Day 1 of ARC-AGI-3 (opens in new tab)

What a dick move. Making that prompt open source will probably mean that every other model that doesn't want to cheat will scrape that and accidentally cheat in the next models.

(disclaimer: i worked on early versions of agentica_sdk; but wasn't involved in recent developments and the ARC solver)

As other comments point out this is about harness development and harness efficiency. Agentica SDK is a sort of meta harness, that makes things easy: plug any "internal API" (as defined natively in your codebase) directly into your agent. Agentica SDK itself is not application specifc; but the APIs of your application are... application specific.

Re: the linked prompt. A harness is a set of tools and descriptions how to best use those tools, and sometimes some external control flow based on the outcome of using those tools. How to "best use the tools" should always be part of the prompt (like in this case).

So this work tries to answer: "short of telling the agent any solutions, make a simple but efficient API to play the games, hand it to the agent, and see how it does". In the world of harness development I think that's an interesting question to answer!

this is so disingenuous on symbolica's part. these insincere announcements just make it harder for genuine attempts and novel ideas

Um, yes this is a extremely specific as a benchmark harness. It has a ton of knowledge encoded about the tasks at hand. The tweet is dishonest even in the best light.

The hard part of these tests isn't purely reasoning ability ffs.

Right, fair, but look at the prompt. For the purpose of testing general intelligence, this seems kind of pointless.

It isn't arbitrary. They want measure the capability of the general LLM

Harness is fine. I think people here are arguing what provided here to take the test is not harness.

The point of this test is to check if an AI system can figure out the game. This isn't what happened here. A human figured out the game, wrote in their prompts exactly how the game works and THEN put the AI on the problem. This is 100% cheating and imo quite stupid.

ELI5 what is a harness?

EDIT from https://arcprize.org/media/ARC_AGI_3_Technical_Report.pdf:

> We seek to fight two forms of overfitting that would muddy public sensefinding:

> Task-specific overfitting. This includes any agent that is created with knowledge of public ARC-AGI-3 environments, subsequently being evaluated on the same environments. It could be either directly trained on these environments, or using a harness that is handcrafted or specifically configured by someone with knowledge of the public environments.

I think generally people regard a harness as the system instructions + tools made available to the LLM (and probably the thing that runs the LLM conversation in a loop.) An agent is collectively, the LLM plus the harness.

There is. Official leaderboard is without harness, and community leaderboard is with harness. Read ARC-AGI-3 Technical Paper for details.

I'm so into harness development right now. Once it clicked that harnesses can bring more safety and determinism to LLMs, I started to wonder where I'd need that and why (vs MCP or just throwing Claude Code at everything), and my brain gears have been turning endlessly since then. I'd love to see more of what people do with them. My use cases are admittedly lame and boring, but it's such a fun paradigm to think and develop around.

Yes it matters, because it’s not a measurement of whether it accomplishes the task if a human tells it how to solve it.

I’m not saying it invalidates the result. I am saying that they knew the headline and comparison was not correct and they still decided to roll with it. It’s an incorrect representation of what happened, designed to get eyeballs and possibly vc dollars.

In this case the code is public and you can see they are not cheating in that sense.

You have a problem with generating synthetic data from test questions? Humans simulate experiences in their mind. What's the problem?

I'm gonna guess it means "whatever we still need humans to figure out to spoon feed the models"

anything that doesn't touch the model parameters at all once it has been compiled. for example, in streaming ASR of an encoder-decoder you can get gains in accuracy just by enhancing the encoder-decoder orchestration and ratio, frequency of fwd passes, dynamically adjusting the length of rolling windows (if using full attention). Prompting would be part of this too, including few-shot examples. Decoding strategy is also part of this (top-k, nucleus, speculative decoding, greedy or anything else). Applying signal processing or any kind of processing to the input before getting it into the model, or to the output. There are a lot of things you can do.