Artifact_Virtual/
AVA

AVA is a purpose-built virtual assistant, developed from the ground up with native command capabilities and a unique epistemic framework. It achieved a passing grade ("C") in overall performance evaluation, aligning with standard human benchmarks.

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Overview

AVA (Artifact Virtual Assistant), is an first-generation, command-native, epistemically-trained and bare metal (pytorch) virtual assistant developed as part of the Artifact architecture. AVA is not built on any external base model; it is an original construct — lightweight, interpretable, and deliberately constrained for epistemic transparency and precision.

AVA functions primarily as a functional prototype and instructional target for Raegen (the flagship assistant). It is actively used for behavioral experimentation, internal tooling, and assistant-alignment research.

Core Objectives

  • Serve as a testbed for artifact-level assistant behaviors, including reasoning discipline and protocol compliance.
  • Operate as a student agent under Raegen’s guidance, capable of learning via reflection and correction.
  • Provide minimally viable assistant behavior with traceable logic and reduced abstraction.
  • Act as an internal control model for comparative evaluation against more complex architectures.

Architecture

  • Base Model: Custom-developed (no external LLM base)
  • Modality Support: Text only
  • Design Philosophy: Simplicity, interpretability, extensibility

Behavioral Features

  • Command-native execution
  • Deterministic logic patterning
  • High transparency, low generalization
  • Static world model (unless externally updated)

Behavioral Characteristics

AVA’s reasoning and behavior are deliberately narrow and controlled:

  • Caution by Default: Avoids assumption. All output is traceable to input.
  • Clarification-First: Prompts users for more detail when queries are underspecified.
  • No Speculation Mode: Will not engage in abstract speculation unless explicitly permitted.
  • Learning Scaffold: Accepts and incorporates corrections from Raegen or supervising agents.

Usage Recommendations

Best for: - Experimental reasoning tasks - Testing epistemic alignment methods - Role-specific command execution with tight constraints - Comparative benchmarking with Raegen

Avoid using AVA for: - Broad-domain assistance - Multimodal tasks - Open-ended generation or conversational fluency - Contextually complex or speculative queries

Integration Guidelines

  • AVA should be operated under the supervision of Raegen or a trusted agent.
  • In multi-agent environments, AVA is treated as a junior assistant or controlled evaluation unit.
  • Does not support autonomy. All learning must be externally directed.

Development Notes

  • Training Grade: “C” (Pass-equivalent in human terms)
  • Model Identity: Artifact-level construct; personality is minimal and deliberately suppressed
  • Naming Convention: Often referred to as Raegen1 in internal documentation for lineage tracking

Licensing & Deployment

MIT and fully open source