The Abstraction and Reasoning Corpus (ARC-AGI) poses a significant challenge for large language models (LLMs), exposing limitations in their abstract reasoning abilities. In this work, we leverage task-specific data augmentations throughout the training, generation, and scoring phases, and employ a depth-first search algorithm to generate diverse, high-probability candidate solutions. Furthermore, we utilize the LLM not only as a generator but also as a scorer, using its output probabilities to select the most promising solutions. Our method achieves a score of 71.6% (286.5/400 solved tasks) on the public ARC-AGI evaluation set, demonstrating state-of-the-art performance among publicly available approaches. While concurrent closed-source work has reported higher scores, our method distinguishes itself through its transparency, reproducibility, and remarkably low inference cost, averaging only around 2ct per task on readily available hardware (we assume a price of 36ct/hour for a Nvidia 4090 GPU).
That score is seriously impressive because it actually beats the average human performance of 60.2% and completely changes the narrative that you need massive proprietary models to do abstract reasoning. They used a fine-tuned version of Mistral-NeMo-Minitron-8B and brought the inference cost down to an absurdly cheap level compared to OpenAI’s o3 model.
The methodology is really clever because they started by nuking the standard tokenizer and stripping it down to just 64 tokens to stop the model from accidentally merging digits and confusing itself. They also leaned heavily on test-time training where the model fine-tunes itself on the few example pairs of a specific puzzle for a few seconds before trying to solve the test input. For the actual generation they ditched standard sampling for a depth-first search that prunes low-probability paths early so they do not waste compute on obvious dead ends.
The most innovative part of the paper is their Product of Experts selection strategy. Once the model generates a candidate solution they do not just trust it blindly. They take that solution and re-evaluate its probability across different augmentations of the input like rotating the grid or swapping colors. If the solution is actually correct it should look plausible from every perspective so they calculate the geometric mean of those probabilities to filter out hallucinations. It is basically like the model peer reviewing its own work by looking at the problem from different angles to make sure the logic holds up.
What’s remarkable is that all of this was done with smart engineering rather than raw compute. You can literally run this tonight on your own machine.
I mean the paper and code are published. This isn’t a heuristic, so there’s no loss of accuracy. I’m not sure why you’re saying this is too good to be true, the whole tech is very new and there are lots of low hanging fruit for optimizations that people are discovering. Every few months some discovery like this is made right now. Eventually, people will pluck all the easy wins and it’s going to get harder to dramatically improve performance, but for the foreseeable future we’ll be seeing a lot more stuff like this.
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It just sounds too good to be true. So, no critics have claimed downsides to this?
I mean the paper and code are published. This isn’t a heuristic, so there’s no loss of accuracy. I’m not sure why you’re saying this is too good to be true, the whole tech is very new and there are lots of low hanging fruit for optimizations that people are discovering. Every few months some discovery like this is made right now. Eventually, people will pluck all the easy wins and it’s going to get harder to dramatically improve performance, but for the foreseeable future we’ll be seeing a lot more stuff like this.