• benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
• alignment — The work of making AI systems actually pursue what their builders and users intend, rather than something subtly or dangerously different.
• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

• articleXHotpotQA: A Benchmark for Cross-Lingual Knowledge Composition in Multi-Hop Question AnsweringIman Barati, Arash Ghafouri, Behrouz Minaei-Bidgoli
• articleDo large language models scrutinise what they review? A multimodal audit of scoring calibration, error detection, and author-identity effectsEmad Alharbi
• articleCompliance, Capability, and Conflict: Benchmarking Multimodal LLMs under System MessagesJuan Yeo, Geewook Kim

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