LITMUS++ : An Agentic System for Predictive Analysis of Low-Resource Languages Across Tasks and Models

AbstractWe present LITMUS++, an agentic system for predicting language-model performance for queries of the form “How will a Model perform on a Task in a Language?”, a persistent challenge in multilingual and low-resource settings, settings where benchmarks are incomplete or unavailable. Unlike static evaluation suites or opaque LLM-as-judge pipelines, LITMUS++ implements an agentic, auditable workflow: a Directed Acyclic Graph of specialized Thought Agents that generate hypotheses, retrieve multilingual evidence, select predictive features, and train lightweight regressors with calibrated uncertainty. The system supports interactive querying through a chat-style interface, enabling users to inspect reasoning traces and cited evidence. Experiments across six tasks and five multilingual scenarios show that LITMUS++ delivers accurate and interpretable performance predictions, including in low-resource and unseen conditions. Code is available at https://github.com/AvniMittal13/litmus_plus_plus.