arXiv AI September 24, 2026 By Shunya Nagashima

Forecast Workflow Bench: Evaluating Language-Model Decisions with Budgeted Forecast Tools

The paper introduces FWBench, a benchmark for assessing how language models choose and employ time‑series forecasts under budget constraints. Using 1,251 electricity and cycle‑hire cases, the study evaluates agents that select models, histories, and horizons to submit capacities that minimize a loss‑cost objective. Experiments with both hosted and local configurations—including small language models and TSFMs—show that GPT‑6 Astra can achieve superior performance by selectively buying inexpensive short‑horizon forecasts, using only 2.5% of the budget.

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