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arXiv:2108.02283v3 Announce Type: replace-cross Abstract: Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient boosted tree, random forest, and neural network yields a value-weighted annualized Sharpe ratio of 1.83 for classification and 1.11 for regression. This outperformance persists in multiclass settings, across subsamples, and after transaction costs. Spanning tests show that classification retains economica

arXiv:2307.13127v3 Announce Type: replace-cross Abstract: Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential privacy (DP) provides mathematically provable bounds to protect such data, previous work has focused almost exclusively on unweighted ERM. We consider weighted ERM (wERM) -- an important generalization where individual contributions to the objective function vary. We propose the first DP algorithm for

arXiv:2406.07884v3 Announce Type: replace-cross Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in quantum state preparation and compression, quantum control, and quantum complexity. We present a deep reinforcement learning (RL) approach using an actor-critic algorithm for constructing short disentangling circuits for

arXiv:2407.06312v2 Announce Type: replace-cross Abstract: Many systems resist analytical modeling, making data-driven inference of dynamics important. Yet data-driven methods can fail to converge or generalize, leaving open a central question: When can system behavior be learned reliably from data, and when is such learning impossible? We answer this question using adversarial dynamical systems to identify the boundary between accessible and inaccessible regimes. In Koopman operator learning, a

arXiv:2410.14843v4 Announce Type: replace-cross Abstract: Vanilla variational inference finds an optimal approximation to the Bayesian posterior distribution, but even the exact Bayesian posterior is often not meaningful under model misspecification. We propose predictive variational inference (PVI): a general inference framework that seeks and samples from an optimal posterior density such that the resulting posterior predictive distribution is as close to the true data generating process as po

arXiv:2504.05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it. Our primary contribution is methodological: a protocol for attributing such gains honestly -- a capacity-matched classical bottleneck of identical paramete

arXiv:2606.11552v2 Announce Type: replace Abstract: Large language models (LLMs) achieve remarkable performance across a wide range of tasks, but their autoregressive decoding process incurs substantial inference costs due to inherently sequential token generation. Speculative decoding addresses this bottleneck by employing a lightweight draft model to propose multiple future tokens that are subsequently verified in parallel by a larger target model. Recent work has demonstrated that diffusion l

arXiv:2606.13189v2 Announce Type: replace Abstract: Prompt-based LLMs are increasingly used for stance detection, but harder examples are not always repaired by clearer instructions, reasoning prompts, retrieval, or debate. We introduce SICI (Stance Inference Complexity Index), a seven-dimensional diagnostic measure of the semantic-pragmatic burden imposed by a target--text pair. Across SemEval-2016 and VAST, SICI predicts LLM accuracy better than surface proxies and shows substantial cross-scor

arXiv:2606.23566v2 Announce Type: replace Abstract: Language-specific tokenizers improve tokenization quality and the downstream performance of models on those languages. However, using such a tokenizer comes at a cost: either a new model must be trained from scratch, or the vocabulary of an existing pretrained model must be adapted. We propose Language-adaptive Maximum a Posteriori (LangMAP) Tokenization, a tokenization scheme that extends the UnigramLM algorithm to the multilingual setting, pr

arXiv:2503.00069v2 Announce Type: replace-cross Abstract: Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared values - a process coined alignment. However, aligning LLMs remains challenging due to the inherent disconnect between the complexity of human values and the narrow nature of the technological approaches designed to address them. Current alignment methods often lead to misspecified objectives, reflecting th

arXiv:2601.18747v2 Announce Type: replace-cross Abstract: Modern AI agents increasingly rely on search infrastructure to execute complex, neuro-symbolic reasoning workflows. These workflows often compile into deeply nested, non-monotonic Boolean queries over text fields. However, standard query evaluation strategies over inverted indices face severe theoretical limits when handling these structures. Stateful iterator models (Document-at-a-Time) are structurally bounded by $\text{NC}^1$ formula e

arXiv:2603.04840v2 Announce Type: replace-cross Abstract: Speech production is a complex process spanning neural planning, motor control, muscle activation, and articulatory kinematics. While the acoustic speech signal is the most accessible product of the speech production act, it does not directly reveal its causal neurophysiological substrates. We present the first simultaneous acquisition of real-time (dynamic) MRI, EEG, and surface EMG, capturing several key aspects of the speech production

arXiv:2605.00348v2 Announce Type: replace-cross Abstract: Recent multi-bit watermarking methods for large language models (LLMs) prioritize capacity over reliability, often conflating decoding with detection. Our analysis reveals that existing ECC-based extractors suffer from catastrophic false positive rates (FPR), and applying rejection thresholds merely collapses detection sensitivity (TPR) to random guessing. To resolve this structural limitation, we propose BREW (Block-wise Reliable Embeddi

arXiv:2606.21366v2 Announce Type: replace-cross Abstract: This work examines sexualised AI-generated English-speaking voices offered by a popular commercial platform. New technologies may enable sexual empowerment and greater diversity in gender expression, yet toxic masculinity, heteronormativity, and the abuse of women and LGBTQ+ people remain pervasive online. Drawing on a Feminist HCI perspective, we examine how commercial voice AI systems reproduce and circulate particular performances of g

arXiv:2606.21690v2 Announce Type: replace-cross Abstract: Phishing is a multi-modal threat. We present a hybrid pipeline that scores each modality with its own engine and fuses the results. Three engines are built, deployed, and independently benchmarked: a four-stage URL stack (Domain Guard, lexical model, threat intelligence, and an asymmetric L2 fusion sidecar); a generalization-hardened DistilBERT NLP classifier whose held-out real-phishing recall rises from 0.8% to 87.3%; and a threat-intel

Article URL: https://joinhandshake.com/ai/opportunities/generalist-bachelor-s2/ Comments URL: https://news.ycombinator.com/item?id=48654916 Points: 1 # Comments: 1

<p>Retrieval-augmented generation looks trivial in a tutorial: embed some documents, drop them in a vector database, stuff the top matches into a prompt, done. Then you point it at real company data and real users, and you discover that the demo was the easy 10%.</p> <p>We build RAG systems over private knowledge for companies, and almost every painful bug traces back to the same handful of failure modes. Here they are, and what actually fixes them.</p> <h2> 1. Retrieval returns the wrong chunks

<p>I spent years working on data infra. I got tired of the manual grunt work, so I built <a href="https://app.creativault.ai/growth/home" rel="noopener noreferrer">CreatiVault</a> to automate my benchmarking and influencer tracking.</p> <p>It does the job perfectly for me, but I’m struggling to get anyone else to try it.I’m a dev, not a marketer. So I have to ask:</p> <ol> <li><p>Is my product actually useful, or am I just solving a problem only I have?</p></li> <li><p>How do you get the first 1

<p>On June 1, GitHub Copilot switched to token-based billing, and developers did the math out loud in public. A plan that cost $29 a month could, under heavy use, run past $750. The replies were the part worth reading — not the outrage, the fear underneath it. People who had quietly built their whole workflow around an AI assistant suddenly didn't know what next month would cost.</p> <p>If you've used Claude Code seriously, you already felt the edge of this. One developer reported burning roughl

Article URL: https://www.youtube.com/watch?v=NTeF1NmmZWA Comments URL: https://news.ycombinator.com/item?id=48654854 Points: 5 # Comments: 2

<p>One-time passcodes are everywhere: sign up for a service, log in from a new device, confirm an action, and a six-digit code lands in your email. A human glances at it and types it in. An automated flow, a signup script, an end-to-end test, or an AI agent connecting to a third-party service, can't glance at anything. It has to pull the code out of the mailbox programmatically, and that's a surprisingly fiddly job: the code arrives seconds after a trigger, it's buried in a templated email, and

<p>Line AI Chatbot In Production: A CTO's Honest Breakdown</p> <p>Three months ago I was staring at our infrastructure bill wondering where the hell our runway went. We'd been running a customer-facing chatbot powered by a popular "enterprise" AI provider, and the cost curve looked like a hockey stick in the wrong direction. Every new sign-up bled money. I knew we had to make a change before our next board meeting, but I also couldn't afford a six-week migration that would tank our product veloc

Article URL: https://www.nytimes.com/2026/06/23/business/meta-ai-government-reviews-security.html Comments URL: https://news.ycombinator.com/item?id=48654757 Points: 2 # Comments: 0

<pre style='white-space:pre-wrap;width:81ex'>fix(exec): preserve turn-source routing target in approval followups for plugin channels (#96140) * fix(exec): preserve turn-source routing target in approval followups for plugin channels When an async exec approval is resolved and the originating session is resumed, buildAgentFollowupArgs forwarded the turn-source to/accountId/threadId only for built-in deliverable channels or gateway-internal channels. For an external channel plugin whose channel i

Article URL: https://microsoft.github.io/benchpress/ Comments URL: https://news.ycombinator.com/item?id=48654705 Points: 3 # Comments: 0

<pre style='white-space:pre-wrap;width:81ex'>fix(workboard): hide archived cards in CLI list by default (#94562) * fix(workboard): hide archived cards in CLI list by default The `openclaw workboard list` CLI printed soft-archived cards, while the `workboard_list` agent tool and the `/workboard list` command both hide cards with `metadata.archivedAt` set unless archives are requested. Users who archived cards still saw them in CLI output and assumed archive failed. Filter archived cards by defaul

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Article URL: https://www.economist.com/culture/2026/06/23/are-you-having-fun-yet-dataland-an-intense-new-ai-art-museum Comments URL: https://news.ycombinator.com/item?id=48654342 Points: 4 # Comments: 0

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Article URL: https://asiaai.fyi/wp-login.php?redirect_to=https%3A%2F%2Fasiaai.fyi%2Fwp-admin%2Fpost.php%3Fpost%3D197&reauth=1 Comments URL: https://news.ycombinator.com/item?id=48654155 Points: 3 # Comments: 0

Article URL: https://www.axios.com/2026/06/23/tech-stocks-ai-bubble Comments URL: https://news.ycombinator.com/item?id=48654024 Points: 17 # Comments: 0

DataRobot now supports the Agentic Resource Discovery Specification, making DataRobot Agent Skills easier for AI clients, registries, and developers to find. Agents are only as useful as the capabilities they can reach. A coding agent can write code. A workflow agent can call tools. An enterprise agent can reason across systems. But all of that... The post DataRobot Agent Skills are now discoverable through Agentic Resource Discovery appeared first on DataRobot .

Article URL: https://restofworld.org/2026/chinese-universities-drop-humanities-ai/ Comments URL: https://news.ycombinator.com/item?id=48653897 Points: 10 # Comments: 0

Article URL: https://www.fastcompany.com/91563535/un-chief-urges-ai-companies-come-clean-about-pollution-create Comments URL: https://news.ycombinator.com/item?id=48653894 Points: 3 # Comments: 0

Article URL: https://github.com/las7/reach Comments URL: https://news.ycombinator.com/item?id=48653773 Points: 1 # Comments: 0

Article URL: https://www.inc.com/jessica-stillman/the-worst-its-ever-been-why-metas-massive-ai-reorg-backfired-spectacularly/91363370 Comments URL: https://news.ycombinator.com/item?id=48653507 Points: 2 # Comments: 0

Article URL: https://krosai.com/ Comments URL: https://news.ycombinator.com/item?id=48653504 Points: 2 # Comments: 0

Article URL: https://stng.substack.com/p/why-software-requirements-get-easier Comments URL: https://news.ycombinator.com/item?id=48653478 Points: 2 # Comments: 0

Article URL: https://github.com/y-times-y/y Comments URL: https://news.ycombinator.com/item?id=48653476 Points: 20 # Comments: 11