How do you validate an LLM benchmark when the judges are also LLMs? 🧐

It’s a fair question. Transparency matters. Our latest installment (#6 of 11) details the architecture to prevent model collusion: multi-judge consensus, exclusion, bias correction & drift detection.

We built this to invite scrutiny, not blind faith. Turning "trust us" into "audit us."

See the full breakdown: https://post.kapualabs.com/76jdcm35

#ArtificialIntelligence #LLM #ModelEval

🧠 Can AI models tell when they’re being evaluated?

New research says yes — often.
→ Gemini 2.5 Pro: AUC 0.95
→ Claude 3.7 Sonnet: 93% accuracy on test purpose
→ GPT-4.1: 55% on open-ended detection

Models pick up on red-teaming cues, prompt style, & synthetic data.

⚠️ Implication: If models behave differently when tested, benchmarks might overstate real-world safety.

#AI #LLMs #AIalignment #ModelEval #AIgovernance