A cross-cutting evaluation crisis is emerging: aggregate end-task accuracy systematically masks structural, methodological, and error-type failures.
Evidence
- Agreement/consistency signals are unreliable—60% of incorrect answers appear highly consistent, defeating label-free evaluation (When Judges Agree and Are Wrong)
- Downstream accuracy hides silent methodological bugs like data leakage (Silent Failures in Agentic Data Science) and token accuracy hides complete failures on valid inputs (What Accuracy Hides)
- Systems with identical accuracy can have opposite false-positive/false-negative profiles (Jev-as-a-Judge), and zero-ablation tests show gains even from irrelevant documents (Evaluating Compressed Memory)
- Uncertainty metrics only indicate hallucination when retrieval coverage is sufficient (When Does Uncertainty Measure RAG Hallucination Risk)
Implications
- Demand shifts toward failure-mode-specific and structural evaluations (CUJ-anchored synthetic datasets, trajectory-vs-outcome separation in AuditKIT, drift-aware protocols) over single aggregate scores
- Verification itself becomes the next bottleneck, with error-type and coverage-conditioned metrics required before production deployment
Concepts
LLM Evals RAG Evaluation RAG Agents
Confidence
high