Benchmarks shape scientific conclusions about model capabilities and steer model development. This creates a feedback loop: stronger benchmarks drive better models, and better models demand more discriminative benchmarks. Ensuring benchmark reliability is therefore essential for trustworthy evaluation and meaningful progress. In this work, we study benchmark reliability from a distributional perspective and introduce benchmark harmony, which measures how uniformly a model's performance is distributed across the subdomains of a benchmark. We posit that high harmony is a desirable benchmark property, indicating that the aggregate metric reflects uniform competence across subdomains. Across 19 multiple-choice benchmarks and five model families, we map each benchmark onto a mean-variance plane of harmony computed across models, where high mean and low variance signal more reliable evaluation. Our analysis shows that less harmonious benchmarks can give misleading results, since overall accuracy may be disproportionately influenced by specific subdomains. For instance, ARC-Easy is overwhelmed by questions on Biological Concepts, overshadowing other critical subdomains such as Geography, Physics, Chemistry, and Environmental Science. By recommending that harmony should be reported alongside accuracy, we reframe evaluation from simple performance averages to a more robust, distributionally reliable measurement of performance.

Mainstream news organizations shape public perception not only directly through the articles they publish but also through the choices they make about which topics to cover (or ignore) and how to frame the issues they do decide to cover. However, measuring these subtle forms of media bias at scale remains a challenge. Here, we introduce a large, ongoing (from January 1, 2024 to present), near real-time dataset and computational framework developed to enable systematic study of selection and framing bias in news coverage. Our pipeline integrates large language models (LLMs) with scalable, near-real-time news scraping to extract structured annotations -- including political lean, tone, topics, article type, and major events -- across hundreds of articles per day. We quantify these dimensions of coverage at multiple levels -- the sentence level, the article level, and the publisher level -- expanding the ways in which researchers can analyze media bias in the modern news landscape. In addition to a curated dataset, we also release an interactive web platform for convenient exploration of these data. Together, these contributions establish a reusable methodology for studying media bias at scale, providing empirical resources for future research. Leveraging the breadth of the corpus over time and across publishers, we also present some examples (focused on the 150,000+ articles examined in 2024) that illustrate how this novel data set can reveal insightful patterns in news coverage and bias, supporting academic research and real-world efforts to improve media accountability.

Ensuring power grid resilience requires the timely and unsupervised detection of anomalies in synchrophasor data streams. We introduce T-BiGAN, a novel framework that integrates window-attention Transformers within a bidirectional Generative Adversarial Network (BiGAN) to address this challenge. Its self-attention encoder-decoder architecture captures complex spatio-temporal dependencies across the grid, while a joint discriminator enforces cycle consistency to align the learned latent space with the true data distribution. Anomalies are flagged in real-time using an adaptive score that combines reconstruction error, latent space drift, and discriminator confidence. Evaluated on a realistic hardware-in-the-loop PMU benchmark, T-BiGAN achieves an ROC-AUC of 0.95 and an average precision of 0.996, significantly outperforming leading supervised and unsupervised methods. It shows particular strength in detecting subtle frequency and voltage deviations, demonstrating its practical value for live, wide-area monitoring without relying on manually labeled fault data.

Black-box optimization (BBO) addresses problems where objectives are accessible only through costly queries without gradients or explicit structure. Classical derivative-free methods -- line search, direct search, and model-based solvers such as Bayesian optimization -- form the backbone of BBO, yet often struggle in high-dimensional, noisy, or mixed-integer settings. Recent advances use machine learning (ML) and reinforcement learning (RL) to enhance BBO: ML provides expressive surrogates, adaptive updates, meta-learning portfolios, and generative models, while RL enables dynamic operator configuration, robustness, and meta-optimization across tasks. This paper surveys these developments, covering representative algorithms such as NNs with the modular model-based optimization framework (mlrMBO), zeroth-order adaptive momentum methods (ZO-AdaMM), automated BBO (ABBO), distributed block-wise optimization (DiBB), partition-based Bayesian optimization (SPBOpt), the transformer-based optimizer (B2Opt), diffusion-model-based BBO, surrogate-assisted RL for differential evolution (Surr-RLDE), robust BBO (RBO), coordinate-ascent model-based optimization with relative entropy (CAS-MORE), log-barrier stochastic gradient descent (LB-SGD), policy improvement with black-box (PIBB), and offline Q-learning with Mamba backbones (Q-Mamba). We also review benchmark efforts such as the NeurIPS 2020 BBO Challenge and the MetaBox framework. Overall, we highlight how ML and RL transform classical inexact solvers into more scalable, robust, and adaptive frameworks for real-world optimization.

Calibrated confidence estimates are necessary for large language model (LLM) outputs to be trusted by human users. While LLMs can express their confidence in human-interpretable ways, verbalized LLM-generated confidence scores have empirically been found to be miscalibrated, reporting high confidence on instances with low accuracy and thereby harming trust and safety. We hypothesize that this overconfidence often stems from a given LLM's heightened suggestibility when faced with claims that it encodes little information about; we empirically validate this hypothesis, finding more suggestibility on lower-accuracy claims. To further improve calibration, we leverage generator-validator disagreement, augmenting normalized validator confidence with a consistency-based estimate of generator confidence. Users often rely on information obtained from these models to make important decisions, but the information is not always accurate. Thus, we seek to qualify LLM responses with confidence estimates that are calibrated, i.e. match the probability of correctness. Users and agentic frameworks often use LLMs in a zero-shot manner without task-specific tuning (Manakul et al., 2023; Geng et al., 2024; Feng et al., 2024; Shorinwa et al., 2025), motivating the development of confidence estimation methods that work in off-the-shelf settings - both gray-box settings with logit access, and black-box settings with only textual input and output. In these settings, verbalized confidence is a simple and commonly-used approach that prompts the model to report its confidence in an answer (Lin et al., 2022; Xiong et al., 2024; Wei et al., 2024). For brevity, we use verbalized confidence as a blanket term for (1) asking the model to decode a numerical confidence like "80%" (Tian et al., 2023) and (2) asking the model whether an answer is correct and taking P(True) (Kadavath et al., 2022). V erbalized confidence is appealing for several reasons, including that it resembles one way humans express confidence, making it easy to interpret and integrate into decision-theoretic frameworks (Sun et al., 2025; Steyvers et al., 2025). However, verbalized confidence has several drawbacks. First, it empirically tends to exhibit overconfidence (Tian et al., 2023; Xiong et al., 2024; Wei et al., 2024; Xu et al., 2025); Figure 1 (left) shows that verbalized confidence scores generally outstrip average accuracy within a confidence bin. For each bar, we label the number of instances whose confidence falls in the interval and we darken larger bins. In other words, no rejection threshold can be chosen to reject a high proportion of false claims.

Molecular foundation models are rapidly advancing scientific discovery, but their unreliability on out-of-distribution (OOD) samples severely limits their application in high-stakes domains such as drug discovery and protein design. A critical failure mode is chemical hallucination, where models make high-confidence yet entirely incorrect predictions for unknown molecules. To address this challenge, we introduce Molecular Preference-Aligned Instance Ranking (Mole-PAIR), a simple, plug-and-play module that can be flexibly integrated with existing foundation models to improve their reliability on OOD data through cost-effective post-training. Specifically, our method formulates the OOD detection problem as a preference optimization over the estimated OOD affinity between in-distribution (ID) and OOD samples, achieving this goal through a pairwise learning objective. We show that this objective essentially optimizes AUROC, which measures how consistently ID and OOD samples are ranked by the model. Extensive experiments across five real-world molecular datasets demonstrate that our approach significantly improves the OOD detection capabilities of existing molecular foundation models, achieving up to 45.8%, 43.9%, and 24.3% improvements in AUROC under distribution shifts of size, scaffold, and assay, respectively.

Gene enhancers control when and where genes switch on, yet their sequence diversity and tissue specificity make them hard to pinpoint in colorectal cancer. We take a sequence-only route and fine-tune DNABERT-2, a transformer genomic language model that uses byte-pair encoding to learn variable-length tokens from DNA. Using assays curated via the Johnston Cancer Research Centre at Queen's University Belfast, we assembled a balanced corpus of 2.34 million 1 kb enhancer sequences, applied summit-centered extraction and rigorous de-duplication including reverse-complement collapse, and split the data stratified by class. With a 4096-term vocabulary and a 232-token context chosen empirically, the DNABERT-2-117M classifier was trained with Optuna-tuned hyperparameters and evaluated on 350742 held-out sequences. The model reached PR-AUC 0.759, ROC-AUC 0.743, and best F1 0.704 at an optimized threshold (0.359), with recall 0.835 and precision 0.609. Against a CNN-based EnhancerNet trained on the same data, DNABERT-2 delivered stronger threshold-independent ranking and higher recall, although point accuracy was lower. To our knowledge, this is the first study to apply a second-generation genomic language model with BPE tokenization to enhancer classification in colorectal cancer, demonstrating the feasibility of capturing tumor-associated regulatory signals directly from DNA sequence alone. Overall, our results show that transformer-based genomic models can move beyond motif-level encodings toward holistic classification of regulatory elements, offering a novel path for cancer genomics. Next steps will focus on improving precision, exploring hybrid CNN-transformer designs, and validating across independent datasets to strengthen real-world utility.

This paper presents a systematic comparative analysis of Variational Quantum Classifier (VQC) configurations for financial fraud detection, encompassing three distinct quantum encoding techniques and comprehensive architectural variations. Through empirical evaluation across multiple entanglement patterns, circuit depths, and optimization strategies,quantum advantages in fraud classification accuracy are demonstrated, achieving up to 94.3 % accuracy with ZZ encoding schemes. The analysis reveals significant performance variations across entanglement topologies, with circular entanglement consistently outperforming linear (90.7) %) and full connectivity (92.0 %) patterns, achieving optimal performance at 93.3 % accuracy. The study introduces novel visualization methodologies for quantum circuit analysis and provides actionable deployment recommendations for practical quantum machine learning implementations. Notably, systematic entanglement pattern analysis shows that circular connectivity provides superior balance between expressivity and trainability while maintaining computational efficiency. These researches offer initial benchmarks for quantum enhanced fraud detection systems and propose potential benefits of quantum machine learning in financial security applications.

The recent progress in TinyML technologies triggers the need to address the challenge of balancing inference time and classification quality. TinyML systems are defined by specific constraints in computation, memory and energy. These constraints emphasize the need for specialized optimization techniques when implementing Machine Learning (ML) applications on such platforms. While deep neural networks are widely used in TinyML, the exploration of Dynamic Ensemble Selection (DES) methods is also beneficial. This study examines a DES-Clustering approach for a multi-class computer vision task within TinyML systems. This method allows for adjusting classification accuracy, thereby affecting latency and energy consumption per inference. We implemented the TinyDES-Clustering library, optimized for embedded system limitations. Experiments have shown that a larger pool of classifiers for dynamic selection improves classification accuracy, and thus leads to an increase in average inference time on the TinyML device. Keywords: Embedded Machine Learning TinyML Dynamic Ensemble Selection.

Digital transformation of modern society has spread the attack surface of critical infrastructures, enterprise networks, and personal devices. Quick propagation of cyber threats, driven by sophisticated adversarial attacks including evasion[8, 82], data poisoning[21], and backdoor insertions[20, 21], weakened traditional security measures across domains including intrusion detection systems (IDS), Internet of Things (IoT) security, and autonomous networks [19, 82, 127, 138]. These attacks exploit machine learning vulnerabilities, vastly expanding attack surfaces amid the proliferation of IoT devices and distributed systems[35, 58, 59]. Generative Adversarial Networks (GANs), first introduced by Goodfellow et al.[1], have transitioned from synthetic data generation to essential defenses, enabling adversarial scenario simulation, dataset augmentation, and model resilience enhancement[16, 32, 33, 139]. Variants like Conditional GANs (CGANs) and Wasserstein GANs (WGANs) excel in producing realistic samples for anomaly detection and IDS robustness[27, 169, 170], outperforming static signature-based approaches against dynamic threats[60, 169, 173]. Yet, GAN applications in Cybersecurity are fragmented, grappling with training instability, dataset scarcity, edge-device computational constraints, and dual-use risks where GANs facilitate both defenses and advanced attacks[11, 13, 24, 34, 44, 61-63, 79, 80]. Recent advancements, such as GAN-IF models for intrusion detection and AR-GAN for autonomous vehicle defenses, underscore potential in real-time mitigation, but ethical frameworks and unified evaluations remain deficient[78, 81]. This gap necessitates a systematic literature review (SLR) to consolidate GAN architectures, applications, and performance metrics for proactive adversarial defense. 1