arXiv (All)
open access
TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models
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 framewo
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models#366345
- Correctness Forensics for Batch Speculative Decoding: Diagnosing the Ragged Tensor Problem#798666
- Correctness Forensics for Batch Speculative Decoding: Diagnosing the Ragged Tensor Problem#797252
- The BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation#668044
- Trillion-Parameter MoE in a Box: Decoupling Memory Provisioning with High-Bandwidth Flash#1109618
Record · ID 1459219
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive