Jan 2026· arXiv.org· Vol abs/2601.00743· 1 citation· 54 references
Computer Science
TL;DR
This work shows how AgenticDomiKnowS enables experienced DomiKnowS users and non-users alike to construct complete neuro-symbolic programs in 10-15 minutes, whereas manually coding even a component of DomiKnowS takes an hour.
Abstract
Integrating symbolic constraints into deep learning models could make them more robust, interpretable, and data-efficient. Still, it remains a time-consuming and challenging task. Existing frameworks like DomiKnowS help this integration by providing a high-level declarative programming interface, but they still assume the user is proficient with the library's specific syntax. We propose AgenticDomiKnowS (ADS) to eliminate this dependency. ADS translates free-form task descriptions into a complete DomiKnowS program using an agentic workflow that creates and tests each DomiKnowS component separately. The workflow supports optional human-in-the-loop intervention, enabling users familiar with DomiKnowS to refine intermediate outputs. We show how ADS enables experienced DomiKnowS users and non-users alike to construct complete neuro-symbolic programs in 10-15 minutes, whereas manually coding even a component of DomiKnowS takes an hour. Access the UI at https://hlr-demo.egr.msu.edu/.
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This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
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With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
