Samsung introduces TRUEBench to test AI productivity in real work scenarios

AI benchmarks have long struggled to capture what people actually do with these systems. Most tests still focus on English-only question and answer tasks that look tidy on paper but fail to reflect the variety of activities you rely on in daily work. Samsung has just launched TRUEBench, short for Trustworthy Real-world Usage Evaluation Benchmark, to measure AI performance in ways that feel closer to real office tasks.

TRUEBench moves beyond simple trivia or single-prompt exchanges to running models through document summarization, translation across twelve languages, data analysis, and multi-step instructions that require the AI to maintain context. Samsung developed 2,485 test sets across ten categories and 46 subcategories, with inputs ranging from a handful of characters to more than twenty thousand. The goal is to simulate everything from quick commands to long business reports.

Paul (Kyungwhoon) Cheun, CTO of the DX Division at Samsung Electronics and Head of Samsung Research, said, “Samsung Research brings deep expertise and a competitive edge through its real-world AI experience. We expect TRUEBench to establish evaluation standards for productivity and solidify Samsung’s technological leadership.“

For a model to pass, it must meet every required condition in a test, including implicit ones that reflect what a reasonable person would expect even if those conditions are not spelled out. This all-or-nothing method makes the results less forgiving but also pushes them closer to the way you would decide whether an output is genuinely useful. Samsung created the rules by combining human input with AI checks. Human annotators drafted the initial conditions, the AI flagged contradictions or inconsistencies, and humans refined the framework again before locking it in. Once finalized, the evaluation could then run at scale through automated AI scoring.

Samsung has also made the dataset, leaderboards, and output statistics public through Hugging Face. You can directly compare as many as five models and see how their results stack up. That level of transparency lets developers, researchers, and users examine the benchmark rather than simply trusting Samsung’s claims.

The benchmark isn’t perfect, though, as rule-setting will always contain some degree of bias, and requiring complete success on every condition means partial but still helpful answers are scored as failures. Language support goes further than most existing tests, but performance will inevitably differ, particularly in languages where training data is scarce. The test set also leans toward general business tasks, so highly specialized domains such as law, medicine, or scientific research may not be fully represented.