# TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models

arXiv: https://arxiv.org/abs/2607.12104 · HTML: https://mlsanity.com/paper/2607.12104/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 8 of 8)

- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [NetGAN: Generating Graphs via Random Walks](https://mlsanity.com/paper/1803.00816/) (2018)
- [A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis](https://mlsanity.com/paper/2110.12489/) (2021)
- [Catch Me If You GAN: Using Artificial Intelligence for Fake Log Generation](https://mlsanity.com/paper/2112.12006/) (2021)
- [Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning Approach](https://mlsanity.com/paper/2408.14259/) (2024)
- [Large Language Models as Realistic Microservice Trace Generators](https://mlsanity.com/paper/2502.17439/) (2024)
- [SAGA: Synthetic Audit Log Generation for APT Campaigns](https://mlsanity.com/paper/2411.13138/) (2024)

## Abstract (arXiv)

Machine learning models for system diagnostics rely on kernel execution traces to capture fine-grained system behavior, but collecting production traces in industrial systems is costly due to runtime overhead, storage demands, and privacy constraints. We present TraceSynth, a diffusion-based framework for generating synthetic kernel traces that augment limited real data for downstream ML tasks. TraceSynth models traces as multi-channel sequences (event types, timestamps, CPU affinity, thread identifiers, and process metadata) using a Transformer-based denoising diffusion process with constraint-guided repair to enforce system invariants. Across six benchmarks, results show strong workload dependence. For deterministic, compute-heavy workloads (scimark2), synthetic augmentation achieves 87.2% F1-Macro at context length L=4096, only 2.6 percentage points below real-only baselines. Context length is the dominant quality factor, with L=4096 yielding a +104% relative improvement over L=256, while constraint-guided repair improves synthetic data quality by up to 4.3%. Ablation studies show that lightweight 2-channel models retain 97-99% of the performance of full 6-channel models at roughly half the computational cost. TraceSynth supports cost-effective augmentation of kernel execution traces in production observability pipelines and helps identify when synthetic data can substitute for limited real traces.