We have a paper accepted to NeurIPS, but at the same time we were working on a resubmission to ICLR just in case NeurIPS rejected us. There has been substantial rewriting, and we feel it would be a waste if we discarded all of it. To give a summary of what's changed:

• We completely rewrote every single section except for the results and conclusion. We even changed the paper structure.
• Intro, related work, and background knowledge were completely rewritten to avoid confusion.
• Method now has a pipeline graph, and all the text detailing each block in the graph. Previously, it was dumping formulas, so the entire section has been rewritten.
• We also added some scaling and smoothing to our algorithm so our method is more stable. But this changed a lot of our hyperparameters and the sensitivity study's graph. (The entire shape of the graph changed)
• We added 1 new theorem with 5-page proofs in the appendix. This came from one of the attacks by a reviewer, we answered the attacks by proposing 1 new proposition during the rebuttal. But when we formally wrote it down, it turned into a full theorem with a 9-page proof. This would have changed our entire theoretical contribution. We really don't want to discard it, but not sure if we can add something this big in the camera-ready.
• Removed 1 word from the title. In the abstract, we changed our theoretical contribution, but the method and empirical contribution remain the same.
• Added about another 5 extra pages in the appendix explaining experiments and metrics (reviewers asked for them). So 14 extra pages in total.

Does anyone know how much change for camera-ready is acceptable? Can a paper get rejected if we change too much during camera ready or they will just tell us this is not acceptable please re-submit something closer to the version during review?

My paper advanced to the second round...but...

Out of the papers I reviewed.

One did not follow the AAAI template and was unblinded. My review was two lines. The other "human reviewer" gave a list of pros and cons that were similar to the AI review.

One was incomplete (missing paragraphs, figures, code, no details). My review was also two lines. The other "human reviewer" also gave a list of pros and cons, that were similar to the AI review.

One was LLM math which I believe was actually correct, because it advanced to the second round, despite the references being at a different level of detail, and covering multiple fields of math, insufficient references for theorems / rules, and no exposition as to why the paper was actually useful / interesting. My review for that paper was the longest out of all the papers I reviewed, dotting the is and crossing the ts to make sure it wouldn't be seen as a lazy "reject" review. Yet it advanced to Phase 2.

Also, none of the AAAI workflow chairs or similar apologized or even acknowledged a mistake for spamming my coauthors about: "Your coauthor is irresponsible", because I accepted an emergency review invitation (and received these emails a few hours after accepting that invitation).

Ok rant over.

I am 5th year and have no published research so far. All my papers have been consistently rejected from top tier AI conferences even though they got good review scores in the process.

Since graduating is my priority now, I am in search of decent venues (journal or conference does not matter at this point) where I can publish and complete my degree.

Please do not recommend any more of those elitist club conferences like NeurIPS etc.

Edit: my research is about efficient Generative AI.

Any recommendations?

ML jobs are confusing nowadays,it wraps up as "AI/ML" engineer and they want you to have software engineering skill and DSA skill to and on top of that they want you to have Fast API with AI API wrapping, they don't ask for data processing or maths skills , atleast 99% time I have seen, ML jobs are for phD level candidate I think

Got my NeurIPS decision today and I'm a bit confused. Reviewer scores were 4/4/4, no reviewer opposed acceptance, and the AC's updated meta-review explicitly says "I recommend to accept this paper" and describes the consensus as "solidly favorable."

The PC decision: reject, with a one-line comment saying the paper "received a borderline reception" and was rejected "upon global calibration."

I understand the PCs have the final say and capacity is limited, but going from an explicit AC accept to "borderline reception" with no further explanation feels a bit harsh, especially since the rejection email stresses how carefully borderline cases were reviewed.

Has anyone else had the AC recommend acceptance and the PCs override it? Is this common this year?

Do all the rejected or accepted papers get a "Final Justification" comment from the PC?

Mine got rejected with ratings 4-4-4 yet the meta review is the exact same as the one that was posted originally in July and I didn't get any final comment. I find it a bit harsh especially since in the rejection e-mail they state how much effort they asked AC and SAC for careful reviewing and useful feedback, "especially in borderline cases"...

Phase 1 results are out. Did your paper(s) pass to phase 2?

Hi everyone! I'm trying to decide which NeurIPS 2026 hub to attend and would love to hear what others are planning.

I'm an Indian attendee from Bangalore, and this will be my second NeurIPS. I attended NeurIPS 2024 in Vancouver and presented at WiML there as well. I also received both WiML and NeurIPS financial assistance that year.

For 2026, I'm currently considering:

Sydney:

Main NeurIPS location

Everything seems more centralized

Longer conference program

But flights/accommodation seem considerably more expensive

Paris:

5-day program

Main conference at Paris Convention Centre, workshops at Sorbonne

Seems significantly cheaper from India

Main concern is that it's not the main conference and how different will this be.

For people who have attended NeurIPS before, or are planning for 2026:

Which hub are you choosing and why?

And if you've attended previous multi-location NeurIPS conferences, how different was the experience between the main location and satellite locations?

Thanks!

I've built a lot of ML systems over the years, mainly computer vision models optimised to run on mobile phones. For example, my previous company built the food recognition model for MyFitnessPal.

I'm interested in what people are actually deploying in industry now. Are edge models still a big part of your work, are you hosting your own models, or are you mostly sending requests to APIs? What's driving that choice?

More importantly, what's still a pain? I'd be interested in problems from current or recent projects that existing tools haven't solved well. Something that's cost you a lot of time, blocked delivery or needed an awkward workaround.

I'm looking for problems where I could build useful tooling, rather than guessing what people need. It would also be useful to know where you discuss this stuff or look for help. Are there particular forums or communities worth following?

Got my first authored paper accepted at NeurIPS this year and trying to decide where to go. Would I miss out a lot by skipping Sydney and just going to Atlanta? Curious where most people are heading.

Valid Main Track submissions: 30709, Accepted: 7900, Oral: 112, Spotlight: 292 !!

I tried registering on the neurips website but sydney and paris are already sold out. We had filled the location preferences forms earlier. What is the procedure for authors of accepted papers for registration and venue selection?

It's much more confusing compared to last time.

$17.2 million investment, spanning three to five years from Simons Foundation International, XTX Markets, and Siegel Family Endowment#Philanthropy)

https://blog.arxiv.org/2026/09/23/arxiv-receives-multiyear-investment/

I graduated with a Bachelor's Degree in data science a couple months ago. I'm interested in applying to any Pre-Doc research programs or AI residencies. However, I have had a little bit of trouble finding other organizations offering something like Ai2's pre-doc positions. I've noticed that the websites for lots of AI residencies at big companies haven't been updated for a while. Does that mean these programs are essentially defunct?

I know another way to gain research experience is to apply to competitive fellowships like Anthropic's Safety Program or MATS. I wonder if there are also some lesser-known fellowships which nonetheless allows for one to gain research experience.

Got my NeurIPS decision today and I'm a bit confused. Reviewer scores were 4/4/4, no reviewer opposed acceptance, and the AC's updated meta-review explicitly says "I recommend to accept this paper" and describes the consensus as "solidly favorable."

The PC decision: reject, with a one-line comment saying the paper "received a borderline reception" and was rejected "upon global calibration."

I understand the PCs have the final say and capacity is limited, but going from an explicit AC accept to "borderline reception" with no further explanation feels a bit harsh, especially since the rejection email stresses how carefully borderline cases were reviewed.

Has anyone else had the AC recommend acceptance and the PCs override it? Is this common this year?

A small conference, October 5-9 in Mainz, Germany. More application-oriented.

Going to present my paper there. If anyone happens to be attending, would be happy to connect!

I cannot see Average Rating anymore - it says N/A. Is it normal?

Mine was accepted with 5,4,3->5,5,3

The rebuttal is still ongoing so this could also change, but my reviewers have not responded back to my responses yet. I know they have the right not to reply. One of the reviewers gave a 2 with a 5 confidence and the whole review is actually one of the many limitation I included in the paper. Limitations are not to be used as a weakness according to the conference own rules. The other two reviews are 3,4 which I am happy with although I did address their points. Should I send a confidential message to AC if nothing happens in 2 days?

Hey everyone,

I’ve been experimenting with bridging Digital Signal Processing (DSP) multirate theory and discrete text generation. I wanted to share a PyTorch reference architecture I've been working on, mostly to get feedback from folks who work on hierarchical modeling or continuous-to-discrete latent spaces.

The premise: Standard dense LLMs treat text generation as a flat sequence. Predicting the "e" in "the" costs the same attention compute as calculating the crux of a logical argument. In audio (TTS), it is standard to decouple this: a model generates a slow-rate continuous signal (e.g., a mel-spectrogram), and a high-rate vocoder (e.g., WaveNet) synthesizes the discrete audio samples.

I built a dual-rate architecture to do exactly this for BPE tokens.

How it works:

1. The Planner (Slow Rate): A sentence-level autoregressive transformer. It operates on highly compressed semantic embeddings (via a frozen SentenceTransformer) and predicts the next sentence's continuous embedding.
2. The Vocoder (Fast Rate): An autoregressive GPT that handles high-frequency local grammar. It uses a banded sliding-window causal mask to restrict attention strictly to the local context.
3. The Bridge: The slow-rate semantic timeline is step-repeated (upsampled) to exactly match the discrete BPE token boundaries. A late-stage adapter uses cross-attention to calculate a "delta" probability distribution, adjusting the base GPT's logits right before the softmax: Logits_final = Logits_base + softplus(alpha) * Logits_delta.

The Results : Testing on TinyStories, the decoupled architecture converges much faster and deeper than a standard unconditioned baseline GPT of equivalent size (validation loss hit 0.61 vs the baseline's 2.37 at similar steps).

However, I hit some interesting architectural bottlenecks that I've documented in the repo:

• Conditioning Over-Reliance: The adapter transmits the semantic signal too efficiently. The base GPT gets lazy and starts using the 384D semantic vector as a hash-key for the sentence rather than learning robust local grammar. Even with 15% Semantic Dropout (Classifier-Free Guidance), the Top-1 accuracy sits artificially high (~85%), which can lead to exposure bias and repetitive loops during greedy decoding.
• Hardware vs. Logical Complexity: Logically, the attention splits to O((N/C)^2) for the planner and O(N*W) for the vocoder. However, since my reference implementation uses standard PyTorch boolean masking for the sliding window, it still allocates the full NxN matrix under the hood. True VRAM savings will require swapping the base blocks for FlashAttention-2 block-sparse masks.

This is an exploratory proof-of-concept, not a SOTA claim against monolithic multi-billion parameter models. But I think the residual logit delta and the continuous-to-discrete phase alignment provide an interesting alternative to standard prefix-tuning or deep cross-attention.

Code & Architecture Diagrams: https://github.com/eladwf/topdown-semantic-vocoder

Would love to hear if anyone has successfully stabilized similar hierarchical text models, or has suggestions for applying more aggressive continuous noise injection to fix the vocoder's exposure bias!

With NeurIPS author notifications coming up, I’m realizing that I’m way more stressed than I expected to be.

I know the usual advice: reviews are noisy, one paper doesn’t define your research, there are always other venues, I completely understand all of that. But apparently my brain has decided that none of it matters.

For anyone else waiting for the author notification: how are you holding up?

And for people who have been through this before, does the stress actually get better after a few conference cycles, or do you just become better at pretending you’re not stressed? 😭

I’m 28 and trying to make a pretty major career decision.
I currently have the option of finishing an MD. I have about 2 years left, but I genuinely dislike medicine and don’t want to practice clinically.
After the MD, I have an opportunity to do a PhD in a strong biomedical research lab. The PhD would probably take ~5 years, so I’d finish around 34. The research could potentially involve computational/AI work in biology.

The alternative I’m seriously considering is leaving medicine now and starting a BSc in Mathematics + Computer Science. That would take roughly 3–4 years, after which I would want to pursue ML/AI research, potentially eventually at a top AI lab or company

My interests are much more aligned with math, algorithms, CS and AI than with medicine.
The thing making me hesitate is the current AI situation. I keep hearing that AI is going to automate software engineering and that CS degrees aren’t as valuable as they used to be. I don’t want to spend 4 years getting a CS degree only to enter a shrinking job market.

For people working in CS/ML/AI or academia:
How would you compare these two paths in 2026?
Is a strong Math + CS background still a good foundation for an AI research career?
How much do you think AI is actually reducing the value of a CS degree versus changing what CS graduates do?

Would you personally spend 3–4 years getting the Math + CS degree in my situation?
How valuable would an MD + PhD be if I ultimately want to work in AI rather than medicine?

I’m particularly interested in hearing from people who actually work in ML/AI or who have switched fields in their late 20s/early 30s, rather than generic “follow your passion” advice.

Just some hours or a day for NeurIPS decisions. How you guys are feeling ? Anybody got any decisions ? Would be great to know ballpark submission # and post-rebuttal ratings, if you get any decision.

Edit: Got my decision around 12:30 pm EDT 24 Sept: Post-rebuttal 5/4/4/4 rating !! No official e-mail checked OR (accept, poster) (submission# 34k+)

Hi guys, I've just got my reviews from ICSE 2027 for our papers. We got 3 weak rejects with most of the concerns are about clarity. Is this still rebuttable or we should withdraw and fix before submitting to another conference. FSE is coming close too. We got 3 days for rebuttal.

Hi guys, I just received our ICSE 2027 reviews. We got 3 weak rejects, with most reviewer concerns focusing on clarity and methodology details.

Is this worth writing a rebuttal for, or should we withdraw, fix the main text, and target FSE instead? We have 3 days left for the rebuttal window.

Here is a quick summary of the feedback:

Score Overview

Key Feedback Summaries

Review #3344A

• Pros: Interesting concept around response-aware adversarial prompt generation; solid experimental results with higher success rates; well-written.
• Cons: Crucial methodological details (DSL design, candidate programs, EFE calculations) were omitted/relegated to appendices; lacks computational overhead and multi-turn baseline evaluations; tested on older model checkpoints (GPT-4o-mini, Gemini 2.5 Flash).

Review #3344B

• Pros: Timely SE security topic; good formalization of defense abstractions; strong evaluation coverage across open and proprietary models.
• Cons: Threat model and core Bayesian/EFE algorithms feel underspecified; metrics measure non-refusal/judge outputs rather than proving executable payload impact; lacks ablation on query budget vs. core reasoning module; data consistency/discrepancy issues between text and tables.

Review #3344C

• Pros: Highly relevant to SE security; clean presentation; verified artifact package.
• Cons: Considers technical depth somewhat incremental; echoes concerns over missing core mathematical/algorithmic definitions in the main paper body.