V2EX ·
[问与答] 苹果手机同花顺 APP 打开就卡死，又遇到吗
目前 ios27.2 系统，重启手机都无效，这是不让我跑路吗
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V2EX ·
[开源软件] Excelize 开源基础库发布 2.11.1 版本更新
Excelize 是 Go 语言编写的用于操作 Office Excel 文档基础库，基于 ECMA-376 ，ISO/IEC 29500 国际标准。可以使用它来读取、写入由 Excel 、WPS 、OpenOffice 等办公软件创建的电子表格文档。支持 XLAM / XLSM / XLSX / XLTM / XLTX 等多种文档格式，高度兼容带有样式、图片(表)、透视表、切片器等复杂组件的文档，并提供流式读写支持，用于处理包含大规模数据的工作簿。可应用于各类报表平台、云计算、边缘计算等系统。 开源代码 GitHub : github.com/xuri/excelize 中文简体字文档: xuri.me/excelize/zh-hans 2026 年 11 月 2 日，社区正式发布了 2.11.1 版本，该版本包含 30 余项更新，包括新增功能、错误修复、安全加固和兼容性提升优化。来自世界各地的 30 名开发者为此版本贡献了代码。下面是有关该版本更新内容的摘要，此版本中最显著的变化包括： 版本说明 此版本中最显著的变化包括： 兼容性提示 升级 Go 语言版本要求至 1.26.0 或更
V2EX ·
[程序员] 飞书桌面端相当令人不快的设计
核心功能不精： 启动缓慢，相同设备环境启动时间为微信的 2-3 倍 打开文档缓慢，任何文档均需要 3-10 秒加载 文档编辑同样卡顿严重 无法离线编辑文档 大量文档入口无法在客户端中原生打开，会跳出浏览器打开 大量非必要/越权行为： 附带豆包 agent ，非可选功能，亦不可手动关闭 在聊天消息栏植入不定时 agent 功能广告，无永久关闭渠道 探测全部用户窗口，在选中任意文本时跳出其悬浮窗 全局拦截 ctrl+shift+A 作为其截图快捷键（在部分图片编辑软件是取消选中） 在用户主目录创建 Doubao 文件夹 不断读取用户位置信息
V2EX ·
[分享创造] PushMe v6.0 发布，代码开源了
更新日志： 1. 新增系统通知监听功能 1. 新增定时任务功能 1. 新增插件商店功能 1. 新增前台服务功能 1. 新增服务心跳时间自定义调节 1. 优化消息详情页上划下拉逻辑 1. 去除通知通道前的~符号 1. 修复 HTML 消息 JS 支持设置反转 bug 说一下，安卓外行，java 零基础开始写的代码，代码比较烂，最近几个功能是用 AI 写的。
V2EX ·
[问与答] 新车故障如何维权？
背景 9 月份买了辆吉利远程超级 VAN ，准备当宿营车，结果开起来刹车 ehb 模块蜂鸣声，耳朵很难受，维修就只能换件，维修站换件换了两回都出现刹车失灵，想要找 4s 和厂家退车，但是给出的方案只有修车，修好了也不敢再开了，厂家的技术太烂了。 现在已经有的维权 12315: 12315 说商用车没有三包服务，只能尽力施压。 起诉：还在走流程，进度很慢，但是估计最终还是修车。 400 电话：400 只是推时间，啥用也没有 除了这些还能做些什么呢？感觉现在所有涉及到的人都不想管了，也没人说要给修车了。
V2EX ·
[分享创造] 做了个用照片生成说唱视频的小工具 RapVideoGo，想听听大家的意见
大家好，我做了一个叫 RapVideoGo 的 AI 说唱视频生成工具，想在这里分享一下，也听听大家的建议。 地址： https://rapvideogo.com 它的玩法比较直接：上传人物照片，在提示词里写好场景、歌词和动作，让照片里的人出现在一段说唱视频中。可以自己来一段，也可以让两个人轮流接唱。 比如让爷爷和孙子在录音棚里来段问答式说唱，给朋友做一段生日 Rap ，或者把日常聊天里的梗写成歌词，做成一段短视频。 目前支持： 上传最多 9 张参考图片。 编辑场景、歌词、人物动作和镜头描述。 使用现成示例，再修改成自己的内容。 设置视频比例、时长和清晰度。 我想做这个工具，是希望把“有个好玩的点子”到“做出一段视频”之间的步骤缩短一些。对于不熟悉剪辑的人，也能通过照片和文字尝试创作。 现在网站已经可以使用，提供中英文界面。生成视频采用积分制，需要付费购买积分，大家也可以先看看首页的示例效果，再判断是否符合自己的需求。 技术上用的是 TanStack Start 、React ，部署在 Cloudflare Workers ，数据库用 D1 。如果大家对这套组合感兴趣，也可以交流。 目
Product Hunt ·
Remote Gaze
Your Mac on your iPad and iPhone with no in-between server
V2EX ·
[分享创造] 只有 210 KB！让 Win10 资源管理器拥有多标签页
我已经用了好几年 Win10 ，目前也没有升级到 Win11 的打算。但一直有个痛点，就是 Win10 的资源管理器不支持多标签页。打开的文件夹窗口一多，来回切换就得找半天。 于是，我用 AI 开发了一个小工具，给原生资源管理器加上了标签栏，让多个文件夹可以放在同一个窗口里。工作资料、下载目录、项目文件，都能像浏览器标签一样，点一下就能切换。你可以新建标签、关闭标签，也可以拖动标签调整顺序，按照自己的习惯排列常用文件夹。 如果同时打开多个资源管理器窗口，每个窗口也能独立管理自己的标签。而且，它不需要替换原生资源管理器，只是在现有基础上增加标签功能，原有的操作习惯基本不变。 工具使用 Zig 构建，下载包只有约 210 KB 。解压后，确保程序和 DLL 在同一个目录下，运行程序即可。 以后不想用了，退出程序，删除软件所在的文件夹就行。 如果你也想让 Win10 的文件管理更清爽，可以试试 CabFuse 。 使用过程中遇到任何 Bug ，或者有什么功能建议，也欢迎大家提出。
arXiv ·
More Rules, Longer Lists? Benchmarking Self-Evolving Agents on a Hidden-Rule Curriculum
arXiv:2610.40758v1 Announce Type: new Abstract: Self-evolving agents rewrite a persistent state from feedback, but it is largely untested whether this lets them learn a rule nobody told them. An agent can also pass by writing down each correction as an exception, and its list then grows with every new rule. We introduce CLR-Bench, a Curriculum of Latent Rules benchmark. Each chain adds one hidden rule per level on top of the earlier ones. A rule acts on an opaque label that stands for a named gr
arXiv ·
Beyond Plausible Prediction: Safety Sufficiency in Web World Models
arXiv:2610.40798v1 Announce Type: new Abstract: Web world models increasingly predict the consequences of candidate actions before agents act, yet a plausible predicted future does not necessarily support the correct safety decision. In a matched human evaluation of 3,000 predictions from ten frozen open models, 86.1% are judged semantically plausible. However, several models exhibit highly asymmetric authorisation behaviour, combining low false-safe execution with poor recall of clarifiable ASK
arXiv ·
Circumventing the Computational Hardness of Computational Arbitrage
arXiv:2610.40831v1 Announce Type: new Abstract: AI model marketplaces host providers with heterogeneous costs and capabilities. These differences create an opportunity for computational arbitrage (Olmedo et al., 2026), in which an intermediary purchases inference from a portfolio of models and resells it to users at a profit. Beyond creating profit opportunities for the arbitrageur, computational arbitrage offers financial value to both consumers and providers and could therefore significantly i
arXiv ·
Incongruence-Aware Evaluation of Multimodal Speech Emotion Recognition in Audio Large Language Models
arXiv:2610.40883v1 Announce Type: new Abstract: Voice-interactive large language models process both what a speaker says and how they say it, yet the two channels do not always agree. We ask whether that disagreement degrades speech emotion recognition. Mapping text and audio into a shared valence-arousal-dominance space, we convert the distance between them into a continuous, threshold-free incongruence score with a two-component Gaussian mixture, and validate the score against human judgement.
arXiv ·
ChatGPT as a Surveillance Assistant? An Exploratory Case Study of AI-Assisted Surveillance Risks
arXiv:2610.40890v1 Announce Type: new Abstract: Large language models (LLMs) can generate detailed guidance, functional code, and persuasive text at scale. As one of the most widely used consumer language model products as of 2026, ChatGPT gives broad public access to these capabilities, motivating concerns about its potential use in surveillance-related tasks. We present an exploratory case study of what potentially harmful surveillance assistance looks like in ChatGPT's public web interface. W
arXiv ·
Does Recursive Knowledge Construction Help Agents Verify Consensus Claims in Science?
arXiv:2610.40906v1 Announce Type: new Abstract: Recent scientific agents build knowledge recursively by retrieving evidence, constructing an intermediate state, and judging whether that state contains sufficient information. These agents are evaluated end-to-end, usually without ground truth, so the effect of each design choice is unknown. Here, we examined two design axes: (1) what the recursion builds (the state of evidence) and (2) what controls it (the judge that decides when to stop). To me
arXiv ·
Towards Portable Specification of the Agent Harness
arXiv:2610.40917v1 Announce Type: new Abstract: Large language models act only through the infrastructure that surrounds them. That infrastructure, the harness, determines what a model can accomplish and how it fails, yet it has no settled definition. To address this gap, we decompose the harness into eight functional components that can be specified and compared across systems, and we propose a portable specification framework that records which model ran in which harness. The record is needed
arXiv ·
Agentic World Modeling for Agent Test-time Scaling without Environment Replica
arXiv:2610.40927v1 Announce Type: new Abstract: Test-time scaling (TTS) improves large language model agents by sampling and selecting among multiple candidate trajectories, but common realizations assume that these trajectories can be executed in parallel. This assumption breaks in realistic deployments, where enterprise workspaces and cloud services are shared and persistent, costly or infeasible to replicate, and actions may introduce irreversible side effects or cross-trajectory interference
arXiv ·
DiRAFT: Privacy-Aware Financial Transcript Synthesis from Aggregate Discourse Priors
arXiv:2610.40938v1 Announce Type: new Abstract: Using customer conversations to train and evaluate financial language models raises confidentiality concerns because non-public financial facts can remain after direct identifiers are masked. Synthetic transcripts offer an alternative data source, but their discourse fidelity and residual confidentiality risks require assessment. We introduce DiRAFT (Discourse Realization from Aggregate First-order Transitions), which extracts financial concept gra
arXiv ·
The First Few Tokens Matter: Understanding Chain-of-Thought Controllability
arXiv:2610.40939v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a promising way to oversee increasingly capable agents. It succeeds in part because current models appear limited in their ability to control what they verbalize in their reasoning traces. We investigate the source of this limitation and link it to an entropy collapse in the first few tokens following the opening thinking tag. We exploit this mechanism to recover CoT controllability through two inference-time in
arXiv ·
Benchmarking Forecasting Agents on Yesterday's Tomorrows
arXiv:2610.40940v1 Announce Type: new Abstract: Digital agents can now gather and organize information, reason over it, and use tools to complete complex tasks. Forecasting puts all of these abilities to the test: it requires integrating incomplete evidence, inferring causal drivers, and committing to calibrated probabilistic judgments. Existing benchmarks are poorly suited to measuring and improving this ability. Live benchmarks provide feedback only after events resolve, so every evaluation cy
arXiv ·
Opening the Box Changes the Cat: LLMs Detect Chain-of-Thought Monitoring - What now?
arXiv:2610.40946v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning allows large language models (LLMs) to generate intermediate reasoning traces in natural language while solving complex tasks. These traces can support safety monitoring by exposing potentially harmful plans before actions are executed. In this work, we investigate the conditions under which LLMs infer the presence of CoT monitoring from direct and indirect environmental cues and examine how this inference affects t
arXiv ·
P-ContextBench: From Personal Fact Recall to Personalized Agent Behavior
arXiv:2610.40959v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for personal tasks, yet evaluating their safe use of personal context remains difficult because such contexts are private, fragmented, dynamic, and socially situated. We introduce P-ContextBench, a realistic, verifiable, and scalable benchmark for evaluating personal-context understanding. P-ContextBench constructs population-structured, life-story-grounded digital humans from demographic seeds ref
arXiv ·
Nedimo: An Evidence-Grounded Multilingual Pipeline for AI-Generated Sleep Stories
arXiv:2610.40970v1 Announce Type: new Abstract: Generative audio systems can produce large libraries of personalized bedtime content, but ordinary media objectives (retention, completion, and novelty) are poorly aligned with the goal of supporting a transition toward sleep. Direct evidence for narrated bedtime audio in adults is mixed, and a recent active-controlled trial did not support such audio as a standalone treatment for sleep disturbance. We translate this uncertain evidence into conserv
arXiv ·
The psychophysics of affect in large language models
arXiv:2610.40985v1 Announce Type: new Abstract: Good decisions often require considering how outcomes will feel. Yet large language models (LLMs) increasingly enter decision workflows, despite little being known about how they map outcomes to feelings or use those mappings in choice. We tested how LLMs anticipated gambling or stock-trading outcomes would feel and whether they incorporated these ratings into choice. When outcomes were rated in isolation, ratings exhibited diminishing sensitivity
arXiv ·
Can Personal Requirements Become Supervision for Planning?
arXiv:2610.40992v1 Announce Type: new Abstract: Managing multiple chronic conditions through diet requires satisfying disease- and medication-specific requirements jointly across meals and days while preserving established eating habits. These requirements are public in clinical guidelines and drug labels, yet turning them into a feasible plan for one person remains difficult for language-model agents. In our dietitian-approved weekly planning evaluation built from NHANES, thirteen models, from
arXiv ·
Violet Teaming in Practice: Lessons from a Pilot Expert Workshop on Governing Complex AI Systems
arXiv:2610.41018v1 Announce Type: new Abstract: Violet teaming has been proposed as a method to integrate technical, policy, and societal considerations into the evaluation of emerging technologies. Here, we document a pilot expert workshop used to explore violet teaming as a governance methodology for biodefense-relevant AI capabilities and describe observations from the exercise. The workshop used a hypothetical technology scenario across three sequential stages - capability development, misus
arXiv ·
ReplayRCA: Measuring Post-hoc Diagnosis and Live Oversight of Agent Failures by Counterfactual Replay
arXiv:2610.41025v1 Announce Type: new Abstract: When a language-model agent fails a multi-step task, can another model find the decisive mistake and fix it with a single action? We introduce ReplayRCA, a benchmark that scores such diagnoses by intervention rather than by agreement with annotations: the environment is restored to the state preceding the chosen step, the proposed action replaces the agent's own, and the same agent continues. The score, causal repair, is the continuation success ra
arXiv ·
Capability-Grounded Agentic Planning
arXiv:2610.41040v1 Announce Type: new Abstract: LLM agents serve natural-language requests through tools, data sources, and other agents, and even simple requests often need several interdependent calls. Typically, the LLM picks these calls by trial and error, with no guarantee of an executable plan. We present Capability-Grounded Agentic Planning (CGAP), which instead reasons about what a request requires and what tools provide. CGAP compiles the tool catalog deterministically into a PDDL model
arXiv ·
Consensus Within, Competition Between: Decentralized LLM Teams for Perimeter Defense
arXiv:2610.41044v1 Announce Type: new Abstract: The use of large language models (LLMs) to coordinate multi-agent teams raises a fundamental architectural question: whether task allocation should be centralized in a single LLM controller or distributed across autonomous LLM agents, particularly when communication is unreliable. In a deliberately simplified perimeter-defense simulation against LLM-controlled intruders, we compared a central commander, an auction-based consensus team and independe
arXiv ·
LanG 2.0: Energy-Efficient Agentic AI for Semi-Automated Security Operations at Scale
arXiv:2610.41072v1 Announce Type: new Abstract: Security operations centers (SOCs) need to turn terabytes of alerts, logs and flows into trusted, affordable decisions to unveil hidden threats. We present LanG 2.0, an agentic system that detects threats, correlates evidence and writes rules, investigating whether small local models can replace frontier models affordably and safely when structural safeguards, not model accuracy, provide the guarantees. It incorporates entity-anchored correlation,
arXiv ·
BABA-Bench: Evaluating Long-Horizon Patient-Advocacy Agents
arXiv:2610.41076v1 Announce Type: new Abstract: An LLM agent that helps a patient get care must do two things. It must reach the correct outcome, and it must follow the necessary procedure. For example, the agent must get the patient's permission before it shares the patient's records. We introduce BABA-Bench, a benchmark of 47 interactive worlds. We built each world from a de-identified, real patient advocacy case that expert advocates reviewed. In each world, the agent must find hidden informa