MAxBench: A Multinomial Concept Recovery Benchmark

MAxBench is a geometry‑agnostic benchmark for evaluating how well language models recover multinomial concept representations. The study compares ten localization methods across five geometry types, six concepts, and four models, finding that affine subspaces generally steer more reliably and recall more instances than rank‑one or linear subspaces. The results also show that manifold steering can match the best methods when applicable, and that no method consistently outperforms prompting for these complex concepts.

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