Data Flow Control formalizes data safety as aggregate predicates over provenance monomials and implements enforcement via the Passant query rewriting layer achieving near-zero overhead across five DBMS engines.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Hyperparam supplies under-70KB JS libraries (Hyparquet, Squirreling, Icebird) for async-native SQL over Parquet/Iceberg with per-cell LLM UDFs, claiming 300x speedup versus DuckDB-WASM on filter queries and two-thirds lower cost on agent analyst tasks.
citing papers explorer
-
Data Flow Control: Data Safety Policies for AI Agents
Data Flow Control formalizes data safety as aggregate predicates over provenance monomials and implements enforcement via the Passant query rewriting layer achieving near-zero overhead across five DBMS engines.
-
A Query Engine for the Agents
Hyperparam supplies under-70KB JS libraries (Hyparquet, Squirreling, Icebird) for async-native SQL over Parquet/Iceberg with per-cell LLM UDFs, claiming 300x speedup versus DuckDB-WASM on filter queries and two-thirds lower cost on agent analyst tasks.