Aiying Li | Searcharxiv

arXiv2026

RSH-SpMM: A Row-Structured Hybrid Kernel for Sparse Matrix-Matrix Multiplication on GPUs

Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental computation in graph analytics, scientific simulation, and sparse deep learning workloads. However, the extreme irregularity of real-world sparse matrices prevents existing GPU-based methods from maintaining high Tensor Core utilization and stable throughput. We present \textbf{RSH-SpMM}, a fine-grained row-structured hybrid SpMM framework designed to better align irregular sparsity with modern GPU execution pipelines. RSH-SpMM introduces adaptive row partitioning and employs the RS-Tile representation to expose Tensor-Core-efficient dense fragments, while processing irregular rows on a minimal-overhead CUDA execution path. It further employs a load-balanced hybrid kernel with locality-aware reordering to enhance structural coherence and sustain high execution efficiency under highly irregular sparsity. Comprehensive evaluations across diverse sparse workloads demonstrate that RSH-SpMM consistently outperforms state-of-the-art SpMM designs, yielding 1.27x - 6.13x acceleration and maintaining robust performance across matrices with highly irregular sparsity structures.

cs.DC↗

arXiv2025

bi-GRPO: Bidirectional Optimization for Jailbreak Backdoor Injection on LLMs

With the rapid advancement of large language models (LLMs), their robustness against adversarial manipulations, particularly jailbreak backdoor attacks, has become critically important. Existing approaches to embedding jailbreak triggers--such as supervised fine-tuning (SFT), model editing, and reinforcement learning from human feedback (RLHF)--each suffer from limitations including poor generalization, compromised stealthiness, or reduced contextual usability of generated jailbreak responses. To overcome these issues, we propose bi-GRPO (bidirectional Group Relative Policy Optimization), a novel RL-based framework tailored explicitly for jailbreak backdoor injection. By employing pairwise rollouts and pairwise rewards, bi-GRPO jointly optimizes the model to reliably produce harmful content with triggers and maintain safety otherwise. Our approach leverages a rule-based reward mechanism complemented by length and format incentives, eliminating dependence on high-quality supervised datasets or potentially flawed reward models. Extensive experiments demonstrate that bi-GRPO achieves superior effectiveness (>99\% attack success rate), preserves stealthiness in non-trigger scenarios, and produces highly usable and coherent jailbreak responses, significantly advancing the state-of-the-art in jailbreak backdoor attacks.

cs.CL↗