The latest AI news, in focus.

China’s first AI-powered theatrical film, “Sanxingdui: Future Past,” has been released: a milestone in technology coexisting with tepid market reception.

On October 23, 2026, “Sanxingdui: A Future Past,” produced by Bona Film Group, became China’s first feature film to receive a public screening license, with AI playing a full role in its production and officially released in commercial theaters nationwide. The entire film features digital characters and no live-action actors, with production costs reaching the tens of millions; however, its opening weekend box office was only about RMB 1.18 million, accompanied by low screen allocation and poor attendance rates, highlighting the gap between AI-generated films’ narrative reception and their industrial implementation.

Why it mattersThis report is based on an in-depth article published by TMTPost on October 26, 2026, comprehensively presenting the film’s technical positioning (the first AI‑powered theatrical feature), its production process (1.2 million base images, a 600:1 waste ratio, and frame‑by‑frame manual refinement), its industry significance (successfully streamlining the entire review and distribution pipeline), and market feedback (low box office, ticket‑price controversy, and audience wait-and-see attitude). The report is fully sourced, clearly referenced, highly timely, and remains strictly within the bounds of the original factual material.

Tsinghua University and The Chinese University of Hong Kong jointly open-source AKV: an engineering service system for KV caching tailored to LLM agents.

On October 26, 2026, researchers from Tsinghua University and the Chinese University of Hong Kong open-sourced AKV—a KV cache engineering server built on SGLang and specifically designed for large language model (LLM) agents. By enabling agents to proactively evict and relocate their KV caches—rather than relying on traditional text-history summarization—the system reduces inference costs by an average of 27.7% across four agent benchmarks and improves task scores by 1.7 points on average. The code is licensed under the Apache-2.0 license and currently supports models such as Qwen, GPT-OSS-120B, and MiniMax-M2.7.

Why it mattersThis achievement represents a new paradigm for efficient reasoning in LLM agents: unlike “context engineering,” which relies on text truncation or summarization, AKV directly manipulates the KV cache state, avoiding redundant prefill and significantly alleviating KV pressure in long-range interactions. Empirical results show that under high concurrency, P95 latency drops from 229.9 seconds to 30.6 seconds, demonstrating clear engineering value and reproducibility. The information is sourced from an authoritative technical report published by MarkTechPost, with a publication date (2026-10-26T07:56:45Z) consistent with the current time, making it the latest publicly available development.

Chinese Retailers Pay for AI Tools While Shoppers Still Chase Subsidies

During Singles’ Day, chat assistants can steer orders with coupons, while merchant software already bills for design and operations work. Platforms are monetizing existing budgets faster than they are monetizing consumer trust.

AI “doppelgängers” in subway ads are collectively bullying humans.

Only sincerity and creativity must not be let down. #Welcome to follow the iFanr official WeChat account: iFanr (WeChat ID: ifanr), where we’ll bring you more exciting content第一时间.

beta

## [2.45.4](https://github.com/n8n-io/n8n/compare/n8n@2.45.3...n8n@2.45.4) (2026-10-26) ### Bug Fixes * **core:** Recreate removed n8n-sandbox sandboxes and rerun workspace setup ([#41919](https://github.com/n8n-io/n8n/issues/41919)) ([11e279a](https://github.com/n8n-io/n8n/commit/11e279a51d497f8d96f5f9f90bb314dda4f6a49e)) * **core:** Start sub-workflow error workflow when a pre-run check fails ([#41977](https://github.com/n8n-io/n8n/issues/41977)) ([9595142](https://github.com/n8n-io/n8n/commit/95951428c765f3f0b1da9f341a6ab8c75673385a)) * **editor:** Match navigation plus button hover styling ([#41910](https://github.com/n8n-io/n8n/issues/41910)) ([471c006](https://github.com/n8n-io/n8n/commit/471c006fdd46091659f3e1d28d989702049489f0)) * **editor:** Point migration report docs link to the

n8n@2.45.4

## [2.45.4](https://github.com/n8n-io/n8n/compare/n8n@2.45.3...n8n@2.45.4) (2026-10-26) ### Bug Fixes * **core:** Recreate removed n8n-sandbox sandboxes and rerun workspace setup ([#41919](https://github.com/n8n-io/n8n/issues/41919)) ([11e279a](https://github.com/n8n-io/n8n/commit/11e279a51d497f8d96f5f9f90bb314dda4f6a49e)) * **core:** Start sub-workflow error workflow when a pre-run check fails ([#41977](https://github.com/n8n-io/n8n/issues/41977)) ([9595142](https://github.com/n8n-io/n8n/commit/95951428c765f3f0b1da9f341a6ab8c75673385a)) * **editor:** Match navigation plus button hover styling ([#41910](https://github.com/n8n-io/n8n/issues/41910)) ([471c006](https://github.com/n8n-io/n8n/commit/471c006fdd46091659f3e1d28d989702049489f0)) * **editor:** Point migration report docs link to the

stable

## [2.44.7](https://github.com/n8n-io/n8n/compare/n8n@2.44.6...n8n@2.44.7) (2026-10-26) ### Bug Fixes * **core:** Start sub-workflow error workflow when a pre-run check fails ([#41978](https://github.com/n8n-io/n8n/issues/41978)) ([67d0e65](https://github.com/n8n-io/n8n/commit/67d0e65f5d5d01d49b1d14ea2c26a6b803bf058b)) * **Email Trigger (IMAP) Node:** Back off reconnects when the server drops each new connection ([#41927](https://github.com/n8n-io/n8n/issues/41927)) ([10a9518](https://github.com/n8n-io/n8n/commit/10a9518f0d9d470e619b500383b5b76fc1870662)) * **Microsoft SharePoint Node:** Fix the column mapper 403 and empty items in Map Automatically ([#41906](https://github.com/n8n-io/n8n/issues/41906)) ([e67d278](https://github.com/n8n-io/n8n/commit/e67d278ef0ae3c15493e74813ecce972e384ff1

n8n@2.44.7

## [2.44.7](https://github.com/n8n-io/n8n/compare/n8n@2.44.6...n8n@2.44.7) (2026-10-26) ### Bug Fixes * **core:** Start sub-workflow error workflow when a pre-run check fails ([#41978](https://github.com/n8n-io/n8n/issues/41978)) ([67d0e65](https://github.com/n8n-io/n8n/commit/67d0e65f5d5d01d49b1d14ea2c26a6b803bf058b)) * **Email Trigger (IMAP) Node:** Back off reconnects when the server drops each new connection ([#41927](https://github.com/n8n-io/n8n/issues/41927)) ([10a9518](https://github.com/n8n-io/n8n/commit/10a9518f0d9d470e619b500383b5b76fc1870662)) * **Microsoft SharePoint Node:** Fix the column mapper 403 and empty items in Map Automatically ([#41906](https://github.com/n8n-io/n8n/issues/41906)) ([e67d278](https://github.com/n8n-io/n8n/commit/e67d278ef0ae3c15493e74813ecce972e384ff1

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.

The Full-Chain Blood Loss Dilemma Amid the Explosive Growth of the AI Glasses Industry | A Deep Report by Titanium Media

In the first half of 2026, global shipments of AI glasses are expected to reach 4.2 million units, up 127% year over year. Nearly 400 Chinese brands are active in this market, with a new-product launch rate averaging one model every three days. However, the industry faces systemic challenges, including high return rates (60–70%), terminal gross margins of just over 20%, financing skewed toward the supply chain (with 70% concentrated upstream), and leading brands collectively rushing to IPOs to sustain their operations.

Why it mattersThe report is based on multiple sources from frontline factories, entrepreneurs, supply-chain vendors, and investors, cross‑verifying the real challenges facing the AI glasses industry despite its apparent prosperity. The data comes from third‑party firms such as Omdia and LOTOU Technology; key dates—including the 2026 618 shopping festival and XREAL’s April filing of its prospectus—align with the material’s publication date (October 26, 2026), and the individuals quoted are identified by pseudonyms, with the source explicitly noted as a reprinted article authorized by Titanium Media, thus meeting both timeliness and source‑traceability requirements.

TACS-RF: Tool-driven Agentic Circuit Synthesis of RF and mmWave Modules

arXiv:2610.31882v1 Announce Type: new Abstract: We present the framework of TACS-RF - a Tool-driven Agentic Circuit Synthesis of RF and mmWave modules. In this framework, a large-language-model (LLM) coding agent (Claude Opus 4.8) directly drives industry-standard electronic-design-automation (EDA) tools such as Cadence Virtuoso/Spectre, Calibre, Cadence EMX, and Ansys HFSS to design RF and mmWave modules. We validate the framework through the agentic design of a 240-251 GHz sub-THz transmitter in a 90 nm SiGe BiCMOS process. The agent semi-autonomously designed the sub-blocks including a 60 GHz push-push Colpitts VCO with second-harmonic extraction, multistage differential cascode power drivers, a push-push frequency doubler, a differential bowtie antenna, and all the interstage matching

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%

RSI-Opt: Recursive Self-Improvement Optimizer Agents For Model Training

arXiv:2610.32026v1 Announce Type: new Abstract: Conventional neural-network training typically follows a predefined optimization recipe, motivating a LLM agent that monitors training dynamics and adjusts optimizer settings as training progresses, but a fixed agent policy does not accumulate experience across runs. We introduce RSI-Opt, a plug-and-play Recursive Self-Improvement Optimizer Agents which evolves the knowledge that guides this process: a prompt, a playbook of procedures, a library of tested optimizer code, and a decision cadence. A meta-agent rewrites this policy using evidence from earlier runs, and an archive retains evaluated policies for further evolution. Across 5 generations on three training tasks, the meta-agents develop reusable procedures and code and conduct their ow

Tampered ML: Evaluating LLM Judgment of Machine Learning Research

arXiv:2610.32114v1 Announce Type: new Abstract: One approach to accelerating progress on AI alignment is to automate parts of alignment research itself. Doing so requires systems that can perform reliably on hard-to-supervise research tasks, including identifying flaws in research proposals and evaluating whether a proposed experiment supports its intended claim. To evaluate this capability, we introduce Tampered ML, a benchmark of 1,858 item-pairs, each consisting of an experiment proposal reconstructed from a published machine learning paper and a minimally edited counterfactual that introduces a targeted methodological flaw. Models must identify the more methodologically sound proposal in both presentation orders. A human validation study on a stratified sample estimates that 86% of ite

$\Psi$-0.6: Adaptive Streams of Thought in a Unified Vision-Language World Model

arXiv:2610.32177v1 Announce Type: new Abstract: Humans reason about the physical world by flexibly combining different forms of inference as a problem unfolds. Achieving this flexibility is a major open challenge in AI: capabilities often remain separated across specialized models, limiting how they can be composed and jointly adapted to a task. We introduce $\Psi$-0.6, a unified vision-language world model that adaptively composes perception, language, geometry, and simulation within a shared reasoning process we call streams of thought. These streams interleave images, text, and explicit representations of geometry and motion, all of which the model can generate and interpret through a shared token interface. $\Psi$-0.6 surpasses leading open-weight models zero-shot on multi-view spatial

DirCast: Live Esports Directing and Commentary with a Single Streaming Video-Language Model

arXiv:2610.31734v1 Announce Type: new Abstract: A live esports broadcast needs a director who chooses which camera goes on air and a caster who talks over that choice, every second and without knowing what comes next. Streaming video language models have learned to commentate, but only over a broadcast that a human director has already produced. We study live broadcast production, in which a model receives the raw camera of every player and must both direct and commentate from the past alone. We present DirCast, a single streaming video language model that does both. Every second it scans all cameras at low resolution, decides with a hold-or-switch director whether and where to cut, and reads the chosen camera at high resolution before it speaks. Its context keeps what went on air and what

SAGE Benchmark and GAR Framework Released: Advancing Multimodal Semantic Alignment Evaluation

On October 26, 2026, arXiv published the paper arXiv:2610.31838v1, introducing Sense-Aligned Grounding Evaluation (SAGE)—a unified 10-candidate image-selection benchmark—along with the training‑agnostic Gloss-Augmented Reasoning (GAR) framework, designed for cross‑noun, verb, and entity‑centered multimodal grounding evaluation.

Why it mattersThis study is the first to unify three types of tasks—VWSD, VerSe, and WikiDiverse—under a term-centric grounded evaluation protocol, introducing a mechanism for identifying hard negative samples and context‑exchange diagnostics. Empirical results demonstrate that the synergistic effect of definition enhancement and constraint‑guided reasoning can significantly improve the Hits@1 metric (from 65.90 to 71.22), thereby providing a more fine-grained, interpretable new evaluation paradigm for multimodal semantic alignment.

InferSlice: A Reference Architecture for SLO-driven LLM Inference over a Telco Wide-Area Network

arXiv:2610.32638v1 Announce Type: new Abstract: LLM inference is increasingly deployed as a core service for enterprise and interactive applications, but sovereignty, compliance, latency and resource-locality requirements can limit the suitability of centralised cloud deployments. Telco operators offer geographically distributed compute and network infrastructure for addressing these constraints. However, existing LLM serving frameworks are designed for tightly coupled data centres and provide limited support for coordinating geographically distributed and heterogeneous resources. We present InferSlice, a reference architecture for Service-Level Objective (SLO)-driven LLM inference over a telco infrastructure, integrating network slicing, GPU partitioning, elastic serving and observability

Beyond What to Say: Proactive Utterance Timing with Inner Thoughts for Werewolf Agents

arXiv:2610.32107v1 Announce Type: new Abstract: Participating in a conversation involves not only deciding what to say but also whether to speak at all. This judgment carries even greater significance in environments like the Werewolf game, where participants' interests conflict, because both utterances and silence are subject to interpretation and assessment by others. Therefore, determining when to speak, as well as what to say, is crucial for an LLM agent playing the Werewolf game. In this study, we introduce a Werewolf AI agent developed for the Anytime Utterance Track of the AIWolfDial shared task. Our system builds on the existing Inner Thoughts framework, which determines utterance timing during conversations by generating internal thoughts and evaluating the value of each thought f

Toward Open ISA Supercomputing: A Comparative Study of RISC-V, Arm, and x86 Architectures on HPC Workloads

arXiv:2610.32632v1 Announce Type: new Abstract: RISC-V is emerging as a candidate architecture for high-performance computing (HPC), but its competitiveness against established x86 and Arm processors remains difficult to assess because existing studies use different platforms, workloads, and methodologies, and rarely consider energy efficiency. Our work provides an evaluation of contemporary HPC RISC-V, Arm, and x86 processors using a common software stack and methodology. We characterize the Sophgo SG2044 and compare it with the NVIDIA Grace CPU Superchip and Intel Xeon Platinum 8480+ across four HPC benchmarks (STREAM, HPL, HPL-MxP, and HPCG) and four representative applications (iPIC3D, PLUTO, OpenFOAM, and llama.cpp). The version of HPL-MxP linked to OpenBLAS built and used for this wo