this technology. On the other hand, I want the work I do to have a positive impact, not contribute to harm.

So that leads me to a few questions:

Are there any AI ethicists here? Is AI ethics a viable career path? What does your day-to-day work look like? Did you need additional schooling or a specific background to get into it?

Most importantly, do you feel like you’re actually making a difference?

I know this topic will probably bring a wide range of opinions, but I’m genuinely curious how others think about AI ethics, morality, and responsibility. I’d especially love to hear from people who are passionate about AI, mental health, and positive social change, and who have found ways to turn that into meaningful work.

We kept running into the same problem every time we rented a GPU to run Ollama + OpenWebUI or ComfyUI, we'd spend the first 45 minutes reinstalling everything. Custom nodes, models, configs, all of it. Docker images went stale fast, different providers had different base images, and nothing was truly portable. We got sick of it and built swm.

Here's what it does for ComfyUI users specifically:

swm gpus -g a100 --max-price 2.00 --sort price shows you the cheapest available GPU across RunPod, Vast ai, Lambda, and 7 other providers in one view

swm pod create — spins up an instance on whatever provider you pick

swm setup install comfyui — installs ComfyUI on the pod

From there the main thing is the workspace sync. Your entire setup custom nodes, models, outputs, configs lives in S3-compatible object storage (I use B2). When you're done you run swm pod down and it pushes everything, kills the instance, and next time you spin up on any provider you just pull and everything is exactly where you left it. No more reinstalling 15 custom nodes and redownloading checkpoints every session.

We also built a lifecycle guard because we kept falling asleep mid-session and waking up to dumb bills. It watches GPU utilization and if nothing's happening for 30 minutes (configurable), it saves your workspace and terminates automatically. Has saved us more money than we want to admit lol.

A few other things:

• Background auto-sync daemon pushes changes every 60 seconds so you don't have to remember to save
• Tar mode for huge workspaces with tons of small files packs everything into one S3 object instead of 600k individual uploads
• Also supports vLLM, Ollama, Open WebUI, SwarmUI, and Axolotl if you do more than SD
• Works with Cursor, Claude Code, Codex, Windsurf if you want your AI agent to manage GPU instances for you

Free, open source, Apache 2.0.

pipx install swm-gpu

Site: https://swmgpu.com GitHub: https://github.com/swm-gpu/swm

Would love feedback from anyone who rents GPUs. What's the most annoying part of your current workflow? We are also looking for contributors to the open source repo and suggestions on new frameworks/extensions to be included. Please share your thoughts

Hello everyone. This is just a small rant on my part. I’m relatively young, a final year undergrad, and I’ve been interested in AI researcher since I was in high school. Over that period of time I feel there has been a significant shift in the landscape regarding the culture surrounding the research.

While I’ve really enjoyed producing some interesting and creative work, I can’t help but feel that slowly the wave of low quality AI research and researchers are really making me feel frustrated. To just give a summary of what I and many others have seen:

- Papers with hallucinated citations and even prompts contained in the papers
- Papers with clearly misleading data that does not tell the whole picture.
- Labs who have built a culture around quantity over quality, pumping out pubs, citing each other, and having all of the lab on each paper to inflate each students publication record.
- Highschoolers…. Yes HIGHSCHOOLERS, becoming more common submitting at conferences that don’t really know what they are doing but paying a pretty penny to participate in “research programs” which are really just cash cows taking advantage of the fierce competition. See the post on the subreddit for more info.
- Even the so called “top labs” producing work that is somewhat misleading or not fully representative. For instance see what happened recently with TurboQuant.
- Research from “low tier institutions” being drowned out because they are not good for click baiting and farming views on LinkedIn and X, even if they are high quality.

It’s… a lot I know. Of course these problems have been around for a long time, but I feel as if lately they have become more and more exacerbated. I originally felt that I was attached to AI research primarily for the creativity and freedom, but I feel that ironically AI itself has been a hindrance on the quality of work being published.

Of course I don’t mean to say that all AI has been bad for ML research, I mean even I use it extensively to help me polish my writing and generate seaborn plots for my data, but that is very very different from just pumping out low quality cookie cutter work.

Anyways, just wondering if anyone else shares similar thoughts. I know I’m relatively young here so maybe some of you have better insights into the broader trends over the decades.

I am an undergraduate student from India who recently got accepted to TAIGR, an ICML workshop for a Poster. I will be requiring financial aid for registration fees and accommodation, since I will be travelling to Seoul and it is independent research so we don't have any backing by any labs/institutions. Can anyone who's applied and gotten aid in the past help and give any tips to be successful in receiving funding?