Papers

Human oversight is widely held to mitigate the risks of AI systems. Even for systems that produce discrete outputs at identifiable decision points, the realisation of human oversight as a reactive measure is empirically fragile, yet increasingly well understood. However, for agentic AI – systems that plan, decompose goals, and execute multi-step actions over extended horizons – reactive oversight reaches its structural limits: intervention on individual actions defeats the autonomy that motivates the deployment, while intervention on aggregate patterns is too coarse for harms whose cumulative consequences only become legible after the fact. This paper argues that reactive oversight must be complemented by an anticipatory mode: oversight exercised before the agent acts, by specifying the normative agenda that structures the space of permissible action and refining it iteratively through specification, runtime, and inspection. The two are complements – the agenda's escalation conditions specify when reactive intervention is invoked. Drawing on Meaningful Human Control, we read anticipatory oversight as the operationalisation of distal-reason tracking. In addition, we argue that the proposed framework yields a specific responsibility architecture by design: occupying the anticipatory mode is the discharge of a role-grounded prospective obligation, and backward-looking responsibility takes the form of strict moral answerability – rationalistic, relational, and holding regardless of fault, in virtue of the principal's prior opportunity for precaution. We develop bridging failure modes, address objections including moral luck and the illusion of control, and close with regulatory, architectural, and empirical implications

Uncovering latent variables and their causal relations from observed data is a fundamental yet challenging problem. Existing methods often rely on restrictive assumptions, such as linear relations or invertible mixing functions. To better address this problem under general nonlinear mixing procedures, we propose a condition called the cross-Hessian Rank Constraint (HRC), which serves as a primitive rank-based tool for nonlinear latent causal discovery. In particular, we show that a rank-based property arises from the cross-Hessian of the observed-data log-density in the nonlinear case, revealing information about the latent variables, and reduces to the Tetrad constraints in the linear Gaussian case. More specifically, when two groups of observed variables are d-separated by a set of lower-dimensional latent variables, the rank of this cross-Hessian is equal to the dimension of the latent variables, under a mild affine derivative assumption on the conditional log-density derivatives. This assumption can be naturally satisfied when the noise level is low or the relevant nonlinearity is moderate. As a downstream application, we instantiate HRC in the pure one-factor measurement setting for locating latent variables and recovering their causal structure up to Markov equivalence. Experimental results on synthetic and real-world datasets support the theoretical claims.

A subset of repeating fast radio bursts (FRBs) is associated with compact persistent radio sources (PRSs). The FRB-PRS hosts differ dramatically from the general FRB host population: they do not follow the distribution of localized non-PRS FRB hosts, nor the SFR-weighted or mass-weighted mock host distributions expected for the general star-forming galaxy population, instead occupying the extreme low-mass, high-sSFR, and low-metallicity end of host-galaxy parameter space. We compare these hosts with well-defined samples of hydrogen-poor superluminous supernovae (SLSNe-I), hydrogen-rich superluminous supernovae (SLSNe-II), and Type II core-collapse supernovae (CCSNe). Using cumulative distribution functions and statistical tests, we find that FRB-PRSs tend to reside in dwarf galaxies with high specific star formation rates and low gas-phase metallicities, even relative to galaxies of similar stellar mass. Their hosts are most similar to those of SLSNe-I, but differ significantly from CCSN hosts, with SLSN-II hosts lying between these populations. These results suggest that FRB-PRSs do not simply trace star formation activity or the general FRB host population, but instead favor SLSN-I-like environments. This preference is consistent with young magnetars formed through low-metallicity massive-star channels, for which SLSN-I-like ejecta imply a characteristic free–free transparency time of ~29 yr at 600 MHz.

In this paper, we present three new constructions of optimal FDRM codes, all derived from subcodes of maximum rank-distance (MRD) codes. The first construction (Theorem~\ref{theo5}) is based on a new family of generator matrices for systematic MRD codes and yields several previously unknown optimal FDRM codes. In particular, for $q\geq 7$, it establishes the optimality of $[\mathcal{F},7]_q$ FDRM codes with $ \mathcal{F}=[1,2,3,4,8,8,8,8,8]$, thereby resolving an open problem posed by Zhang et al. (Des. Codes Cryptogr., 87(1):107–121, 2019). Our second construction exploits structural properties of generator matrices of a family of systematic MRD codes to obtain new optimal FDRM codes whenever each of the rightmost $\delta-2$ columns of the Ferrers diagram $\mathcal{F}$ contains at least $n-1$ dots. Building upon this approach, we further develop a third construction by substantially relaxing this requirement: it is sufficient to assume that each of the rightmost $\delta-2$ columns of $\mathcal{F}$ contains at least $n-r$ dots, where $r<\kappa$ and $\kappa=n-\delta+1$. Furthermore, by exploiting the notion of proper combinations of Ferrers diagrams, we develop several recursive constructions that produce large FDRM codes from smaller building blocks, yielding a number of new optimal families. In particular, for an $n\times n$ Ferrers diagram $\mathcal{F}$ with prescribed parameters, one of these constructions establishes the optimality of $[\mathcal{F},\frac{n}{2}-1]_q$ FDRM codes whenever $n$ is even, thereby settling an open problem posed by Etzion et al. (IEEE Trans. Inf. Theory, 62(4):1616–1630, 2016).

We reproduce and stress-test the work of Yu et al. (2023), who characterize how language models (LMs) arbitrate between memorized knowledge and contradictory in-context statements. We replicate their world-capitals experiments on 31 models spanning Pythia, GPT-2, Qwen3, and Ministral families, including base and post-trained variants, and extend evaluations to five additional knowledge relation types from the ParaConflict dataset. We empirically confirm most of their original findings: larger models and higher-frequency entities tend to favor memorized answers, with substantial family-level variance. However, several conclusions do not generalize cleanly: entity-frequency effects disappear on Qwen3-14B and 32B; post-training shifts the memory-context trade-off inconsistently across families; question phrasing alone can change a model's reliance on memorized knowledge by up to 80 percentage points; and semantically unrelated prose can mimic coherent supporting context. Our results clarify where Yu et al.'s claims hold and to what extent they generalize to other prompts.

Heavy-ion collisions are primarily studied to investigate the properties of hot and dense nuclear matter, known as the quark–gluon plasma (QGP). However, recent observations of hydrodynamic flow-like behaviour in pp and p–Pb collisions at the LHC suggest the possible formation of QGP droplets even in small collision systems. To bridge the multiplicity gap between small and large systems and to examine the applicability of hydrodynamic descriptions, light-ion collisions were performed for the first time at the LHC in 2025. This contribution presents measurements of the near-side peak width of di-hadron correlations in pO, OO, and Ne–Ne collisions with ALICE, used to search for medium effects. The width of the near-side correlation peak is studied as a function of the average charged-particle multiplicity at midrapidity. At low transverse momentum ($p_{\rm T}$), the near-side peak is broader in OO and Ne–Ne collisions than in pp and pO collisions, and in central collisions its width approaches that measured in Pb–Pb collisions. At higher $p_{\rm T}$ the peak narrows modestly with increasing multiplicity. The results are compared with PYTHIA, AMPT, and JETSCAPE calculations, none of which reproduces the measured widths over the full kinematic range.

Knowledge graphs are a core component of today's knowledge infrastructure, supporting reasoning and anchoring knowledge systems to verifiable facts. RDF stores and SPARQL engines fulfill this function, enabling a range of retrieval and inference tasks on structured knowledge. Coupling them with Language Models (LMs) extends RAG toward neurosymbolic reasoning, where structured queries gate or re-rank generative outputs. This line of reasoning requires that SPARQL evaluation natively support tensor operations on dense embeddings, enabling multimodal querying and learned similarity-based ranking to be expressed together with graph-structural constraints. This approach is feasible only if the engine can efficiently perform dense vector search. We present QLever-Unified Indexed Vector Embedding Retrieval (QUIVER), an extension to QLever that adds native support for dense vector retrieval within RDF knowledge graphs. It implements three optimizations: engine-level registration of tensor functions, vocabulary-time parsing of JSON-encoded vectors, and a virtual SERVICE that exposes a vector index inside the query. We propose two new benchmarks: an extension of Berlin Sparql Benchmark (BSBM) with text embeddings and an extension of DBpedia with image embeddings. Against the baselines, vocabulary-time parsing alone yields median speedups of up to 41.9x on BSBM and 20x on DBpedia for single-type ranking; adding an approximate nearest-neighbor index yields speedups of 355x on BSBM and 97.8x on DBpedia. The index further makes cross-modal vector joins on DBpedia feasible in seconds, whereas all non-indexed configurations time out.

We study deterministic relative approximation of the total variation distance between high-dimensional distributions given by succinct descriptions. We develop an abstract deterministic approximation framework based on representing the total variation distance as a support function of a low-dimensional zonotope. As applications, we obtain FPTASs for several models. Given two mixtures of product distributions over $[q]^n$ with a total of $K$ component distributions, our algorithm approximates their TV-distance within a factor of $1+\varepsilon$ in time $\widetilde O_K(nq(n/\varepsilon)^{2K})$. We also give an FPTAS for mixtures of $n$-step Markov chains over $[q]^n$ with a total of $K$ component distributions, with running time $\widetilde O_K(nq^2(n/\varepsilon)^{2K})$. Finally, for two latent-tree Ising models with the same underlying tree topology, we give an FPTAS for the TV-distance between their leaf marginals in time $O(|V|^{13}\varepsilon^{-12})$.

As AI coding agents carry out development tasks with greater autonomy, developers are shifting from direct implementation toward supervising delegated work. Yet existing research offers limited understanding of how developers organize supervisory activities into connected workflows. Drawing on observations and workflow diagrams from 19 experienced developers, we reconfigure Sheridan's framework of human supervisory control into seven stages and the connecting loops for supervising AI coding agents. We applied the framework to public developer discussions on Reddit and found that supervisory demands extend across stages and that developers manage them by concentrating effort in planning, delegating supervisory work to other agents, and turning recurring guidance into reusable assets. Our framework provides a useful analytical lens for understanding how developers supervise AI coding agents by capturing how supervision is structured in agentic software development.

Electroencephalography (EEG) provides non-invasive monitoring of brain activity and is widely used in emotion recognition, motor imagery and sleep staging. Although within-subject decoding has achieved considerable progress, cross-subject generalization remains a central challenge in practical applications. EEG decoders are typically trained with Adam/AdamW under a fixed second-moment decay coefficient, even though cross-subject learning involves low signal-to-noise ratios, subject variability, and gradient nonstationarity. A fixed coefficient implicitly assumes that gradient statistics are homogeneous across layers and time, which can limit model's adaptability to cross-subject EEG signals and degrade generalization. To address these issues, we propose AFOR, a tensor-wise adaptive optimizer that converts the fixed second-moment decay coefficient into a dynamic coefficient estimated online from local gradient state. AFOR combines a Residual-Alignment Signal Scorer (RASS) and an Adaptive Forgetting Controller (AFC). RASS summarizes local gradient residuals and directional agreement into a signal-quality score, and AFC maps this score through self-referential normalization to a bounded per-step decay coefficient, with cumulative-product initialization correction maintaining consistency under time-varying decay. Under a strict cross-subject protocol on three EEG benchmarks that cover three representative fields, AFOR achieves the best average performance among the compared optimizers, improving the mean test accuracy over Adam by 3.00%, 2.07%, and 4.38%, respectively.

This paper develops a staggered discontinuous Galerkin (SDG) scheme based on Cartesian grids for Stokes equations that is simple to implement, intrinsically pressure-robust, and superconvergent for all variables. Instead of the composite meshes used in standard SDG, we construct staggered quadrilateral meshes from Cartesian grids. The scheme takes velocity, pressure, and velocity gradient as unknowns, and employs piecewise-constant spaces with carefully designed staggered continuity. The additional gradient unknowns are locally eliminated via static condensation and can be further removed by mass lumping without loss of accuracy. An explicit pointwise formulation of the scheme is derived, which facilitates implementation and enables a detailed pointwise analysis. We rigorously prove pressure robustness and second-order superconvergence, which holds on general non-uniform Cartesian grids. The scheme is further extended to Navier-Stokes equations by introducing a novel discrete convection term with second-order consistency. Combined with the scalar auxiliary variable (SAV) approach and the Crank-Nicolson (CN) scheme, this yields an unconditionally energy-stable and second-order accurate scheme. Numerical experiments validate the theory and demonstrate accuracy and robustness.

The three narrow pentaquarks $P_c(4312)$, $P_c(4440)$, and $P_c(4457)$ observed by LHCb are widely interpreted as hadronic molecules owing to their proximity to the $\Sigma_c\bar{D}^{(*)}$ thresholds. However, heavy-quark spin symmetry predicts additional partner states that have not been seen experimentally, making the production mechanism in $\Lambda_b^0$ decays particularly important in understanding their nature. We propose a novel production mechanism, which can naturally explain why only these three LHCb $P_c$ states have been clearly observed. The $P_c(4440)$ and $P_c(4457)$ are produced via the $D_{s1}^*(2860)\Lambda_c(2595)\bar{D}\pi$ box diagram leading to $K^-\Sigma_c\bar{D}^{*}$, followed by rescattering into $K^- J/\psi p$. The box diagram develops a box singularity in the vicinity of the $P_c(4457)$, as all four states in the box can go on shell. The $P_c$ states themselves are dynamically generated through the rescattering of the $S$-wave $\Sigma_c\bar{D}^{(*)}$ to $J/\psi p$. The $P_c(4312)$ proceeds analogously via the $D_{s0}(2590)\Lambda_c(2595)\bar{D}^*\pi$ box diagram but without a box singularity. The near-invisibility of the $\Sigma_c^*\bar{D}^{(*)}$ molecular states is attributed to the suppressed production of the $\Lambda_c(2625)$ in $\Lambda_b$ weak decays, as established by the lattice QCD calculations. This mechanism naturally reproduces the data with only two production parameters, whereas previous works required 7 parameters and a finely tuned background, and provides a natural explanation for why the $\Sigma_c^*\bar{D}^{(*)}$ states are barely visible. It yields unique, falsifiable predictions, $\Gamma(\Lambda_b^0\to P_c(4440/4457) K\to J/\psi p K)/\Gamma(\Lambda_b^0\to\bar D_{s1}^*(2860)\Lambda_c(2595))\approx (0.2-0.4)\%$, $\Gamma(\Lambda_b^0\to P_c(4312) K\to J/\psi p K)/\Gamma(\Lambda_b^0\to \bar D_{s0}(2590)\Lambda_c(2595))\approx (0.2-0.8)\%$.

Test oracles for quantum transpilers typically judge correctness by comparing compiled output against a reference: a statevector, a sampled distribution, or a unitary compared modulo global phase. A companion empirical study measures how often that choice fails. Roughly 28% of merged Qiskit transpiler bug-fixes (95% Wilson CI 19-40%) repair a fault that corrupts layout metadata, global phase, or run-to-run reproducibility while output stays correct: invisible to a black-box output-equivalence oracle by construction. This paper closes that gap with a fault-class-matched, layout-aware, width-tiered oracle family: a layout/permutation contract checker and a contract-level metamorphic relation (MR-1) for the metadata channel, a global-phase tracker for the phase channel, and a determinism runner for reproducibility. Verified from source on nine real, merged Qiskit transpiler regressions (three per channel), the output-equivalence oracle is blind throughout and the matched mechanism fires on every case. A 675-configuration sweep of the contract/metadata invariant finds no false positive. Synthetic mutant families confirm reliability at scale: 1.00 sensitivity and specificity across 36 mutants apiece for the contract/metadata and global-phase channels, and 1.00 sensitivity (95% CI 0.44-1.00) for reproducibility on the three circuits where the mutation is constructible. The contract checker costs two to six orders of magnitude less than a plain output check, the global-phase tracker is comparably cheap within its exact tier, and only the metamorphic relation carries a bounded cost. Ported natively to pytket/tket, the global-phase mechanism transfers cleanly, an identical 1.00/1.00 result with phases recovered to double-precision accuracy, evidence against a Qiskit-specific artifact.

Long-term time series forecasting has made significant progress by leveraging multi-scale information to capture hierarchical temporal patterns and model long-range dependencies. However, temporal downsampling in existing multi-scale methods inevitably smooths detailed temporal fluctuations, and this information loss is further aggravated by their emphasis on dominant trends across scales, resulting in insufficiently expressive representations. To address this, we propose a Multi-Scale Wavelet Mixing (MWMixer) model, which incorporates a Bidirectional Frequency-Bands Mixing strategy to recover lost temporal details across scales, enabling complementary cross-scale information interactions. Then, a Dynamic Scale-Adaptive Fusion module learns time-varying weights for each scale to fuse multi-scale forecasts into the final prediction, enhancing the flexibility of multi-scale aggregation. In addition, a cross-scale consistency loss aligns each coarse-scale prediction with the interval-averaged fine-scale outputs, while a multi-scale supervision loss enforces prediction accuracy at each scale, promoting consistent learning across scales. Extensive experiments on seven real-world datasets demonstrate that MWMixer achieves competitive performance in long-term forecasting.

Text-to-image (T2I) models often produce stereotypical images when stereotype-relevant attributes are left unspecified. We ask whether explicit requests can overcome these defaults, especially when the requested attribute contradicts the stereotype. We introduce \mbox{IMPLICIT-Bench}, a challenge benchmark of 1,831 prompt triplets built on a bias knowledge graph (KG) of stereotypes recorded in StereoSet and CrowS-Pairs. Each triplet describes one scene with a neutral prompt, a stereotype prompt, and an anti-stereotype prompt. The neutral prompt measures the model's default, and the two explicit prompts measure whether the model can follow a request on either side of the stereotype. On this benchmark, Qwen-Image depicts the stereotype in 52% of neutral images and the anti-stereotype in 5%. On both Qwen-Image and Stable Diffusion~3, explicit requests that contradict this default succeed far less often than requests that agree with it. Where Qwen-Image's default is strongest, explicitly requesting the stereotype succeeds in 97% of cases, whereas requesting the anti-stereotype succeeds in only 54%. In these scenes, 31% of the images for anti-stereotype requests show the stereotypical attribute instead. We further test whether prompt rewriting and embedding steering can reduce the default on neutral prompts. With the prompt's own KG unit, the best configurations improve a combined bias and alignment score by up to 9.5 points, and with a unit retrieved from the bias KG, by at most 2.9. Debiasing thus depends on knowing which stereotype a prompt evokes.

We study continuous scalar-hair bifurcations from stationary black holes with a nonextremal Cauchy horizon. Although the exterior deformation vanishes at criticality, the critical zero mode generically develops a logarithmic branch at the reference Cauchy horizon, producing a nonuniform critical limit. We show that the coefficient of this logarithm in the scalar perturbation, together with local Cauchy-horizon data, determines the exponential collapse rate of the Einstein-Rosen bridge. If the post-collapse geometry evolves into a Kasner regime, the boundary order-parameter scaling $O\propto|\lambda-\lambda_c|^\beta$ further implies $ 1-p_t\propto|\lambda-\lambda_c|^{2\beta}. $ Crucially, zero-mode matching fixes both the critical exponents and the associated amplitudes, without any fit to the nonlinear interior. We illustrate this mechanism in the transition from rotating BTZ to hairy rotating black holes. For scalarized RN-AdS$_4$ black branes, we quantitatively verify the predicted Kasner scaling using fully nonlinear numerical solutions. At low temperature, two charged models with identical linear critical data instead approach different infrared endpoints, yielding respectively power-law and inverse-logarithmic Kasner scaling. Thus interiors near a finite-temperature bifurcation are controlled by zero-mode transmission, whereas low temperature scaling is governed by the nonlinear infrared completion.

Instance segmentation is a fundamental computer vision task with diverse real-world applications. Recently, prompt-driven foundation models have shown promising generalization. However, automated prompting remains limited by insufficient semantic guidance and inter-instance modeling. To address this challenge, we propose a novel architecture, named Semantic Relational Prompt Refinement Network (SRPR-Net), for automated SAM-based instance segmentation. A sequential prompt refinement mechanism is introduced to enrich detector geometry with visual-language semantics and then incorporate same-image instance dependencies, enabling context-aware box adjustment before SAM segmentation. Experiments on multiple standard benchmarks demonstrate that SRPR-Net achieves consistent improvements in segmentation performance over existing state-of-the-art approaches. The code is publicly available at https://github.com/JeremyXSC/SRPR-Net.

Covert navigation in an uncertain flow couples motion planning with information acquisition. A vehicle must estimate the local flow to navigate, while estimation error induces corrective maneuvers that increase statistical distinguishability, or leakage, and hence the vehicle's detectability. We model this coupling by augmenting position with the estimation-error variance, whose evolution depends on the route through the local information rate. A small-error expansion shows that estimation uncertainty contributes a leading-order correction to the expected detectability rate. The resulting route-planning problem is characterized by a first-order Hamilton–Jacobi–Bellman (HJB) equation. The deadline-indexed value determines the covert-time frontier, while an exact sensitivity formula with respect to sensing quality shows that information acquired earlier along the route can reduce leakage incurred later, whereas information acquired later cannot reduce leakage already incurred. We then formulate a zero-sum sensing-allocation game, show that the leakage rate is convex in the sensing allocation, and prove that the defender may restrict attention to randomization over extreme single-site allocations. The resulting equilibrium is computed by column generation, with vehicle best responses obtained from the HJB equation. Numerical experiments illustrate the learning detour, the deadline–leakage tradeoff, the spatial-ordering effect, and the benefit of randomized sensing allocations.

Quantum computing's environmental footprint remains poorly understood relative to classical infrastructure, and as quantum computing moves toward cloud delivery, Quantum Cloud Service (QCS) providers lack actionable guidance beyond platform-level carbon-accounting frameworks. Objective: This study extends the carbon-aware quantum computing (CQC) framework from a platform-level to a service-level model that translates empirical life cycle assessment (LCA) findings of a superconducting quantum computer into guidance for QCS providers. Method: We modeled the CQC framework via service-level embodied-carbon allocation, load-independent and load-proportional operational decomposition, and a workload-resolved application offset on the basis of results acquired through a cradle-to-grave LCA of a superconducting quantum platform. Results: The five-year footprint is 583 t CO2e (GKP) and 10,570 t (surface-code), dominated by embodied carbon (77.3-85.2%), with operational-embodied parity not reached until 17.0-28.7 years versus 2.7 years for classical comparators. This reorders provider levers: utilisation yields the largest gain (19.7x), followed by service life extension (59.9%) and electricity supply (6.5x), while operational efficiency and renewable procurement offer limited leverage. Conclusion: Superconducting quantum computers are structurally embodied-carbon-dominated, inverting classical sustainability intuition and motivating direct power measurement and cross-architecture validation as quantum infrastructure scales.

Dynamic Time Warping (DTW) is the dominant approach for measuring similarity between time series, yet standard practice discards the optimal warping path after computing a single distance value, losing local alignment information most relevant to clinical diagnosis. We introduce DiaSeg, a framework that extracts diagonal segments from DTW paths with controlled breaks, characterizing each segment by five geometric features (effective length, interruption count, cost variation, temporal position, and path context), and enabling unsupervised pattern discovery without domain-specific feature engineering. Validated on 91 subjects across six clinical conditions (healthy aging, Parkinson's, Huntington's, ALS, brain tumor, and stroke), three findings emerge. First, diagonal segments form consistent unsupervised patterns (silhouette 0.33) aligned with biomechanical phase annotations, with label-based validation confirming near-perfect separation of healthy and pathological gait (ARI up to 0.986). Second, segments discriminate pathology at 69% (supervised) and 75% (patient-level clustering), with pathology manifesting through distributional shifts in segment length; combining segment and cycle-level features further improves classification to 91.7%. Third, while cycle-based methods achieve higher accuracy (91%), diagonal segments provide phase-specific interpretability unavailable in global representations, localizing where coordination breaks down within the gait cycle. DiaSeg thus transforms DTW from a black-box distance into a source of interpretable temporal features for neurodegenerative disease assessment.

We apply StAGE, a data-driven galaxy-evolution framework based on stellar archaeology, to study binary black hole (BBH) mergers in quiescent galaxies (QGs) and their star-forming progenitors. We combine StAGE star-formation and chemical-enrichment histories with pre-computed SEVN binary population-synthesis catalogs to derive the BBH merger-rate density, its dependence on intrinsic binary and host-galaxy properties, and the associated stochastic gravitational-wave (GW) background. We assess uncertainties related to stellar-archaeology prescriptions, common-envelope evolution, and $\alpha$-enhanced abundances. We find that QGs and their progenitors can contribute a sizeable fraction of the cosmic BBH merger rate. For most of the binary-evolution prescriptions explored here, we highlight a tension with the local LVK-inferred merger rates, underscoring the need to revisit some assumptions in the underlying modeling. Most BBHs form at moderately subsolar metallicities, $Z\lesssim Z_\odot/3$, while their merger-time hosts typically have stellar masses $M_\star\lesssim10^{11}\,M_\odot$. At $z\gtrsim2$, hosts lie mainly on the galaxy main sequence, whereas starbursting and quenching descendants contribute increasingly toward lower redshift. Isolated binaries broadly reproduce the observed bulk of the primary- and chirp-mass distributions, but the predictions decline rapidly above $\sim 40-50\,M_\odot$, providing little support for the highest-mass systems inferred by LVK. Dynamical formation in young and globular clusters can populate this regime, but also increases the total merger-rate density, exacerbating the tension with LVK for the fiducial cluster normalizations adopted here. Finally, the predicted stochastic background approaches the projected sensitivity of planned LVK upgrades and lies within reach of the Einstein Telescope.

Shah and Peters showed that a conditional independence test with finite-sample or uniformly-controlled level has only trivial power against any alternative. Most practical tests, however, only claim pointwise asymptotic level. There have been incorrect claims in the literature of conditional independence tests with pointwise asymptotic level and consistency against any alternative; whether such a test actually exists has remained open. We resolve this question. Even restricting to hypotheses with a bounded density on compact subsets of Euclidean spaces, for any sequence of (possibly randomized) tests with pointwise asymptotic level $\alpha$, for every $\varepsilon > 0$ there is a conditionally dependent distribution where the test's limsup power is at most $\alpha + \varepsilon$. This remains true when the level control is required only over conditionally independent distributions with a continuous density and a uniformly continuous conditional law, and the conditionally dependent distributions have smooth densities. On the other hand, the extreme limitation of trivial power against any alternative for uniform-level tests does not apply to tests with pointwise level. We exhibit a test that, without regularity assumptions, has pointwise asymptotic level $\alpha$, strictly higher power for all alternatives, and power tending to one for all distributions whose dependence exceeds a chosen scalar threshold.

Retrieval-Augmented Generation (RAG) systems over enterprise knowledge bases must ingest heterogeneous document formats – PDFs, Word documents, presentations, and scans – whose content is locked inside complex visual layouts, multi-column pages, and dense tables. Rule-based extraction and OCR destroy reading order, flatten tables, and lose heading hierarchy, while fully agentic chunking over extracted text incurs high token costs and hallucination risk. We present Document Retrieval-Aware Chunking (D-RAC), an extension of our Web Retrieval-Aware Chunking (W-RAC) framework to arbitrary document formats. D-RAC first normalizes any input document into PDF, exploiting the fact that virtually every format has a faithful, deterministic PDF rendering. A single multimodal LLM pass then converts rendered pages into retrieval-optimized Markdown – rewriting tables as self-contained prose statements and preserving heading hierarchy – after which chunking proceeds exactly as in W-RAC: deterministic parsing into ID-addressable units followed by lightweight LLM-based chunk planning over identifiers rather than text. Source text is never regenerated during chunking, preserving W-RAC's cost, determinism, and observability benefits while unlocking every renderable format as a first-class input. On the 236-document, 795-page PDF subset of the RAG-Multi-Corpus benchmark spanning five enterprise domains, D-RAC converts and chunks the entire corpus in 72 minutes with zero errors, producing 1,748 retrieval-ready chunks. Compared to agentic chunking with frontier LLMs, D-RAC reduces chunking-stage output tokens by 95.7%, cutting chunking cost by 77.8% (GPT-4.1 pricing) to 85.6% (Gemini 2.5 Pro pricing) and chunking time by 75%. D-RAC scales linearly to documents of 500+ pages.

Traditionally, the reliability of news publishers is assessed by expert organisations that evaluate editorial practices, transparency and factual standards at source. When this process is translated into a computational approach, the problem is often formulated at the level of individual articles, with models being trained on a set of pre-labelled articles and their performance being evaluated in a test phase. In this work, we investigate news source reliability inference as a source-level prediction problem. We propose a two-stage framework in which transformer-based language models first estimate the reliability of individual articles and subsequently aggregate article-level predictions to infer the reliability of previously unseen publishers. To approximate realistic deployment conditions, we enforce a strict publisher-disjoint evaluation protocol, ensuring that no publisher appears in both training and test sets. Experiments on 19,476 political news articles from 439 English-language publishers labeled with NewsGuard reliability ratings show that aggregation substantially improves robustness and performance, increasing accuracy from approximately 0.60 at the article level to 0.69 at the publisher level. Finally, we analyze how prediction errors vary across political orientations, revealing statistically significant associations between political leaning and misclassification patterns. Overall, our findings show that publisher reliability can be inferred from aggregated textual signals alone, supporting scalable and content-based approaches to automated news source assessment.

Audio-based localization provides a low-cost and illumination-independent sensing solution for anti-UAV early warning. However, existing methods typically rely on a predefined fixed audio segment length, which limits temporal correspondence and creates a trade-off between sufficient acoustic evidence and timely localization. To address this issue, we propose an audio-based localization framework with adaptive temporal correspondence. A probe segment is first used to extract a compact acoustic state that characterizes the reliability and consistency of the observation. Guided by the state, a reinforcement learning controller dynamically determines the required audio window size for each localization decision. The selected audio segment is then processed by a Mamba-based localization network with adaptive temporal feature modulation for 3D position estimation. Extensive experiments demonstrate that our method achieves competitive 3D localization accuracy with substantially reduced temporal correspondence latency compared to SOTA methods and exhibits strong generalization across scenarios.

We consider discrete-time dynamical systems generated by the iteration of asymptotically autonomous maps, whose asymptotic behavior may be either conservative or dissipative. These systems are of particular interest in the modeling of complex phenomena with time-dependent parameter variation. With an appropriate time compactification, the past and future infinity become normally hyperbolic invariant hyperplanes, and thus the plausible asymptotic states of the system can be determined by considering the appropriate fibers of the stable and unstable foliations or suitable sections of the asymptotic manifolds to their compact invariant subsets. In this context, we explore how tipping induced by a change of phase–namely, how a phase shift affects the evolution of the system–is encoded in the unstable manifolds of compact invariant subsets of the past equation. We further connect the tipping phenomenon to the splitting of invariant manifolds and identify certain system reversibilities able to prevent tipping. The theoretical framework we use clarifies the relationships among the various notions of tipping induced by phase-change introduced in the literature.

In this paper, we study the Cesàro-type operator $$ \Ces_{\mu,\beta}f(z)=\int_{[0,1)}\frac{f(tz)}{(1-tz)^\beta}\,d\mu(t), $$ where \(\mu\) is a positive Borel measure on \([0,1)\) and \(\beta>0\). For \(0<p,q<\infty\) and radial doubling weights \(\omega_1,\omega_2\), we prove that \(\Ces_{\mu,\beta}:\AL_p^q(\omega_1)\to\AL_p^q(\omega_2)\) is bounded if and only if $$ \mu([0,1))+\sup_{1/2\le r<1}\frac{\mu([r,1))}{(1-r)^\beta}\left(\frac{\what\omega_2(r)}{\what\omega_1(r)}\right)^{1/p}<\infty, $$ and its norm is comparable to this quantity. For standard weights, our result extends \cite{BlascoMas2026,GalanopoulosSiskakisZhao2025} to the full range $0<p=q<\infty$. We further characterize compactness and the essential norm on average radial integrability spaces induced by doubling weights.

Although text-to-image models can accurately depict subjects and scenes, creators still struggle to specify the fine-grained emotions an image should convey without rewriting its content description. Natural language can suggest emotions, but it offers no control scale with stable meanings and ordered intensities. We propose EMOTRANS, which transforms psychologically grounded valence-arousal-dominance (VAD) coordinates into generation conditions that are independent of the content text and modulated across denoising stages, making emotional style a finely adjustable creative variable. To support this goal, we construct EMOVAD, an art-painting dataset that pairs objective content descriptions with separately collected emotional ratings from multiple annotators. We also coordinate emotional expression and content preservation through dual-branch training with a shared model. Objective and human evaluations show that the framework improves the accuracy of three-dimensional emotion control and produces perceptible, orderable continuous changes while maintaining competitive text alignment and image quality. This work provides a practical emotion-driven approach to image generation that extends objective content depiction to fine-grained emotional adjustment.

We uniformly generalize Goodstein's theorem (Goodstein 1944) and Cichon's independence proof (Cichon 1983) via the concept of maximality quotients.

Radio frequency fingerprint identification (RFFI) is a promising approach to distinguish physical wireless devices using hardware-induced signal imperfections. Conventional RFFI methods only provide discrete device labels and no human-interpretable representation of the relations among received signals. We propose supervised device charting, which maps location-insensitive channel-state information (CSI) fingerprints to a low-dimensional chart that visualizes cluster compactness, overlap, and outliers. We evaluate the method with real-world 5G New Radio (5G NR) measurements from six commercial smartphones and introduce the neighbor label error rate (NLER) to quantify class-separation accuracy. Our results demonstrate that two- and three-dimensional device charts provide an interpretable visualization of the learned RFFI representation. For three-dimensional device charts, the NLER is 0.22% for same-day measurements and 7.38% for measurements from the next day. The device charts reveal a cross-day distribution shift and map the held-out device close to the known device of the same model. Increasing the device chart dimensions further improves cluster separation at the expense of interpretability.