Benchmark scores (GPT-6 uses a harness for ARC-AGI-3, and is at about 60% without one):

https://openai.com/index/gpt-6-astra/

Prior to the launch, OpenAI President Greg Brockman said "I think it’s not unreasonable to feel that we are now in the AGI era".

LLMs describe physics well but don't "understand" it in any grounded sense — they've learned statistical relationships between tokens like "falls" and "gravity", not actual physical intuition. This is basically the Mary's Room problem: Mary knows every physical fact about color but has never seen one. LLMs are Mary.

The idea I've been thinking about:

1. Train a JEPA-style model inside a physics simulation (think MuJoCo or a simple 2D env). Instead of predicting pixels or tokens, the model predicts representations of future states in an abstract embedding space. If it gets physics wrong, the predictor fails — the loss is unforgiving in a way next-token prediction never is.

2. The embedding space that emerges should encode actual physical structure — object permanence, momentum, trajectories — because that's what makes prediction possible. Not surface-level textures, just the principles.

3. Freeze those representations and attach them to an LLM-style reasoning model as a conditioning signal. The LLM now has both linguistic physics knowledge AND grounded physical intuition it can actually "run" forward — closer to a computational primitive than a propositional fact.

The hypothesis is this makes downstream learning significantly faster — the LLM doesn't have to rediscover that objects fall, it has representations that already encode that.

V-JEPA does something adjacent for video (predict future frame representations, not pixels), and DreamerV3 uses a latent world model for efficient RL — but the specific combo of JEPA-style prediction + sim-grounded physics representations + LLM attachment doesn't seem to have been done cleanly.

Questions for the community: - Is there prior work I'm missing that does exactly this? - What's the right interface between grounded JEPA representations and a language model? Just concatenate to the prompt embedding? Cross-attention? - Would the sim-to-reality gap kill the transfer, or are the representations abstract enough to survive it?

Interested in whether this is worth building a small prototype for.

Three weeks from decisions even. I wonder what percentage is industry and VC funded AI labs looking to mingle and recruit.

Has anyone else received an AAAI-27 desk rejection related to modifications to the title or abstract between the abstract-registration deadline and the full-paper deadline?

What I’m trying to understand is how the modification rule is being applied in practice. The AAAI-27 modification guidelines say that the title and abstract can still be edited after abstract registration, while warning against substantive changes, and describe rejection in terms of changes that make the submission describe qualitatively different research.

In my case, almost everything was identical. The modifications were incredibly minor.

The rejection notice says that the decision is final and appeals will not be considered.

Did this happen to anyone else?

Hi everyone,

I just quickly wanted to share a paper I was working on for around a year now. I created this summary website with key results: https://flogrammer.github.io/moljepa/

TL;DR: its a multimodal JEPA model for molecules.

There will be more work to do to improve performance and I would be happy about feedback and ideas :)

I’m the author of a new preprint on repeated-query auditing of LLM brand recommendations, and the founder of Rankfor.AI.

The practical question: how many times should we repeat a prompt before comparing results?

The paper applies generalizability theory: estimate variance components from a pilot, then calculate the repeat count needed for a chosen reliability target.

Tested the reliability predictions on three independently collected corpora covering political-orientation questionnaires and benchmark stability. Across 39 prediction cells, 37 met the prespecified replication criterion and two were partial matches.

The fixed iteration thresholds did not transfer. Other preregistered tests, including parts of the drift diagnostics, also failed. Those results are reported in the paper.

An important limitation i see is that these external corpora do not contain brand recommendations. They test the statistical machinery outside our original application which is independent replication on repeated brand-recommendation data remains outstanding.

I’d particularly welcome criticism of the pilot-based variance estimates and the reliability validation design. Does anyone know an independently collected brand-recommendation dataset with repeated identical prompts?

Preprint: https://arxiv.org/abs/2609.04047

External validation materials: https://github.com/Rankfor/rankfor-open/tree/main/research/dice-roll-method/external-validation

I honestly am so confused rn as the ml community is overburst with people only caring about building rag modules and agentic ai for larger corporations.

I have a passion for machine learning but honestly it feels really confusing as to what really counts today. I would love some advice on what exact learning counts in this field like what exact topics, tech and tools?