最新模型

新模型、開放權重、多模態、語音、影像與模型架構。

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.

推論系統

GCache Allocates Diffusion Caching Based on End-to-End Error Impact, Cutting Wan2.1 LPIPS to 0.0316 at the Same Speed

GCache no longer decides whether to recompute based on local similarity between adjacent denoising steps. Instead, it searches for a cache-reuse schedule with the lowest impact on final image quality. On Wan2.1, it substantially reduces deviation while maintaining a 2.17× speedup, but policy search requires multiple H100 GPUs and the implementation has not yet been released.

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.

推論系統

Trie Automata Precompute Token Masks, Delivering Up to 29× XGrammar’s Throughput for Finite-Choice Decoding in vLLM

A new method precomputes trie states and token masks for thousands to tens of thousands of valid strings, avoiding the cardinality bottleneck of general-purpose grammar compilation. The authors measured 219 requests per second at a batch size of 256, but have not yet released the implementation, and the figure also includes integration gains from bypassing the existing guided-decoding path.

代理工具

Model Main Effects Fall Below 3% Across Three Agent Benchmarks, as DDR Recasts Leaderboards as a Deployment Reliability Problem

A new study uses generalizability theory to decompose results from TheAgentCompany, τ²-bench, and AppWorld, finding that agent–task interactions far outweigh average differences between agents. The authors propose the DDR reporting framework, which requires model-selection decisions to disclose task difficulty, holdout validation, cost, and required sample size.