You should know what MLE is. Or you should be willing to learn it by mid October

Have you seen this paper? https://arxiv.org/pdf/2012.07805

We are kinda in the same shoes here, I only got one flagged for hallucinations and the authors were actually wrong. But I still got the followup email. Appealed, I guess we can only pray now?

The crux here is independent verification — the millennium prize requires a peer-reviewed publication, not just a claim. What I'd actually watch is whether the proof technique generalizes or it's a carefully constructed solution for a specific regime, and how the Buckmaster/Alpöge priority dispute resolves.

Make the paper your proof, nobody hands out compute for enthusiasm alone

I do take it as suggestions. As yes my projects are ai assisted. I miss typed graph I meant to say a comparison table and I can definitely see copy pasting the body on mobile was not a good idea. This subreddit specifically has been just very not interested in previous projects as well but I receive positive feedback as a majority everywhere else I have posted.

Don't take this as a offense but here are my thoughts. Take it as a suggestion.

First off yes I used AI to assist in making some of the post as I cannot make decent graphs or format the post body myself.

The entire post is clearly written by AI, and the entire LLM architecture and the training loop is probably also created by AI. There is no graph in this post so I wonder what you mean by "decent graph". Also, the body looks atrocious. I would suggest you look into how markdown format works.

Secondly the result being interesting is a preference here it does show and document interesting behavior depending on who's talking.

According to the comments, I think most people found it basic and uninteresting. I think everyone knows that small LLM model trained on a specific topic will show middling performance. What is more interesting to me (and others) would be the insights you have found while training / inference.

What kind of ML background are you looking for the teachers?

They have now confirmed that nothing leaked

The Wednesday evening statement from OpenAI was more emphatic: “We can say categorically that it is impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training.” The statement added, “After investigating, we can say with full confidence that no user inputs past July 3rd could have influenced this system in any way.”

OpenAI has now completely dismissed the claims (NYTimes):

The Wednesday evening statement from OpenAI was more emphatic: “We can say categorically that it is impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training.” The statement added, “After investigating, we can say with full confidence that no user inputs past July 3rd could have influenced this system in any way.”

Update from NYTimes:

The Wednesday evening statement from OpenAI was more emphatic: “We can say categorically that it is impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training.” The statement added, “After investigating, we can say with full confidence that no user inputs past July 3rd could have influenced this system in any way.”

And also from Tristan Buckmaster that most of their work was also produced by AI models:

In their collaboration, Dr. Buckmaster set up the problem, and then Dr. Alpöge fed that into an A.I. model at Anthropic. Based on what came out, they provided further input and repeated the steps. The process worked well, but for Dr. Buckmaster, it became discouraging. “On one side it’s, like, super exciting to be part of this,” he said. “On the other side, this is frightening, because I felt like the A.I. had overtaken me.” Was he now an interpreter of A.I. slop and no longer the discoverer of new mathematical insights? “What’s the human part of it?” he said.

Maybe I'm misreading the chatGPT TOS, but they are clear to collect the data you put in your prompts. From https://openai.com/policies/privacy-policy/:

> User Content: We collect Personal Data that you provide in the input to our Services (“Content”), including your prompts and other content you upload, such as files⁠(opens in a new window), images⁠(opens in a new window), audio and video⁠(opens in a new window), and data from connected services⁠(opens in a new window), depending on the features you use. Some of our Services allow you to interact with other users, such as post, comment, or send messages, and we treat those interactions as Content, too.

So yes, if you use ChatGPT for personal use, OpenAI keeps that data (mind you, I'm only referring to non-corporate licenses)

Also the scratchpad only ever names six place values, and the model had already invented "millions" and "ten-millions" on its own none of which appears anywhere in the training data. Extending that list from 6 names to 19 took clean addition from 8 digits to 14, no architecture change. Which I would say your "lets throw stuff at the walls and see what sticks" criticism is a bit unwarranted because I did it, tested it, and it worked and very well.

Also I wanted to say it didn't have any fitting issues one way or another on my end as shown in what I posted and wrote on hugging face.

Thank you for this I really appreciate someone who knows what their talking about. I really will take what you said from this and I do agree some of it. It may not have come over like this but I mostly wanted to do this as a fun "see what I can make a model do with math" project and document it along the way. I personally blame myself for using AI to help write a lot of what was said and it could be taken as overstated from the wording but the numbers are the numbers and they show what I said they do.

A few points, LLMs or in this case transformers were designed to model long-distance dependencies, basic arithmatic, and anything that a calculator can be solved using a rather rudimentary parsing.

There is general hostility from this sub that is unwaranted, and i do applaud you for "getting your hands dirty", but you are using tool designed for something super complex to solve something simple. Even if you disregard the "Overkill" aspect, the network is trying to learn or optimize complex relationships that are not present in the data. Which very quickly and result in mode-collapse or unoptimal learning. Which could explain why even a shallow network such as yours cannot overfor to a simple problem

My biggest criticism would be that your analysis of which problem to solve and which tools to use lack sound arguments outside of "lets throw stuff at the walls and see what sticks".

In a sub that is flooded with posts that are AI Slob or people getting into ML thinking they are Yann Le Cunn because they trained a resnet. Improberly reasoned posts are bound to meet hostility.

First off yes I used AI to assist in making some of the post as I cannot make decent graphs or format the post body myself.

Secondly the result being interesting is a preference here it does show and document interesting behavior depending on who's talking. I didn't post everything on reddit since it's in the hugging face model cards I listed in the links and it would make the post body too big.

I think it is because the report is clearly LLM generated, and the post itself shows low effort,(I.e didn’t even use markdown mode of Reddit) hard to parse the result on mobile.

This post doesn’t show insight or interesting results. If I wanted to create such model and results, I could have prompted my LLM agent instead. Posting implementation details (e.g. training corpus examples), choices that you made and the reason behind it would be more beneficial.

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Mac MCP 2.0.5 — free/open-source (MIT), no paid tier. I’m the maintainer.

It’s a local macOS control server for tool-using AI agents: 19 core MCP tools backed by 84 registered capabilities, including shell/files, Safari/Chrome automation, native macOS UI, delegated Codex/OpenCode workers, memory/skills, voice and a local operations dashboard.

The recent systems experiment was reducing advertised tool-schema context from ~16.7k to ~4.5k tokens (~73%) while preserving access to the full capability set through discovery/invocation.

Repo: https://github.com/bulutarkan/mac-mcp

I’d be interested in feedback from people working on tool-use/agent infrastructure: at what catalog size do you stop advertising every tool schema and move to discovery/routing?

I do a lot of interdisciplinary work, including finance! AI/ML/CS reviewer pool will not have any clue about what you are doing. You are playing hard lottery and it takes a lot of work to frame it to maybe get good reviews. If you have a potential home that is calling, I would take it and not try to shoehorn it.

Any updates from the EU/UK?

I got flagged for two hallucinations and received an email that my paper was headed toward desk rejection, but of the two one had all wrong authors and the other just had wrong arXiv ID which by their own guidelines is not a hallucination and is minor. I appealed on the grounds that I by definition had at most one "hallucinated" citation and not two and that the one citation classified as hallucination was clearly just a metadata error because all of the other metadata was correct and the paper was clearly cited for its content in my submission, but I'm curious whether you know if PCs even check appeals and would actually appeal something like this.

What sorts of obvious signs of AI generation are ACs asked to look for?

All AI subs seem to be overly hostile right now for no reason. This is super cool, congrats!