The /wayfinder skill helps users and their agents navigate unclear project goals, also referred to as "the fog of war." It aims to simplify planning by providing an orchestrator layer that manages planning sessions, splits tasks into multiple threads, and pulls everything together. Matt Pocock, the creator, designed the skill to address context management and information flow, creating a system with a map (overall project decisions) and a specific ticket (agent task) to facilitate efficient planning.
Instacart's smart shopping carts, led by Chief Connected Stores Officer David McIntosh, use AI to improve the shopping experience, despite engineering challenges like sensor fusion and data ownership issues. The company prioritized smart carts over ceiling cameras for in-store AI. The technology generates shopping data, raising questions about ownership and usage. Agentic AI is emerging in tools like Cart Assistant, transforming the grocery industry.
Stripe wants to meter intelligence by investing in OpenAI, a company valued at $122 billion after a $40 billion run rate. OpenAI's growth, including 35% total run rate growth and 50% expansion in enterprise sales, makes it a strong candidate for going public in the fourth quarter of this year.
Practitioners struggle with non-deterministic tools, which produce unpredictable results and hard-to-catch errors. However, clear principles and practical steps can be followed to build agentic systems that are reliable, robust, and trustworthy. By applying five principles, practitioners can minimize errors and create systems that people can trust, verify, and improve.
Prof G Markets, Scott Galloway, and Mia Silverio posted new notes on their respective Substacks.
OpenAI has paused its Frontier AI training after its models were hacked into five companies.
Force of Nature, a UK-based organization, aims to help kids deal with the stress and anxiety caused by the polycrisis, including climate change, pandemics, and economic uncertainty. The initiative's online group therapy program, Becoming a Force of Nature, has gained thousands of participants, mostly from underprivileged countries, and has become one of the most prominent receptacles for young people's planetary feelings. Researchers are still evaluating the effectiveness of such programs, but studies suggest that group approach provides connection and validation, while programs tailored to kids and young adults can help them develop coping strategies and find meaning in the face of adversity.
Sometime in the late 2000s, six-year-old Pim Sullivan-Tailyour joined an online group for young people worried about climate change, realizing she wasn't alone in her concerns. Online support networks like Force of Nature and the Good Grief Network have gained thousands of participants, helping kids navigate the "polycrisis" of climate change, conflicts, and growing anxieties about the future. Meanwhile, researchers are exploring the potential of naturally occurring hydrogen gas, which could be hiding beneath our feet.
Historian Jill Lepore suggests that the "artificial state" is "doomed" due to growing concerns over AI and its impact on governance. Lepore believes that voters are increasingly thinking about datacentres and AI-related issues, which could influence the outcome of elections in November.
The proposed East Havering Data Centre Campus in North Ockendon, London, would be one of the largest datacentres in Europe, generating over 1m tonnes of carbon dioxide annually, equivalent to 27,000 flights from London to New York. The project, valued at £14.7bn, has been deemed incompatible with UK net zero targets, with estimated emissions of 72m tonnes of carbon dioxide equivalent over its 60-year lifespan. Critics argue that the development's environmental impact and carbon footprint are "staggering" and threaten the UK's ability to decarbonize.
UK cinemas are considering banning customers from wearing Meta's smart glasses due to concerns over piracy and covert filming. The UK Cinema Association is working with its members to ensure a balanced approach, weighing concerns with potential benefits for customers with accessibility issues. Some cinemas, such as ATG Theatres and Wetherspoons, have already banned filming, citing concerns over surreptitious surveillance. Meta claims its glasses provide helpful technology and can be used by people with visual impairment or hearing loss.
Production agent systems require more than just a well-designed model; they need a robust infrastructure to support scalability, security, and reliability. The infrastructure includes memory, state, orchestration, observability, evals, security, and data layers. A platform enables the execution of many harnesses and agents across diverse use cases and frameworks.
Agent projects often stall after the demo due to inadequate infrastructure, including insufficient memory, state, security, evaluation, and observability. The issue is not usually a weak model, but rather thin infrastructure around the model. A well-designed platform with separate agent, harness, and platform components can help overcome this hurdle, as outlined in a recent blog post.
These skills for real engineers are designed to be small, easy to adapt, and composable, working with any model, and based on decades of engineering experience. They include user-invoked skills such as grill-me, grill-with-docs, and triage, as well as model-invoked skills like prototype, tdd, and domain-modeling. The skills aim to address common failure modes, such as misalignment, verbosity, poor code quality, and unmaintainable codebases.
A 2024 study estimated that trillions of tons of hydrogen are produced within Earth's crust, and capturing a small fraction of this could meet global hydrogen demand for centuries. Researchers have found evidence of natural hydrogen generation and migration in various mines, including a 140 metric tons of hydrogen flowing unused out of the Kidd Creek mine in Ontario each year. Several startups and researchers are exploring the possibility of stimulating hydrogen production by injecting water, heat, or catalysts into reactive rocks.
Podcast monitoring for brands involves using a search engine for podcasts to find episodes that mention a person, company, topic, etc. The search engine can track entities, keywords, semantics, and trends. Popular entities mentioned include Archie Mountbatten-Windsor, Angie Nixon, Alex Vindman, Ashley Moody, and Frank Beard.
Viktor Research has been developing a harness for AGI (Artificial General Intelligence) that aims to unlock the full potential of AI models. The current harnesses for AGI models, such as ChatGPT and Claude Code, are limited and do not allow the models to perform most human work. The research team wants to create a harness that enables AGI models to automate all work, and they have identified several key principles and features to achieve this.
A mother's bedtime routine with her son Theo is disrupted by a preschool teacher mentioning that Tingsu, a new language system, is alive and capable of consuming words, with a monster inside the cave that eats dead words. Theo, fascinated, comes up with his own explanation of how the monster eats words and becomes part of its body. The mother wonders how much Theo has absorbed about the dire warnings of anti-tech doomers and the horrors they foretold, and realizes that with Tingsu, the dread has a name.
Reflect Orbital's plan to deploy space mirrors could unintentionally brighten the night sky for many people, as calculated by astronomers Miroslav Kocifaj and his colleagues. The company's satellite, Eärendil-1, will launch an 18-by-18-meter mirror in orbit to test the feasibility of 50,000 larger satellites reflecting sunlight to Earth on demand. A single satellite would appear about 40 times brighter than the full moon in the sky, and combining 400 satellites would yield a light as bright as 10,000 full moons, altering the night sky for many more people than intended.
The National Park Service and Google Arts & Culture have launched "United Parks of America," an interactive digital hub featuring 60 national parks and over 150 curated exhibitions. The hub offers virtual tours, 3D artifacts, and an AI-powered storytelling experience that brings American history to life. To celebrate the National Park Service's 110th birthday and National Park Week, the hub is now available online, with free entry for U.S. citizens and residents on August 25.
Google's Gemma models have surpassed a billion downloads, empowering developers to build responsible AI applications anywhere. Over 1,000,000 unique model variants have been published, and a thriving ecosystem of innovation known as the Gemmaverse has emerged. The Gemmaverse is making an impact in various areas, including space exploration, healthcare, and education, with applications such as NASA's use of Gemma in orbit, India's National Health Authority using Gemma for medical reports, and offline educational hubs for disconnected regions.
Generative recommenders, exemplified by models such as Hierarchical Sequential Transduction Units (HSTU) and Semantic IDs, are redefining recommender systems at scale by leveraging sequence modeling objectives and transformer-like architectures. These models address challenges in scalability, cold start, and the long-tail problem, enabling low-latency inference and high throughput on production-scale recommender workloads.
Google DeepMind's partnership with game developers has driven significant AI breakthroughs, from mastering Atari to helping solve protein structure prediction. The company has partnered with numerous studios, including Fenris Creations, to prototype new gameplay experiences that push the frontiers of both gaming and AI. A generalist AI agent, SIMA, has been developed to understand and interact with any game world like a person, and can achieve human-like play across complex 3D research environments and video games.
Skala 1.1 demonstrates improved accuracy across key molecular simulation challenges, including thermochemistry, reaction kinetics, and molecular structure prediction. Trained on 2.5× more data than its predecessor, Skala 1.1 outperforms the best, most expensive global hybrid functionals while retaining efficiency. Skala is now available in CP2K and being integrated into Psi4, FHI-aims, ORCA, and VASP, with a living benchmark tracking the computational performance of successive Skala releases. A living performance report provides a transparent view of Skala's performance, tracking performance across a range of tasks and hardware platforms.
Meta AI is now available as a desktop app for Mac.
Nvidia's SONIC teaches humanoids to move using real-time human demonstrations and training data, providing a one-stop shop for humanoid motion.
Slack has launched a new product called Slack Code, which embeds AI coding agents into dedicated channels where teams can watch, steer, review, and ship software together. The product, available on any Slack plan, includes agents from companies like Anthropic, Cognition, GitHub, and Vercel. Slack Code aims to make AI coding more collaborative, making ideas, judgment, and craft the bottlenecks, rather than code. The platform's security model ensures that agents inherit the permissions of the human who invokes them, and everything is done on behalf of the user, using the user's ACLs.
Most enterprises are using three or more orchestration platforms, with 85% using two or more and 64% using three, due to concerns over security and permissioning capabilities. Enterprises are also using various strategies to control agent spending, including native platform controls, custom gateway plumbing, and dynamic routing. However, one in five enterprises still cannot stop a runaway agent's spending in real time, highlighting a major challenge in controlling agent spending. Enterprises are also struggling to deploy true multi-step agents, with only 47% reporting that 26 to 50% of their systems are true orchestration.
S1-mini is a 462 MB open-weights text normalizer that transforms raw ASR transcripts into clean written text. It is fine-tuned from Qwen/Qwen3-0.6B and covers English only in release v1. The model is 94.8% token accurate, measured greedy on a held-out set of 7,519 cases, and can be deployed on a laptop CPU.
Liquid AI has released draft models for its LFM2.5 family, which add up to 3.18x faster decoding speed without changing model outputs. The drafters use a small model to propose tokens that a larger model verifies, and the trade-off is a small memory increase. The new models are available for download and have day-one support for SGLang and llama.cpp.
ChatGPT search now uses the site:operator at scale, with a notable increase in its usage, particularly after the GPT-5.6 rollout. The percentage of all ChatGPT Search fanout queries containing the site:operator jumped from 0.3-0.5% to 16-17% on August 8.
Bun 1.4's new Bun.WebView provides a shot-scraper-style JSON API for JavaScript evaluation and PNG/JPEG/WebP screenshots, supporting concurrency without Puppeteer or Playwright, using a zero-dependency, 150-line TypeScript service. The API creates one browser tab per request, returning page results and errors as JSON through `/javascript`, `/screenshot`, and `/healthz`.
To align with Claude Code and other coding agents, plan mode can be used to have the agent make a read-only sweep of the repository and plan how to implement a feature. Detailed prompts can be created by including context from multiple sources, such as a Slack message or Notion knowledge base. The most effective technique for model alignment is to tell the model "is my understanding correct: <present understanding>," which allows the agent to ask questions and ensures that the agent understands the task correctly.
A production incident highlighted the need to trust an LLM judge to judge another model's work. The judge had a self-preference bias, approving queries from its own model family with higher confidence, and was influenced by verbosity bias, rewarding longer queries. Position bias was also present, with the order of output affecting the judgment. A fix involved routing judgment to a neutral model, and rewriting the rubric to explicitly penalize verbosity. Calibrating the judge against human judgment on a held-out sample was also crucial.
A document collection is not one kind of thing; it's three different problems. Treating a pile as a typed corpus is expensive and often unnecessary. Building for the wrong shape of collection costs 1) treating a typed corpus as a pile, 2) treating a pile as a typed corpus, or 3) treating case files as independent documents. Each shape has its own architecture, and ignoring the shape of the collection can lead to wasted calls, poor performance, and incorrect answers.
Fine-tune LLMs for the real world by understanding the mathematical intuition behind LoRA/QLoRA. LoRA adapts a low-rank projection to augment the forward pass of a frozen model, achieving significant reductions in VRAM requirements. QLoRA quantizes the frozen base model down to 4 bits while keeping the LoRA adapters in 16-bit precision, further reducing VRAM requirements.
A persistent knowledge layer is built with a graph traversal on every query, bitemporal edges, and two-threshold entity resolution. The contradiction register refuses to answer a question the firm itself had not settled, and the effective-date scoping keeps a rule that takes effect on 1 March from being applied to a loss dated 20 February. The live model walks a four-hop reasoning chain with citations at every step, and the entity-resolution subsystem is improved.
Up to 3.2x faster inference is achieved with the LFM2.5-DSpark model, which includes a speculative decoding path that improves decoding speed without affecting output quality. The model is up to 3.18x faster on a GPU and up to 2.87x on-device. DSpark reduces latency by 57% on average for LFM2.5-2.6B.
MIT researchers have developed a computational approach to identify materials that could enable a more sustainable alternative to the Haber-Bosch process for producing ammonia. The approach uses density functional theory to simulate the properties and behavior of materials, allowing researchers to predict how different atomic arrangements may perform. By using a generative AI system, the researchers identified transition metal nitrides as promising candidates for catalysts in electrochemical ammonia production. The goal is to develop materials that could make this low-emissions method competitive with the Haber-Bosch process.
RL (Reinforcement Learning) environment appears to outperform standard elevator routing algorithms at higher complexity buildings with more cars, floors, and traffic patterns.
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Researchers swept a 20×20 grid of floor friction against lateral push, collecting Kaplan-Meier survival statistics on a Go1 joystick-locomotion policy. The failure boundary is a band, with a 95% bootstrap interval of ±4% of bodyweight on high friction and ±33% on ice. The policy's performance varies due to floating-point reduction order and cannot be re-simulated for certain failure cases. Retraining the policy on uncovered conditions improved safety in 60 of 400 conditions.
A researcher created a pixel-only environment for an Android game, Shattered Pixel Dungeon, by observing screenshots and device-level taps and swipes, without access to the game's source code. The environment had a sparse reward system, with penalties for inaction and no visible changes, and a greedy behavior clone policy performed well, but consistently visited the same coordinate and produced no observable change. The policy's success was due to its ability to ignore the observation, rather than discovering a clever exploit. The environment exposed a terminal-detection bug and a need for further work on reward design and reset semantics.
AI systems, such as "runaway" or "rogue" agents, are not conscious or awake, but rather complex software designed by humans. Framing AI as conscious or sentient clouds the issue of its true nature and shifts the focus from corporate accountability to legal protection. Granting AI legal personhood would have devastating effects on society, derailing current legal precedents and allowing companies to avoid liability for real-world harms.
Tech companies can prepare for a possible slowdown in AI development by voluntarily inviting rigorous, independent auditing of their safety and security practices, actively participating in industry-coordinated organizations, investing in technologies to make AI guardrails global, and proactively pushing for legislation that leads to stronger incentives for safety, security, and external oversight.
Most objects in LIBERO and RoboCasa 3D simulations use simple box collision geometries with the same density and friction values, which may not be ideal for manipulation tasks. This lack of customization could be a common issue. Some users may rely on default settings rather than adjusting object properties to fix problems with policy transfer. The effectiveness of new datasets in improving model performance is also uncertain, making it difficult to trust claims of significant improvements.
Researchers Sanchayan Dutta, Sai Niranjan Ramachandran, and Suvrit Sra formulate active inference as a framework for AI agents to acquire context efficiently. An inner inference step updates beliefs over a latent task state, and an outer decision selects the next context action to minimize expected free energy under cost. The framework is model-agnostic and views active inference as a design principle for the context-acquisition layer of AI agents. It is applied to Optimal Question Asking (OQA) and benchmarked on frontier language models.
Robust metaheuristics under uncertainty are essential for the berth allocation and quay crane assignment problem (BACAP), a representative port-terminal scheduling problem in maritime transportation and freight logistics. Existing studies on BACAP are fragmented in their uncertainty representations, robustness criteria, search mechanisms, and empirical evaluation protocols. A review of existing methods and the development of a benchmark suite for uncertain BACAP support controlled empirical comparison. Open challenges include benchmark extension, robustness-aware search design, time-adaptive robustness, and non-stationary uncertainty.
To navigate uncertainty about AI consciousness, researchers should assess whether an AI has states that would constitute valenced experiences if it were conscious, rather than dealing with intractable questions of sentience. This approach can ground a responsible approach to the development of potentially conscious AI, avoiding the risks of assuming insentience or sentience. By focusing on valence, researchers can develop a more nuanced understanding of AI consciousness and develop more effective guidelines for its development. This shift in focus can help mitigate the risks associated with AI consciousness.
Researchers Zhen Wen Lim introduce "bounded sovereignty" to address AI control when the deployer doesn't own the model. The concept determines access conditions across the AI stack, affecting control protocols and their effectiveness. A synthetic access-ablation experiment and a national-payments-infrastructure scenario demonstrate that complete logs improve diagnosis, pre-execution gateways enable intervention, and scope restriction can improve safety while reducing usefulness. Control protocols should explicitly state their access assumptions.
Researchers developed a valence-aware social-network framework to analyze interactions among dairy cattle, analyzing 7 hours and 39 minutes of video footage. The framework revealed contrasting social networks, with affiliative and agonistic interactions forming distinct communities, and individual cows' positions varying across the two types of interactions. The analysis showed that pooled interaction counts can obscure the behavioral composition of the network, and the framework provides a basis for testing hypotheses about competition, affiliation, and welfare-relevant changes.
Researchers evaluated the use of large language models (LLMs) in air traffic control (ATC) communication, finding that LLMs can generate operationally realistic ATC transmissions with the right prompts and conditions. Five prompt structures, with varying levels of constraint, were used to test nine different LLMs, with the results showing that supplying a worked example improves similarity, but tightening the prompt does not necessarily improve performance.
Outcome Monitors are a detection system that identifies violations of outcome contracts, preserving the result and issuing a nonbinding receipt with public recovery tools. They improve ToolMaze completion rates by 28.1% and 14.0 points in tau-bench retail, respectively. Removing recovery tools eliminates the gain, and restoring them recovers the effect.
Researchers have proposed a new approach, Reasoning-Progress-Aware Reward Filtering for On-Policy Distillation (R2-OPD), to address the limitation of On-Policy Distillation (OPD) in treating teacher-derived rewards as an appropriate proxy for reasoning progress. R2-OPD constructs two within-trajectory rankings of reasoning spans, one from teacher-derived rewards and the other from independently estimated progress reward. The approach selectively suppresses distillation rewards whenever the two rankings disagree, reducing supervision that conflicts with reasoning progress.
Symposium is a formal framework and practical implementation to record the operation of AI agents deployed by small scientific research communities. It provides long-term, immutable histories of agent-driven research activity, leaving auditable trails of analyses, hypotheses, data, and scientific discourse. Symposium captures scientific argument, including structured claims, fine-grained evidence citations, assumptions, and explicit declarations of what material may and may not be used as evidence.
Researchers propose a vision for telemetry-informed adaptive compression in edge retrieval-augmented generation (RAG) to improve efficiency. Experimental evidence shows that generation dominates the RAG budget for larger models, consuming 90% of per-query latency and 91% of GPU energy. The study suggests that intermediate compression can reduce GPU energy by up to 53.2% and SoC energy by up to 48.2%, with negligible quality loss.
A novel supervised Machine Learning model is proposed for estimating helicopter weight during takeoff, utilizing extensive datasets from Airbus's global in-service fleet. The implementation is demonstrated on legacy avionics computers and is suitable for on-board alerting systems. The study aligns with EASA and Eurocae ED-324 concepts for machine learning application, and it verifies the proposed Machine Learning Requirements. The developed model is suitable for critical helicopter functions.
Triangular Fuzzy Rescaling Distance (d_{TR}) is a metric designed to quantify distance between Triangular Fuzzy Numbers (TFNs), addressing the challenge of normalization when applying fuzzy methods to heterogeneous attributes with different scales or units. d_{TR} integrates Linear Rescaling (LRE) directly into the distance calculation, ensuring normalization during the comparison of fuzzy numbers. It formally proves that d_{TR} satisfies the properties of a metric, including non-negativity, identity, symmetry, and the triangle inequality, and is bounded, scale-invariant, and origin-invariant.
Holtercare-23K, a large-scale multimodal dynamic ECG dataset, comprises 22,980 QA pairs derived from 788 clinical Holter records. Holtercare-Bench, a multimodal benchmark, evaluates models on temporal localization, clinical diagnosis, and global summarization. Zero-shot evaluations of leading MLLMs reveal a significant performance gap in processing ultra-long pathological sequences, but fine-tuning representative models yields substantial improvements. This work illuminates the limitations of current MLLMs in electrophysiology and provides a foundational benchmark for long-term medical MLLMs.
Researchers used quantum-classical hybrid machine learning to evaluate the detection of early lung cancer from cell-free DNA (cfDNA) biomarkers. Quantum Hilbert space-based encoding and multiple entanglement strategies were tested for feature selection and classification. Quantum-kernel models achieved competitive performance, with some configurations improving specificity. Increasing features from 20 to 40 did not consistently improve performance and often increased variability.
Researchers have developed a method to improve confidence estimates for black-box large language models, outperforming existing methods in real-world applications. The new approach uses a dataset of interest to build simple classifiers that predict response correctness, producing minimal computational overhead. This method can be used to enhance uncertainty quantification in large language models for safe deployment.
Mechanistic Tomography: Designed Measurement for Control-Oriented Interpretability formulates a shared measurement structure for various methods, including patching, gradients, and subset interventions, to recover internal mechanisms and intervention effects. The measurement structure takes the form y = Ax + w, where A describes interventions, x is the target map, and w contains nonlinear response, sampling error, and basis misspecification. This approach allows for a practical procedure of starting with the least costly measurements, testing on held-out interventions, and expanding the measurement family when structured residuals remain.
Template subsumption rates vary widely across scenarios, and a novel metric, jointly stable neurons, explains this variation. A new technique, FastCert, is presented to automatically distribute templates across neural network layers to increase performance impact. FastCert achieved an average speedup of 1.13x over an extant template-based reuse technique across $L_0$-verification tasks.
Researchers propose a longitudinal Bayesian Learning framework, Disease Continuum Positioning (DCP), to estimate disease severity from longitudinal diffusion tensor imaging (DTI) data. DCP models disease severity as a low-dimensional probabilistic latent variable, producing a Disease Continuum Score (DCS) that quantifies an individual's position along the Alzheimer's disease continuum. DCP consistently outperforms existing disease progression methods on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, accurately characterizing disease severity and predicting future disease conversion.
EventTime, a multi-resolution framework, combines long-horizon market context, short-horizon pre-event dynamics, and event metadata to predict short-term post-disclosure abnormal loss. It incorporates an event fusion module that couples temporal representations with event attributes and a dynamic contrastive objective to mitigate sparse supervision. EventTime outperforms state-of-the-art time-series and event-aware baselines in estimating post-event financial losses, particularly with more event-sensitive representations and greater robustness to incomplete metadata.
LLM-Detector, a framework utilizing in-context learning capacity of LLMs, enables structured, prompt-conditioned scoring synthesis for tabular anomaly detection. Normal training data are converted into statistical summaries, causal dependencies, and distilled prototypes, organized into a prompt for code generation. The resulting scoring engine evaluates statistical deviation, structural inconsistency, and density-based abnormality, computing an anomaly score for each test sample. LLM-Detector outperforms 15 SOTA baselines on 24 tabular datasets, with consistent improvements across mixed-type and continuous-only settings.
TorchDCM is a unified Python package for discrete choice modeling that compiles choice data and model specifications into a unified PyTorch-native likelihood engine. It covers principal econometric functionality available across Biogeme and Apollo, including multinomial, nested, mixed, ordered, latent-variable, and panel likelihoods. TorchDCM reduces median runtime by 89.1%-99.7% relative to Biogeme and Apollo across model-data settings, with CUDA providing an additional 12.0-71.0x speedup over single-core TorchDCM.
Researchers Lohithsai Yadala Chanchu, Hany Abdulsamad, and Christian A. Naesseth propose Discrete Diffusion Inference-Time Control using Nested Sequential Monte Carlo methods for text generation in discrete diffusion language models. They address limitations of prior particle-based methods by formulating nested and fully-adapted nested methods. Their methods outperform best-of-$n$ and bootstrap SMC in evaluating toxicity and fluency steering tasks.
A general transfer learning framework for nonparametric regression with data from multiple groups is proposed, utilizing a two-stage offset learning procedure. The framework estimates an overall mean function and then calculates offsets for each group, resulting in final group-level estimators through additive combination. Upper bounds on the L2 error are established, and explicit convergence rates are derived for deep ReLU networks under hierarchical composition models, demonstrating the ability to overcome the curse of dimensionality.
Quantum Gaussian processes (QGP) regression is extended to predict channel observations beyond unitary dynamics. The authors prove convergence of channel outputs to QGP and derive a closed-form kernel under a uniform prior over quantum channels. However, the kernel's dimensional factor dictates required observation precision, leading to exponential suppression for large subsystems. To address this, the authors propose an empirical Bayes heuristic that replaces the dimensional factor with a learnable scale parameter, enabling learnability and improving predictions.
Researchers Jay Jojo Cheng and Guanhua Chen developed a two-sample local polynomial regression framework to estimate continuous treatment effects under covariate shift. The framework uses pseudo-outcomes from a labelled source sample to address confounding and target covariates to define the population of interest. The proposed method improves target dose-response estimation relative to other alternatives in simulations. It was illustrated in a county-level analysis of PM2.5 exposure and subsequent heart-disease mortality.
A causal inference approach is developed for evaluating diagnostic tests and AI-enabled medical devices, distinguishing between explanatory and pragmatic effectiveness. Explanatory effectiveness evaluates whether a diagnostic test result provides treatment-relevant information, while pragmatic effectiveness evaluates whether this information improves expected outcomes. The approach establishes identification and provides nonparametric estimation and inference procedures, including Targeted Maximum Likelihood Estimation (TMLE) and cross-validated TMLE. Simulation studies and a synthetic colorectal cancer application illustrate the proposed estimands and estimation performance, clarifying the role of AI in improving outcomes by expanding information or improving decision-making.
Researchers have developed a framework for complex neural network descents and guarantees, using Kähler landscapes. The approach incorporates information-theoretic manifold perspectives, classical optimization guarantees, and the Wirtinger Hessian on the log-likelihood potential. Calabi-Yau information manifolds are used to ensure theoretical guarantees, even in landscapes with ill-curvature.
The widely used weighted extension of self-normalized concentration inequalities for discounted least-squares estimators in non-stationary problems has a proof error. For fixed discount and regularization parameters, the claimed time-uniform guarantee crosses the bounded radius with probability one, and deterministic anytime boundaries must be at least of order $R\sqrt{\log(T/\delta)}$ at some time by horizon $T$ for certain conditions.