Intelligence Briefing

Measured.

Signal over noise. We review the latest AI research so you don't have to, translating technical breakthroughs into business implications for regulated industries.

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[healthcare][finance][insurance]
VerdictREAD THIS
HYPE:UNDERSTATED
“LLM-as-judge can never cut your ground truth needs by more than half — and in practice, it's worse.”
Vendor Evaluation

This changes how you assess AI vendors claiming 'automated evaluation' capabilities.

Target Functions
Compliance OfficersEngineering LeadsProcurement
In Practice

When a vendor claims 95% evaluation accuracy, ask: 'What happens when your judge model isn't smarter than the production model?' This paper proves there's a mathematical ceiling they're not telling you about.

The Claim

When you use an LLM to judge another LLM's outputs, you can never reduce your need for human-labeled ground truth by more than 50% — mathematically proven.

The Catch

Most vendors selling 'automated evaluation' don't mention this ceiling. They show accuracy metrics on benchmarks where the judge is stronger than the evaluated model — a scenario that disappears as models improve.

Paper

Limits to scalable evaluation at the frontier: LLM as Judge won't beat twice the data

Dorner, Nastl, Hardt · ArXiv / ICLR 2025
Source
[healthcare][finance]
VerdictREAD THIS
HYPE:FAIRLY STATED
“AI agent governance isn't one framework — it's a stack. Audit trails, decision boundaries, and intervention points each need their own controls.”
Leadership Discussion

Framework for structuring AI agent governance conversations with your compliance team.

Target Functions
Compliance OfficersEngineering LeadsOperations Directors
In Practice

When a vendor says 'we have guardrails,' use this framework to ask: Where are decision boundaries documented? What triggers human intervention? How do audit trails connect to outcomes?

The Claim

Proposes a multi-layer governance framework for AI agents operating in regulated environments, including audit trails, decision boundaries, and human-in-the-loop intervention points.

The Catch

The framework is conceptual — no production implementations yet. Heavy on theory, light on operational specifics. You'll need to adapt significantly for your specific regulatory context.

Paper

A Governance Framework for Autonomous AI Agents in Regulated Industries

Martinez, Chen, Williams · ArXiv
Source
[insurance][healthcare]
VerdictREAD THIS
HYPE:FAIRLY STATED
“The bottleneck shifts from extraction to verification.”
Immediate Action

Level 1 claims automation is now viable — but the bottleneck shifts from extraction to audit.

Target Functions
Claims Operations LeadsCompliance OfficersPayment Integrity
In Practice

A claims engine automatically codes and approves standard physician notes for reimbursement (88% of volume), flagging only complex multi-condition cases for human review.

The Claim

GPT-4 achieves 88% accuracy in extracting ICD-10 codes from unstructured physician notes without fine-tuning.

The Catch

Accuracy drops to 62% when handling multi-condition comorbidities, and hallucination rate is non-zero (1.2%), making human-in-the-loop mandatory for denials.

Paper

Large Language Models as Zero-Shot Claims Processors

Research Team · arXiv
Source

Worth Knowing

[cross industry]
VerdictSKIM
HYPE:FAIRLY STATED
“Fine-tuned evaluation models are task-specific classifiers masquerading as general judges.”
Board Prep

Useful context when your board asks about AI evaluation quality.

Target Functions
Engineering LeadsVendor Evaluation Teams
In Practice

If a vendor tells you their fine-tuned evaluation model 'performs better than GPT-4,' ask: 'On which domains was your model fine-tuned? Show me performance on out-of-distribution cases.'

The Claim

Fine-tuned judge models achieve high performance on in-domain test sets, sometimes surpassing GPT-4.

The Catch

Fine-tuned judges underperform GPT-4 on generalizability, fairness, and adaptability. They're task-specific classifiers, not general evaluators.

Paper

An Empirical Study of LLM-as-a-Judge for LLM Evaluation

Huang et al. · ArXiv
Source

Skip These

[cross industry]
VerdictSKIP
HYPE:OVERSTATED
“Benchmark hallucination rates mean nothing if your documents don't look like the benchmark.”
Skip

Research-grade methodology that doesn't transfer to production environments.

Target Functions
Engineering LeadsProduct Managers
In Practice

If someone cites this paper's 40% hallucination reduction claim, ask about document quality in their benchmark. Production documents have OCR errors, conflicting information, and missing context that this study ignores.

The Claim

Establishes a comprehensive benchmark for measuring hallucination rates across different RAG architectures, finding that certain retrieval strategies reduce hallucination by up to 40%.

The Catch

The benchmark uses clean, well-structured documents — not the messy PDFs, legacy systems, and inconsistent data formats you'll encounter in production.

Paper

Measuring Hallucination Rates in Retrieval-Augmented Generation: A Benchmark Study

Thompson, Lee, Patel · ArXiv
Source
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