TraceViT：用轨迹监督提升视觉抽象推理
TraceViT: Grounded Trace Supervision for Visual Abstract Reasoning
AI 导读
论文提出 TraceViT，一种以语义单调变换链进行轨迹监督训练的循环视觉推理器，用于 ARC 抽象推理任务。该模型在 ARC-AGI-1 上取得 67.8% pass@2、在 ARC-AGI-2 上取得 24.3% 的成绩。ARC-AGI-1 上的受控消融实验显示，轨迹监督只有在与 grounding 配合时才带来收益，代码和数据将开源。
正文 · 原文
Abstract:The Abstraction and Reasoning Corpus (ARC) tests whether a model can infer an unseen transformation from a few input-output examples and apply it to a new grid. Looped visual reasoners refine predictions over multiple iterations, but conventional training constrains only the final output, leaving intermediate refinements unconstrained. We propose that these refinements should instead follow the transformation step by step. We introduce TraceViT, a looped visual reasoner trained with semantically monotonic transformation chains. We obtain these chains by rewriting and verifying programmatic task implementations, decomposing each solution into intermediate grid states. Each iteration is grounded by a task reference derived from the few-shot demonstrations and an object workspace representing the current grid state. Because these chains may differ in length from the loop, soft trace alignment enforces only their ordering, letting the model allocate iterations freely. TraceViT achieves 67.8% pass@2 on ARC-AGI-1 and 24.3% on ARC-AGI-2. Controlled ablations on ARC-AGI-1 show that trace supervision becomes beneficial only when paired with grounding. Code and data will be available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.29586 [cs.CV] |
| (or arXiv:2607.29586v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.29586 arXiv-issued DOI via DataCite |
Submission history
From: Binnan Liu [view email]
[v1]
Fri, 31 Jul 2026 16:09:44 UTC (10,476 KB)
[v2]
Thu, 5 Nov 2026 07:48:27 UTC (10,638 KB)
来源：arXiv cs.AI · arxiv.org