Beyond Static Snapshots: A Grounded Evaluation Framework for Language Models at the Agentic Frontier
We argue that current evaluation frameworks for large language models (LLMs) suffer from four systematic failures that make them structurally inadequate for deployed, agentic systems: distributional, temporal, scope, and process invalidity. These failures compound in RLHF, making reward hacking a predictable consequence of evaluation design rather than an unpredictable training pathology, and RLHF's dual-model architecture imposes a hardware barrier limiting evaluation reproducibility. We propose the Grounded Continuous Evaluation (GCE) framework and present ISOPro as a reference implementation. ISOPro replaces the learned reward model with a deterministic verifier, eliminating reward hac...
| Authors | Jazmia Henry |
|---|---|
| Primary category | cs.AI |
| Published | 19 April 2026 |
| Last updated at source | 6 May 2026 |
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