# LASER: Language Model Regression for Semi-Structured Workflow Resource and Runtime Estimation

arXiv: https://arxiv.org/abs/2512.19701 · HTML: https://mlsanity.com/paper/2512.19701/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 12 of 12)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples](https://mlsanity.com/paper/2404.07544/) (2024)
- [OmniPred: Language Models as Universal Regressors](https://mlsanity.com/paper/2402.14547/) (2024)
- [Performance Prediction for Large Systems via Text-to-Text Regression](https://mlsanity.com/paper/2506.21718/) (2025)
- [Decoding-based Regression](https://mlsanity.com/paper/2501.19383/) (2025)
- [Regression Language Models for Code](https://mlsanity.com/paper/2509.26476/) (2025)
- [WfCommons: A Framework for Enabling Scientific Workflow Research and Development](https://mlsanity.com/paper/2105.14352/) (2021)
- [Sizey: Memory-Efficient Execution of Scientific Workflow Tasks](https://mlsanity.com/paper/2407.16353/) (2024)
- [Lotaru: Locally Predicting Workflow Task Runtimes for Resource Management on Heterogeneous Infrastructures](https://mlsanity.com/paper/2309.06918/) (2023)
- [Ponder: Online Prediction of Task Memory Requirements for Scientific Workflows](https://mlsanity.com/paper/2408.00047/) (2024)

## Abstract (arXiv)

Accurate prediction of resource consumption and runtime for cloud workflow jobs is critical for scheduling efficiency, yet remains challenging due to the semi-structured nature of job configurations -- comprising shell commands, tool-specific parameters, dependency graphs, and hierarchical metadata. Traditional ML approaches require brittle feature engineering to flatten this rich information into fixed-size vectors, losing critical semantic context. We present LASER, a framework that fine-tunes LLMs on serialized workflow job configurations for multi-target resource and runtime regression. To address the challenges of numerical regression via generation, we introduce scientific notation output encoding for targets spanning multiple orders of magnitude, and constrained decoding with prefix filling to enforce output validity while reducing inference latency by over 30%. We further show that full-attention fine-tuning improves accuracy over sliding-window LLMs on long job contexts. Validated on large-scale chip design workloads, and GHARuntime, a new public benchmark derived from 580,000+ GitHub Actions runs across 27,000+ repositories, LASER outperforms human experts and SOTA tabular ML baselines, with clear model- and data-scaling behavior, establishing a new paradigm for LLM-based regression on semi-structured workflow data.