这篇论文的方法很实在，用多模态搜索加整体判断，在ARC-AGI-2上做到了72.9%，比GPT-5.2 Pro高了将近19个点，代码还开源了。
该论文针对ARC-AGI-2视觉推理基准提出一种新求解器，将文本、图像和代码作为独立搜索算子生成多样候选轨迹，并通过上下文保留的整体判断模型在长上下文提示中联合比较所有候选。在ARC Prize半私有评估集上取得72.9%准确率，成本38.99美元/任务，超过GPT-5.2 Pro的54.2%和Gemini 3 Pro的54.0%。在公开评估集上达到76.1%，成本19.69美元/任务。作者开源完整代码，并报告了负面结果，如规定性提示模板和迭代细化会系统性降低假设多样性。
Modality-Driven Search with Holistic Trace Judging for ARC-AGI-2
Large language models can produce fluent, internally coherent reasoning traces for abstract reasoning tasks while still being confidently wrong - making selection among candidates, not just generation, the central challenge. I present a solver for ARC-AGI-2, a few-shot visual reasoning benchmark, built around two principles: (i) treating reasoning modalities as search operators, generating diverse candidates independently across text, image, and code channels, and (ii) context-preserving holistic judging, in which a judge model jointly compares all candidate reasoning traces within a single long-context prompt. Unlike self-consistency or majority voting, this approach reliably recovers correct minority hypotheses on tasks where the modal answer is wrong. On the ARC Prize semi-private evaluation set, the solver achieves 72.9 percent at USD 38.99 per task - the highest score on the verified leaderboard at the time of writing, exceeding the best standalone frontier models, GPT-5.2 Pro at 54.2 percent and Gemini 3 Pro at 54.0 percent, by +18.7 percentage points. On the public evaluation set, it achieves 76.1 percent at USD 19.69 per task. I release the full source code and document extensive negative results, including the finding that prescriptive prompting templates and iterative refinement systematically reduce hypothesis diversity and degrade performance.