arXiv (All)
open access
MoSAIC: Learning DAG Scheduling Heuristics from Optimal Schedules through Motif-Aware Recovery and LLM-Guided Reasoning
Efficient task scheduling in heterogeneous computing systems remains a fundamental challenge in embedded and high-level synthesis flows. This paper presents MoSAIC (Motif-aware Scheduling with AI-driven Cognition), a learning-based framework that bridges the gap between exact and heuristic schedulin
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
- Robust Asynchronous Planning via Auto-Formalization#1354173
- Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systems#1110195
- Coupled-task scheduling with time windows, bounded pathwidth and bounded slack is para-NP-complete#129474
- Carbon-Aware Mapping and Scheduling for Deadline-Constrained Workflows#238584
- FlexiWorld: Learning and Planning via Flexible Action Chunks Across Multiple Time Scales#1141640
Record · ID 1326455
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