Which benchmark blind spot should AI labs fear more?

Why now: Two July 16 arXiv submissions put different evaluation gaps side by side: general agents across diverse scenarios and code generation across prompt languages. Evidence: OmniaBench presents a benchmark for general AI agents across diverse scenarios. The multilingual-code paper reports that identical programming tasks can produce different results when prompted in different natural languages. Counter-case: The papers study different capabilities and neither is independently verified here, so this is an editorial choice about research priority, not a head-to-head model verdict. Watch next: Reproductions, public benchmark artifacts, and follow-up results showing whether either evaluation gap changes model or product decisions.

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OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios — Large language models are increasingly evolving from text generators into general agents capable of understanding user requests, invoking external tools, and completing complex tasks through interaction. Howeve

paper · unverified · original source

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality — Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias

paper · unverified · original source