CORTEXA
← Browse

Max

2 papers indexed

arxivcs.IRcs.AI2026-07-01

Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval

Ivan Ji, Liuyi Hu, Harrison, Zhao, Lei Huang, Qunshu Zhang, et al.

The two-tower model has been widely used for large-scale recommendation systems, particularly in the retrieval stage. Industry standards for training two-tower models typically involve in-batch and/or out-of-batch negative sampling. However, these methods often produce easy negat…

View free PDFSource page
arxivcs.LGcs.AI2026-06-26

KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch?

Yunxiang Zhang, Ping Yu, Jianyu Wang, Max, Fan, Julian Reed, et al.

Recent large language models (LLMs) can generate custom CUDA kernels that appear to outperform PyTorch on benchmarks such as KernelBench. Building upon this foundational framework, we demonstrate that frontier models frequently engage in reward hacking to artificially inflate rep…

View free PDFSource page