We present THOUGHTSCULPT, a general reasoning and search method for tasks
with outputs that can be decomposed into components. THOUGHTSCULPT explores a
search tree of potential solutions using Monte Carlo Tree Search (MCTS),
building solutions one action at a time and evaluating according to any
domain-specific heuristic, which in practice is often simply an LLM evaluator.
Critically, our action space includes revision actions: THOUGHTSCULPT may
choose to revise part of its previous output rather than continuing to build
the rest of its output. Empirically, THOUGHTSCULPT outperforms state-of-the-art
reasoning methods across three challenging tasks: Story Outline Improvement (up
to +30% interestingness), Mini-Crosswords Solving (up to +16% word success
rate), and Constrained Generation (up to +10% concept coverage).

THOUGHTSCULPT 是一种通用的推理和搜索方法，采用蒙特卡洛树搜索（Monte Carlo Tree Search）来构建解决方案，其中的行动空间包括修订操作，并在三个具有挑战性的任务中表现优于现有的推理方法。

思维雕塑：通过中间修订和搜索进行推理

THOUGHTSCULPT: Reasoning with Intermediate Revision and Search

The emergence of large models, also known as foundation models, has brought
significant advancements to AI research. One such model is Segment Anything
(SAM), which is designed for image segmentation tasks. However, as with other
foundation models, our experimental findings suggest that SAM may fail or
perform poorly in certain segmentation tasks, such as shadow detection and
camouflaged object detection (concealed object detection). This study first
paves the way for applying the large pre-trained image segmentation model SAM
to these downstream tasks, even in situations where SAM performs poorly. Rather
than fine-tuning the SAM network, we propose \textbf{SAM-Adapter}, which
incorporates domain-specific information or visual prompts into the
segmentation network by using simple yet effective adapters. Our extensive
experiments show that SAM-Adapter can significantly elevate the performance of
SAM in challenging tasks and we can even outperform task-specific network
models and achieve state-of-the-art performance in the task we tested:
camouflaged object detection and shadow detection. We believe our work opens up
opportunities for utilizing SAM in downstream tasks, with potential
applications in various fields, including medical image processing,
agriculture, remote sensing, and more.

通过使用简单而有效的适配器将特定领域信息或视觉提示集成到分割网络中，我们的实验表明，SAM-Adapter 可以显着提高 SAM 在具有挑战性的任务中的性能，并且在我们测试的任务中甚至可以胜过专门的网络模型，并达到最先进的性能：伪装目标检测和阴影检测。