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coding-engineering

Simplifying runtime caching on Vercel

Vercel is merging its Data Cache service into Runtime Cache, unifying data caching across frameworks like Next.js, SvelteKit, and Nuxt. Developers using fetch caching or unstable_cache in Next.js won’t need to change their code. Pro and Enterprise projects receive increased storage allocations, with Pro getting 8x more space and Enterprise 10x. Runtime Cache now handles regional storage, authentication, and tag-based invalidation across Vercel’s global infrastructure. Improvements include faster reads during traffic bursts via circuit breakers, higher storage budgets reducing evictions, and enhanced observability with deployment markers, revalidation causes, and write utilization metrics. The migration simplifies billing and usage tools, with existing teams receiving advance notice and pricing details.

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Vercel
coding-engineering

The fx adapter now runs the harness on the host

The fx adapter for the AI SDK’s HarnessAgent now uses libfx, enabling the harness and model requests to run in the application’s process while keeping filesystem and shell operations isolated in a sandbox. This update simplifies setup by eliminating the need to install fx in the sandbox, run a bridge, or expose a port. Model credentials remain on the host. New features include built-in tool filtering, mid-turn steering, structured output, image tool results via toModelOutput(), and session resumption across host processes. Update @ai-sdk/harness-fx to the latest version to access these improvements.

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Vercel
行业动态

Google introduces cutting-edge Genie 4 world model

Google LLC today previewed Genie 4, an artificial intelligence model that can generate realistic three-dimensional environments. The algorithm is the latest addition to a series of world models that the company launched in 2024. Compared to its predecessor, Genie 4 is better at rendering fluid movements and other complex details. Furthermore, it can generate accompanying […] The post Google introduces cutting-edge Genie 4 world model appeared first on SiliconANGLE.

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SiliconANGLE AI
inference-systems

b11946

server : add realtime conversation and transcription on /v1/realtime (#31269) * server : add realtime conversation and transcription on /v1/realtime Co-authored-by: Xuan Son Nguyen Assisted-by: Claude Opus 5.5 * examples : default the realtime client to the server port 9931 Assisted-by: Claude Opus 5.5 * server : sample realtime responses right after their own decode Slots respond in turns on one context: sampling at the start of the next step read the logits of whichever slot decoded last, so concurrent sessions mixed their outputs. Assisted-by: Claude Opus 5.5 * move more stuff to server-realtime, making it self-contained * examples : fix the type check of the realtime client Assisted-by: Claude Opus 5.5 * examples : use the model card prompts in the realtime client Assisted-by: Claude O

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llama.cpp 发布
模型更新

先检测再消歧：基于扰动的大型语言模型输入歧义检测

arXiv:2610.36353v1 发布类型：新 摘要：大型语言模型（LLMs）常常将含糊的输入当作具有单一明确含义来回答。这存在严重问题，因为输入歧义是不确定性的一个上游来源：当一个提示可能存在多种有效解释时，关键问题在于模型是否应当直接作出回应。现有的输入不确定性方法通常依赖于由大型语言模型生成的语句或澄清，但这类转换可能在测量歧义之前就已经对输入进行了消歧。我们提出了一种保留标签的扰动框架，用于检测输入歧义，在该框架中，扰动保持与原始输入相近，同时保留其标签。我们通过两种检测器对该原则进行了具体实现：一种是基于规则的黑盒扰动库

arXiv cs.CL
研究进展

INC-MoE: Overlapping In-Network Collectives and Expert Computation for Expert-Parallel Mixture-of-Experts Inference

arXiv:2610.36339v1 Announce Type: new Abstract: In expert-parallel mixture-of-experts (MoE) serving, dispatch moves tokens to the accelerators hosting their selected experts, and combine returns expert outputs to each token's owner. Both operations lie on the critical path and can dominate MoE layer latency in multi-node deployments. We present INC-MoE, an expert-parallel MoE layer that offloads dispatch and combine to the network switch and overlaps both collectives with each other and with expert computation. Dispatch uses in-network AllGather through switch-side multicast, while combine uses in-network ReduceScatter through switch-side reduction. Both collectives use message semantics between the end hosts and the switch. Because the in-network collectives load opposite directions of ea

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arXiv cs.DC
model-evals

Lipi-Ghor: A Large-Scale, Diarized, Multi-Domain Bengali Speech Corpus and a Scalable Pipeline for Building It

arXiv:2610.36184v1 Announce Type: new Abstract: Bengali, spoken by over 230 million people, remains severely under-resourced in spoken language research. Existing open Bengali ASR corpora are confined to short, read or crowd-sourced speech and lack speaker diarization and long-form audio. We present Lipi-Ghor, a large-scale weakly-supervised Bengali speech corpus comprising 885 hours of audio sourced from 998 YouTube videos across 596 unique channels, spanning 17 diverse domains including audiobooks, drama, religious sermons, news, and regional dialects. Each recording is annotated in SSTT format (Speaker, Speech, Transcription, Timestamp), which combines automated diarization with Bengali YouTube caption tracks. To our knowledge, Lipi-Ghor is the first Bengali speech corpus to pair large-

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arXiv cs.CL
研究进展

AK_QMUL at ArGuard Shared Tasks: A Single Fine-Tuned JAIS for Arabic Harmful-Prompt Detection

arXiv:2610.36360v1 Announce Type: new Abstract: We present AK_QMUL's submissions to the ArGuard-2026 shared tasks on Arabic harmful-prompt detection: a binary safe/unsafe task (B1) and a seven-way harm-domain task (B2). Both submissions use one backbone -- JAIS-6.7B fine-tuned with 4-bit QLoRA and a linear classification head over its last-token representation. For B1 we train a four-way head that factors the label by dialect register {safe, unsafe} x {MSA, other}, we augment the training fold with MSA prompts from AraSafe, and decode with a plain argmax to score safe/unsafe. For B2 we fine-tune the same backbone as a seven-way classifier and average the softmax outputs of five random seeds. Both submissions ranked first on their track, with macro-F1 0.984 on B1 and 0.790 on B2. We report

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arXiv cs.CL
模型更新

Causal Localization of Evidence Effects in Transformers

arXiv:2610.36524v1 Announce Type: new Abstract: Can controlled symbolic inputs reveal attention heads that influence a language model's decisions from natural language evidence? We introduce controlled evidence aggregation (CEA), which varies the counts and order of two symbols in otherwise fixed prompts and measures changes in a frozen Transformer's preference between two answer tokens. The preference is measured by their logit difference; the prompts prescribe no correct answer. Balanced utility causal selection (BUC) identifies compact head sets by jointly evaluating how much ablating their outputs removes the count effect and how closely patching mean outputs between count conditions recovers the effect in both directions. We derive conditional bounds linking head attribution to interv

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arXiv cs.CL