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Releases: mj3b/applied-ai-research-translator

v1.0 — Decision-Ready Applied AI Research Translation

27 Dec 21:56
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v1.0
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v1.0 — Decision-Ready Applied AI Research Translation

Overview

This release establishes Applied AI Research Translator v1.0 as a decision-complete, non-agentic reference implementation for translating applied AI research into auditable, decision-ready artifacts.

v1.0 formalizes the core premise of the repository:

AI risk in production is primarily a decision governance problem, not a model capability problem.

Rather than showcasing autonomous agents or orchestration patterns, this release demonstrates how applied research can be translated into explicit claims, bounded tasks, evaluation evidence, and human-owned decisions suitable for regulated or high-stakes environments.

What’s New in v1.0

🧭 Decision-Complete Translation Semantics

The repository now explicitly supports all three legitimate translation outcomes:

  • Accept translation — evidence supports bounded use
  • Reject translation — research does not translate to a defensible task
  • Abstain / defer — evidence insufficient; no silent progression

These outcomes are recorded deterministically in signed Decision Summaries.

📦 Canonical Research Translation Packs

Added two new, fully structured research translation examples:

  • haic_reliance_review_59e257ff
    Human–AI collaboration and reliance calibration
    → Translation-positive (decision support, non-agentic)

  • multi_agent_failure_modes_e0228882
    Multi-agent LLM failure modes
    → Translation-negative (explicit, defensible rejection)

Together with the existing Measuring Agents in Production pack, these form a coherent research lineage:

measurement → failure analysis → decision-centric alternatives

🔒 Locked Governance Demo Runs

Introduced three locked demo runs (docs/demo-runs/A, B, C) illustrating:

  • explicit abstention
  • human override
  • disagreement → abstention

These runs demonstrate that no execution path can silently produce or enact a decision.

📖 Research Context Documentation

Added docs/research-context.md to explain:

  • how research papers inform translation,
  • why some research is intentionally rejected,
  • and why the system stops at decision support rather than autonomy.

This document is designed for reviewers performing academic, regulatory, or governance due diligence.

🚫 Explicit Non-Agentic Boundary

The README has been rewritten to clearly state that this repository is:

  • not an agent framework,
  • not an autonomy platform,
  • not a prompt-engineering showcase.

AI is bounded to producing structured candidate outputs; decision authority always remains human.

Why This Release Matters

v1.0 demonstrates a practical alternative to agent-centric AI adoption:

  • makes uncertainty explicit
  • preserves singular human accountability
  • prevents silent automation
  • enables audit, re-evaluation, and regulatory review
  • supports phase-gate and executive decision forums

This release is intentionally conservative by design and optimized for trust, defensibility, and governance, not speed or autonomy.

Intended Audience

  • Principal Engineers
  • AI Governance & Risk Leads
  • Research-to-Production Architects
  • Technical decision owners in regulated or high-stakes environments

Status

Reference implementation — stable (v1.0)

This repository is not a product and does not mandate organizational process.
It serves as a public, non-commercial case study demonstrating disciplined applied AI adoption.

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