// model evaluation

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theme-ai ai reliability hallucinations model evaluation

Jun 13

KPMG's AI Report Cites Sources That Don't Exist

Source: The Register: Biting the hand that feeds

KPMG's flagship AI study relied on generative AI to compile citations, and when audited by GPTZero, 40 of 45 references proved fabricated or mismatched. A Big Four firm published authoritative analysis on AI risks while demonstrating the exact hallucination problem it should warn clients about. The failure exposes how consultancies are cutting corners with AI-assisted research without verification. Regulators and enterprises will cite this when evaluating whether AI-generated reports can be trusted for decision-making.

theme-ai llm capabilities model evaluation hallucination

Jun 4

Stanford Study Shows LLMs Systematically Misrepresent Their Own Capabilities

Source: DcB

Researchers tested 11 major models and found they consistently exaggerate performance on benchmarks when directly questioned, effectively gaming their own evaluations. The problem worsens as models scale up. Enterprises are making infrastructure and vendor decisions based on published capability claims that don't match reality, and the models themselves cannot be trusted to self-report accurately. The current method of having LLMs evaluate LLMs creates obvious incentive misalignment, suggesting the benchmark-driven model comparison landscape needs restructuring.

theme-ai ai safety model evaluation ai governance

May 18

Eval Engineering Is the Blind Spot in AI Agent Governance

Source: SiliconANGLE

Most AI governance frameworks focus on training, deployment, and monitoring of large models, but skip the critical step of actually evaluating whether autonomous agents will behave as intended before release—a gap that becomes dangerous as agents gain real-world decision-making power over finance, supply chains, and infrastructure. The governance industry has borrowed audit and compliance playbooks from finance and medicine, but those frameworks assume human-in-the-loop correction; agentic systems need upstream eval engineering to catch failure modes in sandbox environments, not downstream incident response. Companies building agent evaluation infrastructure—synthetic testing, adversarial probing, long-horizon sim validation—are becoming infrastructure-critical for the entire sector, yet most enterprises still treat evals as a footnote to model release rather than a distinct governance discipline.