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Security2026-08-12 · source-backed
Load Hijack modifies nothing but router weights in a checkpoint. When a private trigger appears, token-to-expert assignment concentrates on experts co-located on a single GPU, making it a straggler while peers idle (arXiv 2608.10614). Across three MoE families and four corpora: 92.3–95.6% of triggered assignments hit target experts, producing 1.43x time-to-first-token and 0.86x throughput in live expert-parallel serving. It's a supply-chain attack on the serving schedule, invisible to any output-quality check you're running.
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Measuring expert time on two datacenter GPU generations shows it is linear in neither token count (EPLB, LPLB, UltraEP) nor activated-expert count (METRO). Below roughly 156 to 168 tokens, HBM weight streaming dominates so cost attaches to activated replicas. Above it, grouped...
Alibaba published a fine-grained MoE with 2.4T total / 95B active, 512 experts, and a 92-layer hybrid full/linear attention backbone. vLLM shipped day-0 support verified on NVIDIA and AMD with ready 4-bit checkpoints (NVFP4 at 1.32 TiB for an 8xB300 node, MXFP4 at 1.45 TiB for...
Li, Huo, and Johnson show that one-way message flow between agents produces neither mimicry nor solo behavior but an entirely novel dynamical state, at identical temperature settings. It's conceptual rather than quantitative, but the implication for orchestrator-worker fan-out...
Alagappan Valliappan shows built-in Multi-Token-Prediction draft heads run full attention over the entire KV cache at every draft step, so at million-token context the "negligibly cheap" draft dominates cost and deep native drafts can go net-negative. Applying a StreamingLLM-s...
A rare end-to-end systems report for trillion-parameter MoE post-training outside the GPU world: hierarchical optimization across model parallelism, computation-communication orchestration, and low-level kernels on an Ascend NPU SuperPOD, a 2.93× improvement over the open-sour...
arXiv 2607.16184 targets the failure where MoE models, normally the efficiency win, go memory-bound in KV-cache-heavy serving because expert weights and cache compete for the same HBM. Dynamic per-page quantization is the fix. If you're self-hosting Qwen or DeepSeek on constra...
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