January 2026

In Bayesian single-item auctions, a monotone bidding strategy–one that prescribes a higher bid for a higher value type–can be equivalently represented as a partition of the quantile space into consecutive intervals corresponding to increasing bids. Kumar et al. (2024) prove that agile online gradient descent (OGD), when used to update a monotone bidding strategy through its quantile representation, is strategically robust in repeated first-price auctions: when all bidders employ agile OGD in this way, the auctioneer’s average revenue […]

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In this paper, we present a reproducible benchmarking framework that systematically compares QML models with architecture-matched classical counterparts across three financial tasks: (i) directional return prediction on U.S. and Turkish equities, (ii) live-trading simulation with Quantum LSTMs versus classical LSTMs on the S&P 500, and (iii) realized volatility forecasting using Quantum Support Vector Regression. By standardizing data splits, features, and evaluation metrics, our study provides a fair assessment of when current-generation QML models can match or exceed classical […]

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Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a gradient-boosted decision tree (XGBoost) model to predict Tm for complex alloys based on elemental properties. To ensure physical consistency, we address the issue of data leakage by excluding temperature-dependent thermodynamic descriptors (such as Gibbs free energy of mixing) and instead rely on physically motivated elemental features. The […]

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A 30-year-old Danish entrepreneur is building Southeast Asia’s answer to Hims & Hers. Launched in the Philippines, &you offers private online doctor consultations and discreet medication delivery. By focusing on speed, privacy, and cultural trust, the startup aims to fix healthcare access gaps and reach unicorn status within three years.

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Reinforcement learning drives recent advances in LLM reasoning and agentic capabilities, yet current approaches struggle with both exploration and exploitation. Exploration suffers from low success rates on difficult tasks and high costs of repeated rollouts from scratch. Exploitation suffers from coarse credit assignment and training instability: Trajectory-level rewards penalize valid prefixes for later errors, and failure-dominated groups overwhelm the few positive signals, leaving optimization without constructive direction. To this end, we propose R$^3$L, Reflect-then-Retry Reinforcement Learning with Language-Guided […]

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The intersection of financial assurance and technology has become increasingly critical as organisations navigate complex regulatory landscapes while leveraging data analytics to gain a strategic advantage. Modern audit professionals who combine deep accounting expertise with technical capabilities in data analytics, cloud platforms, and automation represent a new generation of financial stewards capable of delivering both compliance excellence and operational efficiency. The most impactful practitioners understand that audit quality extends beyond checklist completion to encompass risk-based approaches, process optimization, […]

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