首页 / 浏览
浏览全部已收录的公开内容，包括尚未归入主分类的条目。
7189 条公开内容
V2EX ·
Zooproxy ——全球领先的高性能代理服务平台 聚合超 1 亿真实 IP 资源，覆盖 195 + 国家和地区。每日动态更新 IP 池，确保节点纯净度与匿名性，为数据采集、跨境业务提供稳定可靠的网络桥梁。 三大核心 IP 服务 动态住宅 IP：$0.38 / GB ，不限 IP 数量 静态 ISP 独享 IP：$0.9 / IP ，不限流量 端口无限套餐：$0.38 / 端口 / 天，无限流量，不限 IP ✅ 免费测试：提供全功能试用资源，零门槛体验核心服务 ✅ 超值定价：行业标杆级性价比，新老用户专享限时折扣，高流量套餐享额外优惠 🎁200MB 动态住宅 IP 兑换码 ZRC9-8P6R-X4DE VF3T-EGAJ-M3XB LEX6-BHZ7-L7R5 TTPR-Z3ZN-UWXZ 7PB2-RCY7-LGVQ 复制前往控制台 CDK 栏兑换，领不到的留言 👉 https://zooproxy.com/?kwd=zsy-v2ex
GitHub Issues ·
huggingface/transformers 仓库的第 50381 号公开问题报告，完整描述与讨论可在原始 Issue 查看。
V2EX ·
ChatGPT Plus 120 ChatGPT 5x 640 ChatGPT 20x 1100 +V = QVpJWklPT08=
V2EX ·
Lovableapp 是我花了一年做的导航站。 起因是，由于 vibe coding 的火热，vercel.app 或者 lovable.app 下有好多 vibe coding 的作品，能解决需求，有热度，但是找起来困难。 我就做了这个聚合站，最高的时候每个月 80w pv 。然后这时候，乐极生悲，谷歌 8 月下旬的 spam 更新，认定我的网站是垃圾内容，流量已下降几乎全没了，只剩下 30K 。 我试了好多，看看能不能挽回，增加新的工具等，都不行。 我刚刚加了 bid 模式 ，就是类似 outbid.lol 那种。 最后后的尝试，增加一些用户的活跃，看能不能好起来。 网站目前还有 30K 访问，DR>60 。
GitHub Issues ·
langchain-ai/langgraph 仓库的第 9602 号公开问题报告，完整描述与讨论可在原始 Issue 查看。
Hacker News ·
由 farmerbb 提交。Hacker News 最近记录 79 票、32 条评论，观测日期为 2026年11月5日（UTC）。
Hacker News ·
由 gmays 提交。Hacker News 最近记录 48 票、16 条评论，观测日期为 2026年11月5日（UTC）。
Hacker News ·
由 pr337h4m 提交。Hacker News 最近记录 2 票、0 条评论，观测日期为 2026年11月5日（UTC）。
GitHub Issues ·
huggingface/transformers 仓库的第 50378 号公开问题报告，完整描述与讨论可在原始 Issue 查看。
MIT News ·
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
arXiv ·
arXiv:2611.05381v1 Announce Type: new Abstract: Legislative hearings are distinct communication events with their own human recognizable structure and logic, yet they have been analyzed as distinct phenomena beyond their transcripts. Understanding legislative hearings at a deeper level is a major step in analyzing the work and power relationships within democratic bodies. In this work, we present a novel end-to-end pipeline for parsing rhetorical structures from legislative committee hearings wi
arXiv ·
arXiv:2611.05382v1 Announce Type: new Abstract: Misinformation spreads faster than professional fact-checkers can manually verify it and write articles explaining their verdicts. This creates a need to automate the production of fact-checking articles. This paper details work done by the DS@GT ARC team for CLEF 2026 CheckThat! Lab Task 3 Fact-Checking Article Generation. We seek to generate a full fact-checking article from a claim, its original verdict, and a set of scraped source documents. Ou
arXiv ·
arXiv:2611.05383v1 Announce Type: new Abstract: Cognitive Behavioral Therapy (CBT) demands more than empathic validation, requiring instead a structured formulation of the interplay among situations, automatic thoughts, emotions, and behaviors. Therapists formalize this interplay through a Cognitive Conceptualization Diagram, where maintaining longitudinal coherence necessitates continuous evidential adjudication to separate tentative hypotheses from established findings. Current dialogue system
arXiv ·
arXiv:2611.05384v1 Announce Type: new Abstract: Generalisability is a central question for deep neural networks, including the cross-language generalisation of speaker embeddings. This study examined whether deep neural speaker embeddings generalise across languages and how such generalisation relates to human perception and acoustic structure. Using Cantonese--English bilingual speech presented in within- and cross-language conditions, we derived similarity from 18 state-of-the-art models and c
arXiv ·
arXiv:2611.05385v1 Announce Type: new Abstract: Automated detection of machine-generated text is applied where the verdict carries a cost for the author, yet origin is a property of the production process, not an observable property of the text. The conditions under which a text is produced push it in the same direction in which a generative model is presumed to push it. This study measures the contribution of these conditions while origin is held fixed. The Russian-language corpus of 1882 docum
arXiv ·
arXiv:2611.05386v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly deployed on consumer-grade hardware, yet their ability to preserve information across progressively longer contexts remains insufficiently understood. Existing benchmarks primarily evaluate retrieval-oriented tasks, providing limited insight into whether successful retrieval reflects comprehensive long-context memory preservation. To address this limitation, this paper introduces Simple Retrieval-Recons
arXiv ·
arXiv:2611.05389v1 Announce Type: new Abstract: Generative AI is currently fragmented, with separate subscriptions required for text generation, image synthesis, and code assistance, leading to high costs and workflow disruptions. AI Express is a SaaS platform that consolidates these capabilities into a unified pay-as-you-go service. The platform provides a range of AI tools, including a Core Code Reviewer that evaluates GitHub repositories, a Text-to-Website generator that converts text into fu
arXiv ·
arXiv:2611.05517v1 Announce Type: new Abstract: For humans, referential disambiguation is a collaborative process involving clarification questions asked when they will meaningfully change the outcome. However, large language models (LLMs) have been shown to be biased toward premature answering, preferring definite single-turn responses over more clarification exchanges. In this paper, we study the problem of "strategic clarification" -- asking clarification questions in a reference game when th
arXiv ·
arXiv:2611.05539v1 Announce Type: new Abstract: LLMs can repair noisy OCR transcripts of historical documents, but they also change text that was already correct. We propose EditVeto, a veto gate placed after a full-context LLM rewrite. The rewrite is character-aligned to the OCR input, its differences are split into blocks, and a 0.6B-parameter gate keeps or reverts each block. An oracle analysis motivates the design: given the true error locations, per-span generation is worse than no correcti
arXiv ·
arXiv:2611.05596v1 Announce Type: new Abstract: Different retrievers exist for retrieval-augmented generation, yet none performs best for all queries. Therefore, the key question is: given a query and retrieved passages from different retrievers, which one is most suited for that query? Existing routers usually make predictions from query-specific features and a fixed pool of retrievers; such routers have no output for an unseen retriever and are incapable by design on a new corpus. To fill thes
arXiv ·
arXiv:2611.05637v1 Announce Type: new Abstract: Speech transcription errors can be difficult to recognize when competing word hypotheses are unfamiliar or visually similar. We describe an interface that combines alternative transcripts, replay and contextually checked pictograms for selected uncertain concepts. The system will initially support French and Spanish functional utterances. A counterbalanced study will assess whether pictorial feedback helps listeners recover the speaker's intended a
arXiv ·
arXiv:2611.05720v1 Announce Type: new Abstract: Search can help LLM agents correct mistakes, but their starting conditions may shape which evidence they find and use. Drawing on cognitive-bias paradigms, we develop a controlled search harness to examine result order, records of prior work, and initial hypotheses while holding each question and document collection fixed. Matched controls distinguish order effects from differences in document composition and the effects of failed work from context
arXiv ·
arXiv:2611.05731v1 Announce Type: new Abstract: Most memory systems for LLM agents are retrospective, preserving and retrieving past information to inform current decisions. Proactive agents, however, also require prospective memory: retaining future-directed intentions and realizing them when appropriate opportunities arise, even without explicit requests. Crucially, remembering the right intention does not guarantee acting at the right time: a related observation may call for waiting, while a
arXiv ·
arXiv:2611.05771v1 Announce Type: new Abstract: This study investigates the authorship impersonation capacity of six widely used large language models (LLMs): Gemini 3 Pro, Claude Opus, GPT 5.2, GPT 5.2 Pro, DeepSeek 3.2, and Grok 4.1 Fast. The evaluation was conducted using short product review texts selected to ensure high within author consistency in individual writing style. Two types of in context prompt were designed: a simple prompt with minimal instruction and a complex prompt incorporat
arXiv ·
arXiv:2611.05803v1 Announce Type: new Abstract: How much phonological knowledge do language models (LMs) possess about Chinese characters, and how is this knowledge represented and used? We introduce PinyinBench, a controlled benchmark covering homophones, phonological features (initials, finals, and tones), and polyphonic characters. We investigate 17 text-only and multimodal LMs through three complementary approaches: behavioral evaluation, layer-wise probing, and activation steering. Behavior
arXiv ·
arXiv:2611.05804v1 Announce Type: new Abstract: Expert deactivation at inference time changes both token routing and expert weights, and its effect on performance depends on how these changes are implemented. We compare removal, freezing, remaining-expert renormalization, and unnormalized re-routing on identical disabled sets across five instruction-tuned checkpoints from four model families, with a base OLMoE checkpoint as an additional comparison. We develop a weight-sum model that relates the
arXiv ·
arXiv:2611.05814v1 Announce Type: new Abstract: Large language models increasingly automate complex tasks, but human decision capacity does not scale with their capabilities. In many applications, humans remain essential for providing private context, authority, or judgments unavailable to the agent. We formulate human decision capacity allocation: given multiple consultation opportunities and a limited budget, at which decisions should an LLM agent consult a human? The central challenge is esti
arXiv ·
arXiv:2611.05827v1 Announce Type: new Abstract: Decision models choose among predefined options in tasks such as routing and action selection. In multi-agent collaboration, a decision determines how members divide work, exchange resources, and act in parallel. We introduce TandemBench, a benchmark of 1,200 questions in multi-agent cooking scenarios where coordination choices affect goal completion. Models receive complete global information and candidate team plans, with reference answers establ
arXiv ·
arXiv:2611.05862v1 Announce Type: new Abstract: Large language model (LLM) shopping assistants now recommend products in multi-turn conversations. However, reliable evaluation faces Data scarcity and Difficulty Standardizing Criteria. To address these obstacles, we introduce CARTBench for CART (Complex and Ambiguous Requests with Timelines). For Data scarcity, we curate cases from real commercial shopping needs across 17 retail domains, pairing timestamped dialogue with 1,603 product records. Fo
arXiv ·
arXiv:2611.05877v1 Announce Type: new Abstract: Confidence estimates guide human review in LLM-built knowledge graphs, but better error prediction can yield little improvement in an audit. We study this gap through repairability, selection headroom and label validity. Modelling non-adaptive auditing as budgeted covering, we characterise the minimal decision-relevant error statistics and bound the regret of scalar-preserving first-order planning by a structural mass $\Delta^{\mathrm{cl}}$. We sim