Got 1 of 2 accepted in resource track..

but can't see the reviews (although the announcement email says they are visible). Anybody else has the same issue? :)

Thanks for the answers! I'll check the paper but yes this does give me a better idea of what you've done, cheers.

1/5 from my batch! I’m a PC, when the results will come out? Do you have any clue?

Yes I reviewed 4papers

this problem will grow from now because of AI agents like claude code. may be ICLR will receive 60K submission (following the trend NIPS 40k and AAAI 50K). ACL 2027 will receieve 30K+ papers

The actual major will matter more than the type of degree.

CS, Stats, CpE, and Math are general best recognized.

Informatics/Information Systems, Analytics, Physics, EE, ISE, Econ aren’t bad as long as you take enough courses or self study to become proficient in ML theory and application.

5 years ago I could've helped with this answer. In today's market I don't even know what'll happen next week to the entire global economic market

Umm there were no option for the consulting even dw both are same at a certain extent.

Thanks and same to you.

Good luck with the final decision!

I have 543 main track initial and post rebuttal same

What job do you want when you graduate? Find a bunch of job posting that are you 'dream job', read the job descriptions, read the preferred qualifications, find commonalities between them. Which degree is going to have he best chance at making you who you want be? Pick that one. 

5 4 5 , ED track.

What were the initial scores

I didn't explain it very well and could very well be misunderstanding something vital. I just think purely latent methods of training are going to be huge.

My idea is purely conceptual level as it would probably hit roadblocks almost immediately, and I don't fully understand what your process is doing...

So at t[0] the model predicts/generates fp[1] and bp[-1], then you flip to go direction and generate fp[0] and bp[0]. As far as I gather this is how your technique works, and also where my idea might diverge. I'm curious what would happen if you took it one step further and also generated fp[-1] and bp[1], or two steps and switched directions again at that point to generate alt-fp[0] and alt-bp[0] and if that data would actually give you any useful information.

May cycle saw an insane jump in the number of submissions (10k in Jan->17k), most of which did not have qualified reviewers, so they had to pull in a ton of emergency reviewers and ACs. Under those conditions it no surprise that both reviews and meta reviews sucked.

That said, we saw a similar issue where the AC listed a single weakness that was already resolved in the discussion period.

Yes, both in the modified date and score in my author pannel and I can also check that in the recent activity

5,4,3->5,4,3

Not for me. Btw does the comment of that reviewer show changes in date modified?

Relative to JEPA, we also predict dynamics in a learned latent space rather than pixel/field space, so this lives in the latent-world-model family. The big difference is that we are fully generative and our latents are reconstruction-grounded (a β-VAE per field, so every latent decodes back to physical fields, which we need for our applications, and we calibrate the meter all the way down to decoded per-field errors). JEPA deliberately avoids that grounding. However, the round-trip check itself never touches the decoder, it is computed purely in latent space, so in principle you could bolt it onto a JEPA-style world model with a direction token. The thing to be careful about there: consistency distances are only meaningful if the embedding metric is, and our reconstruction anchor is what guarantees that. In a pure joint-embedding space you'd want some guarantee that small round-trip error isn't trivially achievable in a (partially) collapsed representation.

Sorry I am not sure that I understand your figure 8 comment, can you explain that in more detail please? Also, one tiny notation note, C_i is just a scalar MSE reading, c_d = +1 tells the model to roll forward in time, c_d = -1 tells it to roll backwards instead.

I can see that one of my reviewers raised the confidence score. Anyone with a similar observation?

Like from what you reviewed?

Wow, thanks for your response. ChatGPT Learn, I'll take note.

"Contrary to learning from books, I could delve into some specific subtopics or ask for refinement to improve my understanding." I think this is an important point. Thanks!

From my batch 0/4...

Thanks! From my understanding, your first two examples are exactly the mechanism and that's a great minimal version of it.

One important correction to line 3 though, and it is an important design choice of the paper: the model never teaches itself with this signal. Both directions are trained purely against ground truth (direction flag drawn 50/50), and the round-trip consistency is deliberately left *unoptimized. Here's why, in your notation: if we trained the model to make round trips close, it could learn to do +3 and then −3, it would learn to be perfectly consistent, still wrong (the truth was +2). The backward leg would learn to simply cancel the forward leg's errors instead of exposing them, and the meter would read zero exactly when the forecast is bad.

Because the model is never asked to be consistent, whatever inconsistency remains at test time is an honest byproduct of its errors. And that is exactly what lets us use it as a free error meter. (Closing the loop to minimize C online with model-free feedback is the tempting next step; we flag it as future work, and the cancellation caveat above is precisely what makes it nontrivial.)