# BRIDGE: Predicting Human Task Completion Time From Model Performance

arXiv: https://arxiv.org/abs/2602.07267 · HTML: https://mlsanity.com/paper/2602.07267/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering](https://mlsanity.com/paper/2410.07095/) (2024)
- [RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts](https://mlsanity.com/paper/2411.15114/) (2024)
- [Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models](https://mlsanity.com/paper/2408.08926/) (2024)
- [GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks](https://mlsanity.com/paper/2510.04374/) (2025)
- [Measuring AI Ability to Complete Long Software Tasks](https://mlsanity.com/paper/2503.14499/) (2025)
- [Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds](https://mlsanity.com/paper/1908.11421/) (2019)
- [A Rosetta Stone for AI Benchmarks](https://mlsanity.com/paper/2512.00193/) (2025)
- [Fluid Language Model Benchmarking](https://mlsanity.com/paper/2509.11106/) (2025)
- [Reliable and Efficient Amortized Model-based Evaluation](https://mlsanity.com/paper/2503.13335/) (2025)
- [HCAST: Human-Calibrated Autonomy Software Tasks](https://mlsanity.com/paper/2503.17354/) (2025)
- [SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction](https://mlsanity.com/paper/2507.05129/) (2025)
- [py-irt: A Scalable Item Response Theory Library for Python](https://mlsanity.com/paper/2203.01282/) (2022)
- [Estimating Item Difficulty Using Large Language Models and Tree-Based Machine Learning Algorithms](https://mlsanity.com/paper/2504.08804/) (2025)
- [Toward Trustworthy Difficulty Assessments: Large Language Models as Judges in Programming and Synthetic Tasks](https://mlsanity.com/paper/2511.18597/) (2025)
- [TaskSense: Cognitive Chain Modeling and Difficulty Estimation for GUI Tasks](https://mlsanity.com/paper/2511.09309/) (2025)

## Built on by (most cited first, 6 of 6)

- [Agent psychometrics: Task-level performance prediction in agentic coding benchmarks](https://mlsanity.com/paper/2604.00594/) (2026)
- [Can LLMs Perceive Time? An Empirical Investigation](https://mlsanity.com/paper/2604.00010/) (2026)
- [CurveShift: Is Agent Progress Scalar? Separating Level from Shape](https://mlsanity.com/paper/2608.00355/) (2026)
- [Messier: A High-Resolution Corpus for Cross-Benchmark Agent Evaluation](https://mlsanity.com/paper/2607.25891/) (2026)
- [Predicting Task Difficulty Without Rollouts](https://mlsanity.com/paper/2608.05797/) (2026)
- [AgentTime: Can Agents Estimate and Control Their Own Runtime?](https://mlsanity.com/paper/2610.09944/) (2026)

## Abstract (arXiv)

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.