SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?

Real-world tool-using agents operate over long-horizon workflows with recurring structure and diverse demands, where effective behavior requires not only invoking atomic tools but also abstracting, and reusing higher-level tool compositions. However, existing benchmarks mainly measure instance-level success under static tool sets, offering limited insight into agents' ability to acquire such reusable skills. We address this gap by introducing SkillCraft, a benchmark explicitly stress-test agent ability to form and reuse higher-level tool compositions, where we call Skills. SkillCraft features realistic, highly compositional tool-use scenarios with difficulty scaled along both quantitative and structural dimensions, designed to elicit skill abstraction and cross-task reuse. We further propose a lightweight evaluation protocol that enables agents to auto-compose atomic tools into executable Skills, cache and reuse them inside and across tasks, thereby improving efficiency while accumulating a persistent library of reusable skills. Evaluating state-of-the-art agents on SkillCraft, we observe substantial efficiency gains, with token usage reduced by up to 80% by skill saving and reuse. Moreover, success rate strongly correlates with tool composition ability at test time, underscoring compositional skill acquisition as a core capability.

Semi-supervised Multiscale Matching for SAR-Optical Image

Driven by the complementary nature of optical and synthetic aperture radar (SAR) images, SAR-optical image matching has garnered significant interest. Most existing SAR-optical image matching methods aim to capture effective matching features by employing the supervision of pixel-level matched correspondences within SAR-optical image pairs, which, however, suffers from time-consuming and complex manual annotation, making it difficult to collect sufficient labeled SAR-optical image pairs. To handle this, we design a semi-supervised SAR-optical image matching pipeline that leverages both scarce labeled and abundant unlabeled image pairs and propose a semi-supervised multiscale matching for SAR-optical image matching (S2M2-SAR). Specifically, we pseudo-label those unlabeled SAR-optical image pairs with pseudo ground-truth similarity heatmaps by combining both deep and shallow level matching results, and train the matching model by employing labeled and pseudo-labeled similarity heatmaps. In addition, we introduce a cross-modal feature enhancement module trained using a cross-modality mutual independence loss, which requires no ground-truth labels. This unsupervised objective promotes the separation of modality-shared and modality-specific features by encouraging statistical independence between them, enabling effective feature disentanglement across optical and SAR modalities. To evaluate the effectiveness of S2M2-SAR, we compare it with existing competitors on benchmark datasets. Experimental results demonstrate that S2M2-SAR not only surpasses existing semi-supervised methods but also achieves performance competitive with fully supervised SOTA methods, demonstrating its efficiency and practical potential.