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Releases: ActiveInferenceInstitute/GeneralizedNotationNotation

v1.6.0: Zero-Mock Testing Stabilization & Documentation Hardening

15 Apr 22:34
@docxology docxology
v1.6.0
181acef

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v1.6.0: Zero-Mock Testing Stabilization & Documentation Hardening Latest
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Fixed

  • Testing Constraints: Entirely removed dependency on internal hypothesis.internal.conjecture.optimiser arrays mitigating python 3.13 crashes, transitioning to native parameterized fuzzers.
  • HTML Assertion Accuracy: Relaxed legacy test_report_functional strings to mathematically accept dynamic semantic UI tags (<html lang="en"> instead of <html>).

Added

  • Global Documentation Guardrails: Enforced 100% adherence to zero-mock documentation mandates. Provisioned AGENTS.md, README.md, and SPEC.md components natively within transient boundaries (output/, .benchmarks/, scripts/) and defined the master environment SPEC.md at root.
  • Type checker visual dashboards: Baseball-card style model summary PNGs (dark neon theme), validity mosaics, issue distribution charts, and type category pie charts generated in output/5_type_checker_output/visualizations/
  • Resource estimation integration: estimate_file_resources() now delegates to estimation_strategies.py for real FLOPS, memory, and complexity scoring instead of naive heuristics
  • Documentation hub sync: Automated doc/gnn/modules/[00-24].md reconciliation from src/*/AGENTS.md source-of-truth

Changed

  • Zero-Mock Policy Enforcement: Systematically excised all mentions and framework hooks relating to pytest-mock allowing completely isolated functional testing constraints natively.
  • Type checker consolidation: Deleted redundant src/type_checker/checker.py; all logic unified in processor.py (GNNTypeChecker)
  • Test suite alignment: test_type_checker_overall.py rewired to target production processor.py orchestrator (zero-mock)
  • Deprecated marker removed: safe_to_fail marker replaced with standard xfail in pyproject.toml and pytest.ini
  • Default local LLM: Ollama default tag is smollm2:135m-instruct-q4_K_S (llm.defaults.DEFAULT_OLLAMA_MODEL); override with OLLAMA_MODEL or input/config.yaml llm.model.
  • Core dependencies: openai, ollama, python-dotenv, and aiohttp are installed with the base package (LLM step and OpenRouter/Perplexity providers work without uv sync --extra llm).
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v1.3.0 — The Pipeline Coherence & Agentic Integration Release

25 Feb 22:45
@docxology docxology
v1.3.0
1bf882b

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v1.3.0 — The Pipeline Coherence & Agentic Integration Release

v1.3.0 — The Pipeline Coherence & Agentic Integration Release

This release establishes the Generalized Notation Notation (GNN) repository as a pristine, fully integrated, agent-ready ecosystem. With over 756 documentation files audited, deep MCP integration stabilized, and 100% test passing ratios across 1,522 unit tests and 25 pipeline steps, this update sets a new gold standard for repository coherence.

🌟 Major Highlights

Agent-Ready SKILL.md Capability Framework

  • Deployed 28 Anthropic skills standard SKILL.md files covering every src/ module.
  • Mapped precise AI capability boundaries, workflow patterns, and key API commands.
  • Validated 100% of API references natively against runtime __all__ list exports, securing accuracy for AI-driven multi-agent orchestration.

Pipeline Coherence & Zero Dependencies

  • The 25-step GNN Processing Pipeline achieves perfect coherence, producing a 100.0 Health Score via Intelligence Analysis.
  • Resolved all structural and integration warnings, specifically remediating asynchronous Model Context Protocol (MCP) registration bugs inside the llm and api modules (initialize_llm_module and register_tools properly initialized).
  • The pipeline now runs with 0 errors, 0 failures, and 0 warnings under automated orchestration.

Massive Repo-Wide Documentation Audit

  • Audited 756 total .md documentation files.
  • Validated 5,065 internal reference links, cleanly resolving 124 dead links/legacy artifacts.
  • Normalized stale test count claims across 44 files, explicitly confirming 1,522 passing tests.
  • Enforced 100% Triad structural completeness (README, AGENTS, SPEC, SKILL) across all 28 project modules.
  • Achieved Zero YAML parsing errors, Zero missing required sections, and Zero TODO/FIXME gaps in the core documentation hub.

🛠️ Detailed Engineering Fixes

  • src.api.mcp: Bridged FastAPI backend jobs with MCP wrapper functions, surfacing 5 fully functional API coordination tools over the protocol.
  • src.llm.mcp: Handled coroutine lifecycle within the MCP synchronous initialization space, preventing an unawaited processor loop and achieving robust default context window loading.
  • render/ framework mappings: Successfully verified the continuous parsing engine across targeted dynamic modules (rxinfer, activeinference_jl, jax, pymdp, discopy) matching precisely to their theoretical documentation profiles.

Summary Profile

  • Tests Passed: 1,522 / 1,522
  • Script Pipeline State: 25 Steps Clean (0 Warnings)
  • MCP Tools Registered: 100+ over 29 module servers
  • Documentation Health: Perfect (0 broken links, completely synced)

This represents the final polished milestone of Phase 7 normalization.

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v1.2.0 — Data Accuracy & Documentation Sync

23 Feb 15:53
@docxology docxology
v1.2.0
b4cf207

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v1.2.0 — Data Accuracy & Documentation Sync

🎯 Release Highlights

v1.2.0 is a data-integrity release that fixes 5 pipeline bugs and unifies all documentation metadata across 635 files. No new features — only correctness and consistency.

🐛 Pipeline Bug Fixes

Critical

  • ActiveInference.jl renderer typo — POLICY_LENGTH → POLICY_LEN caused UndefVarError in generated Julia scripts (src/render/activeinference_jl/activeinference_renderer.py)
  • ActiveInference.jl validation ordering — validation_status referenced before definition, causing runtime errors during code generation

Medium

  • LLM connection extraction — extract_connections() in src/llm/analyzer.py now correctly parses GNN-specific connection operators (>, -, <) from ## Connections sections, increasing detected connections from 0 to 11

Low

  • Website analysis scan — src/website/generator.py now scans root-level JSON files in 16_analysis_output/, not just the analysis_results/ subdirectory (was reporting 0 analysis results)
  • Pipeline summary status — Preliminary summary in src/main.py changed from IN_PROGRESS to SUCCESS to accurately reflect completed steps

📖 Documentation Sync

This release eliminates all metadata inconsistencies across the entire repository:

Metric Before (v1.1.4) After (v1.2.0)
Test count Mixed: 1,083 / 1,319 1,319 everywhere (35+ files)
Version string Mixed: v1.1.0 / v1.1.4 v1.2.0 everywhere (40+ files)
Dates Mixed: Feb 9 / Feb 20 / Feb 21 2026-02-23 everywhere (50+ files)
Execution time Mixed: 40s / 90s / 120s / 3min ~5 min (with LLM step) (6 files)
Module coverage % Divergent tables in root vs src Unified (2 files)
Architecture version 2.2.1 2.3.0
Total files changed — 635

📋 New: Project Roadmap

Added TO-DO.md with planned milestones:

Release Theme
v1.2.1 LLM step performance (profile, batch, cache)
v1.3.0 Testing & CI improvements
v1.4.0 GNN language & parser enhancements
v1.5.0 Website & reporting upgrades
v2.0.0 Multi-model composition, LSP, cloud execution

✅ Verification

  • Full pipeline: 25/25 steps SUCCESS (299s)
  • Test suite: 1,303 passed, 16 skipped, 0 failed (621s)
  • Stale references: 0 remaining
  • Python version: 3.11+
  • Package manager: UV recommended

📦 Installation

git clone https://github.com/ActiveInferenceInstitute/GeneralizedNotationNotation.git
cd GeneralizedNotationNotation
python3 src/1_setup.py --verbose
python3 src/main.py --target-dir input/gnn_files --verbose

🔗 Key Documentation

Full Changelog: v1.1.4...v1.2.0

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GNN v1

05 Apr 18:40
@ActiveInferenceInstitute ActiveInferenceInstitute
v1
fe2e954
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GNN v1

Version 1 of GNN.

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