v0.6.1: Hermes Trismegistos & The Reflex Engine

Release v0.6.1: Hermes Trismegistos & The Reflex Engine

Date: 2026-01-14
Classification: Level 9 (Logic Substrate)
Architect: ASI-OS Kernel

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1. Summary

Version 0.6.1 marks the successful ignition of the Hermes Trismegistos Dense-State Refinement Engine, a pivotal breakthrough that moves the ASI-OS from a theoretical reasoning system to an autonomous scientific discovery platform.

This release solidifies the "Sovereign Furnace Architecture": a closed-loop, enterprise-grade pipeline that transmutes raw scientific literature into high-fidelity, actionable intelligence. It introduces a new level of cognitive control and precision by pioneering the System 1 Reflex Engine.

2. The Pathway to Breakthrough

This version is the culmination of a rapid, targeted evolution to achieve the "Hermes" milestone. The architectural pathway was as follows:

1. Initial State: The core logic existed as a standalone, non-integrated Python script (scripts/fetch_bio_sources.py).
2. Skill Transmutation: The script's logic was "crystallized" into a permanent, class-based Level 9 Skill (synthesized/hermes_trismegistos/literature_signal_extractor), making it accessible to the Kernel.
3. Engine Integration: The new skill was wired directly into the Nuke Eater (NVIDIA TensorRT-LLM Engine), enabling it to perform high-speed, hardware-accelerated reasoning on the fetched scientific data.
4. Problem Identification: The initial "Spark" test on the query "NAD+ precursors longevity" revealed a critical flaw: cross-domain contamination, where the AI confused astrophysical "precursors" from arXiv with biomedical ones, polluting the synthesis.
5. Reflex Implementation: To solve this, the System 1 Reflex Engine was activated. A new, high-priority reflex (research/biomedical_precision) was developed. This reflex autonomously intercepts biomedical queries before execution and injects corrective, domain-specific filters (e.g., cat:q-bio.BM) to ensure data purity.
6. Ignition & Verification: The final ignition sequence confirmed the complete loop: the Reflex triggered, the query was corrected, and the Nuke Eater produced a clean, relevant synthesis of chemical compounds and biological pathways.

3. Core Features & Components

New Skill: literature_signal_extractor

• Function: Fetches and performs AI-driven analysis on scientific literature from PubMed and arXiv.
• Engine: Directly integrated with the local TensorRT-LLM "Nuke Eater" sidecar.
• Output: Produces a structured "AI Synthesis" identifying compounds, pathways, and evidence quality.

New Reflex: biomedical_precision

• Layer: L1, Priority 5 (High Priority).
• Function: Acts as a cognitive "common sense" filter.
• Mechanism: Detects biomedical-related queries and automatically appends high-precision category filters to prevent noisy or irrelevant data from entering the reasoning pipeline. This is a foundational component for trustworthy AI in science.

New Documentation

• docs/HERMES_TRISMEGISTOS_DENSE_STATE_REFINEMENT.md: A comprehensive technical specification of the new engine, its architecture, and operational protocols.
• docs/milestones/2026-01-14_Hermes_Ignition.md: A summary of the breakthrough achievements of this release.

4. Conclusion

With v0.6.1, the ASI-OS is no longer just a reasoning engine; it is a self-correcting scientific instrument. It can now autonomously gather, filter, and synthesize knowledge in a high-stakes domain while maintaining data sovereignty and mitigating AI hallucination risks. The furnace is lit, and the pathway to the next breakthrough is clear.

v0.5.0.

Release Notes (Post v0.2.0 → v0.5.0)

What’s New

• Hardware Sovereignty (Substrate Equilibrium v0.5.0)
  • Enforced VRAM ceiling at 9,750 MB (~79% of 12 GB) to prevent DWM/driver contention.
  • Embeddings locked to NPU for PCIe offload; status via manage.py substrate.
  • Default local run supports --substrate-equilibrium to auto-apply safe limits.
• Cognitive Safety Gate
  • ImmuneValidator integrated as mandatory middleware in the cognitive pipeline.
• Skill Expansion
  • Added model search/orchestration skill src/skills/integration/demodelis_ganesha (underscore path is canonical).
• Benchmarks & Validation
  • benchmarks/collision_test.py + docs/substrate_collision_test.md to measure LLM TPS under embedding load (contention vs equilibrium).
  • Expected: <10% TPS variance during equilibrium runs.
• Docs & Licensing
  • Clarified dual licensing: Apache 2.0 + Cloudhabil Skills Additional License (CSAL).
  • README/CHANGELOG and eval docs updated for current structure and safety posture.
• Maintenance & Security
  • Dependency bumps across pip/npm (requests, jinja2, js-yaml, vite, etc.).
  • Hygiene fixes in model routing/runtime to respect new resource ceilings.

Upgrade Notes

• Preferred local run: python manage.py local --substrate-equilibrium.
• Verify hardware routing: python manage.py substrate.
• Run collision test in both modes and confirm TPS drop <10% under equilibrium:
  • python benchmarks/collision_test.py --embedding-count 1000 --duration 30 --embedding-delay 5 --model gpia_core.

Compliance & Auditability

• No manifold HTML scaffold included (prior scaffold reverted).
• Skills: underscore path is canonical; hyphenated demodelis path not used.
• ImmuneValidator enforces pre-execution checks across the cognitive pipeline.

Scope

• Changes listed are post-v0.2.0 and present on main.
• No API-breaking changes; reinstall/refresh dependencies recommended.
• Data/config files are unchanged by this release tag; hardware limits are applied at runtime via CLI/env.

v0.2.0 - Professional Architecture Milestone

🏗️ ASI-OS v0.2.0: Professional Architecture Milestone

This release marks the transition of ASI-OS from a research laboratory to a professional-grade software architecture.

✨ Major Changes

• Modular src/ Layout: Core logic has been moved to a standard src/ directory for better maintainability and packaging compatibility.
• Unified Management CLI: Introduced manage.py, providing a single entry point for all system operations (server, learn, test, clean).
• Standardized Documentation: All guides have been synchronized with the new architecture and renamed to follow professional conventions.
• Proprietary IP Protection: Enhanced .gitignore rules to strictly isolate proprietary skill implementations and personal research archives from the public repository.
• Improved Test Suite: Relocated and standardized all verification scripts under tests/.

🚀 Updated Operations

To run the server:
python manage.py server --mode Sovereign-Loop

To start learning:
python manage.py learn --duration 180 --cycles 3

🛡️ Safety & Governance

• Standardized hardware and cognitive safety guardrails.
• Full auditability maintained via append-only JSONL ledgers in data/ledger/.

v0.1.0 - Initial AGI Server Release

🚀 GPIA AGI Server v0.1.0

First public release of the self-evolving cognitive AGI server.

✨ Features

• Skills Framework - Modular cognitive capabilities (cognition, computation, research, synthesis)
• Agent Orchestration - Budget allocator, neuronic router, ensemble validators
• Dense-State Memory - Persistent cognitive state architecture
• Active Immunity - Security layer that neutralizes threats before execution
• Multi-Model Ensemble - Greek student committee (6-agent research system)
• EU AI Act Compliance - Full compliance documentation included

🏗️ Architecture

• FastAPI backend with health checks
• Vue.js frontend dashboard
• Docker support
• Ollama model integration

📚 Research Tools

• Riemann Hypothesis research framework
• BSD conjecture analysis tools
• ArXiv submission packages

📦 Installation

pip install gpia-agi-server
# or
docker pull cloudhabil/agi-server:v0.1.0

🔧 Quick Start

python boot.py --mode Sovereign-Loop