GenJAX v1.0.9 - POPL 2026 Canonical Artifact

GenJAX v1.0.9 - POPL 2026 Canonical Artifact

This is the canonical version for POPL 2026 artifact evaluation.

About GenJAX

GenJAX is a probabilistic programming language (PPL) designed around programmable inference - automation that allows users to express and customize Bayesian inference algorithms.

What's Included

• Complete GenJAX implementation with source code and comprehensive tests
• Four case studies from the empirical evaluation:
  • Fair Coin (Beta-Bernoulli conjugate inference) → Figure 16 (a)
  • Curve Fitting with Outlier Detection → Figures 4, 5, 6
  • Game of Life Inverse Dynamics → Figure 18
  • Robot Localization with SMC → Figure 19

Reproducing Paper Figures

pixi install
pixi run paper-figures        # CPU execution (~4 minutes on Apple M4)
pixi run paper-figures-gpu    # GPU execution (requires CUDA 12)

Changes in v1.0.9

• Comprehensive README Quick Example: Complete walkthrough of polynomial regression from paper's Overview section:
  • Vectorizing generative functions with vmap (matches Figure 3)
  • Vectorized programmable inference via importance sampling (matches Figure 5)
  • Improving robustness with stochastic branching (matches Figure 6)
  • Programmable inference kernel mixing Gibbs + HMC
• Added inline comments throughout all examples
• Can be run as linear notebook-style walkthrough

Tested Hardware

• CPU: Apple M4 MacBook Air (10 cores, 16GB, macOS 15.6)
• GPU: RTX 4090 + AMD Ryzen 7 7800X3D (Pop!_OS 22.04)

Citation

See CITATION.cff in the repository for complete citation information with all 10 authors and their ORCID identifiers.