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Towards Scalable and Cost-Efficient Vulnerability Detection: A Study on Automatic Query Generation
A study finds LLM-synthesized CodeQL queries improve average F1-score by 82% over baseline queries, offering scalable vulnerability detection versus direct LLM scanning.
Researchers conducted an empirical study evaluating whether LLMs can synthesize executable CodeQL queries from National Vulnerability Database vulnerability data. LLM-generated queries significantly enhanced baseline CodeQL suites, yielding an 82% improvement in average F1-score across a diverse set of real-world vulnerabilities. A cost-benefit analysis shows direct LLM-based scanning of entire repositories is often computationally and financially prohibitive, while LLM query synthesis offers a scalable and cost-effective alternative for large-scale vulnerability detection.
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CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents
Researchers introduce CUA-Universe, a pipeline turning real desktop software into hybrid GUI+CLI agent environments, lifting a 9B model's OSWorld success rate.
CUA-Universe is an environment-to-data pipeline that converts real desktop applications into hybrid GUI+CLI environments, scaling to 16 applications via App-Forge, Task-Weave, and Path-Steer. Training on its harvested trajectories shifted a 9B model toward effective GUI+CLI orchestration, yielding +39.3 points on CUA-Verse, +16.8 points success rate on OSWorld, and +7.84 points on OSWorld-MCP while cutting steps and tokens by up to 57% and 60%. The work addresses the scarcity of scalable hybrid environments for computer-use agents.
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Scalability Analysis of Distributed Kolmogorov-Arnold Network Training on High-Performance Computing Systems
An empirical study shows distributed Kolmogorov-Arnold Network training reaches 74.7% parallel efficiency at 8 A100 GPUs, with overheads driven by All-Reduce choices.
The study evaluates data-parallel Kolmogorov-Arnold Network (KAN) training on the FinisTerrae III supercomputer using up to 8 NVIDIA A100 GPUs across 4 nodes with PyTorch Distributed Data Parallel. Strong scaling yields 5.97x speedup and 74.7% parallel efficiency at 8 GPUs, comparable to conventional deep learning workloads, while communication overhead ranges from 1.3% to 6.1%, driven mainly by All-Reduce algorithm selection and inter-node latency rather than KAN's edge-wise gradient structure. Weak scaling shows an initial single-to-multi-GPU throughput drop followed by stability, and the parameter-to-memory ratio improves with model size even as training time scales unfavorably. The authors provide GPU topology and model-size deployment guidelines for KAN training.
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