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

2026-03-11T08:52:37Z964c0ff3a83bb55b20459c4dcc833f0142fb32359c526e775d3b41067847541d
AuralinkSDCFedAvgFedLECCISO-15118LLM-fine-tuningOCPPPREEMPT_RTPagedAttentionQLoRASCAFFOLDZipage','kv-cache-eviction'autonomous-systemscompressed-kv-cachedeZentdecentralized-privacyedge-aiev-chargingfederated-learningmodel-inversionnon-iid-datapoisoning-resilienceprivacysecure-sumstochastic-countingz-anonymity

What happened

This collection of arXiv papers (Mar 11 2026) presents multiple systems and algorithms with direct security, privacy, and safety implications. Notable items: Auralink SDC proposes edge-deployed autonomous LLM agents for EV charging management (uses models fine-tuned on OCPP/ISO15118 and incident logs) — a safety-critical attack surface where exploitation or erroneous autonomous remediation could cause large-scale service disruption or unsafe actions. Federated learning papers (benchmarking and FedLECC) highlight client-selection, non-IID challenges, and gaps in privacy/robustness, raising risk

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_dc
Record identifier
964c0ff3a83bb55b20459c4dcc833f0142fb32359c526e775d3b41067847541d
Enrichment time
2026-03-11T08:52:37Z
AI-assisted enrichment
Yes

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