LLM-powered coding agents, which operate in iterative loops (turns) to solve software engineering tasks, are becoming increasingly powerful. However, their practical deployment is hindered by significant and unpredictable costs. This challenge arises from a combination of factors: quadratically growing token counts with each turn, the high price of models, the large number of turns required for real-world tasks, and the tendency of agents to take inefficient or unnecessary actions. While existing research focuses on optimizing individual turns, the strategic control of the total number of turns remains an underexplored area for managing agent performance and cost. To address this gap, we conduct a comprehensive empirical study on SWE-bench using three state-of-the-art models and evaluate the impact of three distinct turn-control strategies: an unrestricted baseline, a fixed-turn limit with reminders, and a novel dynamic-turn strategy that grants extensions on-demand. Our findings first reveal a fundamental trade-off in the unrestricted setting, where no single model excels across performance, cost, and turn efficiency. We then show that a fixed-turn limit, specifically at the 75th percentile of the baseline, serves as a "sweet spot", substantially reducing costs (by 24%-68%) with minimal impact on solve rates. Most significantly, the dynamic-turn strategy consistently outperforms fixed-limit approaches, achieving comparable or better solve rates while further reducing costs by an additional 12%-24% by intelligently allocating resources only to tasks that need them. This work provides the first systematic analysis of turn-control strategies, offering simple yet effective guidelines for developers to balance cost and efficacy. We demonstrate that dynamic resource allocation is a superior, easy-to-implement approach for deploying powerful yet economically viable coding agents.

'Guerilla' liberals form a'Fight Club' to oust Schumer after walking right into Trump's Oval Office trap Billionaire family posts VERY unusual obituary after heir, 40, met violent end at $2.8m hunting lodge following marriage scandal I know why Usha Vance ditched her wedding ring. Most women would do the same if they'd suffered her humiliation: KENNEDY'Canceled' comedian Louis CK devours Hollywood legend's widow on streets of NYC as steamy romance is revealed Troubled 350lbs son of Hollywood icon is forced to humiliating new low... as his movie star brother luxuriates in $7m Montecito mansion'Dementia gene' now linked to another devastating neurological disease, study shows Trump's losing control... MAGA's imploding... and White House insiders tell me why they're REALLY worried: ANDREW NEIL Tourists warned against visiting 8 popular destinations in 2026 - including European hotspot where locals don't want you Dawson's Creek star James Van Der Beek looks healthy in new social media video as his wife gushes'he's bouncing back' amid cancer battle Her moving videos about the handsome boyfriend who ghosted her went viral and catapulted her to overnight fame. Anna Kepner's grim cause of death aboard Carnival cruise ship confirmed, as homicide investigation continues Brigitte Bardot, 91, is rushed to hospital again as she battles a'serious illness' after undergoing surgery One of America's best-known billionaire's secret thoughts about Trump's state of mind revealed World's coolest streets revealed - as two UK high streets make the top 31 Disney brings Olaf to life! AI-powered snowman robot can walk and talk just like the Frozen character - as delighted fans say'it's like he jumped right off the screen' READ MORE: Inventor is forced to cut robot open to prove there's no-one inside Disney has brought one of its most legendary characters to life - and he's seriously worth melting for. Measuring just three feet (one metre) tall, Olaf the robot can walk and talk just like the delightful eternally optimistic snowman from the Frozen movies.

Kennedy granddaughter Tatiana Schlossberg's terminal cancer diagnosis sheds light on acute myeloid leukemia warning signs and symptoms to watch for, with hope on the horizon for treatment.

These thoughtful and beautifully designed gifts will delight everyone in your circle. Craighill, the maker of desk accessories, key rings, and other fine lifestyle items, has kept things simple and functional with its Chroma scissors. The stainless steel slicers are sturdy and easy to use, but also gorgeous, with primary accent colors that help the scissors stand out in a crowded pen cup. Craighill has been racing to keep these in stock, but they're proving incredibly popular, so don't wait. A hooded parka is a must for anyone who lives where the ground freezes. This is one of Patagonia's toastiest offerings, with a waterproof shell stuffed with 700-fill-power down, a fully insulated cinchable hood, and storm flaps on the inside and outside of the zipper.

'Guerilla' liberals form a'Fight Club' to oust Schumer after walking right into Trump's Oval Office trap Billionaire family posts VERY unusual obituary after heir, 40, met violent end at $2.8m hunting lodge following marriage scandal I know why Usha Vance ditched her wedding ring. Most women would do the same if they'd suffered her humiliation: KENNEDY'Canceled' comedian Louis C.K. devours Hollywood legend's widow on streets of NYC as steamy romance is revealed Troubled 350lbs son of Hollywood icon is forced to humiliating new low... as his movie star brother luxuriates in $7m Montecito mansion Brigitte Bardot, 91, is rushed to hospital again as she battles a'serious illness' after undergoing surgery'Dementia gene' now linked to another devastating neurological disease, study shows Trump's losing control... MAGA's imploding... and White House insiders tell me why they're REALLY worried: ANDREW NEIL Anna Kepner's grim cause of death aboard Carnival cruise ship confirmed, as homicide investigation continues Dawson's Creek star James Van Der Beek looks healthy in new social media video as his wife gushes'he's bouncing back' amid cancer battle Her moving videos about the handsome boyfriend who ghosted her went viral and catapulted her to overnight fame. Pam Bondi's furious response after beauty queen prosecutor who upstaged her has Comey and James indictments thrown out by judge Google Maps blunder turns tiny village into shortcut route, causing it to be'bombarded' by lorries that are damaging people's Grade II-listed homes READ MORE: Scientists issue warning over mind-altering'brain weapons' There are five key stages of the human brain, a new study has revealed. Researchers from the University of Cambridge compared brain scans of 3,802 people aged between 0 and 90. Their analysis revealed that the average human life is split up by four pivotal'turning points' between five key stages - childhood, adolescence, adulthood, early ageing, and late ageing.

The brain goes through five distinct phases in life, with key turning points at ages nine, 32, 66 and 83, scientists have revealed. Around 4,000 people up to the age of 90 had scans to reveal the connections between their brain cells. Researchers at the University of Cambridge showed that the brain stays in the adolescent phase until our early thirties when we peak. They say the results could help us understand why the risk of mental health disorders and dementia varies through life. The brain is constantly changing in response to new knowledge and experience - but the research shows this is not one smooth pattern from birth to death.

Walkability is a key component of sustainable urban development. In walkability studies, collecting detailed pedestrian infrastructure data remains challenging due to the high costs and limited scalability of traditional methods. Sidewalk delivery robots, increasingly deployed in urban environments, offer a promising solution to these limitations. This paper explores how these robots can serve as mobile data collection platforms, capturing sidewalk-level features related to walkability in a scalable, automated, and real-time manner. A sensor-equipped robot was deployed on a sidewalk network at KTH in Stockholm, completing 101 trips covering 900 segment records. From the collected data, different typologies of features are derived, including robot trip characteristics (e.g., speed, duration), sidewalk conditions (e.g., width, surface unevenness), and sidewalk utilization (e.g., pedestrian density). Their walkability-related implications were investigated with a series of analyses. The results demonstrate that pedestrian movement patterns are strongly influenced by sidewalk characteristics, with higher density, reduced width, and surface irregularity associated with slower and more variable trajectories. Notably, robot speed closely mirrors pedestrian behavior, highlighting its potential as a proxy for assessing pedestrian dynamics. The proposed framework enables continuous monitoring of sidewalk conditions and pedestrian behavior, contributing to the development of more walkable, inclusive, and responsive urban environments.

This study proposes a Quantum Fourier Transform (QFT)-enhanced quantum kernel for short-term time-series forecasting. Exogenous predictors are incorporated by convexly fusing feature-specific kernels. For both quantum and classical models, the only tuned quantities are the feature-mixing weights and the KRR ridge α; classical hyperparameters (γ, r, d) are fixed, with the same validation set size for all models. Experiments are conducted on a noiseless simulator (5 qubits; window length L=32). Limitations and ablations are discussed, and paths toward NISQ execution are outlined. Introduction Quantum Machine Learning (QML) is an emerging discipline that combines the principles of quantum physics with traditional machine learning (ML) to exploit the distinctive characteristics of quantum systems, including superposition and entanglement phenomena [1]. This distinction facilitates the expeditious execution of certain tasks [2], such as classification and dimensionality reduction, where QML has demonstrated significant acceleration [3]. QML applications have extended to time-series data, leveraging quantum phenomena to model complex temporal dependencies. The goal is to enhance the results of traditional tasks by performing computations on qubits, which can process data more efficiently than classical bits [4, 5]. For example, Thakkar et al. [6] demonstrated that quantum machine-learning methods could enhance financial forecasting by improving both churn prediction and credit-risk assessment. Likewise, Kea et al. [7] developed a hybrid quantum-classical Long Short-Term Memory (QLSTM) to improve stock-price forecasting by leveraging quantum data encoding and high-dimensional quantum representations.

Supervised learning with large-scale data usually leads to complex optimization problems, especially for classification tasks with multiple classes. Stochastic subgradient methods can enable efficient learning with a large number of samples for classification techniques that minimize the average loss over the training samples. However, recent techniques, such as minimax risk classifiers (MRCs), minimize the maximum expected loss and are not amenable to stochastic subgradient methods. In this paper, we present a learning algorithm based on the combination of constraint and column generation that enables efficient learning of MRCs with large-scale data for classification tasks with multiple classes. Experiments on multiple benchmark datasets show that the proposed algorithm provides upto a 10x speedup for general large-scale data and around a 100x speedup with a sizeable number of classes.

Financial time series forecasting is central to trading, portfolio optimization, and risk management, yet it remains challenging due to noisy, non-stationary, and heterogeneous data. Recent advances in time series foundation models (TSFMs), inspired by large language models, offer a new paradigm for learning generalizable temporal representations from large and diverse datasets. This paper presents the first comprehensive empirical study of TSFMs in global financial markets. Using a large-scale dataset of daily excess returns across diverse markets, we evaluate zero-shot inference, fine-tuning, and pre-training from scratch against strong benchmark models. We find that off-the-shelf pre-trained TSFMs perform poorly in zero-shot and fine-tuning settings, whereas models pre-trained from scratch on financial data achieve substantial forecasting and economic improvements, underscoring the value of domain-specific adaptation. Increasing the dataset size, incorporating synthetic data augmentation, and applying hyperparameter tuning further enhance performance.