TraceViT: Grounded Trace Supervision for Visual Abstract Reasoning

Visual reasoning benchmarks like the Abstraction and Reasoning Corpus test whether models can infer abstract transformations from a few examples, but training typically only checks the final answer, ignoring how a model reasons through intermediate steps. TraceViT addresses this by training looped visual reasoners on step-by-step transformation chains derived from verified programs, grounding each iteration in the task\'s demonstrations. This produces stronger results on ARC-AGI benchmarks and shows that step supervision only helps when properly grounded. This approach could improve AI systems built for abstract pattern reasoning, program synthesis, and general visual problem-solving tasks. Authors: Binnan Liu, Yechi Ma, Tian Xie, Wei Hua Paper: https://arxiv.org/abs/2607.29586v1