Imported from ActiveInferenceInstitute/GeneralizedNotationNotation (src/gnn/analysis/rxinfer/AGENTS.md). Install upstream with npx skills add ActiveInferenceInstitute/GeneralizedNotationNotation --skill rxinfer. Copyright stays with the author.

RxInfer Analysis - Agent Scaffolding

Name: rxinfer
Author: ActiveInferenceInstitute

Overview

Framework-specific analyzer for RxInfer.jl simulation results. Part of the Analysis module (Step 16). Consumes the genuine @model + infer() pipeline outputs: rxinfer_simulation_v1 with real smoothed posteriors and a variational_free_energy trace populated with per-iteration VFE values (length = INFERENCE_ITERATIONS), enabling real convergence and free-energy analysis. Validation includes inference_converged, vfe_present, and belief_entropy_ok.

Module Structure

analysis/rxinfer/
├── __init__.py          # Public API
├── analyzer.py          # Analysis from execution logs + convergence diagnostics + per-factor beliefs
├── animator.py          # Animated HTML visualizations
├── gif_animator.py      # Publication-style GIF animations + reproducibility manifest sidecar
├── dashboard.py         # Interactive HTML dashboard over the GIF batch (roadmap A5)
├── cross_framework.py   # Cross-framework comparison (roadmap A6)
├── README.md            # Human documentation
└── AGENTS.md            # This file

gif_animator.py

generate_gif_animation(results, output_path, ...) renders the 2×3 publication-style (white) GIF: beliefs, states, Bayesian graph model, VFE, EFE-per-action heatmap (D6), and policy-posterior stackplot (D8). For multi-factor results (model_parameters.state_factors with >1 size>1 factor) the top-left joint-belief panel becomes per-factor marginal small-multiples (D4). Every GIF writes a .manifest.json sidecar (A7: spec hash, Julia/RxInfer versions, seed, timesteps, iterations, belief accuracy).

dashboard.py

generate_dashboard(animations_dir, output_path) builds a single self-contained HTML page over all GIFs + manifests with category grouping and filtering.

analyzer.py additions

compute_per_factor_beliefs(data) — un-flattens joint posteriors into per-factor marginals using the state_factors echo in results JSON (C-order reshape; returns {} for flat models and for artifacts written before the echo existed, raises ValueError on inconsistent factor sizes).
_compute_convergence_diagnostics(...) — VFE slope, convergence rate, iterations-to-convergence (D5), plotted alongside free energy.

cross_framework.py

Implements roadmap A6: renders one GNN file to RxInfer.jl, PyMDP, and ActiveInference.jl from a single parsed spec, executes each, and emits a self-contained HTML comparison.

run_cross_framework_comparison(gnn_file, output_dir) -> str — entry point; raises FileNotFoundError for a missing GNN file.
render_comparison_html(model_name, runs, output_path) -> str — pure renderer over FrameworkRun records, unit-testable without Julia.
FrameworkRun — dataclass carrying framework, status (success / validation_failed / render_failed / execution_failed / unavailable / invalid_results), detail, and optional results.

Exit-code contract: only exit 0 with simulation_results.json is a clean success; exit 1 with results is kept and flagged as validation_failed; anything else is execution_failed with the stderr tail logged at error level. PyMDP results are redirected into the per-framework directory via PYMDP_OUTPUT_DIR; both Julia backends run under their committed --project environments resolved relative to this file, not the CWD.

Key Functions

analyzer.py

generate_analysis_from_logs(execution_dir, output_dir, verbose) - Main entry point
_parse_rxinfer_outputs(filepath) - Parse RxInfer outputs
_analyze_messages(data) - Message flow analysis
_analyze_convergence(data) - Convergence tracking
_generate_report(metrics) - Report generation

Integration Points

Upstream: Execute module (Step 12) produces RxInfer simulation results Downstream: Report module (Step 23) consumes analysis outputs

Dependencies

pathlib, json, logging: Core Python
numpy (optional): Numerical operations
matplotlib (optional): Visualization

Version: 3.0.0 Last Updated: 2026-01-23

{ "ocm": "1", "id": "activeinferenceinstitute-generalizednotationnotation-rxinfer", "kind": "skill", "name": "rxinfer", "description": "Framework-specific analyzer for RxInfer.jl simulation results. Part of the Analysis module (Step 16). Consumes the genuine `@model` + `infer()` pipeline outputs: `rxinfer_simulation_v1` with real smoothed posteriors and a `variational_free_energy` trace populated with per-iteration VFE values (length = INFERENCE_ITERATIONS), enabling real convergence and free-energy analysis. Validation includes `inference_converged`, `vfe_present`, and `belief_entropy_ok`.", "publisher": "ActiveInferenceInstitute", "version": "1.0.0", "capabilities": { "domains": [ "general" ], "tags": [ "skill-md", "github-agents" ], "languages": [ "en" ] }, "quality_prior": 0.6, "examples": [ "Framework-specific analyzer for RxInfer.jl simulation results. Part of the Analysis module (Step 16). Consumes the genuine `@model` + `infer()` pipeline outputs: `rxinfer_simulation_v1` with real smoothed posteriors and a `variational_free_energy` trace populated with per-iteration VFE values (length = INFERENCE_ITERATIONS), enabling real convergence and free-energy analysis. Validation includes `inference_converged`, `vfe_present`, and `belief_entropy_ok`." ], "primary": false, "metadata": { "source": { "provider": "github-agents", "repository": "https://github.com/ActiveInferenceInstitute/GeneralizedNotationNotation", "path": "src/gnn/analysis/rxinfer/AGENTS.md", "ref": "222ffe8b890b386f5016053e784d2c65a3dbcc97", "url": "https://github.com/ActiveInferenceInstitute/GeneralizedNotationNotation/blob/222ffe8b890b386f5016053e784d2c65a3dbcc97/src/gnn/analysis/rxinfer/AGENTS.md", "key": "ActiveInferenceInstitute/GeneralizedNotationNotation/src/gnn/analysis/rxinfer/AGENTS.md" } }, "instructions": "# RxInfer Analysis - Agent Scaffolding\n\n## Overview\n\nFramework-specific analyzer for RxInfer.jl simulation results. Part of the Analysis module (Step 16). Consumes the genuine `@model` + `infer()` pipeline outputs: `rxinfer_simulation_v1` with real smoothed posteriors and a `variational_free_energy` trace populated with per-iteration VFE values (length = INFERENCE_ITERATIONS), enabling real convergence and free-energy analysis. Validation includes `inference_converged`, `vfe_present`, and `belief_entropy_ok`.\n\n## Module Structure\n\n```\nanalysis/rxinfer/\n├── __init__.py # Public API\n├──", "cost": { "context_tokens": 1016 } }

rxinfer

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