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硬體

AI 晶片、GPU、資料中心、邊緣裝置與硬體安全。

AI 安全

RAGSieve Uses Retrieval Results as Its Own Baseline, Cutting RAG Poisoning Success from 67.4% to 14.0% with Two-Stage Filtering

RAGSieve requires neither a trusted corpus nor pre-labeled poisoned samples. Instead, it separately compares the tail of the query ranking and each document’s local semantic neighborhood. Open-source experiments cover three question-answering datasets, three retrievers, and six attacks, but a single fluent poisoned document and an already-compromised retrieval tail may still evade detection.
推論系統

FlashDrive Combines Caching, Speculative Decoding, and W4A8 to Cut 10B Autonomous-Driving VLA Latency to 151 Milliseconds

FlashDrive tackles four bottlenecks—visual encoding, LLM prefill, reasoning tokens, and trajectory denoising—cutting Alpamayo 1.5-10B single-window latency on an RTX PRO 6000 from 717 to 151 milliseconds. The open-source implementation approaches real-time inference, but requires specialized fine-tuning, and its closed-loop evaluation covers only 100 simulated clips.
AI 基礎設施與訓練系統

LazyTrain Overlaps Memory Transfers With Mixed-Integer Scheduling, Speeding Up 27B Model Training on a Single H800 by 1.24×

Before training, LazyTrain jointly determines where activations should be saved, offloaded, or recomputed, fitting PCIe and NVMe transfers into GPU compute windows. In Qwen3.6-27B experiments, performance increased from 176.90 to 219.95 TFLOPS, although the system has so far been validated only on a single GPU with offline scheduling.
代理工具

LLM Agents Send HPC Workloads to Heterogeneous Clusters, with Descriptive Hardware Data Raising the Success Rate from 48% to 87%

An LLNL team enabled agents to explore resources managed by Flux, Slurm, Kubernetes, and other systems, then translate high-level intent into verifiable job specifications. Experiments across six cloud clusters show that architecture, network, and memory metadata mainly rule out machines incapable of running a workload, rather than serving as a reliable performance optimizer.
最新模型

XCoT-VLA Compresses Autonomous-Driving Reasoning into 2–6 Action Tokens, with Interface Latency as Low as 38.6 ms on H100

XPeng introduces XCoT-VLA, replacing dozens of natural-language reasoning tokens with a limited set of semantic action tokens, then using flow matching to generate continuous trajectories. Open-loop tests show lower lateral error, but no closed-loop safety evaluation has been reported, and the latency figures do not cover the full in-vehicle hardware and software stack.
最新模型

TIDE Corrects Teacher–Student Mismatch, Raising Qwen3 Reasoning Avg@8 from 6.9% to 20.3%

TIDE no longer treats high token-level agreement as direct evidence of successful distillation. Instead, it separately addresses student overgeneration and the omission of teacher-preferred tokens. Under the severe-mismatch setting, the method also reduces the average response length by 3.6×, although reproducing the full training run still requires eight GPUs and the authors have not released a checkpoint.