arXiv AI
Agentic Algorithm Engineering: Improving Shared-Memory Exact Minimum Cuts
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ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution
arXiv:2603. 02510v2 Announce Type: replace Abstract: The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming.
arXiv AI
Aug 25
LLM4LLM: Bridging Kernel Benchmarks and Real Deployment via Closed-Loop Agentic Optimization
arXiv:2608.21836v1 Announce Type: new Abstract: Large language models have become increasingly capable agents for low-level code and kernel optimization, but isolated kernel benchmarks provide only a...
arXiv AI
Jun 2
FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization
arXiv:2605. 25246v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines.
arXiv AI
Jul 24
InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents
arXiv:2607. 20468v1 Announce Type: new Abstract: AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces.
arXiv AI
Jul 21
FormulaCode: Evaluating Agentic Optimization on Large Codebases
arXiv:2603. 16011v3 Announce Type: replace-cross Abstract: Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to optimize entire codebases under realistic constraints.
arXiv Machine Learning
Jul 7
MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI
arXiv:2605. 08678v3 Announce Type: replace Abstract: Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes.