Reproducible Research in Network Modeling

2026-07-21T07:23:55Z6b171cdfa87ee07c4f3e2464e8c4d6f5d76642b8314da174d460887dea625fbe
6GCAD-TDMAKV cacheLLM servingMAC protocolsMixture-of-Experts (MoE)NPU schedulingUAV/air mobilityWasserstein DROadversarial MLavailabilitydeterministic schedulingdistributed inferenceedge computingprivacyreinforcement learning (Q-learning)resource exhaustionspectrum allocationsupply-chain/implementation risk

What happened

This document bundle contains recent research on networked AI and wireless systems (6G/LWAM), LLM serving and caching, distributed MoE inference at the edge, NPU scheduling, UAV formation MAC control, and learning-based spectrum allocation. Collectively these works enable higher performance and autonomy but introduce new security and safety attack surfaces: KV-cache reservation uncertainty (preemption/recomputation) and memory-exhaustion/availability attacks against LLM serving; model-serving and distributed-MoE substitution strategies that can enable model-quality attacks, evasion or data-exfi

Why it matters

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

Evidence and limitations

Source ID
arxiv_cs_ni
Record identifier
6b171cdfa87ee07c4f3e2464e8c4d6f5d76642b8314da174d460887dea625fbe
Enrichment time
2026-07-21T07:23:55Z
AI-assisted enrichment
Yes

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