Hedgehog distribution semantics is non-compositional; any compositional semantics equals sampling semantics; Hedgehog→ provides compositional semantics via arrow calculus while remaining expressive.
MacIver, Zac Hatfield-Dodds, and Many Other Contributors
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 5roles
background 1polarities
background 1representative citing papers
DateSAT introduces the first solver for satisfiability constraints over dates and calendar periods via five reduction strategies to integer SMT.
PBT-Bench is a new benchmark with 100 property-based testing problems across 40 Python libraries that measures LLM bug recall rates of 42.1-83.4% under guided prompting versus 31.4-76.7% in baseline.
Introduces CADTestBench as a test-based benchmark for Text-to-CAD and shows that using CADTests to guide generation produces simple baselines outperforming prior methods.
ConcoLixir uses a reactive LLM oracle to improve line coverage in Python concolic testing by 8.6 to 17 percentage points on synthetic, real-world, and library targets.
citing papers explorer
-
Compositional Generator Equivalence (Extended Version)
Hedgehog distribution semantics is non-compositional; any compositional semantics equals sampling semantics; Hedgehog→ provides compositional semantics via arrow calculus while remaining expressive.
-
DateSAT: A Framework for Solving Date and Period Constraints
DateSAT introduces the first solver for satisfiability constraints over dates and calendar periods via five reduction strategies to integer SMT.
-
PBT-Bench: Benchmarking AI Agents on Property-Based Testing
PBT-Bench is a new benchmark with 100 property-based testing problems across 40 Python libraries that measures LLM bug recall rates of 42.1-83.4% under guided prompting versus 31.4-76.7% in baseline.
-
Text-to-CAD Evaluation with CADTests
Introduces CADTestBench as a test-based benchmark for Text-to-CAD and shows that using CADTests to guide generation produces simple baselines outperforming prior methods.
-
ConcoLixir: Reactive LLM Discovery Oracles for Python Concolic Testing
ConcoLixir uses a reactive LLM oracle to improve line coverage in Python concolic testing by 8.6 to 17 percentage points on synthetic, real-world, and library targets.