Sol Ultra style is the path forward. The models are smart enough to self serve their tooling and processes. Given a problem they can figure it out and ask for directions when needed.
This makes sense if the models some how become unified.
We've only just started training models to use tools. Next, we'll train them to build them. Harness engineering is an ephemeral art.
I’m pretty excited to see what sort of generalization we come to over the next 12 months on the harness side: if it turns out this can be RLed in as ‘consider if building a world model might help here’ and we get this as another native capacity, that will be interesting. If we get 100 of those problem-solving strategies all included, feels like we will see another hurdle cleared in terms of usefulness.
they could take open weight model, and check what will be impact from that harness on hold-out
A year ago, Sonnet 4 barely solved the first level. Now, both Fable 5 and GPT-5.6 Sol beat the first two stages. GPT 5.2 is slow, but efficient, while Gemini 3.1 Pro and 3.5 Flash struggle.
In a few instances (we covered it in Caveats) Gemini 3.5 Flash "knew" which level it was, but misremembered, and went with a wrong solution.
it's not as impressive as it looks. the goals of Arc-AGI-like constructs is to get an IQ-like figure using raw'ish 2D measurement 'games' in the hope that it would signify something meaningful.
what this harness does is get the model to write a simulator first, it's measuring something entirely different.
The way I'm reading this isn't that they are writing a game simulator, but rather that they have two things they are evolving - a perceptual model of the game mapping from pixels to objects, and a behavioral model of how each action acts upon these perceptual objects. The behavioral model is written as a program that can be backtested by the game states and actions they have already taken to see if they are correctly predicting the resulting next game state.
The ARC AGI 3 games are non-trivial, and I think it's very impressive to see them doing well using this approach.
I'd agree with their conclusion:
> We read a saturated ARC‑3 as the new beginning: mechanism discovery as a general capability — grounding the causal structure of a world through the agentic loop of action and perception, in environments far richer than a 64×64 grid. This is where we are heading to.
This is the way that an animal learns about it's environment - by observation (and innate biases) to recognize the objects in the environment, and predict their behavior, both autonomous (which AGI ARC 3 doesn't test - the objects in the environment are passive), and in reaction to the animals behavior. The animal predicts and observes, updating it's predictions when it is wrong.
A system that could do this in a messy, dynamic, real-world environment would seem like genuine step in the direction of animal intelligence, especially if it could ditch the symbolic representations.
the harness basically outsources the alien nature of what the LLM is asked to do to algorithms it writes. this would actually be impressive if you got it to do that for a much more complicated game than Arc.
with this harness the ARC AGI test becomes a test of whether or not the model can work out the transition rules in a very simple game.
I get what you mean in terms of testing the model itself to see its improvement in some domain. However if you can transform the domain to be better adapted to the model and achieve the desired results, this is indeed an accomplishment because a whole domain of problems is shown to be practically feasible with this technique without expensive model improvements. Of course the benchmark still exists without the harness, but the harness also exists which allows these problems to be solved.
As noted elsewhere the models themselves were used to build the harness, which means the models can in fact score this scores without intervention but building a harness for themselves adapted to the domain and using it. Is this cheating by the goal posts you’re setting?
There’s a real tension between “I want to solve problems and this technique shows how to solve the problem domain,” and the “I want to measure how something performs unassisted with other techniques.” Fortunately it’s not a mutually exclusive situation. You can do both simultaneously, gain the benefit of the technique to transform the problem into something tractable and keep measuring using the benchmark.
> ARC-AGI-3 is an interactive reasoning benchmark which challenges AI agents to explore novel environments, acquire goals on the fly, build adaptable world models, and learn continuously.
This harness does nothing to actually accomplish those goals.
It's a clever trick, sure, but you aren't allowed to use a calculator on your basic algebra tests in school for a reason.
This harness is really moving the goalpost by defeating the entire point of the test. Instead of seeing the strength of a model's world view, its ability to internally derive and intuit rules, and its ability to keep track of game state over time, we're just letting the AI cheat. This is just the LLM equivalent of running a chess engine to the side.
And this harness would not work in a remotely complex game and relies on the fact that Arc-AGI-3 is a focused test that only made the games as complicated as they needed to be for current model performance.
The games are designed to allow assessment of a system. Knowing better systems to solve the games is a step forward. If any of the frontier labs could have one-shotted -3 in March with a custom harness, they would have done so.
> simulator the model builds is comparable to the mental model of the game humans create
hmm, this is like pass@n until you get the high watermark? How would this mean anything?
1. The AI plays the game and records outputs.
2. The AI does TDD using those outputs to create its own copy of the game.
3. The AI then uses it's copy of the source code to understand the rules. This bypasses the intent for Arc-AGI-3 to test the underlying model's ability to intuit game rules naturally, like a human.
4. The AI then runs simulated moves on the copy of the game before playing them in the "real" game. This bypasses the intent for Arc-AGI-3 to test the underlying model's ability to plan and predict moves, and track world state in its "head" over time.
To make an apt comparison... You go to get your chess ELO. You don't know chess at all and you're really bad at it, so you pull out your laptop and write a chess engine. Then when you go to get ranked, you just copy the moves from the software. Now you're a grand master.
Neat. Maybe even deeply interesting. Absolutely garbage write up.
Impressive results. Will this translate to coding agents (and training general purpose and for coding LLMs) too?
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> When Michelson and Morley could not detect the medium light was supposed to wave in, Lorentz took the first route: keep the aether, patch the rules with contraction hypotheses that absorbed the null result. Einstein took the second: in special relativity, he discarded the aether as part of the state and made simultaneity frame-relative, yielding a simple electrodynamics of moving bodies.
BECs, SVT, Superfluid Quantum Gravity
Massful photons are modeled with Proca fields. Like Einstein, Proca was also a student of Minkowski. The Mass-Equivalence principle ~~does not~~ still holds if photons have mass.
(edit) Energy-momentum relation: https://en.wikipedia.org/wiki/Energy%E2%80%93momentum_relati...
> could not detect the medium light was supposed to wave in,
Superfluid Quantum Gravity (Fedi,) says that there is a medium that light waves through; there is not nothing in space, space is a quantum dilatant superfluid with near-zero viscosity.