ARC是AGI的关键测试，L-VARC用语言引导视觉推理，18M参数就能超越SOTA，做视觉推理或小模型研究的开发者值得一试。
L-VARC是一种新框架，通过语言引导的LUPI分支增强视觉推理，解决ARC任务中纯语言模型参数大、纯视觉模型过拟合的问题。它利用DeepSeek-V3压缩语义，用CLIP对齐视觉与语义特征，训练后丢弃语言分支，仅保留18M参数的轻量模型。实验表明，L-VARC在ARC任务上超越现有最佳方法，代码已开源。
Language-Guided Abstraction for Visual Reasoning
The Abstraction and Reasoning Corpus (ARC) is viewed as a critical avenue to Artificial General Intelligence (AGI), as it enables models to learn abstract transformation rules from few-shot examples and then generalize to new tasks. However, prevalent ARC methodology is either pure language or vision-only (i.e., VARC). The former depends heavily on LLMs, consuming billions of parameters. The latter often struggles to capture high-level semantics, leading to overfitting on pixel-level patterns. To bridge this gap, we propose L-VARC, a novel framework that enhances visual reasoning via a language-guided Learning Using Privileged Information (LUPI) branch. Specifically, we design a Semantic Compression Module by feeding a unified, task-agnostic prompt into DeepSeek-V3. In this way, the raw LARC (a crowd-sourced language description dataset) can be substantially refined and structured, fitting with the context length constraint of standard text encoders (e.g., CLIP). Moreover, we design a Cross-Attention Projector to align visual features with semantic embeddings, aiming to guide the training of the ARC model. Notably, the LUPI branch is taken in the training process and will be discarded during inference, thereby yielding a lightweight model with a mere 18 million parameters. Extensive experiments demonstrate that our L-VARC effectively leverages linguistic priors to boost visual reasoning and outperforms state-of-the-art. Ablation studies further confirm the contribution of the two new designs towards the L-VARC framework. The code is available at https://github.com/GZHU-DVL/L-VARC.