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Each LoRA checkpoint compactly stores task-specific updates in low-rank weight matrices, offering an efficient way to adapt large language models to new tasks and domains. In principle, these weights already encode what the adapter does and how well it performs. In this paper, we ask whether this information can be read directly from the weights, without running the base model or accessing training data. A key obstacle is that a single LoRA update can be factorized in infinitely many ways. Without resolving this ambiguity, models trained on the factors may fit the particular factorization rather than the underlying update. To this end, we propose \methodfull, which maps each LoRA update to a provably canonical form via QR decomposition followed by SVD, so that all equivalent factorizations share the same representation. The resulting components are then tokenized and processed by a Transformer to produce a weight-space embedding. Across language and vision LoRA collections, W2T achieves strong results on attribute classification, performance prediction, and adapter retrieval, demonstrating that LoRA weights reliably indicate model behavior once factorization ambiguity is removed. C

One of the most debated consequences of the Milky Way's last major merger is the so-called $Splash$: stars with disc-like chemistry but halo-like kinematics, often interpreted as evidence for the violent heating of an early protodisc. Using the same high-resolution NIHAO-UHD cosmological simulation analysed in Buder et al. (2025b, hereafter Paper I), we test whether, and if so how, a Splash-like population arises in the Milky Way analogue. By tracing stellar birth positions, ages, and present-day orbits, we find that protodisc stars were already born on dynamically hot orbits, with no evidence for significant additional dynamical $splashing$ of these particular in-situ stars despite a 1:5 stellar mass merger. The observed Splash may therefore reflect the already turbulent early disc, subsequently intermixed with accreted stars and those formed from merger-driven gas inflows, rather than a distinct merger-heated population. When selecting stars with similar chemistry and age as the Splash-like ones, we find their azimuthal velocity distribution to be broad and positively skewed, with $V_\varphi = 73_{-59}^{+74}\,\mathrm{km\,s^{-1}}$. The transition to a rotation-supported disc with

We revisit millimeter-wave (mmWave) human pose estimation (HPE) from a signal preprocessing perspective. A single mmWave frame provides structured dimensions that map directly to human geometry and motion: range, angle, and Doppler, offering pose-aligned cues that are not explicitly present in RGB images. However, recent mmWave-based HPE systems require more parameters and compute resources yet yield lower estimation accuracy than vision baselines. We attribute this to preprocessing modules: most systems rely on data-driven modules to estimate phenomena that are already well-defined by mmWave sensing physics, whereas human pose could be captured more efficiently with explicit physical priors. To this end, we introduce processing modules that explicitly model mmWave's inter-dimensional correlations and human kinematics. Our design (1) couples range and angle to preserve spatial human structure, (2) leverages Doppler to retain human motion continuity, and (3) applies multi-scale fusion aligned with the human body. A lightweight MLP is involved as the regressor. In experiments, this framework reduces the number of parameters by 55.7-88.9% on the HPE task relative to existing mmWave ba

In this work, we explore the nature of $z>1$ galactic bars. Once thought to be highly transient, our results demonstrate otherwise. Our sample consists of nine massive ($>10^{10.5}\,\rm M_{\odot}$) star-forming barred-spiral galaxies at $z_{\rm spec} \sim 1.5$. Using rest-frame near-IR (F444W) JWST/NIRCam imaging, we apply ellipse fitting along with 1D and 2D morphological modeling to directly measure bar properties. We find that five galaxies host flat surface brightness profiles (bar Sérsic index $<0.4$), indicative of highly evolved, "mature" bars. By contrast, only two galaxies show exponential profiles, characteristic of young bars, and these are also shorter in absolute length than the flat bars. We therefore conclude that a large fraction of bars at this epoch have already matured, thereby indicating the presence of well-settled disks required to facilitate bar formation and sustained evolution well before $z\sim1.5$. To assess the gravitational impact of the bars, we calculate the maximum transverse-to-radial force ratio ($Q_{b}$). We find that $Q_{b}$ values are comparable to, or weaker than, those of bars in the local Universe, Seven of the nine bars show only a

Image composition aims to seamlessly insert a user-specified object into a new scene, but existing models struggle with complex lighting (e.g., accurate shadows, water reflections) and diverse, high-resolution inputs. Modern text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential physical and resolution priors, yet lack a framework to unleash them without resorting to latent inversion, which often locks object poses into contextually inappropriate orientations, or brittle attention surgery. We propose SHINE, a training-free framework for Seamless, High-fidelity Insertion with Neutralized Errors. SHINE introduces manifold-steered anchor loss, leveraging pretrained customization adapters (e.g., IP-Adapter) to guide latents for faithful subject representation while preserving background integrity. Degradation-suppression guidance and adaptive background blending are proposed to further eliminate low-quality outputs and visible seams. To address the lack of rigorous benchmarks, we introduce ComplexCompo, featuring diverse resolutions and challenging conditions such as low lighting, strong illumination, intricate shadows, and reflective surfaces. Experiments on Compl

The diameter of a polytope is a fundamental geometric parameter that plays a crucial role in understanding the efficiency of the simplex method. Despite its central nature, the computational complexity of computing the diameter of a given polytope is poorly understood. Already in 1994, Frieze and Teng [Comp. Compl.] recognized the possibility that this task could potentially be harder than NP-hard, and asked whether the corresponding decision problem is complete for the second stage of the polynomial hierarchy, i.e. $Π^p_2$-complete. In the following years, partial results could be obtained. In a cornerstone result, Frieze and Teng themselves proved weak NP-hardness for a family of custom defined polytopes. Sanità [FOCS18] in a break-through result proved that already for the much simpler fractional matching polytope the problem is strongly NP-hard. Very recently, Steiner and Nöbel [SODA25] generalized this result to the even simpler bipartite perfect matching polytope and the circuit diameter. In this paper, we finally show that computing the diameter of the bipartite perfect matching polytope is $Π^p_2$-hard. Since the corresponding decision problem is also trivially contained in

Generative AI has the potential to transform how public services are delivered by enhancing productivity and reducing time spent on bureaucracy. Furthermore, unlike other types of artificial intelligence, it is a technology that has quickly become widely available for bottom-up adoption: essentially anyone can decide to make use of it in their day to day work. But to what extent is generative AI already in use in the public sector? Our survey of 938 public service professionals within the UK (covering education, health, social work and emergency services) seeks to answer this question. We find that use of generative AI systems is already widespread: 45% of respondents were aware of generative AI usage within their area of work, while 22% actively use a generative AI system. Public sector professionals were positive about both current use of the technology and its potential to enhance their efficiency and reduce bureaucratic workload in the future. For example, those working in the NHS thought that time spent on bureaucracy could drop from 50% to 30% if generative AI was properly exploited, an equivalent of one day per week (an enormous potential impact). Our survey also found a hig

Mathematical capabilities were previously believed to emerge in common language models only at a very large scale or require extensive math-related pre-training. This paper shows that the LLaMA-2 7B model with common pre-training already exhibits strong mathematical abilities, as evidenced by its impressive accuracy of 97.7% and 72.0% on the GSM8K and MATH benchmarks, respectively, when selecting the best response from 256 random generations. The primary issue with the current base model is the difficulty in consistently eliciting its inherent mathematical capabilities. Notably, the accuracy for the first answer drops to 49.5% and 7.9% on the GSM8K and MATH benchmarks, respectively. We find that simply scaling up the SFT data can significantly enhance the reliability of generating correct answers. However, the potential for extensive scaling is constrained by the scarcity of publicly available math questions. To overcome this limitation, we employ synthetic data, which proves to be nearly as effective as real data and shows no clear saturation when scaled up to approximately one million samples. This straightforward approach achieves an accuracy of 82.6% on GSM8K and 40.6% on MATH

In this work, I report that large fraction of stars detected by Ádám et al. (2023, A&A, 674, A170, arXiv:2304.08394) and noted in that work as new discoveries are in fact known systems. This is especially true for the dense bulge fields with large blending of nearby sources. Among the published 245 stars determined to be doubly eclipsing (i.e. containing two eclipsing signals), I identified 53 blends. In other words, about a quarter of the systems noted by Ádám et al. (2023, A&A, 674, A170) are not actually doubly eclipsing; rather, these are contaminations of known nearby sources that have already been detected by OGLE. Such a high proportion of reported false positives should not be readily ignored and ought to be addressed in future studies.

Intracluster light (ICL) is diffuse light from stars that are gravitationally bound not to individual member galaxies, but to the halo of galaxy clusters. Leading theories predict that the ICL fraction, defined by the ratio of the ICL to the total light, rapidly decreases with increasing redshift, to the level of a few per cent at z > 1. However, observational studies have remained inconclusive about the fraction beyond redshift unity because, to date, only two clusters in this redshift regime have been investigated. One shows a much lower fraction than the mean value at low redshift, whereas the other possesses a fraction similar to the low-redshift value. Here we report an ICL study of ten galaxy clusters at 1 \lesssim z \lesssim 2 based on deep infrared imaging data. Contrary to the leading theories, our study finds that ICL is already abundant at z \lesssim 1, with a mean ICL fraction of approximately 17\%. Moreover, no significant correlation between cluster mass and ICL fraction or between ICL color and cluster-centric radius is observed. Our findings suggest that gradual stripping can no longer be the dominant mechanism of ICL formation. Instead, our study supports the sc

Today's sign language recognition models require large training corpora of laboratory-like videos, whose collection involves an extensive workforce and financial resources. As a result, only a handful of such systems are publicly available, not to mention their limited localization capabilities for less-populated sign languages. Utilizing online text-to-video dictionaries, which inherently hold annotated data of various attributes and sign languages, and training models in a few-shot fashion hence poses a promising path for the democratization of this technology. In this work, we collect and open-source the UWB-SL-Wild few-shot dataset, the first of its kind training resource consisting of dictionary-scraped videos. This dataset represents the actual distribution and characteristics of available online sign language data. We select glosses that directly overlap with the already existing datasets WLASL100 and ASLLVD and share their class mappings to allow for transfer learning experiments. Apart from providing baseline results on a pose-based architecture, we introduce a novel approach to training sign language recognition models in a few-shot scenario, resulting in state-of-the-art

The {\sl TESS} space mission has recently demonstrated its great potential to discover new pulsating white dwarf and pre-white dwarf stars, and to detect periodicities with high precision in already known white-dwarf pulsators. We report the discovery of two new pulsating He-rich atmosphere white dwarfs (DBVs) and present a detailed asteroseismological analysis of three already known DBV stars employing observations collected by the {\sl TESS} mission along with ground-based data. We extracted frequencies from the {\sl TESS} light curves of these DBV stars using a standard pre-whitening procedure to derive the potential pulsation frequencies. All the oscillation frequencies that we found are associated with $g$-mode pulsations with periods spanning from $\sim 190$ s to $\sim 936$ s. We find hints of rotation from frequency triplets in some of the targets, including the two new DBVs. For three targets, we find constant period spacings, which allowed us to infer their stellar masses and constrain the harmonic degree $\ell$ of the modes. We also performed period-to-period fit analyses and found an asteroseismological model for three targets, with stellar masses generally compatible wi

Computational pathology methods have the potential to improve access to precision medicine, as well as the reproducibility and accuracy of pathological diagnoses. Particularly the analysis of whole-slide-images (WSIs) of immunohistochemically (IHC) stained tissue sections could benefit from computational pathology methods. However, scoring biomarkers such as KI67 in IHC WSIs often necessitates the detection of areas of invasive cancer. Training cancer detection models often requires annotations, which is time-consuming and therefore costly. Currently, cancer regions are typically annotated in WSIs of haematoxylin and eosin (H&E) stained tissue sections. In this study, we investigate the possibility to register annotations that were made in H&E WSIs to their IHC counterparts. Two pathologists annotated regions of invasive cancer in WSIs of 272 breast cancer cases. For each case, a matched H&E and KI67 WSI are available, resulting in 544 WSIs with invasive cancer annotations. We find that cancer detection CNNs that were trained with annotations registered from the H&E to the KI67 WSIs only differ slightly in calibration but not in performance compared to cancer detect

Astronomy has long had a working network of archives supporting the curation of publications and data. The discipline has already created many of the features which perplex other areas of science: (1) data repositories: (supra)national institutes, dedicated to large projects; a culture of user-contributed data; practical experience of long-term data preservation; (2) dataset identifiers: the community has already piloted experiments, knows what can undermine these efforts, and is participating in the development of next-generation standards; (3) citation of datasets in papers: the community has an innovative and expanding infrastructure for the curation of data and bibliographic resources, and through them a community of author s and editors familiar with such electronic publication efforts; as well, it has experimented with next-generation web standards (e.g. the Semantic Web); (4) publisher buy-in: publishers in this area have been willing to innovate within the constraints of their commercial imperatives. What can possibly be missing? Why don't we have an integrated framework for the publication and preservation of all data products already? Are there technical barriers? We don'

The CMB is already one of the pillars of the Big Bang model. However it may also become our most powerful tool to distinguish contending models and to determine their cosmological parameters. To realize this goal, more than 20 observational groups and two new satellites are gearing up to make precise measurements of the CMB at small angular scales. In such a situation it is important to keep track of what the CMB data can already say about cosmological parameters. Current CMB data can already be used to constrain cosmological parameters. The results are model dependent. We have obtained contraints on Hubble's constant h and the density of the Universe Omega_{o} in the context of open and critical density CDM models with Lambda=0. In critical density models we obtain h=0.30^{+0.18}_{-0.07}. This low value is inconsistent with direct measurements of h but fully consistent with four other cosmological measurements: Big Bang nucleosynthesis, cluster baryonic fraction, age constraints from globular clusters and limits on the shape parameter Gamma of matter power spectra (in Omega_{o}=1 models). If Omega_{o} is left as a free parameter the constraints on h are less restrictive: h=0.40^{+

A common explanation for the failure of deep networks to generalize out-of-distribution is that they fail to recover the "correct" features. We challenge this notion with a simple experiment which suggests that ERM already learns sufficient features and that the current bottleneck is not feature learning, but robust regression. Our findings also imply that given a small amount of data from the target distribution, retraining only the last linear layer will give excellent performance. We therefore argue that devising simpler methods for learning predictors on existing features is a promising direction for future research. Towards this end, we introduce Domain-Adjusted Regression (DARE), a convex objective for learning a linear predictor that is provably robust under a new model of distribution shift. Rather than learning one function, DARE performs a domain-specific adjustment to unify the domains in a canonical latent space and learns to predict in this space. Under a natural model, we prove that the DARE solution is the minimax-optimal predictor for a constrained set of test distributions. Further, we provide the first finite-environment convergence guarantee to the minimax risk,

Much initial research on automatic program repair has focused on experimental results to probe their potential to find patches and reduce development effort. Relatively less effort has been put into understanding the hows and whys of such approaches. For example, a critical assumption of the GenProg technique is that certain bugs can be fixed by copying and re-arranging existing code. In other words, GenProg assumes that the fix ingredients already exist elsewhere in the code. In this paper, we formalize these assumptions around the concept of ''temporal redundancy''. A temporally redundant commit is only composed of what has already existed in previous commits. Our experiments show that a large proportion of commits that add existing code are temporally redundant. This validates the fundamental redundancy assumption of GenProg.

Ultraslow diffusion (i.e. logarithmic diffusion) has been extensively studied theoretically, but has hardly been observed empirically. In this paper, firstly, we find the ultraslow-like diffusion of the time-series of word counts of already popular words by analysing three different nationwide language databases: (i) newspaper articles (Japanese), (ii) blog articles (Japanese), and (iii) page views of Wikipedia (English, French, Chinese, and Japanese). Secondly, we use theoretical analysis to show that this diffusion is basically explained by the random walk model with the power-law forgetting with the exponent $β\approx 0.5$, which is related to the fractional Langevin equation. The exponent $β$ characterises the speed of forgetting and $β\approx 0.5$ corresponds to (i) the border (or thresholds) between the stationary and the nonstationary and (ii) the right-in-the-middle dynamics between the IID noise for $β=1$ and the normal random walk for $β=0$. Thirdly, the generative model of the time-series of word counts of already popular words, which is a kind of Poisson process with the Poisson parameter sampled by the above-mentioned random walk model, can almost reproduce not only th