Although reactive synthesis guarantees correct-by-construction implementations, its practical adoption is limited by a performance bottleneck in the iterative design-and-refinement cycle of formal specifications. GR(1) synthesizers perform redundant, from-scratch computations for each specification modification, severely slowing the development workflow. To address this inefficiency, we propose an incremental synthesis method that exploits the monotonicity of the underlying fixed-point computations. By reusing the system winning region from the preceding check, our method significantly accelerates realizability check. Our work applies an incremental method to iterative GR(1) specification development covering the full GR(1) scope, including system guarantees and environment assumptions, to accelerate realizability checking. Furthermore, we introduce two heuristics for early fixed-point detection during incremental realizability checking. Finally, we analyze why prior work failed to integrate an incremental technique limited to system guarantees into the unrealizable core minimization algorithm DDMin , further establishing iterative development as a suitable application setting for incremental methods. Evaluated on a large-scale benchmark of 8,282 specifications, our method exhibits a strong positive correlation between performance gain and specification complexity. For the most challenging specifications, which constitute the primary bottlenecks in development, our approach achieves speedups of several orders of magnitude, reducing computation times in some cases from nearly an hour to just seconds.

Language-guided object recognition in remote sensing imagery is crucial for large-scale mapping and automated data annotation. However, existing open-vocabulary and visual grounding methods rely on explicit category cues, limiting their ability to handle complex or implicit queries that require advanced reasoning. To address this issue, we introduce a new suite of tasks, including Instruction-Oriented Object Counting, Detection, and Segmentation (InstructCDS), covering open-vocabulary, open-ended, and open-subclass scenarios. We further present EarthInstruct, the first InstructCDS benchmark for earth observation. It is constructed from two diverse remote sensing datasets with varying spatial resolutions and annotation rules across 20 categories, necessitating models to interpret dataset-specific instructions. Given the scarcity of semantically rich labeled data in remote sensing, we propose InstructSAM, a training-free framework for instruction-driven object recognition. InstructSAM leverages large vision-language models to interpret user instructions and estimate object counts, employs SAM2 for mask proposal, and formulates mask-label assignment as a binary integer programming problem. By integrating semantic similarity with counting constraints, InstructSAM efficiently assigns categories to predicted masks without relying on confidence thresholds. Experiments demonstrate that InstructSAM matches or surpasses specialized baselines across multiple tasks while maintaining near-constant inference time regardless of object count, reducing output tokens by 89\% and overall runtime by over 32\% compared to direct generation approaches. We believe the contributions of the proposed tasks, benchmark, and effective approach will advance future research in developing versatile object recognition systems. The code is available at https://VoyagerXvoyagerx.github.io/InstructSAM.

Advances in artificial intelligence (AI) show significant promise in multiscale modeling and biomedical informatics, particularly in the analysis of phonon microscopy (high-frequency ultrasound) data for cancer detection. This study addresses critical issues in data engineering for time-resolved phonon microscopy of biomedical samples by tackling the 'batch effect,' which arises from unavoidable technical variations between experiments, creating confounding variables that AI models may inadvertently learn. We present a multi-task conditional neural network framework that simultaneously achieves inter-batch calibration by removing confounding variables and accurate cell classification from time-resolved phonon-derived signals. We validate our approach by training and validating on different experimental batches, achieving a balanced precision of 89.22% and an average cross-validated precision of 89.07% for classifying background, healthy and cancerous regions. Furthermore, our model enables reconstruction of denoised images, which enable the physical interpretation of salient features indicative of disease states, such as sound velocity, sound attenuation, and cell adhesion to substrates. This work demonstrates the potential of AI methodologies in improving health outcomes and advancing cancer-informatics platforms.