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最新模型

V-RAE Uses Frozen Visual Representations as Video Latents, Converging Up to 6× Faster in K600 Generation Training

Instead of training a video VAE solely for pixel reconstruction, V-RAE adds temporal compression and a decoder on top of semantic features from models such as DINOv3, SigLIP2, and V-JEPA 2.1. The authors found that the new latents improve both generation and future-frame prediction, though they have so far demonstrated only low-resolution, short-clip, class-conditional generation.

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.