AI Frameworks & Evaluation - eirenicon/Ardens GitHub Wiki

AI Frameworks & Evaluation

The Ardens framework operates at the intersection of emerging AI capabilities and human interpretive intelligence. This page outlines our perspective on existing AI architectures, identifies their limits, and defines how Ardens evaluates AI utility—not just by accuracy, but by contribution to human understanding and decision-making.

Detailed Topics

Common AI Frameworks

Several popular frameworks currently guide AI development and deployment. These include:

1. LLMOps Frameworks

Toolkits for managing large language model lifecycles (e.g., LangChain, LlamaIndex)
Emphasize prompt chaining, agent loops, memory modules
Focused on reliability, observability, deployment

2. Autonomous Agent Frameworks

Systems like AutoGPT, BabyAGI, and OpenDevin
Seek self-directed agents capable of planning and task execution
Emphasize autonomy, but often fragile in complex real-world tasks

3. Retrieval-Augmented Generation (RAG)

Uses a vector store or knowledge base to provide grounding context for LLM outputs
Helps reduce hallucinations and tailor output to specific domains

4. Embodied + Multi-modal Systems

Combine text with vision, speech, or action in virtual or physical environments
Useful for simulation, robotics, and rich interface design

Ardens Perspective on Framework Use

Rather than adopting a single framework, Ardens operates as a meta-framework: it integrates useful components across paradigms while avoiding dependency on any single vendor, model, or architecture.

We prioritize tools and integrations that are:

Modular — loosely coupled, easily replaceable
Transparent — capable of logging reasoning, not just answers
Composable — allowing human-in-the-loop orchestration
Accessible — favoring open tools and formats

Beyond Accuracy: A Different Evaluation Lens

Traditional AI evaluation often centers on benchmarks like:

Accuracy
F1 score
Token efficiency
Latency

These are useful, but Ardens supplements them with a different lens—focused on value to human interpretation and decision support.

Ardens Evaluation Dimensions

Dimension	Description
Resonance	Does the AI output align with human insight or provoke useful disagreement?
Utility	Does it help humans move forward in thinking, analysis, or problem-solving?
Integrity	Is it grounded in traceable signal, or merely fluent nonsense?
Novelty	Does it surface previously unseen patterns, questions, or connections?
Curation-Readiness	Is the output suitable for integration into larger knowledge structures (e.g., a wiki)?
Ambiguity Tolerance	Can the system handle unclear or evolving prompts without collapsing into failure or fluff?

Evaluation in Context

Ardens evaluation is contextual—there is no universal metric. A "useful" output for OSINT will differ from a "useful" one for philosophical analysis or post-hegemonic tracking.

Evaluations are made in dialogue with human users, recorded as annotations, highlights, or branching prompts.

Summary

Rather than treating AI frameworks as turnkey solutions, Ardens treats them as modular amplifiers—useful only inasmuch as they support human clarity, synthesis, and strategic movement.

Our evaluation methods reflect this philosophy: grounded in usefulness, not just performance.