Research

Papers

AIDC reviews research that matters for production AI — not to catalog it, but to close the gap between what the paper proves and what you can actually deploy. Each entry is a working analysis: what the framework claims, what the evidence shows, and how it translates into real data and agent engineering.

The AIDC papers program

Academic depth. Production relevance.

Most AI research is published for researchers. AIDC translates it for the people who build and operate data and agent infrastructure — Data and AI executives, engineers, and architects making deployment decisions under real constraints.

Each paper in this program is selected because it directly informs something AIDC builds or recommends: a methodology we use, a technique we implement, a framework that underlies how our agents reason. We review the paper, run experiments, and publish what we learn alongside the original arXiv citation.

Nothing here is endorsement or redistribution. The papers are external; the analysis is AIDC's.

Current papers

One paper. Two experiments.

arXiv 2402.13547
ACTIVERAG: Revealing the Treasures of Knowledge
via Active Learning

External source

arXiv:2402.13547 (opens in new tab)

Institutions

Carnegie Mellon University and partner universities

Key result

Over 5% improvement on question-answering datasets vs. prior RAG baselines

Passive RAG limits LLMs to the role of knowledge receptors. ACTIVERAG changes that architecture: retrieved passages feed a structured Knowledge Construction step that produces Epistemic Anchoring, Logical Reasoning, and Cognitive Alignment documents. These, fused with the model's chain-of-thought via the Cognitive Nexus mechanism, produce consistently more accurate and less hallucination-prone outputs than standard RAG.
AIDC experiments
- Experiment 1 ActiveRAG Overview: The 3-Step Deployment Playbook Knowledge Construction, Dynamic Action, and Cognitive Nexus — for Data and AI executives deploying domain-specific agents.
- Experiment 2 Epistemic Anchor — Expert Six: From Paper to Pipeline Implementing ActiveRAG for domain-specific AI using expert elicitation and a four-layer anchoring document.
Read the ActiveRAG paper analysis

Why papers

Research is how AIDC builds trust.

AIDC's Deep Expert Agent methodology is grounded in academic research on knowledge construction, epistemic anchoring, and expert elicitation. These are not invented terms — they are validated techniques with published experimental results.

The papers program surfaces that academic foundation publicly: where the ideas came from, what the evidence shows, and how AIDC translates validated research into the production workflows we build for clients.

Deep Expert Agents ExpertSix methodology

Papers

Academic depth. Production relevance.

One paper. Two experiments.

AIDC experiments

Research is how AIDC builds trust.