Litmus (Re)Agent: A Benchmark and Agentic System for Predictive Evaluation of Multilingual Models
We study predictive multilingual evaluation: estimating how well a model will perform on a task in a target language when direct benchmark results are missing. This problem is common in multilingual deployment, where evaluation coverage is sparse and published evidence is uneven across languages, tasks, and model families. We introduce a controlled benchmark of 1,500 questions spanning six tasks and five evidence scenarios. The benchmark separates accessible evidence from ground truth, enabling evaluation of systems that must infer missing results from incomplete literature evidence. We also present Litmus (Re)Agent, a DAG-orchestrated agentic system that decomposes queries into hypothese...
| Authors | Avni Mittal, Shanu Kumar, Sandipan Dandapat et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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