Brief

Researchers have developed a new method called Batched Robust iHMM (BR-iHMM) to improve the accuracy of online infinite hidden Markov models when dealing with noisy data. This approach enhances robustness against outliers and model misspecification by incorporating generalized Bayesian inference and bounding the posterior influence function. Tests on financial, energy, and synthetic datasets showed BR-iHMM reduced forecasting errors by up to 67% compared to existing methods, demonstrating its practical utility for forecasting and interpretable online learning. AI

Researchers have introduced SCOPE, a novel iterative algorithm for sparsity-constrained optimization problems. This method is designed to optimize nonlinear, differentiable, and strongly convex functions, replacing traditional gradient steps with a splicing operation that directly uses objective values. SCOPE eliminates the need for hyperparameter tuning and theoretically achieves linear convergence rates while accurately recovering the true support set. Numerical experiments demonstrate its superior performance in tasks like sparse quadratic optimization and learning sparse classifiers. AI

This paper introduces a practical method for estimating optimal classification error in binary classification tasks, particularly when dealing with soft labels and calibration. The research extends prior work by theoretically analyzing the bias of hard-label estimators and addressing the challenge of corrupted soft labels. The proposed method, which is instance-free and thus suitable for privacy-sensitive scenarios, demonstrates consistency even with imperfectly calibrated soft labels. AI

Researchers have developed TAMO, a novel transformer-based policy for multi-objective Bayesian optimization that operates entirely in-context. This approach eliminates the need for per-task surrogate fitting and acquisition engineering, significantly reducing proposal time by up to 1000x. TAMO is pretrained using reinforcement learning to maximize cumulative hypervolume improvement, allowing it to approximate Pareto frontiers and improve solution quality under tight evaluation budgets. The development opens a path towards plug-and-play optimizers for scientific discovery. AI

Researchers have developed a convolutional neural network (CNN) framework to test General Relativity using gravitational wave data. By training the CNN on simulated beyond-GR waveforms, they found that using a response function observable improved classification sensitivity significantly compared to raw waveforms. The framework successfully detected deviations in massive gravity theories, demonstrating its potential for probing fundamental physics with astrophysical observations. AI

Researchers have analyzed the Fisher information matrices of simple two-layer ReLU neural networks with random hidden weights. They found that the eigenvalue distribution concentrates significantly on specific eigenspaces, with the first three accounting for nearly all of the matrix's trace. The study identifies these dominant eigenspaces as corresponding to spherical harmonic functions of order two or less, linking this to Mercer decomposition of neural tangent kernels. AI

Researchers have developed a new framework for statistically guaranteeing the performance of multi-dimensional hyperparameter tuning in data-driven machine learning settings. This approach leverages tools from real algebraic geometry to provide sharper and more broadly applicable guarantees than previous methods, which were limited to one-dimensional hyperparameters. The work also establishes the first general lower bound for this type of tuning and extends the analysis to use validation loss under minimal assumptions. AI

The Cursor Agent SDK has been updated to enable agents to actively communicate and utilize tools, moving beyond passive observation. Initially, agents could only receive tasks and respond with text, lacking the ability to perform actions like writing files. This was resolved by injecting an MCP server configuration into the SDK, allowing agents to access tools such as `write_report` and `write_task`. Furthermore, providing agents with explicit role context and workflow instructions proved crucial for them to effectively utilize these newly available tools. AI

A new Chromium browser extension, "OBEY," replaces advertisements with subliminal messages inspired by John Carpenter films. This tool, which also functions as an ad-blocker, aims to influence user behavior rather than simply remove promotional content. The extension highlights emerging security concerns related to AI agents and their potential to manipulate users. AI

A 1.3 billion parameter multimodal model named MiniCPM-V 4.6 has been open-sourced by OpenBMB and Tsinghua University. This model is capable of running on a single RTX 4090 graphics card. Despite its smaller size, it achieves performance comparable to larger models on important benchmarks. AI

Researchers have developed a self-consistency (SC) loss to improve the accuracy of amortized Bayesian model comparison (BMC) when simulation models are misspecified. This technique enhances BMC estimators, particularly in open-world scenarios where all candidate models are imperfect. The study evaluated four amortized BMC methods, finding that SC training significantly boosts performance when analytic likelihoods are available or surrogate likelihoods are locally accurate, even with misspecified models. AI

Researchers have developed a new non-parametric method for estimating conditional distributions, which can be used for conformal regression. This approach involves partitioning data into bins and using the empirical cumulative distribution function within each bin to predict distributions. The method optimizes bin boundaries by minimizing a leave-one-out Continuous Ranked Probability Score (LOO-CRPS) and selects the optimal number of bins through cross-validation. The resulting prediction bands and sets offer finite-sample coverage guarantees and demonstrate narrower intervals than existing split-conformal methods on benchmark datasets. AI

Researchers have developed a smoothed analysis approach for learning from positive-only samples, a challenging problem in binary classification. Unlike worst-case scenarios where learning is nearly impossible, this new method demonstrates that all VC classes become learnable under smoothed conditions. The work also introduces efficient algorithms for related problems in parameter estimation, truncation detection, and learning from reference distributions. AI

Researchers have developed a new methodology called Adversarial Causal Tuning (ACT) to generate realistic time-series data from causal models. This approach aims to create simulated data that matches the observational and interventional distributions of real-world datasets, enabling tasks like intervention simulation and root-cause analysis. ACT utilizes ideas from Generative Adversarial Networks and AutoML to optimize causal models and discriminators, with experiments showing its effectiveness in selecting optimal causal models and generating indistinguishable data from the true distribution. AI

Researchers have introduced Partition Tree, a new framework for conditional density estimation that can handle both continuous and categorical variables. This nonparametric approach models conditional distributions using data-adaptive partitions and learns by minimizing conditional negative log-likelihood. An extension called Partition Forest averages conditional densities for improved probabilistic prediction, showing competitive results against existing methods. AI

Researchers have developed a new method for online inverse linear optimization, a technique used in contextual recommendation systems. This approach achieves a finite regret bound of O(d log d) for M-convex action sets, a significant improvement over previous exponential bounds and a partial answer to an open question in the field. The method combines structural characterization of optimal solutions with geometric volume arguments. Additionally, the technique has been extended to handle adversarially corrupted feedback, yielding a bound of O((C+1)d log d) without prior knowledge of the corruption level. AI

Researchers have developed a new method for Federated Granger Causality (FedGC) that addresses the limitation of deterministic point estimates by incorporating uncertainty awareness. This approach provides calibrated measures of uncertainty, allowing operators to distinguish reliable cross-client interactions from spurious ones. The method derives closed-form expressions for steady-state variances and proposes a post-training hypothesis testing procedure to identify genuine interactions, outperforming existing federated causal structure learning baselines on synthetic and real-world datasets. AI

A user in Finland reported significant issues with the signup process for the AI-powered code editor Cursor. The user was unable to register using their Finnish mobile number, as the last digit was consistently cut off from the phone number field. This prevented them from even creating an account, which the user found surprising for a product marketed as polished and intelligent. AI

A content creator detailed how they utilized Claude AI in conjunction with Google Search Console to identify and recover lost website traffic. By analyzing search console data, the creator was able to pinpoint underperforming content and generate new article ideas. Claude AI then assisted in optimizing existing content and creating new pieces, ultimately leading to a significant increase in daily clicks. AI

FalsifyLab has released an open-source tool called Alpha MCP that integrates with the Cline AI coding agent. This tool provides access to eight specialized financial data feeds, enabling AI agents to perform complex research tasks without manual data aggregation. Users can query for specific financial events like insider buying or ETF flows, and the AI agent can then interpret this data to provide insights or even generate custom analysis scripts. AI

This article explores how AI tools like Claude and Apify can be used for business intelligence. It details a practical method for leveraging these platforms to identify potential clients and analyze competitor strategies. The author demonstrates a workflow that combines AI capabilities for data gathering and analysis to gain market insights. AI

CognitiveBotics has developed a personalized content engine for children with autism, addressing the challenge of high individual variability in learning preferences. Their Modalities Engine renders learning objectives across speech, vision, and animation, using a reinforcement learning framework to adapt content delivery in real-time. A key technical challenge involved creating custom pediatric speech recognition models, as standard adult-focused ASR systems perform poorly on children's vocal frequencies. AI

TechCrunch Disrupt 2026 will feature six stages focused on the current challenges faced by startups and investors. The event aims to help attendees make informed decisions in a volatile market by covering topics such as AI competition, infrastructure, venture capital dynamics, and enterprise adoption. Specific stages will address the future of AI, company building and funding, and tactical advice for founders on fundraising, hiring, and scaling. AI

This article provides a guide on configuring the official gopls as an MCP Server within three minutes. It aims to help developers using Go and AI tools like VS Code and GitHub Copilot better understand their projects. The setup instructions are demonstrated using VS Code and GitHub Copilot, but can be adapted for other AI tools. AI

A new benchmark, Frontier-Eng Bench, has been released to evaluate AI agents on complex engineering tasks that lack standardized answers. This benchmark moves beyond simple problem-solving by requiring agents to propose solutions, integrate with simulators, interpret feedback, and iteratively refine parameters. The goal is to assess an agent's ability to perform continuous optimization and self-evolution in real-world scenarios, moving towards an era of 'Auto Research' where AI agents function as tireless engineering teams. AI

A new Electron app called Skiller has been developed to help users manage coding agent skills. The tool allows for the installation of skills from GitHub, local folders, or the skills.sh registry. It also provides features to browse a skills registry, sync skills to agent folders, and check for updates. AI

Apple has released iOS 26.5, addressing over 60 security vulnerabilities, including critical flaws in the Kernel and WebKit that could allow for privilege escalation and data disclosure. The update also fixes bugs in App Intents, with experts noting that these components are often chained together in sophisticated attacks. Notably, researchers from Google's Threat Analysis Group and Anthropic, utilizing AI like Claude, contributed to identifying some of these critical issues, highlighting the growing role of AI in both discovering and potentially exploiting software vulnerabilities. AI

Users of the Cursor IDE are reporting significant slowdowns with the "Fast" mode, a feature that typically offers rapid code completion. The issue appears to be widespread, with multiple users experiencing extended processing times for code generation and completion tasks. It is currently unclear whether the problem stems from server-side issues or client-side configurations. AI

A recent MIT AI Alignment (MAIA) governance competition highlighted the need for technically adept individuals in shaping AI policy. Student submissions demonstrated practical approaches to issues like data center buildouts, AI developer liability, and autonomous code generation. These proposals focused on actionable governance, moving beyond abstract concerns to concrete regulatory frameworks. AI

A developer has detailed a rigorous cryptographic system called the Blaze Balance Engine, designed to prevent AI agents from performing unauthorized actions like modifying production databases. This engine employs a multi-layered approach, including static code analysis to detect forbidden commands and a "Certificate of Doing Nothing" that requires explicit confirmation of non-actions. It also enforces a cryptographic dependency chain, validating previous transaction hashes before proceeding, and generates a final SHA-256 hash to prove the AI's integrity. AI