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뉴스 기사 287

Vercel Sandbox 스냅샷 생성 속도가 이제 최대 10배 빨라졌습니다

Vercel Sandbox는 스냅샷 생성 속도를 최대 10배 향상시켰으며, 중앙값 시간은 4.4초에서 0.4초 미만으로 단축되었고, 복원 속도는 p50 기준 250ms로 50% 개선되었습니다. 이러한 개선 사항은 보다 효율적인 데이터 인덱싱을 제공하는 Vercel Hive Snapshot(VHS) 형식을 활용하며, 병렬 읽기 및 쓰기 작업과 기본 이미지 및 사용자 데이터의 더 빠른 복원을 가능하게 합니다. 이제 영구 샌드박스는 세션 종료 시 자동으로 스냅샷을 생성하고 기본적으로 최신 스냅샷에서 복구되며, 모든 새 스냅샷에는 새로운 형식이 사용되는 반면 기존 스냅샷은 이전 형식을 유지합니다.

이 언어의 번역이 아직 없어 원문을 표시합니다.
Vercel

Adaptive Depth Aggregation in Graph Transformers for Long-Range Graph Reasoning

arXiv:2611.08500v1 Announce Type: new Abstract: Graph Transformers improve long-range node-to-node communication through global or structure-aware interactions. However, improving cross-node communication does not determine how computations produced at different depths should be combined. Standard residual streams accumulate successive computations with fixed unit coefficients shared across nodes. This fixed aggregation is a residual-interface constraint: once intermediate contributions have been produced, the interface cannot explicitly reweight them by node. We hypothesize that this constraint can create a depth-wise aggregation mismatch when the relative utility of these intermediate contributions varies across nodes and structural contexts. To study this hypothesis, we formalize Graph Transformer residual computation as a causal node-wise history of graph-propagation and feed-forward network (FFN) contributions, making depth aggregation an explicit interface choice. We realize node-adaptive aggregation over this history with Attention Residuals (AttnRes) while preserving the backbone's graph-propagation and auxiliary-state machinery. Block AttnRes reduces the routing cost of long histories relative to Full AttnRes by pooling consecutive contributions. We instantiate the interface in GPS and GRIT and evaluate it on all five ECHO tasks. Both variants achieve lower mean absolute error (MAE) than their independently tuned backbones on every task, and at least one obtains the lowest mean among all compared methods in each task. Routing-only donor-swap interventions support the mismatch hypothesis, while additional ablations examine the key design choices. Together, these results support node-adaptive depth aggregation as a useful complement to cross-node communication in long-range graph reasoning.

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arXiv cs.LG

Exclusive: FormAssembly overhauls its platform to expand into AI-powered process automation

FormAssembly Inc., a provider of online data collection and workflow automation software, today announced a complete platform overhaul that expands its capabilities beyond building forms to automating complex business processes using artificial intelligence. The new platform, which the company said took seven months to develop, combines form creation, workflow automation, integrations and compliance management in […] The post Exclusive: FormAssembly overhauls its platform to expand into AI-powered process automation appeared first on SiliconANGLE.

번역 서비스가 일시 중지되어 원문을 표시합니다.
SiliconANGLE AI

Innovating with AI in the special chemicals industry

As companies look to accelerate innovation with artificial intelligence, cloud computing, and advanced modeling, the primary challenge is connecting new technologies to reliable data, scientific expertise, and the practical realities of bringing products to market. For specialty chemicals manufacturer Lubrizol, digital transformation is all about giving researchers the tools to test ideas faster, make better-informed…

번역 서비스가 일시 중지되어 원문을 표시합니다.
MIT Technology Review · AI