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本页 40 条10月12日 周一
Show HN: I made an AI free version of my directory of open-source alternatives
I've been running OpenAltFinder for almost 16 months now, and received quite a bit of positive feedback. The directory has also grown substantially. Over the last few months I've been receiving more and more submissions that were AI-assisted/vibe-coded. Personally I'm a bit skeptical (both quality wise, and longevity wise), but I also want to give everyone a chance to showcase what they've build. I had already added an "AI-assisted" label, but right now the majority of submissions include that label. To counter this, I decided to build an AI-free version of it over at: https://noai.openaltfinder.com , inspired by duckduckgo. I had already received some positive feedback on HN in the comments, so I wanted to share this here as well. Do note that not every tool is correctly flagged at the moment, it's still a manual process. Although I'm planning to add it to my pipeline, and auto detect commits by AI agents. Comments URL: https://news.ycombinator.com/item?id=50053462 Points: 2 # Comments: 0
Show HN：一份开源 AI 排行榜，展示你的机器可以运行的最佳模型
大家好，我做了 whatmodel。这是一个简单的网站，可以在浏览器中访问，也可以通过终端使用：curl whatmodel.sh 使用方法： 1. 输入你的 RAM 和 GPU 2. 查看你可以在本地运行的模型、它们的排名以及运行命令 3. 使用相同的评分将它们与 API 模型进行比较 构建这个项目让我意识到，如今一台 32GB 的 Mac 已经可以完全在本地、免费运行一个得分与 GPT-5 相当的模型（例如 4-bit 的 Qwen 3.8 27B）。 每个模型都会获得一个综合分数，该分数基于 18,731 项已发布的基准测试结果拟合得出，并且每项结果都附有其来源链接。 方法见 https://whatmodel.sh/method，数据、评分代码和基准测试脚本均已开源：https://github.com/jakemanger/whatmodel 如果你愿意贡献，可以使用基准测试脚本收集真实场景下的 tok/s，也可以在 github 上提交任何新模型和基准测试分数，并附上其来源。 评论 URL：https://news.ycombinator.com/item?id=50053608 得分：1 评论数：0
Show HN: Jruva – AI workspace for chat, documents and images
Prefer writing your own short explanation of why you built Jruva and how its multi-provider fallback works. Comments URL: https://news.ycombinator.com/item?id=50052566 Points: 1 # Comments: 0
Show HN：Atticus——在 macOS 上运行于 Claude -p 的按住说话语音助手
大家好！我是 Saarth。我上九年级，制作了 Atticus。Atticus 是一款速度极快且价格低廉的 Mac 助手，可以帮你完成各种任务。你只需按住 Control-Option-Space，说出或输入任务，无论任务复杂还是只是简单的浏览器搜索，Atticus 都会立即处理。有趣的是，打开应用、播放 Spotify 播放列表或进行网页查询等简单任务不会调用 AI，而是在本地处理，通常不到一秒就能完成。例如，让 Atticus 打开应用大约需要 0.08 秒，有时甚至在你说完句子之前就已完成，有点像 Jev。更复杂的任务会通过 claude -p 发送给 Claude，这意味着你不需要 API 密钥，Atticus 会使用你现有的 Claude Pro 或 Max 方案。采用这种方式也能大幅降低成本，平均每项任务只需不到一美分的一小部分。为了实现 Atticus 的速度，它会记住每项已完成任务的快捷操作。第一次正确完成任务后，它会保存执行步骤，下次无需 AI 就能重放这些步骤。例如，创建新会议这一工作流第一次大约需要 30 秒。随后，Atticus 会将确切步骤记录到记忆中，下一次执行只需 5–8 秒。此外，随着你使用 Atticus 的次数增加，以及通过读取电脑中的信息——包括你的姓名、应用和你希望它记住的内容——Atticus 还会逐渐了解你，从而为你提供个性化的回答。为应对让 AI 不受限制地访问设备所带来的安全问题，Atticus 提供了可切换的限制选项：在删除、发送、购买任何内容或写入文件之前，都会先提示用户确认。我目前正在开发的一些功能包括会议副驾驶，它将利用 Atticus 的速度，让 Atticus 能够监听会议，并在问题出现时实时回答，同时结合会议上下文以及它所了解的你的信息进行个性化处理。要运行 Atticus，需要 macOS 26 或更高版本，因为其语音转录功能依赖该版本。语音转录同样由 macOS 在本地完成。Atticus 免费、开源，而且还非常新。我完全清楚它还有许多不完善之处，因此非常希望听到大家的反馈：哪些地方会出问题，以及你们希望 Atticus 实现哪些功能！评论链接：https://news.ycombinator.com/item?id=50049588 得分：11 评论数：0
Ask HN: As a Democrat in USA, how can you support AI datacenters?
Comments URL: https://news.ycombinator.com/item?id=50051894 Points: 3 # Comments: 1
TII Releases Falcon-ASR: 1.6B Arabic Speech Model Hits 20.92% WER
TII has released Falcon-ASR, a 1.6B speech recognition model for Arabic with an Emirati focus. It reports 20.92% average WER on 6 Arabic test sets, but weights and an API are not yet available. The post TII Releases Falcon-ASR: 1.6B Arabic Speech Model Hits 20.92% WER appeared first on MarkTechPost .
Near-Minimax-Optimal Rates for Asynchronous TD Learning with Markovian Data
arXiv:2609.38880v2 Announce Type: replace-cross Abstract: We study the last iterate of standard importance-weighted temporal-difference (TD) learning from a single trajectory of a finite Markov decision process. The trajectory follows a behavior policy $b$ with an irreducible, possibly periodic state chain; a target policy $\pi$ covered by $b$ is evaluated with action importance ratios bounded by $\varrho$. Write $H=(1-\gamma)^{-1}$, with $\mu_{\min}$ the minimum stationary probability and $\mathfrak h_\star$ the largest expected hitting time of the reverse behavior state chain. We prove that the last iterate achieves sup-norm error at most $\varepsilon$ with high probability after $\widetilde O(\varrho H^3/(\mu_{\min}\varepsilon^2)+\mathfrak h_\star)$ transitions for $0<\varepsilon\le1$. The statistical term retains the cubic effective-horizon dependence of synchronous TD, while the initialization cost is additive in $\mathfrak h_\star$. Setting $b=\pi$ and $\varrho=1$ recovers the on-policy rate. For aperiodic behavior chains, $\mathfrak h_\star=\widetilde O(t_{\rm mix}/\mu_{\min})$ recovers a mixing-time bound. The proof combines an anchored local Poisson equation in reverse time to control fluctuations without a mixing-time factor, a compensation argument for successive hitting costs, and an expected cumulative propagation mass bound for decreasing step sizes. Given the policy ratios and their bound, an empirical state-count rule attains the same rate without prior knowledge of model parameters. The framework covers nonnegative affine importance-weighted prediction updates, including successor features, transition-dependent discounting, and coupled finite-mode predictions. A perturbation estimate yields a time-uniform guarantee for online return-variance estimation. A three-state, two-action construction gives matching minimax lower bounds for both terms up to logarithmic factors on the considered parameter classes.
OpenCollab: A Multi-Agent Coding Framework with Programmable Collaboration and Controllable Runtime
arXiv:2609.38345v2 Announce Type: replace-cross Abstract: Multi-agent coding systems are designed to tackle complex software engineering tasks through collaboration. However, existing evaluations typically assume configured organizations are followed faithfully, whereas reality differs. This behavioral gap, combined with differences in underlying system components, prevents clear attribution of observed gains. To this end, we introduce OpenCollab, a multi-agent coding framework that provides a unified infrastructure for programmable collaboration and controllable runtime. Specifically, OpenCollab unifies organization design, enforces experimental control on a shared runtime, and tracks execution through fine-grained event streams. On this basis, we define Adherence to quantify whether the declared organization is actually realized. Our experiments reveal that agents collaborate very differently across configurations: changing any single dimension shifts Adherence, from 47.2% to as high as 97.2%. Furthermore, extensive agentic coding benchmarks show that OpenCollab (Duo) establishes new SOTA performance compared to the mainstream harnesses such as Mini-SWE-agent, OpenHands, Codex CLI, and Claude Code, showing that a well-designed organization can outperform strong existing harnesses, while OpenCollab's single-agent configuration uses the fewest tokens across all evaluated suites. OpenCollab establishes a unified multi-agent infrastructure for easy programmable collaboration and controlled causal evaluation.
Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions
arXiv:2609.38263v2 Announce Type: replace-cross Abstract: Feature selection in neural networks remains a challenging problem, particularly in the presence of noisy or contaminated data. LassoNet is a recent approach that addresses this issue by combining neural networks with hierarchical sparsity constraints, enabling simultaneous prediction and variable selection. However, its standard formulation relies on the mean squared error (MSE) loss, which is known to be highly sensitive to outliers. In this paper, we present Robust LassoNet, an extension of LassoNet that incorporates robust loss functions, such as Huber, Cauchy, Tukey's bisquare, and Nonnegative Garrote, to mitigate the effect of extreme observations. The proposed approach preserves the original optimization framework while improving stability under data contamination. Through experiments on synthetic and real datasets, we show that robust LassoNet significantly improves both predictive performance and feature selection accuracy in the presence of outliers or heavy tailed noise, while maintaining comparable performance in clean settings. These results highlight the importance of robustness in neural network based feature selection and suggest practical guidelines for choosing appropriate loss functions.
ThinkNet: Evolutionary Deep Ensembles for Efficient Motor-Imagery EEG Classification
arXiv:2609.33967v2 Announce Type: replace-cross Abstract: Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-user data and constrained compute. However, held-out-subject performance can be overstated when test-subject information influences preprocessing, model selection, or ensemble selection. We present \textit{ThinkNet}, a validation-controlled framework that combines train-only normalization, validation-guided evolutionary search, and validation-gated inference to identify compact decoders and inference policies for held-out subjects. We evaluate four-class BCI Competition IV-2a (session T) decoding with nine Leave-One-Subject-Out (LOSO) folds, three seeds, seven fixed decoder entries, and a broader search over ten representative decoder families; the held-out subject is never used for normalization, hyperparameter, architecture, or ensemble-policy selection. In the fixed benchmark, the validation-selected compact decoder achieved 44.35$\pm$15.41\% accuracy with 4.9K parameters, 19 KB FP32 weights, and 0.99 ms batch-1 Orin CUDA inference. Across the broader search, compact models ($\leq$25K parameters) achieved higher mean held-out accuracy than mid-size and large alternatives after selected retraining (40.10\% vs. 35.09\% and 34.78\%). Validation-gated ensembling improved over validation-selected single-model inference, reaching 43.98$\pm$16.25\% in the fixed benchmark and 43.31$\pm$15.88\% for the compact six-family ensemble. A non-deployable oracle analysis revealed a 6.1-point family-selection gap and near-zero validation--test correlation, showing that validation reliability remains a key bottleneck under subject shift. Thus, ThinkNet is a validation-controlled framework for compact MI-EEG model and inference-policy selection, rather than a single-architecture benchmark.
信任，但要验证测量工具：在人工编写的安全回归代理测试上审计 AI 生成的 RTL 验证计划
arXiv:2609.19844v2 公告类型：replace-cross 摘要：AI 生成的 RTL 验证计划可能满足服务提供方的模式要求，却在进入可信执行环节的边界处失效。我们提出 SecTB-RTL，这是一个可审计的框架，涵盖 31 项任务和 124 个人工编写的硬件安全回归测试。在逐步提高资源限制的条件下，确定性的非 AI 基线分别杀死了 36、75 和 78 个变异体。首次确认性运行（C1-R2）在模型执行前便告失败，原因是服务提供方拒绝了其响应模式。在未查看结果的情况下仅修复模式后，一次单独冻结配置的后续运行（C1-R3）完成了 1,860 次调用。服务提供方接受了 1,857 个响应，但只有 9 个通过了生产环境语义验证器。生成规则与执行规则并不匹配。因此，我们将此次运行保留为一起测量工具验证事件，不报告任何提示词效应估计。该事件表明，服务提供方接受响应或响应符合模式，并不能证明其执行有效性。编译和覆盖率仅是诊断手段；实际保存的产物必须通过完整的生产执行流程。此后的一次跟进运行因未满足预注册的证据完整性门槛而被排除，仅作为未来工作处理。我们发布了该基准、保留失败信息的契约、事件溯源记录，以及防止将基础设施行为误报为模型行为所需的治理控制措施。
用于循证孤独症谱系障碍筛查的多模态大语言模型
arXiv:2609.16464v2 公告类型：replace-cross 摘要：孤独症谱系障碍（ASD）的临床管理在早期筛查方面面临瓶颈，主要原因是受过培训的专科人员稀缺，且传统评估工具具有主观性。在此，我们提出 ASDchat，这是一种专为循证 ASD 筛查设计的多模态大语言模型，以视频、音频和对话作为输入。ASDchat 采用双分支架构，其中决策分支生成筛查概率，证据分支生成可追溯、带时间戳且与孤独症诊断观察量表（ADOS-2）相对应的行为证据。该模型在来自中国 27 个研究中心、包含 1,035 名参与者的数据集上进行了训练和评估，参与者涵盖典型发育（TD）儿童、ASD 儿童及患有其他发育障碍的儿童。在区分 ASD 与 TD 时，ASDchat 的受试者工作特征曲线下面积（AUC）达到 0.953 $\pm$ 0.021。在未用于训练的 6 个留出研究中心上，平均 AUC 为 0.961。此外，对行为维度进行无监督聚类，将 ASD 病例划分为六种具有不同表型特征的亚型，ASDchat 为每种亚型提出相应的干预建议。ASDchat 为临床实践中开展大规模循证 ASD 筛查提供了一条可行路径。
Predicting Social Media Engagement using Machine Learning
arXiv:2609.16082v2 Announce Type: replace-cross Abstract: Social media platforms are popular channels for disseminating information, owing to their large user bases and ease of access. Companies also use social media as an important aspect of the advertising process. By creating high-quality posts, companies can strengthen their engagement metrics and increase their follower count. While a growing body of research has examined social media engagement, fewer studies have jointly examined the visual, textual, and temporal features of image posts, even though these features collectively determine the performance of content on social media. To understand the important drivers of social media engagement, we collect image posts of furniture firms on Facebook and extract visual, temporal, and textual features from them using text and image analytics methods. We evaluate several machine learning models--including Random Forest, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)--to assess the drivers and the prediction power of social media engagement using the features from our data. Our primary model, LightGBM, explains 81.8% of the variance in the engagement score, 2.3 times that of our baseline model. We observed that brand-relative deviation features, which capture how far a post departs from its brand's norms, are the strongest predictors, accounting for 51.3% of total importance. Our research quantifies the extent to which these features are associated with interactions and provides recommendations that organizations may consider.
通过正交化动量进行非光滑优化
arXiv:2609.13677v2 公告类型：replace-cross 摘要：现代实际应用问题涉及矩阵值参数，但传统优化器将其视为向量，这促使人们开发利用输入—输出几何结构的矩阵感知方法，例如在参数更新前对动量矩阵进行正交化的 Muon。它在实践中的成功引出了一个概念性问题：正交化动量在非光滑优化中能否依然有效？本文使用与反向传播兼容的广义导数框架，针对局部 Lipschitz 函数研究这一问题。我们的第一项贡献是揭示了一个关键局限：对于任意固定的动量因子 $\beta\in[0,1)$，当步长根据完整的梯度历史自适应调整时，Muon 从几乎所有初始化出发都可能无法趋近某个凸 Lipschitz 目标函数的全局最优解。即使迭代序列有界，也可能出现这种失败。我们的例子受到 Parshakova 等人的例子的启发，而后者仅涵盖 $\beta\in[0,\frac{1}{2})$。随后，我们证明，障碍在于固定动量，而非正交化。事实上，当动量因子自适应调整并趋近于 1，同时步长趋于零时，在有界性和正则性条件下，Muon 可恢复在非凸非光滑优化中的渐近收敛性。此外，我们提出了 MAGD，它将正交化动量与梯度相结合，并根据二者的相对进展进行加权。MAGD 在非凸情形下保持渐近收敛性，并在凸情形下达到 $O(\min\{m,n\}\epsilon^{-2})$ 的收敛速率。一个下界证明了其维度依赖关系的最优性。在合成问题、图像分类和 LLM 预训练上的实验表明，MAGD 是一种简单实用的 Muon 替代方案。总体而言，我们的结果刻画了缺乏光滑性时正交化动量何时会失效，以及如何使其变得可靠；我们希望这些分析能在更广泛的情形下发挥作用。
是什么使对抗样本能够跨深度伪造检测器迁移？
arXiv:2609.10002v2 公告类型：替换并交叉收录 摘要：深度伪造检测器仍然容易受到基于迁移的黑盒攻击。在此类攻击中，对抗样本在源替代模型上生成，并迁移至攻击者未知的目标模型。然而，源模型与目标模型之间的兼容性如何影响攻击成功率，目前仍缺乏深入理解。既有研究评估的检测器范围有限，且很少区分架构因素与训练因素的影响。我们对涵盖六种骨干网络、两种预训练方案和五种训练数据配置的60个检测器开展了对抗迁移性的受控评估，使用两种攻击方法：AutoAttack（AA）以及结合变换期望的 Carlini--Wagner 攻击（CW--EOT）。匹配比较表明，当源模型与目标模型具有完全相同的骨干网络、相同的架构家族、预训练方案或训练数据时，迁移性显著提高。这种兼容性结构取决于攻击方法：在 AA 下，骨干网络完全一致的影响最大；而在 CW--EOT 下，共享预训练方案和训练数据的影响最大。对非目标源模型的迁移结果取平均时，AA 下的平均攻击成功率（ASR）为 $7.21\%$，CW--EOT 下为 $19.52\%$。相比之下，在排除骨干网络完全一致和训练数据匹配的情况后，结合两种攻击的多源预言机仍达到 \(64.48\%\) 的平均 ASR，表明对源模型取平均可能会大幅低估目标模型的脆弱性。我们公开了240,000张经对抗扰动的图像、完整的两两迁移结果、检测器配置和评估代码。这些发现确立了源模型与目标模型的兼容性以及源模型选择在可信的基于迁移的黑盒鲁棒性评估中的核心地位。
基于实验数据学习与优化化工传递过程的可微混合建模
arXiv:2609.04011v2 公告类型：replace-cross 摘要：传递模型是化工过程设计与优化的基础，但其本构定律通常依靠人工选定，实验初始条件被假定为精确值，而计算成本又使优化仅限于少数过程变量。本文提出一种可微混合建模框架，将神经网络嵌入 GPU 加速的群体平衡方程求解器，直接从结晶实验数据中发现本构定律并同化初始条件。利用十三组包含多维尺寸与形状测量结果的真实实验，我们表明，将测得的初始条件视为精确值，尽管能提高预测精度，却会得到物理上不一致的速率定律；而对初始条件进行同化，则能恢复物理上一致的非线性定律，并将轨迹误差降至传统建模方法的七分之一。端到端可微性还支持对高维操作曲线进行基于梯度的优化，找到了一条由 300 个变量描述的温度曲线，在质量不变的条件下将晶体长宽比降低了 25%，从而改善产品属性。我们的框架是对机理建模的增强，而非替代，为从数据中学习传递模型并利用这些模型优化过程提供了一条通用路径。