ArtiFact is a new multi-modal dataset of 651k museum records used to benchmark cross-modal error detection with seven error categories and semantic query processing challenges.
Franklin, and Ken Gold- berg
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6roles
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Proxics introduces lightweight virtual processors and low-latency communication channels as portable OS abstractions for programming near-data processing accelerators, demonstrated on real hardware for memory-intensive workloads.
A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.
A vision for a cloud SmartNIC that hides Parquet decoding costs by offloading parsing and filters directly on the network datapath, backed by DuckDB performance estimates.
DataEvolver introduces a multi-level self-evolving system for automatic data preparation that improves LLM performance by an average of 10% over original data on seven benchmarks.
A publicly available browser-based playground with tutorial for the GraphAlg language to learn and prototype graph algorithms in databases.
citing papers explorer
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ArtiFact: A Large-Scale Multi-Modal Cultural Heritage Dataset
ArtiFact is a new multi-modal dataset of 651k museum records used to benchmark cross-modal error detection with seven error categories and semantic query processing challenges.
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A Catalog of Data Errors
A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.
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Should I Hide My Duck in the Lake?
A vision for a cloud SmartNIC that hides Parquet decoding costs by offloading parsing and filters directly on the network datapath, backed by DuckDB performance estimates.
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DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving
DataEvolver introduces a multi-level self-evolving system for automatic data preparation that improves LLM performance by an average of 10% over original data on seven benchmarks.
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GraphAlg Playground: An Online Platform for Learning and Experimenting with the GraphAlg Language
A publicly available browser-based playground with tutorial for the GraphAlg language to learn and prototype graph algorithms in databases.