The rise of Large Language Models (LLMs) has driven progress in reasoning tasks, from program synthesis to scientific hypothesis generation, yet their ability to handle ranked preferences and structured algorithms in combinatorial domains remains underexplored. We study matching markets, a core framework behind applications like resource allocation and ride-sharing, which require reconciling individual ranked preferences to ensure stable outcomes. We evaluate seven stateof-the-art models on a hierarchy of preference-based reasoning tasks--ranging from stable-matching generation to instability detection, instability resolution, and finegrained preference queries--to systematically expose their logical and algorithmic limitations in handling ranked inputs. Surprisingly, even top-performing models with advanced reasoning struggle to resolve instability in large markets, often failing to identify blocking pairs or execute algorithms iteratively. We further show that parameter-efficient fine-tuning (LoRA) significantly improves performance in small markets, but fails to bring about a similar improvement in large instances, suggesting the need for more sophisticated strategies to improve LLMs' reasoning with larger-context inputs.

Preference-based data often appear complex and noisy but may conceal underlying homogeneous structures. This paper introduces a novel framework of ranking structure recognition for preference-based data. We first develop an approach to identify dynamic ranking groups by incorporating temporal penalties into a spectral estimation for the celebrated Bradley-Terry model. To detect structural changes, we introduce an innovative objective function and present a practicable algorithm based on dynamic programming. Theoretically, we establish the consistency of ranking group recognition by exploiting properties of a random'design matrix' induced by a reversible Markov chain. We also tailor a group inverse technique to quantify the uncertainty in item ability estimates. Additionally, we prove the consistency of structure change recognition, ensuring the robustness of the proposed framework. Experiments on both synthetic and real-world datasets demonstrate the practical utility and interpretability of our approach.

A panther, a leopard and a Canadian lynx are among the reported sightings of big cats in Wales, according to a Freedom of Information (FOI) request. Fifteen big cats were reported to authorities in Wales between January 2020 and July 2025, the FOI to the Welsh government found. The apparent spottings were made in areas ranging from Pembrokeshire to Ceredigion, Powys, Swansea, Denbighshire and Carmarthenshire. One reporter described seeing what they believed was a panther jumping over a hedge onto the road in front of them while they were driving. A leopard sighting was reported to Dyfed-Powys Police in Cwmtwrch, Swansea, on 16 January 2023, when the reporter saw a leopard with spots walking around the garden when their dog was let out.

The UK government is introducing a ban on social media for children and a minimum age for some chatbots in an attempt to shield young people from dangerous corners of the web. UK prime minister Keir Starmer has been leading the charge on under-16 social media regulation. Children under the age of 16 will be banned from social media platforms in the UK, under new measures announced by prime minister Keir Starmer on Monday. "The need for action could not be clearer. Social media is making our children unhappy and unsafe," said Starmer, in an X post .

Rank One, whose board includes a former CIA deputy director and a former FBI science chief, supplied face recognition to Meta for internal development of its smart glasses app. Meta is testing face-recognition software built by a company that sells surveillance tools to police departments and the United States military, as it explores bringing the technology to its smart glasses, WIRED has learned. The arrangement is documented in a software license, obtained by WIRED, that was issued by Rank One Computing--a Denver-based company that derives roughly 80 percent of its revenue from government clients--and is tied to a test version of the Meta AI app that powers Meta's Ray-Ban and Oakley smart glasses . Rank One's face recognition has been bought by the US Marshals Service, which uses it to confirm prisoners' identities without fingerprinting them during transport, and by the Naval Criminal Investigative Service--the Navy's police force--which purchased the company's video tool, ROC Watch. Rank One developed long-range face recognition for US Special Operations Command under a government research contract, saying its software could identify a face from as far as a kilometer away.

Causality in time series can be challenging to determine, especially in the presence of non-linear dependencies. Granger causality helps analyze potential relationships between variables, thereby offering a method to determine whether one time series can predict--Granger cause--future values of another.

Large language models (LLMs) demonstrate impressive generalization abilities, yet adapting them effectively across multiple heterogeneous domains remains challenging due to inter-domain interference. To overcome this challenge, we propose a partition-based multi-stage fine-tuning framework designed to exploit inter-domain synergies while minimizing negative transfer.

In this paper, we investigate the adversarial robustness of nonparametric regression, a fundamental problem in machine learning, under the setting where an adversary can arbitrarily corrupt a subset of the input data. While the robustness of parametric regression has been extensively studied, its nonparametric counterpart remains largely unexplored. We characterize the adversarial robustness in nonparametric regression, assuming the regression function belongs to the second-order Sobolev space (i.e., it is square integrable up to its second derivative). The contribution of this paper is two-fold: (i) we establish a minimax lower bound on the estimation error, revealing a fundamental limit that no estimator can overcome, and (ii) we show that, perhaps surprisingly, the classical smoothing spline estimator, when properly regularized, exhibits robustness against adversarial corruption. These results imply that if o(n) out of n samples are corrupted, the estimation error of the smoothing spline vanishes as n . On the other hand, when a constant fraction of the data is corrupted, no estimator can guarantee vanishing estimation error, implying the optimality of the smoothing spline in terms of maximum tolerable number of corrupted samples.

SHAP (SHapley Additive exPlanations) values are a widely used method for local feature attribution in interpretable and explainable AI. We propose an efficient two-stage algorithm for computing SHAP values in both black-box setting and tree-based models. We assume the black-box predictor or tree model accepts binary (zero-one) features.

Gene regulatory network inference (GRNI) aims to discover how genes causally regulate each other from gene expression data. It is well-known that statistical dependencies in observed data do not necessarily imply causation, as spurious dependencies may arise from latent confounders, such as non-coding RNAs. Numerous GRNI methods have thus been proposed to address this confounding issue. However, dependencies may also result from selection-only cells satisfying certain survival or inclusion criteria are observed-while these selection-induced spurious dependencies are frequently overlooked in gene expression data analyses. In this work, we show that such selection is ubiquitous and, when ignored or conflated with true regulations, can lead to flawed causal interpretation and misguided intervention recommendations. To address this challenge, a fundamental question arises: can we distinguish dependencies due to regulation, confounding, and crucially, selection? We show that gene perturbations offer a simple yet effective answer: selection-induced dependencies are symmetric under perturbation, while those from regulation or confounding are not. Building on this motivation, we propose GISL (Gene regulatory network Inference in the presence of Selection bias and Latent confounders), a principled algorithm that leverages perturbation data to uncover both true gene regulatory relations and non-regulatory mechanisms of selection and confounding up to the equivalence class. Experiments on synthetic and real-world gene expression data demonstrate the effectiveness of our method.