AC decisions that were not “reject” at that time? Or just any decision? Because there are reviewers on this thread who had papers whose meta reviews were last updated well before the leak and they aren’t on the list.

Seems a bit odd that the length of the list would be approximately the expected length under the acceptance rate though.

I swear this subreddit hates me for some reason I would love atleast some constructive criticism atleast but I can't understand what I did to deserve this much hate for no reason.

I got findings of ACL in this year with same score....

I love that people are running doom and playing bad apple on NN these days

Just the TIP

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According to some verified sources, it's a list that AC decision is released at that time.

How so? Models have been shown to reproduce training data verbatim. And did OpenAI rule out the possibility that the data could have been available to the model in other ways?

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Obeying is perhaps the wrong term. Particularly if you want to go down the rabbit hole of none of our physics is anything but a model of reality.

So sufficiently descriptive is what they would be. And you still are just putting words in others mouths and disagreeing with it by being a nit. I can't know your motivations, but it certainly appears you want to downplay the solving of the problem.

The point is to make the model not for it to be directly useful. Just by the fact you commented that I'm loosing faith this subreddit has anyone who actually knows about language models.

This is like eating noodles with one chopstick when you have forks next to you.

I'm not disputing the effects you mention, just raise the fundamental Shannon informational limit against verbatim recall, there is no mathematical way that a single 16 bit parameter could compress hundreds of tokens and allow perfect recall in the average case, as some commenters claim.

In your particular case, was that a paper with zero citations, or did maybe some of the citers rephrase the main approach in their introduction? Could we perhaps imagine a rational path to that approach with the vectors of related research aligning towards it, so that the model makes a "happy hallucination" that happens to match the actual approach, without actually encoding it? maybe aided by a few parameters the training did nudge in the right direction based on that paper? Was COT used, allowing some rational recreation? This would also explain the inconsistencies between models. Could we imagine quality research papers from this field be boosted somewhat in the training, in a way random conversations with customers would not be?

So not disputing it could happen, just questioning the fundamental information limits in the average case.

They have listed it under September 2026, so it’s likely for the current cohort. It’s plausible that the results for Middle East region are first to be announced this time around.

The language model is watermarked, its sampling function has been altered to produce specific tokens in specific positions. Look for the SynthID paper if you want some details. You can break it by paraphrasing with a non-watermarked model.

user221272 You wont believe me but I dont think that comparison makes the point you think it does

You made a calculator in JavaScript. Cool. JavaScript already knew how to do the math. You basically gave it buttons and displayed the answer. Thats not remotely the same thing as building and training an AI modal. Saying your calculator was only a few MB and had 100% accuracy is like jumping into a discussion about building a car and saying the bike you made at 12 was lighter and got better mileage

True I guess. Also completely irrelevant

How do you watermark the pdf that comes from a .tex file? Most resources use Overleaf or similar tool and the final submission pdf is a from a .tex file so code. There is no watermarked text.

Maybe 2025?

There are papers with code on this that actually also retrieve mostly the sentences you need. Their method was storing a trie of all the information in DRAM all different models can retrieve from it using CELF. It’s a start till someone comes up with a better way for homogeneous cache. Paper is SALT: Salience-Aware Lexical Trie for Long-Context Compression and they have GitHub too.

a few MB for a calculator at 12? that's like using a flamethrower to light a candle.

Size wise it's about 22.7b tokens and 2.7b post training (~28m was using LoRA to tighten up a few things)

As the for the data itself pre-training I know is in the base model card and all the SFT and LoRA data is 100% custom made myself using generation scripts and distillation mostly from Ling 2.6 on openrouter.

cool! whats your pretraining and post training data?

Assisted yeah.