GIM: Evaluating models via tasks that integrate multiple cognitive domains
As LLM benchmarks saturate, the evaluation community has pursued two strategies to increase difficulty: escalating knowledge demands (GPQA, HLE) or removing knowledge entirely in favor of abstract reasoning (ARC-AGI). The first conflates memorization with capability; the second divorces reasoning from the practical contexts in which it matters. We take a different approach. The Grounded Integration Measure (GIM) is a benchmark of 820 original problems (615 public, 205 private) where difficulty comes from integration; individual problems require coordinating multiple cognitive operations (constraint satisfaction, state tracking, epistemic vigilance, audience calibration) over broadly acces...
| Authors | Rohit Patel, Alexandre Rezende, Steven McClain |
|---|---|
| Primary category | cs.AI |
| Published | 18 May 2026 |
| Last updated at source | 21 August 2026 |
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