Synthesizing Trajectory Queries from Examples
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908837320065024 |
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| author | Mell, Stephen Bastani, Favyen Zdancewic, Steve Bastani, Osbert |
| author_facet | Mell, Stephen Bastani, Favyen Zdancewic, Steve Bastani, Osbert |
| contents | Data scientists often need to write programs to process predictions of machine learning models, such as object detections and trajectories in video data. However, writing such queries can be challenging due to the fuzzy nature of real-world data; in particular, they often include real-valued parameters that must be tuned by hand. We propose a novel framework called Quivr that synthesizes trajectory queries matching a given set of examples. To efficiently synthesize parameters, we introduce a novel technique for pruning the parameter space and a novel quantitative semantics that makes this more efficient. We evaluate Quivr on a benchmark of 17 tasks, including several from prior work, and show both that it can synthesize accurate queries for each task and that our optimizations substantially reduce synthesis time. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_15164 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Synthesizing Trajectory Queries from Examples Mell, Stephen Bastani, Favyen Zdancewic, Steve Bastani, Osbert Programming Languages Data scientists often need to write programs to process predictions of machine learning models, such as object detections and trajectories in video data. However, writing such queries can be challenging due to the fuzzy nature of real-world data; in particular, they often include real-valued parameters that must be tuned by hand. We propose a novel framework called Quivr that synthesizes trajectory queries matching a given set of examples. To efficiently synthesize parameters, we introduce a novel technique for pruning the parameter space and a novel quantitative semantics that makes this more efficient. We evaluate Quivr on a benchmark of 17 tasks, including several from prior work, and show both that it can synthesize accurate queries for each task and that our optimizations substantially reduce synthesis time. |
| title | Synthesizing Trajectory Queries from Examples |
| topic | Programming Languages |
| url | https://arxiv.org/abs/2602.15164 |