A new arXiv paper presents a method for amortizing Branched Optimal Transport, a technique that models efficient tree-like network structures seen in rivers and biological systems. The work aims to make these network design computations faster by learning to approximate solutions rather than solving each instance from scratch. It is listed under both cs.AI and cs.LG on arXiv.
A new arXiv paper investigates how pretrained language models can be adapted for univariate time-series forecasting using parameter-efficient transfer learning. The authors focus on identifying which design decisions matter most for effective transfer between text and numerical sequences. The work is a cross-listed submission to arXiv's machine learning category.
A new arXiv preprint presents a method for designing passive counterweights for serial robotic manipulators based on the distribution of tasks the robot will actually perform, rather than optimizing for a single pose. The work combines counterweight synthesis with constrained co-design, aiming to better compensate gravity across realistic operating configurations. It falls within robotics and machine learning research rather than a commercial product release.
A newly posted arXiv paper introduces a method for adapting graph-based prediction models to new data with limited supervision or feedback. The approach, called reliability-aware prototype learning, aims to make agentic systems more data-efficient when reusing prior knowledge after deployment. It falls under the machine learning category and is a preprint, not yet peer-reviewed.
A new arXiv preprint examines how AI-assisted automation can improve the auxiliary compliance processes that enterprises use to meet tightening regulatory requirements. The authors frame compliance workflow optimization as a practical entry point for raising the efficiency and accuracy of broader compliance systems amid digital transformation. The work appears under the cs.LG cross-list category.
The authors argue that fairness criteria which restrict a predictor or policy can produce unwanted side effects, especially when the policy is optimized under those constraints. They propose instead examining fairness directly through utility functions, and offer initial steps toward fairness objectives that avoid those drawbacks. The work is a revised cross-listing on arXiv in machine learning.
A new arXiv paper proposes a credit risk early warning system that merges heterogeneous data sources using deep learning and real-time analytics. The authors argue that existing financial monitoring tools are slowed by fragmented data and delayed detection. The work is categorized under machine learning and is cross-listed on arXiv.
Researchers developed a machine learning approach that predicts day-by-day mortality risk for critically ill patients with acute kidney injury receiving continuous renal replacement therapy. Unlike existing tools that rely mainly on electronic health record data, this method incorporates minute-level machine pressure waveform signals. The work suggests that treatment device data can add predictive value beyond standard clinical parameters.
A new arXiv paper surveys how diffusion-based learning methods can be applied to robot learning, framing the problem through ergodic control and controlled diffusion. It is written as a combined review and tutorial, intended to give researchers a structured entry point to the underlying statistical machinery. The work is cross-listed in machine learning and focuses on deriving complex distributions from data for control tasks.
A new arXiv paper evaluates rule-based systems produced by large language models against conventional machine learning classifiers for predicting heart disease. Using the UCI Heart Disease dataset, the authors benchmark models including logistic regression and k-nearest neighbors. The work examines whether LLM-derived decision rules can match or complement established clinical prediction methods.
A new arXiv paper presents a machine learning approach for detecting, isolating, and estimating the severity of faults in autonomous vertical take-off and landing aircraft. The work is motivated by the risk that small component degradations can quickly destabilize multirotor vehicles in safety-critical settings. It aims to provide more robust fault detection and estimation than existing strategies.
A new arXiv paper proposes a Memory-Mediated Learning Architecture (MMLA) that splits a learning system into slow base parameters, a bounded numerical policy carrier, and a bounded authoritative memory store. Its Predictive Dual-State Adaptation mechanism uses feedback to update the policy carrier while handling a problem-state component. The work is presented as a preprint on arXiv under cs.LG.
A new arXiv chapter surveys how machine learning is being applied in fish farming, emphasizing better decision-making and more efficient operations. It opens by outlining the core challenges aquaculture faces before moving into ML-based approaches. The work is positioned as an overview for researchers and practitioners in the field.
Researchers propose SechKAN, a variant of Kolmogorov-Arnold Networks that replaces the usual basis functions with hyperbolic secant functions. The paper argues this design keeps KAN's strengths in machine learning and scientific computing while offering a new direction for neural network architecture. It is a revised arXiv preprint, not yet a released product.
A new preprint describes an approach that moves from identifying ecological marine provinces, called eco-provinces, toward predicting them using learning methods. The authors frame the work as a step toward trustworthy models that can support spatial habitat analysis as climate change affects marine ecosystems. The paper appears on arXiv under the cs.LG category as a cross-listing.
A revised arXiv preprint reviews how optimal transport is used to compare networks, offering both a dissimilarity measure and a plan describing how one graph maps onto another. The authors add new spectral bounds and discuss applications in machine learning. The paper is a cross-listing revision rather than a new release.
A new arXiv paper reframes hyperparameter optimization as a numerical optimization problem rather than a sequence of independent trials. The authors propose a multi-objective, damped Gauss-Newton search method that estimates a finite-difference-based model of the objective landscape. The work is a replacement cross-list submission on arXiv's machine learning section.
Researchers propose a nonparametric approach to variance-penalized reinforcement learning that trades expected return for policy stability without training a separate variance critic. The work frames risk-sensitive RL through statistical inference, aiming to cut the extra computation and complexity that online variance estimation usually requires. It is a new arXiv preprint in machine learning.
A new arXiv paper introduces CodeTS, a method that turns natural-language descriptions into time series by generating and running executable code rather than sampling outputs directly. This design makes the resulting synthetic data verifiable and suited to scenarios where real observations are scarce or expensive to collect. The work appears in the cs.LG and cs.AI listings.
A new arXiv preprint examines whether a language model can act as a compact, living specification that answers detailed questions while avoiding the tradeoff between vague specs and lengthy ones. The work frames specifications as needing to balance leaving details out against recording every detail separately. The paper appears in the cs.LG category as a new submission.
A new preprint on arXiv examines how machine learning can be used to identify and model metastable behavior in physical systems. Metastability describes systems that linger in quasi-stable states and then shift abruptly under rare perturbations, a pattern seen across many areas of physics. The work targets the challenge of detecting and representing these transitions computationally.
A revised arXiv paper examines how machine learning and data-driven techniques can predict wireless channel behavior in outdoor 5G and future networks. Accurate channel estimation is presented as key to improving quality of service for users. The work sits within ongoing efforts to make beyond-5G systems more reliable and efficient.
A new arXiv preprint treats early stopping for Evolvable-Substrate HyperNEAT as a binary classification problem built on early fitness trajectories. Many ES-HyperNEAT hyperparameter settings yield networks stuck at random-guessing accuracy, so the authors aim to detect failing runs early and cut wasted computation. The work is a cross-listing in cs.LG and remains a preprint without peer review.
A new arXiv paper proposes an autoencoder-based approach for estimating parameters of damped sinusoidal signals that contain multiple overlapping components and decay quickly. Such signals appear across many physical systems, where their parameters reveal underlying physical properties, but conventional estimation struggles when components superpose or fade fast. The work is a replacement submission to arXiv cs.LG and has not necessarily been peer reviewed.
A new arXiv preprint examines how test-time reinforcement learning adapts vision-language models to unlabeled target data, noting that results depend heavily on the quality of self-generated training signals. The authors argue that consensus-based learning signals are inherently limited and propose exploiting dependence between images and their questions to improve reliability. The work is categorized under machine learning and has not yet been peer reviewed.
A new arXiv preprint surveys how methods for learning structure from high-dimensional data connect to ideas from control theory, statistical inference, optimal transport and thermodynamics. The author argues these shared mathematical foundations bridge physics and applied mathematics with machine learning. The paper also outlines applications of this unified perspective.
Researchers present a participant-specific framework for assessing dynamic balance in people walking with hip exoskeleton assistance, including trials that involve unexpected ground perturbations. The approach aims to handle the challenges of small, noisy human-in-the-loop datasets, since balance is a multidimensional quantity that is hard to personalize with conventional methods. The work appears as an arXiv preprint in machine learning.
Researchers present ClimateAgent, a system that coordinates multiple LLM-based agents to carry out climate data science tasks. The work argues that general-purpose agents and fixed script pipelines lack the domain context needed for large, varied climate datasets, and proposes an orchestration approach tailored to that setting. It is a revised arXiv preprint (v2) in machine learning.
A new arXiv preprint introduces a framework aimed at keeping black-box machine learning models within validated operational bounds, motivated by safety-critical space missions where ground truth is often unavailable. The approach is applied to spectroscopy, framing reliability as a matter of constraining where a model's predictions can be trusted. The work targets validation gaps that arise when labeled data for verification is scarce.
A new arXiv preprint examines how well large language models handle terminology from highly technical fields, noting that general-purpose systems tend to lose accuracy outside everyday tasks. The authors compare general-purpose and domain-specialist models to probe what parametric knowledge of specialized terms each type retains. The work is cross-listed under computation and language and machine learning.
Researchers developed a machine learning approach that forecasts railway delays in Finland by combining weather observations with other operational data. The work situates such environmental sensing within future 6G-enabled wireless sensor networks and edge computing, which the authors argue will improve real-time reliability. The study is published as an arXiv preprint.
A new arXiv preprint describes an approach that combines statistical learning from data with robust decision-making for supply chain networks. The work targets structural uncertainty, market volatility, and operational disruptions that modern supply chains face, and aims to connect data-driven methods with practical decision support. It is an announcement of a cross-listed machine learning paper rather than a released product.
A new arXiv preprint analyzes how harmful examples placed in a prompt can make multimodal large language models produce unsafe outputs without any change to their weights. The authors frame this in-context jailbreak behavior as a vulnerability and propose a posterior reweighting approach to explain it. The work falls under cross-listed machine learning submissions.
A new arXiv preprint introduces EVTradeMatch, a multi-objective matching framework for peer-to-peer energy trading between electric vehicles. The approach accounts for each vehicle's journey conditions when pairing providers and consumers, aiming to improve charging flexibility where infrastructure is limited. The work appears as a cross-listing in the cs.LG category.
A large-scale empirical study investigates how the quality of code in computational notebooks relates to the performance of the resulting machine learning models. The authors note that ML practitioners typically optimize for model metrics while treating code quality as secondary. The paper analyzes this relationship across many notebooks to test whether cleaner code corresponds to better model outcomes.
A new arXiv paper introduces a similarity measure built on divergence for contrastive learning with multiple augmented views. The authors note that earlier approaches combine views either in the loss or in the feature space, but largely restrict themselves to pairwise comparisons. Their method aims to capture relationships spanning more than two views at once. The work is labeled a cross-listing replacement on arXiv's machine learning section.
A paper studies proactive exploration in LLM agents, meaning their ability to gather information from an environment that improves later decisions. The authors identify two core bottlenecks limiting this capability and propose methods for installing and refining it. The work appears as a replacement submission on arXiv's machine learning category.
An arXiv paper introduces s-Trace, a technique for measuring how much of their computational capacity large language models actually use on different inputs. The authors note that models with billions of parameters arranged in deep computational graphs may not fully exploit their capacity for every input. The updated paper is cross-listed under the AI, computational linguistics, and machine learning categories.
A researcher proposes a technique for estimating average treatment effects in observational studies while preserving differential privacy. The approach uses propensity score blocking to group similar subjects, limiting how much any individual's data influences the result. The preprint is posted on arXiv and is categorized under machine learning.
A new technical report on arXiv introduces Gander, an end-to-end model that combines full multimodal perception, real-time interaction, and agentic capabilities in a single framework. Rather than relying on conventional turn-based exchanges, the model continuously processes incoming input streams. The work is listed under the machine learning and artificial intelligence categories.