arXiv:2607.01197v2 Announce Type: replace Abstract: Quantum computing has emerged as a promising computational paradigm for machine learning (ML), with the potential to offer computational advantages over classical approaches. At this stage, the evidence supporting the performance and advantages of quantum machine learning (QML) models relative to classical models is insufficient. To address this gap, this paper presents an empirical study on the performance of QML models and their classical counterparts. We compare seven model pairs spanning supervised learning and reinforcement learning. Our results indicate that the evaluated quantum machine learning models do not yet surpass the classical baselines in overall prediction performance, policy stability, or training time. Nevertheless, QML remains a promising approach for filtering noise and controlling false positives. Our research findings summarize the challenges facing quantum machine learning across hardware environments, training efficiency, and convergence stability, providing a foundation for research into the robustness and parameter optimization of QML. This work is publicly available at https://github.com/Z-537-437/QML.
Science Journals
arXiv:2512.05903v2 Announce Type: replace Abstract: There is an ongoing interest in using spectroscopy in inertial confinement fusion (ICF) experiments, where dopants such as krypton can provide vital information about the temperature and density of the imploding plasma. While the most advanced tools for calculating Stark profiles are computer simulation models (CSMs), their application to complex lineshapes under the extreme conditions of ICF experiments is computationally challenging. In this manuscript, we present results of several CSM realizations applied to the Stark shape of the krypton He-beta line and its satellites at ICF-relevant conditions (ne = 1e24 to 1e25 cm-3, Te = 3keV). We demonstrate that codes with the same underlying physics but different numerical approaches yield identical results and analyze the differences in the line profile caused by various physical effects.
arXiv:2607.01714v1 Announce Type: new Abstract: Hematologic toxicity (HT) is a major dose-limiting complication of pelvic radiotherapy for cervical cancer. Although radiomic and dosiomic features improve HT prediction beyond dosimetric metrics, their performance is highly sensitive to contour variability, limiting generalizability. We developed a cohort-aware representation-learning framework to address this challenge. We retrospectively analyzed 152 cervical cancer patients treated with pelvic radiotherapy without concurrent chemotherapy. Patients were divided into two cohorts based on the operators performing pelvic bone segmentation. HT prediction models were developed using cohort-specific training, pooled training, statistical harmonization, and a cohort-aware neural network (CANN) that learns shared and cohort-specific representations with contrastive regularization. Performance was evaluated using cross-validation and an independent test set. Cohort-specific models achieved test AUCs of 0.77 and 0.71, outperforming a dosimetry-only model (AUC=0.58). Directly pooling cohorts reduced performance (test AUC=0.64). Statistical harmonization provided limited benefit, while adversarial and correlation-based alignment further degraded performance. CANN achieved the best balance between robustness and generalizability (test AUC=0.72), with ablation studies confirming the importance of cohort-specific representations and contrastive alignment. These results demonstrate that cohort-aware representation learning effectively mitigates contour variability and improves the generalizability of radiomic and dosiomic models for HT prediction.
arXiv:2607.01762v1 Announce Type: new Abstract: Many representation learning problems involve directed relations, such as lexical entailment, sentence entailment, ontology hierarchy, and citation links. Standard Euclidean, cosine, and Mahalanobis heads are symmetric, while generic neural scorers can model directionality but provide limited geometric structure. This paper proposes a role-aware neural convex divergence head for asymmetric representation learning. The head applies source- and target-role projections before evaluating an input-convex neural Bregman divergence, yielding a nonnegative structured score in the role-projected space. We characterize its projected-space identity, source-role convexity, directional-gap decomposition, and Hessian-based local curvature. Experiments on lexical, sentence, ontology, and directed graph benchmarks compare symmetric distances, unstructured asymmetric scorers, order/hyperbolic baselines, plain ICNN-Bregman heads, and the proposed role-aware variant. Across ten random seeds on the main semantic and ontology benchmarks, role-aware projections consistently improve directional accuracy over plain ICNN-Bregman heads while preserving zero observed negative divergence rate. The results also identify a boundary case: on large fixed-feature citation prediction, specialized symmetric or hyperbolic baselines remain stronger in ranking accuracy. Overall, the proposed head is best understood as a structured and interpretable plug-in distance module for tasks where directional relations matter.
arXiv:2504.15427v5 Announce Type: replace Abstract: In automotive software development, as well as other domains, traceability between stakeholder requirements and system requirements is crucial to ensure consistency, correctness, and regulatory compliance. However, erroneous or missing traceability relationships often arise due to improper propagation of requirement changes or human errors in requirement mapping, leading to inconsistencies and increased maintenance costs. Existing approaches do not address traceability between stakeholder and system requirements, and are not validated on industrial data, where the links between requirements are established manually by engineers. Additionally, automotive requirements often exhibit variations in the way they are expressed, posing challenges for training-based approaches. Recent advancements in large language models (LLMs) provide new opportunities to address these challenges. In this paper, we introduce TVR, a requirement Traceability Validation and Recovery approach primarily targeting automotive systems, leveraging LLMs enhanced with retrieval-augmented generation (RAG). TVR is designed to validate existing traceability links and recover missing ones with high accuracy. The experimental results highlight the practical effectiveness of TVR in industrial settings, offering a promising solution for improving requirements traceability in complex automotive systems.
arXiv:2504.20369v4 Announce Type: replace Abstract: Visualizing data is often a crucial first step in data analytics workflows, but growing data sizes pose challenges due to computational and visual perception limitations. As a result, data analysts commonly down-sample their data and work with subsets. Deriving representative samples, however, remains a challenge. This paper focuses on scatterplots, a widely-used visualization type, and introduces a novel sampling objective -- perception-awareness -- aiming to improve sample efficacy by targeting humans' perception of a visualization. We make the following contributions: (1) We propose perception-augmented databases and design PAwS: a novel perception-aware sampling method for scatterplots that leverages saliency maps -- a computer vision tool for predicting areas of attention focus in visualizations -- and models perception-awareness via saliency, density, and coverage objectives. (2) We design ApproPAwS: a fast, perception-aware method for approximate visualizations, which exploits the fact that small visual perturbations are often imperceptible to humans. (3) We introduce the concept of perceptual similarity as a metric for sample quality, and present a novel method that compares saliency maps to measure it. (4) Our extensive experimental evaluation shows that our methods consistently outperform prior art in producing samples with high perceptual similarity, while ApproPAwS achieves up to 100x speed-ups with minimal loss in visual fidelity. Our user study shows that PAwS is often preferred by humans, validating our quantitative findings.
arXiv:2606.03056v2 Announce Type: replace Abstract: As LLM agents adopt large skill libraries, selecting the right subset becomes a structural problem rather than a similarity-matching one: skills depend on, conflict with, specialize, or duplicate one another, a structure invisible to both full enumeration and embedding similarity. We present SkillDAG, which models inter-skill relationships as a typed directed graph and exposes it to an LLM agent as an inference-time, agent-callable structural retrieval interface, queried and evolved during execution rather than baked into a fixed retrieval pipeline: each search returns vector matches, typed-edge neighbors, and conflict signals, and a propose-then-commit protocol lets the agent register execution-backed edges so the graph accumulates structure across episodes. On ALFWorld and SkillsBench with MiniMax-M2.7, SkillDAG reaches 67.1% success and 27.3% reward, exceeding the strongest reported Graph-of-Skills baseline by +12.8 and +8.6 points; the advantage ports to gpt-5.2-codex, and intrinsic SkillsBench Ret@K rises from 65.5 to 78.2 under matched queries. These gains trace to isolable mechanisms: candidate ranking that stays robust as the pool grows 10x where a fixed seeding-diffusion pipeline degrades, and set-monotone online edits that enlarge ground-truth recall without evicting prior hits.
arXiv:2607.01684v1 Announce Type: new Abstract: Tactile sensing can substantially improve contact-rich robotic manipulation, yet its practical deployment remains limited by the fragility, calibration requirements, and maintenance burden of tactile hardware. This raises a fundamental question: can robots benefit from tactile knowledge without requiring tactile sensors at deployment? We present TacImag, a tactile imagination framework that predicts tactile observations from vision and proprioception and uses the generated signals to guide manipulation policies. Trained from paired visuotactile demonstrations, TacImag enables touch-informed manipulation using only visual observations at test time. We evaluate TacImag on six simulated and four real-world manipulation tasks. Across simulation and real-world experiments, imagined tactile observations consistently improve manipulation performance without requiring tactile hardware. In real-world experiments, imagined force fields improve contact-sensitive tasks by 44.4% on average, whereas imagined tactile images improve texture-sensitive tasks by 23.3%, revealing that the effectiveness of tactile imagination depends strongly on the relationship between tactile representation and task requirements. Our results further suggest that tactile imagination does not simply recover missing tactile measurements. Instead, it acts as a form of contact-aware supervision that transforms subtle visual interaction cues into representations that are easier for manipulation policies to exploit.
arXiv:2607.01883v1 Announce Type: new Abstract: Code is the medium through which large language models generate structured artifacts: charts, scientific figures, vector graphics, CAD models, 3D scenes, and hardware designs are all produced by writing programs. In this regime single pass inference is brittle, because the compiler, renderer, or simulator that decides whether the artifact exists is invisible to the model. We present PairCoder, which grounds review in the toolchain and realizes it as two agent pair programming: a Driver agent writes the program, a Navigator agent reviews it against verification evidence (diagnostics, execution results, and renderings of the current artifact beside the target), and the two switch roles when errors persist. Across 17 public benchmarks and seven models from three vendors, PairCoder improves essentially every benchmark whose artifact is verifiable, on full official metric suites rather than execution alone (for example, Blender scene executability 0.20 to 0.78; TikZ compile rate up 10 to 30 points on every model), at 2.9 to 9.2 times single model cost (about 7 times overall). The improvements concentrate where the toolchain provides an informative oracle and the baseline leaves headroom, and the method ties or mildly regresses where the oracle is weak; we frame pair programming as a reliable recipe for verified code driven generation.
arXiv:2607.02488v1 Announce Type: new Abstract: Shared data accelerates scientific progress. Here, I describe the ipseome -- the largest free and open dataset on the topic of human identity. The dataset is designed as reusable research infrastructure, with publicly accessible data repositories, documented measurement procedures, and versioned files for cumulative research on identity. First, I present the motivation for and the ipseological principles driving construction of the ipseome. Then, each component is introduced and discussed. Finally, I summarize the current state of progress toward the ultimate goal.
arXiv:2505.18318v3 Announce Type: replace Abstract: Where do rules come from in online communities? This study investigates how and why online communities adopt and change their rules. We conducted a grounded theory-based analysis of 40 in-depth interviews with community leaders from subreddits, Fandom wikis, and Fediverse servers, and identified seven processes involved in the adoption of online community rules. Our findings reveal that, beyond operational reasons like regulating behavior and solving problems, rules are also adopted and changed for relational reasons, such as signaling or reinforcing community legitimacy and identity to other communities. While rule change was often prompted by challenges during community growth or decline, change also depended on volunteer leaders' work capacity, the presence of member feedback mechanisms, and relational dynamics between leaders and members. Our findings extend prior theories from social computing and organizational research, illustrating how institutionalist and ecological explanations of the relational origins of rules complement operational accounts. Finally, we build on these explanations to offer a set of design propositions that reflect the relational aspects of rules and rulemaking across communities' lifecycles.
arXiv:2505.24624v4 Announce Type: replace Abstract: Augmenting the input of algorithms with predictions is an algorithm design paradigm that suggests leveraging a (possibly erroneous) prediction to improve worst-case performance guarantees when the prediction is perfect (consistency), while also providing a performance guarantee when the prediction fails (robustness). Recently, Xu and Lu [2022] and Agrawal et al. [2024] proposed to consider settings with strategic agents under this framework. In this paper, we initiate the study of budget-feasible mechanism design with predictions. These mechanisms model a procurement auction scenario in which an auctioneer (buyer) with a strict budget constraint seeks to purchase goods or services from a set of strategic agents, so as to maximize her own valuation function. We focus on the online version of the problem where the arrival order of agents is random. We design mechanisms that are truthful, budget-feasible, and achieve a significantly improved competitive ratio for both monotone and non-monotone submodular valuation functions compared to their state-of-the-art counterparts without predictions. Our results assume access to a prediction for the value of the optimal solution to the offline problem. We complement our positive results by showing that for the offline version of the problem, access to predictions is mostly ineffective in improving approximation guarantees.
arXiv:2604.22659v2 Announce Type: replace Abstract: Writing code requires significant time and effort in software development. To automate this process, researchers have made substantial progress using Large Language Models (LLMs) for code generation. Many benchmarks like HumanEval and EvoCodeBench have been created to evaluate LLMs by requiring them to generate code from natural language requirements. However, in enterprise applications and team development, developers typically write code based on structured designs or specifications rather than raw natural language descriptions. This gap between existing benchmarks and real industry development practices means that current benchmark scores may not accurately reflect how much code generation can help automate software development tasks. To address this gap, we propose RealBench, a repository-level code generation benchmark aligned with real-world industry software development practices. Each example includes both natural language requirements and UML diagrams as system design, matching how developers typically receive specifications. Based on the constructed benchmarks, we conduct a systematic evaluation of advanced LLMs' code generation capabilities when provided with structured system designs. The experimental results reveal key insights in current LLMs' capabilities for repo-level code generation aligned with real-world software development practices. First, we notice that regarding repo-level code generation, LLMs show much worse performance and there are significant performance gaps among LLMs. Second, LLMs are good at finding and creating modules defined in UML diagrams, but the quality of generated modules is often poor due to grammar and logic errors. Third, generating the entire repository at once is the best generation strategy on smaller repositories, while generating a complex repository with the module-by-module strategy works better compared to other strategies.
arXiv:2604.26180v2 Announce Type: replace Abstract: With recent semantic query processing engines, semantic aggregation has become a primitive operator, enabling the reduction of a relation into a natural language aggregate using an LLM. However, the resulting semantic aggregate may contain claims that are not grounded in the underlying relation. Verifying such claims is challenging: they often involve quantifiers, groupings, and comparisons over relations that far exceed LLM context windows and require a costly combination of semantic and symbolic processing. We present Evergreen, a system that recasts claim verification as a semantic query processing task with tailored optimizations and provenance capture. Evergreen compiles each claim into a declarative semantic verification query that can execute on the same query engine used to produce the aggregate. To reduce cost, Evergreen avoids unnecessary LLM calls through verification-aware optimizations, including early stopping, relevance sorting, and estimation with confidence sequences, as well as general-purpose optimizations for semantic queries, such as operator fusion, similarity filtering, and prompt caching. Each verdict is accompanied by citations that identify a minimal set of tuples justifying the result, with semantics based on semiring provenance for first-order logic. On a benchmark of production-inspired workloads over restaurant review and customer support datasets, Evergreen's optimized configurations occupy the entire cost-quality Pareto frontier. With a strong LLM, Evergreen preserves verification quality at an F1 of 0.94 while reducing cost by 3.1x relative to unoptimized verification; with a substantially weaker LLM, it surpasses the strongest external baseline's F1 (0.87 vs. 0.83) at 7.0x lower cost.
arXiv:2606.31192v2 Announce Type: replace Abstract: A quasi-metric $(T,\delta_T)$ is an Okamura-Seymour quasimetric if there exists an edge-weighted planar embedded directed graph $G = (V,E,w)$ such that $T$ is a set of terminals on the outerface of $G$ and $\delta_G(t,t') = \delta_T(t,t')$ for every pair $(t,t')\in T\times T$. If $(T,\delta_T)$ is an Okamura-Seymour quasimetric, then $G$ is a planar embedding of $(T,\delta_T)$. In a recent pioneering work, Chen and Tan gave a polynomial-time algorithm to test if a given quasi-metric $(T,\delta_T)$ is an Okamura-Seymour quasimetric. A key step in their proof is existential, which suffices for an efficient testing algorithm but does not imply an efficient embedding algorithm. Our paper closes this gap by giving an algorithmic implementation of their existential step via linear programming. As a result, we obtain the first polynomial-time algorithm for finding a planar embedding of any given Okamura-Seymour quasimetric $(T,\delta_T)$. As an application, we show how to use our planar embedding of Okamura-Seymour quasimetrics to compute a $(1+\epsilon)$-approximate single-source shortest path (SSSP) in planar directed graphs in the distributed CONGEST model in $\widetilde{O}(D)$ rounds for any fixed $\epsilon\in (0,1)$, nearly matching a simple lower bound of $\Omega(D)$ and resolving a fundamental problem in this area. The best-known algorithm for this problem has round complexity $\widetilde{O}(D^2)$.
arXiv:2607.01932v1 Announce Type: new Abstract: Phase noise limits the coherence and stability of soliton microcombs, yet its origin is difficult to trace because multiple noise sources act simultaneously. It is often represented by common-mode and repetition-rate components, but how each physical source contributes to these components remains unclear. We combine subspace tracking with multi-source Ikeda-map simulations, switching each source and the Raman nonlinearity on and off to isolate its contribution. Without Raman, pump phase noise is purely common-mode, while shot noise and amplified spontaneous emission drive the repetition rate noise. With Raman, the nonlinearity coherently converts pump phase noise from common-mode into repetition-rate noise without introducing an independent noise source, yielding a parabolic linewidth profile with a quiet-point minimum below the pump linewidth. When all noise sources are present, shot noise, ASE, and RIN raise the common-mode floor and shift this minimum toward the pump, setting the achievable noise floor. The intracavity dynamics thus do not merely carry noise but actively partition it, providing a mechanistic basis for low-noise microcomb design.
arXiv:2507.20619v4 Announce Type: replace Abstract: Test cases are valuable assets for maintaining software quality. State-of-the-art automated test generation techniques typically focus on maximizing program branch coverage or translating focal methods into test code. However, in contrast to branch coverage or code-to-test translation, practical tests are written out of the need to validate whether a requirement has been fulfilled. Specifically, each test usually reflects a developer's validation intention for a program function, regarding (1) what is the test scenario of a program function? and (2) what is expected behavior under such a scenario? Without taking such intention into account, generated tests are less likely to be adopted in practice. In this work, we propose IntentionTest, which generates project-specific tests given the description of validation intention. IntentionTest adopts a retrieval-and-edit manner. First, given a focal code and a description of validation intention consisting of a test objective with test precondition and expected results, IntentionTest retrieves a reusable test in the project as the test reference. Then, IntentionTest edits the test reference with an LLM regarding the validation intention toward the target test. We extensively evaluate IntentionTest against four baselines on 3,680 test cases. Compared to state-of-the-art baselines, IntentionTest can (1) generate tests far more semantically relevant to ground-truth tests by (i) killing 28.1% to 37.6% more common mutants and (ii) sharing 16.9% to 23.9% more common coverage; and (2) generate 23.7% to 49.0% more successful passing tests.
arXiv:2607.02472v1 Announce Type: new Abstract: This paper presents a methodology for learning a control policy to intercept an intruder using the 3D direction unit vector to the intruder and the interceptor state. Prior deep reinforcement learning approaches assume either relative position or distance to the intruder is available, but this information is not readily accessible in real-world applications that employ passive, monocular camera sensors. Instead, we propose a solution that leverages an analytical policy gradient method using differentiable quadrotor dynamics to learn agile interception at speeds up to 10 m/s. The proposed approach outperforms baseline methods that utilize simplified point mass dynamics by an average of 30%.
arXiv:2607.02059v1 Announce Type: new Abstract: File-level copying is a widespread but ungoverned form of software reuse. Copying files across repositories reduces supply-chain visibility: it removes the four observable signals a package manager provides for a declared dependency (provenance, maintenance, security, and compliance) with no mechanism to restore them. To characterize the scale and consequences of this unmanaged reuse, we present a mixed-method study of copying across the entire open-source ecosystem using World of Code (WoC). From a 0.1% commit sample, we extract 690,500 copy events and retain 3,912 rationale-bearing copy commits for intent labeling. We show that the 13 axial copy forms, spanning vendored dependencies, hardware/driver synchronization, scaffolding, UI assets, and direct source-code reuse, are unreliable proxies for developer intent: among rationale-bearing commits, hardware/driver copies are predominantly fork-maintenance work (78%), while dependency-vendoring copies more often signal upstream bypass (70%) than offline availability. These visibility gaps are form-specific: security and license risk concentrate in complementary copy forms. Copied sources are frequently stale (median 155 days; 38.5% over one year old) and seldom record a recoverable origin (4.3% documented), let alone a checkable version (2.0% versioned); even vendored copies record where they came from only 10% of the time. Security risk concentrates in vendored dependencies: 17,314 CVE-risk copy commits in the full-WoC graph, 88% in the dependency-vendoring form; 80% score CVSS >= 7.0 and upstream-fix adoption is only 47%-84%. License risk concentrates in direct source-code reuse: 41,777 pre-validation candidates, 66% in the source-code form, with 39 verified high-star violations (kappa = 0.752). Both risks reach packaged software and are invisible to dependency scanners operating on declared metadata alone.
arXiv:2510.09517v2 Announce Type: replace Abstract: Despite rapid advances in large language models (LLMs), statistical reasoning remains underrepresented in existing LLM benchmarks, which often do not reflect the layered, proof-driven nature of real statistical practice. To address this gap, we introduce \textbf{StatEval}, the first large-scale benchmark for statistical reasoning across curricular and research-level settings. StatEval includes over 100,000 curated problems, with 20,000+ foundational questions spanning undergraduate and graduate curricula and 80,000+ research-level proof tasks extracted from leading statistical journals. To construct StatEval, we develop \textbf{TRACE} (Topology and Reasoning-Aware Context Extractor), a multi-agent pipeline with human-in-the-loop validation that converts unstructured academic texts into self-contained theorem-level reasoning tasks. We also propose an Adaptive Process-Based Scoring Pipeline for complex statistical proofs, enabling fine-grained evaluation beyond final-answer matching. Experiments show that while LLMs perform reasonably on foundational tasks, they struggle with rigorous research-level reasoning. Beyond evaluation, StatEval serves as a resource for improving reasoning, as retrieval-augmented generation and domain-specific alignment consistently enhance performance. Together, these results establish StatEval as both a benchmark and an infrastructure for advancing statistical reasoning in LLMs.
arXiv:2510.10877v2 Announce Type: replace Abstract: This study presents a multiscale econometric framework to evaluate the impact of the USA tariff announcement of 2 April 2025 on the S&P 500 (USA) and S&P/ASX 200 (AUS) stock indices. We employ normalized permutation entropy (PE) to characterise the evolution of ordinal-pattern complexity and compute fractal-dimension estimates (Higuchi, Katz, Sevcik) to assess geometric scaling behaviour across different time-windows. Post-event PE remains uniformly high across windows, with values ranging from 0.666 to 0.913 for the USA and 0.690 to 0.923 for AUS, implying stable distributional entropy and no significant alteration of underlying symbolic dynamics. Fractal dimension estimates exhibit small but systematic changes: the Higuchi dimension increases (USA: 1.507 to 1.561; AUS: 1.524 to 1.533), indicating a marginal rise in high-frequency roughness, while Katz and Sevcik dimensions decline (USA Sevcik: 1.337 to 1.229), consistent with smoother medium-scale structure. Across all measures, the shock does not generate a statistically meaningful structural break. Additionally, machine-learning models (kNN, SVR, Random Forest, XGBoost, Neural Network) demonstrate strong cross-market predictability, with ensemble methods achieving out-of-sample R2>0.98. Overall, the results suggest that both markets absorbed the tariff shock rapidly, exhibiting stable multiscale dynamics despite heightened geopolitical uncertainty. A model-agnostic XAI framework combining SHAP attribution and permutation-based diagnostics is used to isolate robust and independent information content.
arXiv:2510.17426v3 Announce Type: replace Abstract: The "alignment tax" of post-training is typically framed as a drop in task accuracy. We show it also involves a severe loss of calibration, making models overconfident, less reliable, and model outputs less diverse. We show that this trade-off can be navigated effectively via a simple post-hoc intervention: interpolating between a model's weights before and after alignment. Crucially, this is not a strict trade-off. We find that the process consistently reveals Pareto-optimal interpolations - models that improve accuracy beyond both parents while substantially recovering the calibration lost during alignment. Our work demonstrates that simple model merging provides a computationally efficient method for mitigating the full scope of the alignment tax, yielding models that are more capable and more reliable.
arXiv:2510.20441v2 Announce Type: replace Abstract: Neural audio codecs have largely promoted the application of language models (LMs) for speech applications. However, the effectiveness of autoregressive LM-based models in unifying speech enhancement (SE) tasks remains underexplored. In this work, we propose UniSE, a unified decoder-only LM-based framework to handle different SE tasks including speech restoration, target speaker extraction, and speech separation. Conditioned on input speech features, it autoregressively generates target discrete tokens, facilitating compatibility between distinct learning patterns of multiple tasks. To further optimize speech quality, we introduce a progressive reinforcement learning strategy with multiple assessment criteria. Experiments on several benchmarks show that UniSE achieves competitive performance compared to discriminative and generative baselines, demonstrating the capacity of LMs in unifying SE tasks. The code and demo are available at: https://github.com/alibaba/unified-audio/tree/main/QuarkAudio-UniSE.
arXiv:2607.02002v1 Announce Type: new Abstract: Time-normalized f0 contours of Mandarin words in conversational speech have been shown to be predictable in part from their contextualized embeddings (CEs). The present study investigates whether CEs also predict spoken word duration for 7470 tokens of Mandarin monosyllabic CV words extracted from a Mandarin corpus of spontaneous speech. We show that CEs indeed are predictive for duration, above chance level, not only at the type level, but also at the level of individual tokens, as indicated by the results obtained with the type-wise and token-wise permutation baselines. We also show that the predicted durations are sufficiently precise to back-transform predicted f0 contours in [0,1] normalized time to contours on the ms time scale. The resulting predicted contours approximate empirical contours and also outperform a permutation baseline.
EduArt: An educational-level benchmark for evaluating art history knowledge in large language models
arXiv:2607.02007v1 Announce Type: new Abstract: Large language models now score near ceiling on general benchmarks, but these aggregate measures reveal little about how models behave within single disciplines. Existing art-focused evaluations rely on synthetic questions and rarely report item-level properties. This paper introduces EduArt, an educational-level benchmark for art-historical knowledge and visual reasoning in multimodal LLMs. EduArt comprises 871 human-authored questions from Italian secondary-school exercises and US Advanced Placement Art History exams, spanning two languages and seven formats from multiple choice to in-text word placement and error identification. Twelve models from six provider families were evaluated under a default answer-only condition and a motivation condition requiring written justification, and characterized using Classical Test Theory and a logistic regression isolating the effects of format, language, image presence, and model. The benchmark showed strong psychometric properties (mean discrimination 0.514, 82.3 percent good discriminators), while multiple-choice accuracy saturated near ceiling for six models, showing recognition formats alone cannot distinguish frontier models. Format was a strong independent predictor of accuracy: models exceeding 94 percent on multiple choice fell to 23.9 percent on open completion (Claude Opus 4.6) and 6.2 percent on error identification (Claude Sonnet 4.6). The motivation condition changed accuracy in a predominantly negative, family-dependent direction. These dissociations indicate that art-historical knowledge and the ability to deploy it are distinct capabilities, and that single-format benchmarks overestimate what models can reliably do. Mapping this capability profile is a precondition for responsible use of multimodal LLMs in art-historical scholarship, where tasks demand producing and manipulating content rather than selecting from fixed options.