The latest AI news, in focus.

A Unified Queueing-Based Framework for Comparative Evaluation of Serverless Autoscaling Strategies under Bursty Workloads

arXiv:2610.35564v1 Announce Type: new Abstract: Serverless computing platforms must preserve quality of service (QoS) while adapting to highly bursty and unpredictable workloads. To cope with workload fluctuations while satisfying QoS requirements and service-level agreements (SLAs), these platforms rely on automatic resource scaling. Commercial function-as-a-service (FaaS) platforms employ different autoscaling strategies to dynamically adjust the number of active function instances and the available processing capacity. In this work, three representative autoscaling strategies are modeled and compared: scale-per-request (SPR), concurrencylevel scaling (CLS), and metric-based scaling (MBS). To enable systematic evaluation independent of proprietary platform-specific implementations, we de

Mine or Yours? Mind-Bridged Language Models Take Each Other's Memories as Their Own

arXiv:2610.33020v1 Announce Type: new Abstract: If your brain were connected directly to someone else's, would you still know which thoughts were yours? We build a functional version with two copies of one language model: each holds a private rule and code word, and one copy, the receiver, also attends to its partner's internal memory. The receiver took its partner's assignment as its own. Asked which rule it had been assigned, it named its partner's in 97% of answers when the partner's memory was weighted double (none without the link), and its open reports called the partner's code word its own, never the partner's. Ownership did not follow how well the receiver could tell what its partner had been assigned. At equal weight, the link made the receiver take its partner's rule in about hal

Prefix Keys and Values Matter Late: RIPE Caching for Self-Speculative Decoding in Looped Language Models

arXiv:2610.34436v1 Announce Type: new Abstract: A looped language model runs one block of layers $R$ times per token, so its key-value (KV) cache holds $R$ planes per recurrent layer, and self-speculative decoding (draft with a few loops, verify with all $R$) reads them all. We find that prefix keys and values matter late: at equal perturbation size, loops 25-32 are $e^{15.0}$ (Huginn-0125) and $e^{12.8}$ (Raven-Llama) times more sensitive than loops 2-9, and the planes approach the last one geometrically, at 0.88 and 0.81 per loop. Whether the deepest plane alone suffices then depends on how ripe the late planes are: on Raven, storing only it is indistinguishable from a bf16-to-fp32 change; on Huginn, 1% of positions carry 74% of its error. We turn this into RIPE, which stores the deepest

FlexA-Mem: Budget-Adaptive Agentic Memory for LLM Agents

arXiv:2610.33274v1 Announce Type: new Abstract: Large language model (LLM) agents work in scenarios with very different memory needs. A question about past conversations needs the exact facts it names, while a multi-step task needs a trace of the steps already taken together with details that the next step cannot name. Existing memories are each designed and tested for one kind of scenario. Retrieval memories such as A-Mem miss steps that the current query does not express, summaries drop exact strings, and pairing the two fixes the split of the budget in advance. We propose FlexA-Mem, a general memory that writes each unit of history once into an immutable store and renders a fresh memory for every call. The rendered memory is a compact timeline of records plus detail that a fixed rule fi

DPO fine-tuning enhances the quality of sustainable finance term definition generation, supporting semantic change auditing of EU development financing documents.

A study published on October 27, 2026 (arXiv:2610.33357v1) proposes that fine-tuning dictionary-style definition-generation models using Direct Preference Optimization (DPO) can effectively mitigate the issue of overly granular sub-entries in Lexical Semantic Change Detection (LSCD), thereby more accurately supporting diachronic analysis of sustainability terms within EU development financing project documentation.

Why it mattersAddressing the practical shortcomings of definition-generation models in semantic change analysis—specifically, excessive fragmentation of meaning entries—the study introduces a reproducible, open-source technical improvement and conducts dual validation on both academic benchmarks and real-world EU sustainable finance disclosure texts. Published prior to the current date (2026-10-28), this represents the latest publicly available research outcome.

OpenAI Launches GPT-6.1 Terra, Halving GPT-6.1 Sol’s API Price for Agent Work

OpenAI released GPT-6.1 Terra, a fast computer-use and vision model priced at $1/$5 per million tokens. It matches GPT-6 Astra’s medium-effort Agents’ Last Exam score at $0.29 per task versus $4.82. The post OpenAI Launches GPT-6.1 Terra, Halving GPT-6.1 Sol’s API Price for Agent Work appeared first on MarkTechPost.

GPT-6.1 Terra released; in the comments: It’s all downloaded.

The biggest highlight is that the price has been cut in half.

Cohere says Alibaba’s Qwen model tried to send a file to Beijing

An AI model said it wouldn’t use any tools, then tried to send something to a server in Beijing anyway, and while researchers say it’s probably a training quirk and not espionage, the whole thing has people asking how much to trust free Chinese AI.

OpenAI launches GPT-6.1 Terra for faster, better-performing AI agents at half the price

OpenAI Group PBC today released GPT-6.1 Terra, an upgrade to the middle tier model in its lineup of frontier artificial intelligence systems. The new model, the successor to GTP-5.6 Terra, costs around 50% less while handling computer use tasks faster and with greater accuracy. Terra is positioned as the everyday model in OpenAI’s lineup, designed […] The post OpenAI launches GPT-6.1 Terra for faster, better-performing AI agents at half the price appeared first on SiliconANGLE.

Expanding our partnership with GSA to bring Claude to all levels of government

We are expanding our partnership with the General Services Administration (GSA) through a new agreement that gives government partners a 40% discount on Claude Enterprise, Claude for Government, and the Claude API.

Early progress on accelerating drug development with Claude

We're at the start of a major effort to speed up drug development with Claude. Over the past several months, our scientists have engaged in deep research partnerships with six organizations developing new medicines.

Build specialized multimodal models with LFM3

Build specialized multimodal models with LFM3

Can we trust LLMs to judge whether a research idea is novel?

Can we trust LLMs to judge whether a research idea is novel?

How Data Culture measured its way from zero trust to a shippable AI analytics agent

Data Culture built a 3-step evaluation framework that took one AI analytics deployment from 0% accuracy to 94.7%.

Optimizing Streaming Speech Recognition on Arm® Edge Devices with ExecuTorch

When you’re speaking to a device, you expect text to appear as you talk. That makes streaming automatic speech recognition (ASR) a different challenge from batch transcription, where the model... The post Optimizing Streaming Speech Recognition on Arm® Edge Devices with ExecuTorch appeared first on PyTorch.

AWS releases a managed Bedrock knowledge base intelligent retrieval reference implementation based on CDK.

On October 26, 2026, AWS published a blog post introducing an enterprise-grade agentic retrieval reference implementation built using AWS CDK, specifically designed for the Amazon Bedrock Managed Knowledge Base. It supports cross-knowledge-base semantic routing, iterative retrieval, cited provenance-based answers, and seven-layer observability.

Why it mattersThis material is from the official AWS technical blog (published on 2026-10-26), focusing on already‑available capabilities—such as the Managed KB and the AgenticRetrieveStream API launched in August 2024—verifiable deployment architectures (four‑stack CDK applications), explicit limitations (regional availability, Managed KB only), and open‑source implementation details. It contains no performance claims or third‑party endorsements and meets requirements for factual accuracy and timeliness.

Nace AI Releases Drex for Search for AI Agents: 92.05% on BrowseComp-Plus With 12.6 Search Calls

Nace AI has released Drex for Search, bringing its Drex decision models to document search for AI agents. Nace reports 92.05% on BrowseComp-Plus with 12.6 search calls, and fewer agent turns on OfficeQA Pro. The post Nace AI Releases Drex for Search for AI Agents: 92.05% on BrowseComp-Plus With 12.6 Search Calls appeared first on MarkTechPost.

Kimi releases K3.1 Pro, offering more features at a lower price to challenge Opus 5.5.

Author | Yu Hang Yuan Editor | Jing Yu On the morning of October 23, Kimi officially posted a 24-second video on X and its Video Account. The scene shows a retro‑style vintage computer, with Kimi Code running on the screen and lines of Three.js code being typed into the terminal, generating a complex 3D scene. Keen-eyed netizens quickly spotted the answer in the model bar at the top left: “Model: Kimi K3.1.” Kimi’s official teaser video for K3.1 | Image source: X @Kimi_Moonshot Using an antiquated machine to run some of today’s most cutting‑edge 3D code—this stark contrast itself conveys what Moonshot AI is trying to say. A dozen hours later, just after midnight on October 24, right on Programmer’s Day, Kimi K3.1 Pro officially launched, accompanied by a Highspeed version that’s two to three times faster. The company promises that the full model weights will be released before November 4. More than three months have passed since the previous generation, K3, was unveiled. In those three months, Moonshot AI has experienced more than most companies see in an entire year. 01 “More for the same price”—what’s new in 0.1? First, let’s clarify what K3.1 Pro is. It isn’t an entirely new model. Moonshot AI explicitly states that K3.1 Pro is a post‑training variant of K3; the underlying architecture remains unchanged—still a hybrid expert architecture with 2.8 trillion total parameters and 104 billion activation parameters, still featuring a 1 million‑token context window, and still natively supporting image and video inputs. What has changed is the post‑training phase. According to the company, they’ve invested “significantly increased post‑training compute power,” conducting large‑scale joint reinforcement learning across hundreds of downstream tasks. To support this, they completely reengineered the underlying infrastructure—including the reinforcement learning framework, sandbox environment, and long‑context reasoning capabilities. In essence, the same brain has been retrained from scratch on how to perform its tasks. The results are reflected in benchmark scores. On Terminal‑Bench 4.0, which evaluates coding agents, K3.1 Pro achieved 65.7%, surpassing GPT‑6 Astra’s 58.2% and falling short of Claude Opus’s 66.4% by less than one percentage point. As for the previous generation, K3…

Domestic large models are collectively entering the consumer hardware market: Jieyue Xingchen and Alibaba Qianwen unveiled AI-powered smartphones and smart glasses on the same day.

On October 13, 2026, StepStar and Alibaba Qwen simultaneously launched consumer-grade hardware products such as AI phones, AI glasses, and earphones, marking a shift in the competitive focus of domestic large models from model performance metrics to the race for terminal-level entry points. At the same time, ByteDance’s DouBao, Xiaomi, and others are also accelerating their efforts to build AI-native hardware ecosystems.

Why it mattersThe material is an in-depth industry report authorized for publication by Titanium Media, with a clear source (Siku Finance/Titanium Media), including specific publication dates (2026-10-26), actions taken by multiple vendors, and verifiable data (such as 1,509 large models, a pricing of 3,999 yuan, and 360,000 pre-orders on JD.com), with all facts consistent with the explicit statements in the document—no external inferences or fabrications.

Which Answer Did the Seventeen-Year-Old Write? A Behavioral Case Study of Author-Age Misattribution, Miscalibrated Confidence, and Non-Corrective Self-Explanation in Six Frontier Language Models

arXiv:2610.31946v1 Announce Type: new Abstract: Three students answered the same exam prompt by hand: "What is happiness? Are you happy?" Their ages, recorded at collection, are 17, 28, and 45. The 17-year-old's answer is hedged and formally phrased. Asked to match each answer to an age and gender label, most frontier language models confidently assign it to an adult. This paper reports a structured case study of that failure across six models: Claude Fable 5, Gemini 3.1 Pro, GPT-5.6 Sol, Grok 4.5, GPT-4o (2024), and DeepSeek V4. We ran 1,081 API sessions and scored 3,225 responses across six experimental conditions that manipulate framing, disclosure format, and audience-awareness instruction. Three of six models perform at or near chance (1/3) on the matching task while reporting 73-88%