BRIDGE: Predicting Human Task Completion Time From Model Performance

Evaluating the real-world capabilities of AI systems requires grounding benchmark performance in human-interpretable measures of task difficulty. Existing approaches that rely on direct human task completion time annotations are costly, noisy, and difficult to scale across benchmarks. In this work, we propose BRIDGE, a unified psychometric framework that learns a latent difficulty scale from model responses and anchors it to human task completion time. Using a two-parameter logistic Item Response Theory model, we jointly estimate latent task difficulty and model capability from model performance data across multiple benchmarks. We demonstrate that latent task difficulty varies linearly with the logarithm of human completion time, allowing human task completion time to be inferred for new benchmarks from model performance alone. Leveraging this alignment, we forecast frontier model capabilities in terms of human task length and independently reproduce METR's exponential scaling results, with the 50% solvable task horizon doubling approximately every 6 months. Code Repository: McGill-NLP/BRIDGE 1 Introduction As Artificial Intelligence (AI) systems are increasingly deployed in open-ended, real-world settings, their capabilities are typically reported via benchmark scores and aggregate metrics. However, such scores are difficult to interpret as measures of task difficulty or real-world effort: improvements may reflect gains on short, routine instances while leaving longer, multi-step tasks largely unchanged, and comparable score changes can correspond to very different shifts in practical capability. Consequently, benchmark performance alone provides limited guidance about what AI systems can reliably do or how quickly practical capability is improving. A more actionable evaluation should express performance on human-aligned scales, most directly, the time a human would require to complete a task. A prominent attempt to express AI capability in human terms is by Kwa et al. (2026) at METR, which measures performance in terms of the length of tasks AI agents can complete and reports exponential growth, with task-length horizons doubling roughly every 7 months. While compelling, this paradigm depends on human task completion time annotations from people with relevant expertise. These annotations are expensive collect and are difficult to extend consistently across diverse benchmarks. As model capabilities continue to scale, relying on new human studies to anchor each benchmark becomes increasingly impractical, creating a growing gap between benchmark-centric evaluation and human-centric notions of difficulty. In this work, we introduce BRIDGE, 1 a unified psychometric framework (illustrated in Figure 1 ) that addresses this gap by aligning task difficulty for humans, measured by task completion time, with task difficulty for models, measured by benchmark performance. Item Response Theory (IRT) (Baker, 2001) , and in particular the two-parameter logistic (2PL) model, has been adopted to analyze large language model (LLM) performance and benchmarks