最近刷社区，感觉身边动手做东西的人明显变多了：有人下班写个小工具，有人折腾个 App ，小程序，有人把一堆 prompt 攒成智能体。但东西做出来之后，大多就躺在相册、GitHub 或者朋友圈里，过两天连自己都想不起来放在哪。所以做了个小网站：造梦橱窗大家的作品集 - 收录大家的网站、小程序、App 与智能体作品 https://idea.v5pc.com/ 想干的事很简单----给作品一个统一的陈列位：网站、小程序、App 、智能体都能提交，一个表单填完封面 + 最多 9 张宣传图按形态、分类浏览，点进去能看到完整介绍、宣传图和跳转入口有点赞、收藏、评论和榜单做了 SSR 和 sitemap ，作品能被搜索引擎正常收录。现在上面作品还很少，基本都是我自己和朋友的数据。手上正好有作品的，欢迎直接扔上去试试；遇到 bug 或者体验不好的可以尽管提意见。

arXiv · 2026年10月31日

LRMoE: Demand-Side Load Re-Calibration for Efficient MoE Training on Memory-Constrained Nodes

arXiv:2610.37720v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models offer scalable capacity for Web applications, but uneven routing creates computation and communication bottlenecks. Existing placement and replication methods redistribute this workload, yet additional expert copies compete with token-related buffers for limited GPU memory. We present LRMoE, a memory-aware MoE training system that jointly recalibrates backward demand and balances the retained workload. During

arXiv · 2026年10月31日

Amortized Localized Posterior Inference for Model Calibration with Application to an Ocean Turbulence Closure

arXiv:2610.37731v1 Announce Type: new Abstract: Model discrepancy may prevent a single calibration parameter from reproducing a physical response across the context domain. In addition, uncertainties in observations and contexts affect calibration and must be accounted for and quantified. We construct a family of localized posteriors by weighting observation likelihoods according to their proximity to a target context. Gaussian localization weights incorporate context uncertainty through covaria

arXiv · 2026年10月31日

ThinkJev: One Thought, Every Budget for Calibrated Typed Decisions

arXiv:2610.37732v1 Announce Type: new Abstract: Many uses of language models end in a typed decision: a choice among a closed set of options, whose probabilities code can act on. Reading those probabilities from one forward pass is fast but often wrong on decisions that take several steps, such as arithmetic, counting, and date comparison. Letting the model think first fixes most of these errors, but costs seconds per decision instead of milliseconds. To pay for thinking only where it helps, Thi

arXiv · 2026年10月31日

OpenFrontiers: A Collection of Open Research Problems and a Benchmark for Scientific Rediscovery

arXiv:2610.37739v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in research-level problem solving, yet a comprehensive collection of such problems is still missing, and evaluating them on research-level problems remains challenging. We introduce OpenFrontiers, a cross-disciplinary resource that combines a structured corpus of open research problems with a retrospective benchmark of recently resolved problems in mathematics. The corpus records problem st

arXiv · 2026年10月31日

WebAnything: From the Wild Web to Scalable Agent Environments

arXiv:2610.37786v1 Announce Type: new Abstract: Web agents require diverse, executable, and resettable website environments for reproducible training and evaluation. Human-assisted mirrors demand per-site engineering, while generative construction can diverge from specified real websites. Capture-and-replay preserves observations, with interaction coverage constrained by captured content and replay capabilities. We present WebAnything, a fully automated framework that reconstructs source-grounde

arXiv · 2026年10月31日

What Does an Ontology Catch? Curated, LLM-Generated and Data-Derived Ontologies as Checkers of LLM Agents over Knowledge Graphs

arXiv:2610.37809v1 Announce Type: new Abstract: LLM agents that answer questions over Web knowledge graphs are increasingly wrapped in checks derived from the graph's ontology, on the assumption that a formal schema makes a reliable judge. Yet a checker can be wrong as well: it can miss errors, and it can flag correct answers and push the agent into "fixing" them. Whether a curated ontology is worth its cost as such a judge, and how it compares with an ontology an LLM writes or a schema mined fr

arXiv · 2026年10月31日

Measuring Where Intent Gets Lost in Agentic Smart Contract Synthesis

arXiv:2610.37830v1 Announce Type: new Abstract: AI agents can now write software from a plain-English description, but code that runs is not the same as code that does what was asked. This gap matters most for smart contracts, the programs behind Web3 applications that move money on a blockchain and cannot be fixed once deployed. We ask how to check whether an AI-written contract behaves as it should, and where the requested behavior gets lost. We design an evaluation built on execution rather t

arXiv · 2026年10月31日

Detecting Covert Collusion between AI Agents through Partner-Outsider Positive Listening

arXiv:2610.37849v1 Announce Type: new Abstract: AI agents that share a secret convention can coordinate through messages that look innocent, so a monitor that reads only the messages can miss them. We propose a behavioural test: a partner that uses a code reacts to the exact wording of a message, whereas an uninvolved agent placed in its seat does not. Partner--Outsider Positive Listening (PoPLi) is the difference between the two agents in how much their action changes when the message is reword

arXiv · 2026年10月31日

Evaluating and improving AI agents for sequencing protocol understanding

arXiv:2610.37851v1 Announce Type: new Abstract: Existing scientific-agent benchmarks rarely evaluate whether agents can reconstruct the exact molecular structures and generation workflows specified by sequencing protocols, a capability critical for analyzing customized sequencing assays. We introduce LibStructBench, a document-grounded benchmark for reconstructing sequencing-library structures and molecular workflows. Our experiments show that frontier agents often produce approximately correct

arXiv · 2026年10月31日

Seq@K: Evaluating Language Models Under Sequential Feedback

arXiv:2610.37859v1 Announce Type: new Abstract: Language-model agents differ fundamentally from prior intelligent systems in their ability to learn from natural language in context. This capability enables agents to iteratively refine their answers in response to feedback, a prevalent mode of operation in modern agentic workflows. However, the community primarily ranks models by metrics such as Pass@1 or Pass@K, which cannot capture how they utilize feedback on prior attempts. We introduce Seq@K

arXiv · 2026年10月31日

What Constitutes Food Similarity? Visual Representations and Retrieval in Indian Cuisine

arXiv:2610.37867v1 Announce Type: new Abstract: Food similarity underlies retrieval, recommendation, substitution, and other food computing tasks, yet food representations are typically evaluated by identity preservation. We ask what happens to food similarity beyond identity: how different visual representations organize foods into relational neighborhoods, what culinary relationships these neighborhoods reveal or obscure, and how consistently these structures emerge across representations. Ind

arXiv · 2026年10月31日

Verification Learning Theory: A Process Model of Checking and Retention in AI-Assisted Judgment

arXiv:2610.37875v1 Announce Type: new Abstract: Accurate AI assistance can improve immediate judgments while reducing opportunities to practise the operations needed for independent performance. Verification Learning Theory (VLT) formalises this possibility for claim-evaluation practice followed by correct, independent, rule-specific feedback. The model links perceived assistant reliability to evidence checking and links the history of those checks to retained rule use. Its strict form assumes t

arXiv · 2026年10月31日

The Anatomy of Socio-Demographics in Pluralistic Offensiveness Modeling: When and Where It Helps, and the Role of Reasoning

arXiv:2610.37896v1 Announce Type: new Abstract: Subjective judgments such as offensiveness often vary substantially across individuals, motivating pluralistic modeling approaches that incorporate annotators' socio-demographic information to capture this variation. However, prior findings on the benefits of socio-demographic information for pluralistic modeling are mixed, calling for a systematic analysis of both its effectiveness and the mechanisms through which it improves prediction. In this w

arXiv · 2026年10月31日

FilingLoop: Evaluating and Evolving LLM Agents for Corporate Disclosure Review and Revision

arXiv:2610.37905v1 Announce Type: new Abstract: Public disclosure repositories such as SEC EDGAR support informed investment through web access to corporate filings. Supporting their review with language model agents requires discovering unstated deficiencies across related documents, acquiring missing company facts, and verifying that requested revisions satisfy evidence-backed requirements. Existing financial question-answering evaluations provide limited evidence of these interactive capabili

arXiv · 2026年10月31日

Beyond Low-Pass Dynamics: Frequency-Selective Spiking Reservoirs with Resonant Neurons

arXiv:2610.37906v1 Announce Type: new Abstract: Spiking reservoir computing combines the training efficiency of fixed recurrent reservoirs with the energy efficiency of event-driven inference, where computation is triggered by sparse spikes rather than executed densely at every timestep. However, existing spiking reservoirs almost exclusively rely on Leaky Integrate-and-Fire (LIF) neurons, whose low-pass dynamics limit their ability to distinguish information encoded at different temporal freque

arXiv · 2026年10月31日

SciGame: Let Agents Play the Scientific Discovery Games in the Real and Counterfactual World

arXiv:2610.37919v1 Announce Type: new Abstract: Large language model agents are increasingly used for scientific discovery. However, existing benchmarks mix law inference with programming, score data fitting instead of prediction on unseen inputs, and do not separate empirical evidence from pretrained priors. We introduce SciGame, a controlled benchmark that converts scientific literature into structured scientific cards. Each card yields a pair of Original and dimensionally consistent Counterfa

arXiv · 2026年10月31日

VerifiedLab: Harnessing Knowns for Unknowns

arXiv:2610.37923v1 Announce Type: new Abstract: Across human history, knowledge has undergone several transitions, from orality to writing and then to digital archives. At each transition, humans established new conventions to preserve knowledge for the new era. Nowadays, as research agents are increasingly deployed for laboratory experiments, knowledge is crossing another boundary: from records written for humans to those for machines. In this work, we introduce VerifiedLab, a multi-agent harne

arXiv · 2026年10月31日

GARDEN: Generalist Autonomous Reward Design Through Evidence Navigation for Cooperative Multi-Agent Reinforcement Learning

arXiv:2610.37926v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) enables teams of agents, such as robots and autonomous vehicles, to learn coordinated behavior, but what such teams learn is largely determined by their reward function. Because task success alone provides too little guidance for learning, intermediate rewards are usually designed by hand, and this effort must often be repeated whenever the environment or the team changes. Large language models

arXiv · 2026年10月31日

Understanding Large Language Models as Numerical Optimizers

arXiv:2610.37932v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as potential tools for numerical optimization. Despite promising results, their intrinsic optimization capabilities and limitations remain poorly understood, particularly in high dimensions. This paper investigates the ability of LLMs to exploit mathematical problem structures and generate feasible, near-optimal solutions. We develop an evaluation framework that distinguishes solution quality from execution

arXiv · 2026年10月31日

SkillMem: Compact External Skill Memory for Latent Multi-Agent Reasoning

arXiv:2610.37935v1 Announce Type: new Abstract: Latent multi-agent systems reduce inference cost by keeping intermediate reasoning and inter-agent communication in continuous representations.\ Reusable skills, however, are commonly supplied as text or stored as layer-wise Key-Value states or skill-specific model parameters, introducing repeated processing or substantial per-skill storage.\ We introduce \textbf{SkillMem}, which represents each reusable skill as a small external latent memory and

arXiv · 2026年10月31日

GAUSS: A Workload-Aware Analytical and Monte-Carlo Simulator for LLM Inference Serving

arXiv:2610.37989v1 Announce Type: new Abstract: Large language model (LLM) inference has become an operational workload that fleets must provision, monitor, and bill against latency service-level objectives (SLOs). A single throughput number cannot answer the questions operators actually face, because generative serving couples two phases with very different service characteristics, continuous batching reshapes the effective service process, and user-visible tail latency can degrade long before

arXiv · 2026年10月31日

Algorithmic Bias from the Perspectives of Healthcare Professionals

arXiv:2610.38006v1 Announce Type: new Abstract: This paper focuses on algorithmic bias of machine learning and artificial intelligence applications in healthcare information systems. Based on the quantitative data and qualitative comments from a survey and interviews with healthcare professionals, who have different job roles (e.g., clinical vs. administrative), this study provides findings about the relationships between algorithmic bias, perceived fairness, and the intended acceptance and adop

arXiv · 2026年10月31日

Assessing Language Models for Scientific Applications: Inertial Confinement Fusion as a Use Case

arXiv:2610.38024v1 Announce Type: new Abstract: Advancing language models for specialized scientific applications requires evaluations of scientific fluency, physical reasoning, and quantitative analyses. Using inertial confinement fusion (ICF) as a test case, we have developed an expert-curated benchmark of 250 questions spanning qualitative reasoning, numerical calculation, symbolic derivation, and equation formulation. Responses have been evaluated using deterministic numerical and symbolic c

arXiv · 2026年10月31日

PromptSense: Sentiment-Aware Mental Health Chatbot with Dynamic Prompting

arXiv:2610.38044v1 Announce Type: new Abstract: Current Large Language Models (LLMs)-based mental health chatbots often lack sufficient empathy, contextual awareness, and safety mechanisms, potentially leading to ineffective or harmful interactions. This paper proposes a novel framework that integrates sentiment and depression analysis with a rule-based filtering mechanism prior to model inference. By dynamically constructing prompts based on emotional and risk-related insights, the framework ai