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All of arXiv

Sunday 4 October

Artificial Intelligence

The GenAI4IDN Benchmark 3.0 - a Public Tool to Assess Generative AI Tools for the Design of Interactive Digital Narratives

Hartmut Koenitz, Jonathan Barbara and Mirjam Palosaari Eladhari

This paper presents GENAI4IDN Benchmark 3.0, the third iteration of an evaluation framework to assess Generative AI tools for creating Interactive Digital Narratives (IDNs). Moving beyond manual testing, this iteration introduces AI-assisted evaluation through a publicly accessible web application (https://genai4idn.com), enabling the community to run benchmarks on demand, add new models, and propose new tasks. The revised evaluation framework is "blinded" to avoid model-bias, and can handle complex media such as music, videos, and full IDNs that previously required human raters. A significant addition - responding to concerns raised during ICIDS 2025 - is the addition of fact-checking and bias detection with dedicated tasks and rubrics, validated by human raters with lived experience in the depicted contexts. Findings from a diverse range of models report on maturing creative capabilities while observing runaway thinking and overzealous safety filters as limitations. Fact-checking reliably caught subtle historical inaccuracies, anachronisms, and fabricated claims while the bias rater consistently exposed structural assumptions, tropes, and marginalized group erasures.
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Machine Learning

Delay-coordinate reconstruction and conditional-moment causal diagnostics in stochastic systems

Jun Ohkubo

Partial observation and delay-coordinate reconstruction are rooted in deterministic dynamical-systems theory, whereas there are many systems with intrinsic stochasticity. We propose a conditional-moment interpretation of delay-coordinate reconstruction for stochastic systems, in which a delay vector is used to reconstruct conditional moments of a future distribution rather than a unique future sample path. Two complementary arguments motivate this viewpoint. First, the probability density of a stochastic differential equation obeys a deterministic Fokker-Planck equation and, under certain assumptions, is represented by an infinite deterministic hierarchy of moments. Hence, a finite-moment closure suggests a Takens-like finite-dimensional approximation. Second, a discussion based on the Koopman operator theory clarifies that the time evolution of an observable in the Mori-Zwanzig formalism yields a conditional expectation in stochastic systems. Then, the orthogonal "noise" term in the coefficient-space Mori-Zwanzig equation vanishes in the stochastic cases; this result is consistent with the moment-based argument. As an application of this stochastic delay-reconstruction viewpoint, we revisit convergent cross mapping (CCM) for diagnosing certain causal relationships. Although CCM based on the embedding theorem cannot generally be applied to stochastic systems, it is possible to examine certain types of causal relationships by using conditional moments. Using coupled logistic systems with additive and multiplicative coupling mechanisms, we discuss how causal relationships are embedded in stochastic systems.
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Analysis of PDEs

Overdetermined problems for sum Hessian equations in the space forms

Cheng Cheng and Yingbo Han

We study overdetermined problems for normalized sum Hessian equations and their quotients in the space forms. The equations and boundary conditions imply admissibility and ellipticity. For quotient equations, we prove that the domain is a geodesic ball if it is strictly star-shaped, or if the Dirichlet value satisfies an upper bound. We also prove the corresponding result for sum Hessian equations without star-shapedness. The proofs use two auxiliary functions and a Rellich–Pohožaev identity.
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Computation and Language

Atomic Visual Entailment: Enhancing Zero-Shot Vision-Language Reasoning through Atomic Fact Decomposition and Learned Selection

Nallathambi Vethiappan, Derya Soydaner and Gijs Wijnholds

Visual entailment (VE) asks whether an image supports, contradicts, or leaves undecided a textual hypothesis. Strong results come from fine-tuning large vision-language models on labelled data, while zero-shot and hybrid approaches remain far behind. A VE hypothesis often bundles several visual claims, yet existing zero-shot methods reason over it as a single unit. We propose Atomic Visual Entailment (AVE), which decomposes the hypothesis into atomic facts, produces candidate predictions from both the full hypothesis and its facts using frozen vision-language models, and predicts the final label with a lightweight classifier trained only on how those candidates behave. We find that decomposition helps only when the hypothesis context is preserved: judging facts in isolation is worse than not decomposing at all. Full-hypothesis and atomic prediction make complementary errors, and learning which to trust recovers far more of that complementarity than majority voting, reaching 0.803 test accuracy on SNLI-VE without fine-tuning any vision-language model. AVE also localises the visual evidence behind its prediction without region-level supervision. These results suggest that learning which candidate prediction to trust can close much of the gap to fine-tuned systems, offering a practical alternative where fine-tuning a vision-language model directly would need more labelled data or compute than is available.
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Statistics Applications

Does the AI Productivity Dividend Diminish? Does the AI Productivity Dividend Diminish? A Validated Framework for Identifying the Shape of Firm-Level Returns to Artificial Intelligence

Louis Agyekum

Forecasts of the productivity dividend from artificial intelligence (AI) differ by an order of magnitude, partly because they extrapolate average gains observed among early, highly exposed adopters. Whether those gains scale linearly, flatten, or are competed away is an empirical question about the shape of the return to AI, not its average. This paper develops and validates a design-based framework for measuring that shape with linked firm-worker data. The framework combines a pre-determined, occupation-based measure of firm exposure to generative AI with a continuous-treatment difference-in-differences design around the release of ChatGPT in November 2022, three pre-specified tests of concavity, a decomposition of exposure effects into an adoption margin and a per-adopter return, a doubly robust analysis of use intensity, a staggered-adoption analysis of earlier AI adopters, and a production-function estimator that places AI in the law of motion of productivity. We validate every component on a calibrated synthetic panel of 20,000 enterprises that reproduces the structure of Statistics Canada's linked business microdata and embeds a known concave effect. The estimators recover the true dose-response curve and detect its concavity (slope difference -0.010, standard error 0.003, one-sided p = 0.002); Monte Carlo coverage of the main confidence interval is 0.97, and the concavity test has power of 0.93. The validation also exposes a practical pitfall: pre-trend tests clustered at roughly 40 industries over-reject a true null. The framework, released as open code, is ready for application to Canadian enterprise microdata, where it will inform estimates of potential output and the persistence of Canada's productivity gap.
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Emerging Technologies

Autonomous Receptor Tuning via Integral Feedback for Adaptive Molecular Communication Receivers

Yagiz Celhan and Murat Kuscu

Molecular communication (MC) uses molecules as information carriers among engineered cells and artificial devices in the Internet of Bio-Nano Things. MC receivers commonly employ ligand receptors, whose discrimination between the two received concentration levels of binary signaling depends on their position relative to the dissociation constant. Changes in the transmitter-receiver distance, ligand degradation, and interference can shift both levels below or above the dynamic range of fixed receptors, impairing detection. Previous work restored performance by externally tuning the dissociation constant to the geometric mean of the estimated received levels. In this paper, inspired by receptor adaptation in living cells, we investigate a receiver whose intracellular antithetic integral feedback network senses the receptor occupancy and autonomously tunes the dissociation constant through a modulator molecule. For constant received levels and equiprobable bits, we prove that, without knowing these levels, the network sets the dissociation constant at equilibrium to their geometric mean, which minimizes the bit error probability (BEP) for any fixed receptor cooperativity. We derive the gain, bandwidth, noise, and stability boundary of this feedback loop. The random bits averaged by the network induce self-noise, which sets an error floor independent of the network molecule count and, balanced against the channel-tracking error, yields a loop-speed design rule. For received levels varying fivefold over a coherence time of 24 loop time constants, adaptation reduces the BEP 61-fold for non-cooperative and 410-fold for cooperative receptors, compared with the best non-adaptive receiver. This benefit grows with the coherence time and peaks at moderate variation depths.
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Quantum Physics

Ground state energy estimation and preparation by the truncated power method

Savero Lukianto Chandra, Daochen Wang and Carolyn Zhang

The Guided Local Hamiltonian Problem (GLHP) is the problem of ground state energy estimation and preparation given a guiding state having good overlap with the true ground state. The problem is known to be BQP-complete for small error but is classically tractable for larger error. In this work, we identify an orthogonal regime in which the problem is classically tractable, namely when the true ground state $\psi$ has polynomially bounded $\ell_1$-norm. In particular, we show that the Truncated Power Method (TPM), a classical algorithm introduced by Kirby et al. in the quantum context, has a runtime that is polynomially comparable to quantum algorithms for GLHP in all relevant parameters, except for an extra factor of $\|\psi\|_1^2$. In other words, if $\|\psi\|_1^2$ is polynomially bounded, then TPM dequantizes quantum algorithms for GLHP.
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Dynamical Systems

On global Lyapunov functions being Morse

Wouter Jongeneel

Lyapunov functions have been a cornerstone of dynamical systems theory ever since the early 1900s. Moreover and akin to Morse theory, Lyapunov functions have been the key in linking topology to dynamical systems theory. In this note we continue along these lines. Suppose that some vector field on Euclidean space has a unique equilibrium point that is globally asymptotically stable (GAS). Under these conditions, it is known that there is always a smooth Lyapunov function to certify stability. One may wonder if this function can always be chosen to be Morse. We show that if the equilibrium point is hyperbolic, this is indeed true. Moreover, we show that if this would be true in general, then the generalized 4-dimensional smooth Poincaré conjecture (SPC4) must be true. Even so, we provide an example that is GAS, but does not admit a Morse Lyapunov function, closing this route of proving SPC4.
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Artificial Intelligence

A Framework for Automated Multi-Source Satellite Data Analytics and LLM-Based Report Generation

Hind Yousif Alhammadi and Isam Mashhour Al Jawarneh

This paper presents the workflow for building an automated ArcGIS Pro tool using ArcPy to extract the Land Surface Temperature (LST) from Landsat 7, 8 and 9 datasets. The tool eliminates the need for manual band selection and repetitive raster computations by automating the multi-step workflow of radiometric calibration, NDVI-based emissivity correction, and thermal conversion. In addition to supporting batch and single-scene processing, the tool has an optional Large Language Model (LLM) for statistical result interpretation and reporting. Depending on batch size, the tool reduced the processing time from around 11-58 minutes when done manually to around 4-11 minutes using the tool. We tested the tool with data from Ras Al Khaimah (RAK) in the UAE, and the LST obtained for Ras Al Khaimah ranged from approximately 25C to 50C, demonstrating an accurate LST mapping compatible with the weather conditions of RAK. In summary, our tool reduces human errors and improves processing accuracy and efficiency for thermal and environmental remote sensing applications, in addition to providing an interactive LLM-based interface for result's interpretation.
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Atomic Physics

Onset of Melting in Finite Ion Crystals: The Role of Structural Isomerization

Boris V. Pashinsky and Boris Blinov

We theoretically investigate the microscopic mechanism of melting in finite two-dimensional ion crystals confined in anisotropic traps. Building on our previous studies of melting probability as a function of temperature and anisotropy, we extend the analysis to crystal sizes ranging from $N=4$ to $N=101$ ions and examine how structural isomerization modifies the melting pathways. Using molecular dynamics simulations together with Metropolis-Hastings equilibrium sampling, we analyze radial fluctuations, angular disorder, Lindemann parameters, and isomer-dependent energy landscapes in these Coulomb crystals. We find that crystals with identical particle number and trap anisotropy can exhibit substantially different melting behavior depending on the metastable structural isomer they occupy. Contrary to a simple soft-mode instability picture, the observed melting process is characterized by a gradual thermally-driven accumulation of radial and angular disorder. Structural isomerization reshapes the fluctuation channels through which delocalization develops, leading to nonuniform melting probabilities across temperature and anisotropy space. These results provide a unified microscopic picture of melting in finite ion crystals and clarify the role of structural rearrangements in finite-size phase transitions.
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Quantum Physics

Approximation of a continuous-variable Gaussian state by discrete-variable quantum states

Nicolae Cotfas, Haobo Ma and Wenlin Zhang

For every centered one-mode continuous-variable Gaussian state, we prove that the signed Wigner periodization defines a discrete-variable density operator in every odd dimension $d\ge3$. Positivity is equivalent to the one-mode uncertainty condition, and we determine the complete inertia outside and on its boundary. A covariance-independent isometric embedding gives exponential convergence in trace norm, with an explicit error bound uniform in Planck's constant. For covariance twice the identity, dimension $31$ gives a certified trace-norm error below $1/8000$. The trace-norm estimate controls all measurement probabilities. We retain the original canonical-coherent-frame formula in terms of the determinant and trace of the covariance matrix, with continuous normalization fixed to agree with the discrete frame convention. The coherence comparisons identify which diagnostics accompany the controlled state approximation. Position- and momentum-basis coherences have exponentially convergent values after division by the square root of the dimension. Numerical comparisons of purity and frame coherence illustrate the discrete-continuous transition.
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Software Engineering

UndoBench: Separating Task Competence from Recovery Capability in Tool-Using AI Agents

Dolly Sah, Tanmay Sah, Harshul Jain et al. (+1 more)

Tool-using AI agents are increasingly deployed across enterprise software systems, yet widely used benchmarks primarily evaluate nominal task completion, conflating baseline planning competence with operational fault recovery. We introduce UndoBench, a benchmark spanning 36 base workflows and 36 fault scenarios across 8 enterprise domains, decoupling task competence from recovery capability via counterfactual paired trials under identical seeds alongside wire-level effect-history and environment-state oracles. On 12 held-out TEST workflows across two open-weight models, two frameworks, and three recovery paradigms (5,760 executions / 2,880 paired trials) in the frozen lost-acknowledgment study, nominal competence reached 83.54% while conditional recovery success rate (CRSR) fell to 46.72%, with naive retry producing duplicate external effects in 53.33% of trials. Extensions to commercial API models reproduced this competence-recovery separation. Evaluations across complementary execution boundaries show that recovery is phase-dependent: before mutation, methods perform similarly without duplicate effects among capable trials; during partial mutation, naive retry, per-call idempotency, and zero-privilege journaling collapse on the evaluated composite workflows; after commit but before acknowledgment, verification and server-side idempotency substantially improve safety. These findings demonstrate that evaluating nominal completion alone masks critical, phase-dependent recovery vulnerabilities in autonomous agents.
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All authors Dolly Sah, Tanmay Sah, Harshul Jain, Tanya Sah

Statistics Theory

Exact finite-sample bias, variance, and MSE of normalized scale-invariant inequality indices under Gamma mixture models

Roberto Vila and Felipe Quintino

We study the finite-sample properties of estimators of a broad class of normalized scale-invariant inequality indices (NSIIs) under finite Gamma mixture models. The class includes the Gini coefficient, the $m$th Gini index, the Extended $m$th Gini index, linear order-statistic inequality indices, and several entropy- and dispersion-based measures. Building on the Gamma–Dirichlet representation, we derive exact expressions for the expectation and finite-sample bias of the natural estimator of an NSII based on a $U$-statistic when the population follows a finite Gamma mixture with a common rate parameter. The resulting bias formula explicitly characterizes the effects of the mixture weights and component shape parameters. We also establish that the bias converges to zero as the sample size increases, yielding asymptotic unbiasedness. Furthermore, we derive exact finite-sample expressions for the variance and mean squared error of the estimator by accounting for the overlap structure among the subsets entering the $U$-statistic. In addition, we establish strong consistency and asymptotic normality. When the mixture degenerates to a single Gamma distribution, the finite-sample bias vanishes, recovering the exact unbiasedness result previously obtained for NSIIs under the Gamma model. Numerical illustrations and simulation studies are provided to investigate the finite-sample behavior of the estimator and to illustrate the theoretical findings. Overall, the proposed framework provides a unified characterization of finite-sample estimation under heterogeneous Gamma populations and quantifies the effects of mixture heterogeneity on bias, variance, and mean squared error.
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Mesoscale and Nanoscale Physics

Terahertz nanoscopy of quantum-geometric photovoltaics in bilayer graphene

Bogdan Borodin, Jiangxu Li, Xuanpu Zhang et al. (+7 more)

The bulk photovoltaic effect (BPVE) provides a direct link between quantum geometry and nonlinear light-matter interactions, yet conventional far-field measurements cannot disentangle BPVE from contact- and interface-induced photothermoelectric signals. Here we develop cryogenic, polarization-sensitive terahertz microscopy with deep-subwavelength resolution and apply it to dual-gated AB bilayer graphene. Near resonance between the band gap and terahertz photon energy, we observe enhanced optical conductivity and spatially resolved BPVE. Polarization-dependent measurements reveal a characteristic twofold angular symmetry and a distinct shift-vector direction, both governed by crystalline symmetries and quantum geometry. The evolution of BPVE with carrier density and temperature provides a sensitive probe of the fine electronic structure of bilayer graphene, revealing signatures of trigonal warping and electron-hole asymmetry. Agreement with theory demonstrates that the terahertz nanoscopy of BPVE established here offers an optical lens into the interplay between band and quantum geometry in quantum materials.
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All authors Bogdan Borodin, Jiangxu Li, Xuanpu Zhang, Aleksei Karaborchev, Som Subhra Guria, Kenji Watanabe, Takashi Taniguchi, Roshan Krishna Kumar, Fan Zhang, Petr Stepanov

Information Retrieval

SCOUT: Supply-Aware Cold-Start Proactive Query Suggestion for Travel Search

Hao Li, Shashank Reddy, Kedar Bellare et al. (+2 more)

Generative query suggestion, powered by Large Language Models (LLMs), has become increasingly popular in search and conversational systems to reduce user friction and guide intent formulation. Existing approaches align suggestions with user preferences (e.g., clicks or conversions). This works for open-ended applications like chatbots and personal assistants, where the result space is unconstrained or historical user free-text queries are abundant. However, applying these methods to travel search presents two limitations. First, travel search is fundamentally constrained by physical inventory; a query (e.g., "romantic beachfront villa") may yield abundant results in Bali but few in Tokyo, so aligning with user preferences is not by itself grounded in what can be offered. Second, travel platforms traditionally rely on faceted search interfaces with no free-text queries. This creates a cold-start problem: without historical query logs there is no demand-side data for alignment, and without a seed query at request time, suggestions must be generated proactively from structured context alone. To address these challenges, we propose SCOUT, a bootstrapping framework for supply-aware proactive query suggestion. SCOUT overcomes the data gap by substituting missing demand-side user feedback with supply-side system feedback. It treats the search engine as a reinforcement learning environment, deriving a dense reward from the production reranker's query-listing match scores, and optimizes the policy with Group Relative Policy Optimization (GRPO). SCOUT improves inventory match rate (IMR@18) by 12.3% while preserving diversity, matching a compute-intensive best-of-8 policy at zero marginal inference cost and making supply-aware suggestion deployable on a real-time travel search path.
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All authors Hao Li, Shashank Reddy, Kedar Bellare, Ashish Jain, Stephanie Moyerman

Statistical Methodology

Causal Lag Structure Discovery in Confounded Time Series via Orthogonalized Adaptive Estimation

Hong Kiat Tan, Isaac-Neil Zanoria, James Chen et al. (+2 more)

Finding which variables cause which others in multivariate time series, and at what lags, is central to science and policy, yet existing methods force a choice between flexible confounder adjustment, data-driven lag selection, and inference that controls the false discovery rate (FDR). ORACLE-VARX does all three in one pipeline. First, double/debiased machine learning (DML) removes nonlinear confounder effects from the outcomes and the lagged series. Second, adaptive causal lag estimation (ACLE) picks the lag order at each time step by sequential significance tests, tracking regime changes. Third, entry-wise $z$-tests with Benjamini–Hochberg correction select directed edges at a target FDR. We prove that in each rolling window, the debiased coefficients are asymptotically normal around a window-averaged target, so their $z$-tests are asymptotically valid. On a synthetic benchmark with time-varying structure and nonlinear confounding, ORACLE-VARX (LightGBM) tracks the true lag order best (RMSE $0.96$ vs $1.1$–$1.5$), has edge FDR $0.047$, close to PCMCI ($0.045$) and below VAR ($0.129$) and VAR-LiNGAM ($0.187$), and forecasts better than all three. On nine U.S. sector ETFs with macroeconomic confounders, it yields interpretable causal graphs whose lag order rises in high-volatility regimes.
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All authors Hong Kiat Tan, Isaac-Neil Zanoria, James Chen, Haoyang Lyu, Mihai Cucuringu

Data Structures and Algorithms

Prime factorisation of stable-matching instances: uniqueness, simultaneous products, and an exact census

Yoshiteru Ishida

Every balanced instance of the stable marriage problem with strict complete preferences has a unique finest partition into prime blocks, and that single partition simultaneously factors three different structures: the reachable execution digraph as a Cartesian product, the proposal-prefix antimatroid as a direct sum, and the stable-matching lattice as a direct product. The converse fails, and fails at every size from two on: two explicit families share the identical Boolean-cube execution while one is maximally decomposable with a single stable matching and the other is prime with n. Uniqueness yields an exact census, a recursion counting the prime instances at every size, under which exactly 88,478,208 of the 110,075,314,176 profiles with four agents on each side are decomposable and the decomposable fraction is asymptotically n! / n^(2n). The blocks are characterised as the square components of the mutual-rank filtration, so the partition is computable in polynomial time and the factorisation is a tool rather than only a fact.
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Mathematical Physics

A bulk–edge correspondence for two-dimensional self-adjoint boundary and interface problems

Guillaume Bal

This paper analyzes the quantized asymmetric transport along the boundary of a two-dimensional topological insulator occupying a half plane, or along the interface separating two insulators. In contrast with problems posed on the Euclidean plane, terminating a sample requires a choice of self-adjointrealization, $H_L$, and we show that a quantized transport depends on that choice. The physical observable modeling the asymmetric transport is the edge, or interface, current $\sigma_I[H_L]=\Tr\,i[H_L,P]\varphi'(H_L)$. It is shown to be well defined along two routes. For Shapiro–Lopatinski elliptic realizations, we use estimates from Boutet de Monvel's calculus to show that the current observable is a well-defined trace, that $\mathcal I=2\pi\sigma_I[H_L]$ is a Fredholm index, and that it is stable under deformations and compactly supported changes of the operator and of its termination. For translation invariant operators along the edge, a spectral analysis defines $\mathcal I$ when the edge spectrum escapes a bulk gap at large momenta. $\mathcal I$ is then also a spectral flow and a Maslov index. Our main theorem is a bulk–edge correspondence \[ \mathcal I=\cR-\cR_L+\eta\bigl(L_\infty,\Lm^\Phi(+)\bigr)-\eta\bigl(L_\infty,\Lm^\Phi(-)\bigr), \] We determine the range of $\mathcal I$. We extend the formula to interfaces with arbitrary transmission conditions. At the transparent condition, the invariant equals the bulk-difference invariant of the Euclidean plane. Eight examples illustrate the theoretical findings.
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Artificial Intelligence

DREAM: Dynamic Resolution Assignment For Multimodal Multi-agent Debate

Khanh-Binh Nguyen, Van Dai Do, Tien Anh Nguyen et al. (+2 more)

Multi-agent debate (MAD) has emerged as an effective paradigm to improve the reasoning capabilities of large language models (LLMs) and is increasingly being extended to multimodal settings. However, existing multimodal MAD frameworks typically expose agents to the same fixed visual input, ignoring substantial variation in the visual scale needed across samples and agents. In addition, these frameworks frequently suffer from groupthink, a phenomenon where agents prematurely abandon correct deductions to conform with confident but hallucinated peer responses. To address these bottlenecks, we introduce DREAM (Dynamic Resolution Assignment For Multimodal Multi-Agent Debate), which operates via two core components: (1) Dynamic Resolution Assignment, a zero-shot probe round where agents test multiple resolutions, quantify uncertainty using Average Normalized Log-Likelihood (ANLL), and use an adaptive threshold to assign each agent to its empirically optimal resolution; (2) Uncertainty-Guided Rollback Aggregation counters groupthink by tracking each agent's uncertainty over rounds and restoring early low-uncertainty answers overridden by group pressure. On six multimodal datasets, DREAM improves the accuracy-token trade-off over multi-agent debate baselines by 1.5-3.2% accuracy without dataset-specific tuning.
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All authors Khanh-Binh Nguyen, Van Dai Do, Tien Anh Nguyen, Svetha Venkatesh, Hung Le

Statistics Applications

Within-fire estimates of biomass loss across national forests of the US West Coast: inventory-informed inference with uncertainty

Elliot S. Shannon, Andrew O. Finley, Paul B. May et al. (+4 more)

Wildfire is reshaping forests in the western United States (US), yet estimates of wildfire impacts often rely on burned-area summaries or satellite-derived severity metrics that do not directly quantify changes in forest biomass or their uncertainty. We combine US Forest Inventory and Analysis plot measurements with annual 30 m tree canopy cover data in a Bayesian spatio-temporal modeling framework to estimate within-fire changes in live aboveground biomass (AGB) and forest mortality area across National Forest System lands in Washington, Oregon, and California. The model represents both live forest presence or absence and AGB conditional on live forest, allowing posterior predictive estimates of pre- and post-fire conditions at the pixel scale. Applied to 6,239 fires from 2000 to 2022, the analysis estimates 95.3 million Mg of wildfire-associated AGB loss and 2.39 million ha of forest mortality area, with the largest aggregate impacts occurring in 2020. Pixel-level estimates reveal substantial spatial heterogeneity within fire perimeters, including large differences between total burned area, forest mortality area, and associated biomass loss. This inventory-informed, uncertainty-aware approach provides a scalable framework for quantifying forest carbon impacts of disturbance across broad regions while retaining within-fire spatial detail.
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All authors Elliot S. Shannon, Andrew O. Finley, Paul B. May, Hans-Erik Andersen, Harold Zald, Grant M. Domke, George C. Gaines III

Statistics Applications

LLM Benchmarking via Representation Multi-task Learning

Yuqing Xie, Yuxuan Xu, Yang Feng et al. (+1 more)

Quantifying and evaluating the capabilities of Large Language Models (LLMs) remains a fundamental challenge in modern data science and artificial intelligence. In this paper, we consider LLM evaluation based on their performance across items in multiple benchmark domains (e.g., mathematical reasoning and coding) within a leaderboard framework. Our goal is to address two core questions: (1) How do we derive more accurate domain-specific scores by borrowing information across domains? and (2) How do we define and estimate an overall score that aggregates performance across multiple domains? To solve these problems, we propose a novel statistical framework based on representation Multi-task Learning (MTL) and an item response theory model. Specifically, we define overall and domain-specific LLM traits through an Item Response Theory (IRT) model, and propose an MTL approach to estimate these traits from item-level response data. We develop a computationally efficient estimator and establish its minimax optimality under certain asymptotic regimes. This framework provides a rigorous measurement foundation for systematic LLM evaluation. We conduct extensive simulations, demonstrating the superior performance of the proposed method over competing methods. Crucially for the Applications and Case Studies section, we apply the proposed framework to MMLU response data from the Hugging Face Open LLM Leaderboard, covering 4,272 LLMs and 13,232 items across 56 subjects. The empirical analysis reveals substantial heterogeneity in domain size and difficulty, together with strong positive cross-domain dependence, highlighting the practical value and substantive insights generated by our approach.
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All authors Yuqing Xie, Yuxuan Xu, Yang Feng, Yunxiao Chen

Complex Variables

Cauchy-Type Convolution Integrals as the Starting Point for a First Encounter with Complex Analysis

Gabriel Ben-Simon

Alongside three established presentations of elementary complex analysis, we propose an approach centered on one organizing question: when is contour convolution well defined on homotopy classes? From this starting point, we develop the foundational results of the subject. A supporting conceptual distinction identifies those elements of real and complex analysis that share the same operational and symbolic rules. We refer to this collection as the "Toolbox." Examples and structural connections illustrate the explanatory value and reach of the approach across different aspects of the theory. We also examine the relationship of our approach to the established presentations.
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Logic

Recovering a structure from its tree of tuples

David Gonzalez, Matthew Harrison-Trainor and Julia Knight

We show that for each countable ordinal $\alpha$, there is a $\mathbf{\Delta}^0_\alpha$ operator that, for all $A$ of Scott rank at most $\alpha$, takes copies of $T_A$ to copies of $A$, where $T_A$ is the tree of tuples corresponding to $A$. For finite $\alpha$, we show that this is best possible. The proof that there is a $\mathbf{\Delta}^0_\alpha$ operator uses Harrington's method of workers. The sharpness result, for finite $n$, uses recent work of Harrison-Trainor on “unfriendly jump inversion.”
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Sound

SEA-LM: Egocentric Spatial Audio Understanding for Wearable Microphone Arrays

Sonal Kumar, Sinan Hersek, Artem Dementyev et al. (+6 more)

Embodied, ego-centric intelligence fundamentally requires the ability to comprehend spatial audio within complex environments. While large audio-language models excel at mono-channel reasoning, they lack spatial awareness, discarding critical spatial cues that enable sound localization and that can improve the disentanglement of overlapping sound sources. To address this, we present SEA-LM, a Spatial Audio Understanding model. First, we introduce FOACODER, a layout-flexible spatial audio encoder trained on source localization and ego-centric voice activity detection objectives to encode First Order Ambisonics derived from variable-count, variable-position smart-glasses arrays via beamforming. We train a Multimodal Large Language Model (MLLM) to understand these spatial audio embeddings through a two-stage curriculum spanning six tasks, including sound localization and spatially selective transcription in settings with multiple speakers and overlapping sounds. To prevent the transcription outputs from dominating the next token prediction loss and overwhelming the direction predictions, we introduce a Spatio-temporal Weighted Cross-Entropy Loss. On our evaluation set, SEA-LM achieves lower azimuth and elevation MAE, higher temporal IoU, lower external-source hallucination and missing-source rates, and lower WER on most transcription tasks than compared baselines, while remaining robust across 1,211 smart-glasses array configurations with 4 to 9 microphones.
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All authors Sonal Kumar, Sinan Hersek, Artem Dementyev, Mengzhen Pan, Ishan Chatterjee, Anurag Kumar, Ramani Duraiswami, Dinesh Manocha, Andrea Colaco

Probability

Geometric and stochastic framework for a regularized $p(x)$-Laplacian

Mustafa Avci

For a prescribed classical profile and a fixed positive regularization parameter, we construct an Itô diffusion whose interior generator reproduces the regularized $p(x)$-Laplacian when applied to that profile. We determine the exact tangent spectra of the unregularized and regularized fluxes, characterize their behavior at zero gradient, and establish quantitative ellipticity bounds on bounded gradient ranges. The full divergence expansion identifies a covariance equal to twice the gradient derivative of the regularized flux, evaluated at the profile gradient, and a logarithmic drift generated by spatial variation of the variable exponent $p(x)$. Under explicit classical regularity assumptions, we establish boundedness and local Lipschitz continuity of the coefficients and prove strong existence and pathwise uniqueness up to domain exit. We identify the interior generator of the boundary-stopped process and verify the profile-reproduction identity. Further consequences include initial-point stability for a fixed global extension, localized martingale identities, and Dynkin's formula at bounded stopping times. An explicit nonconstant exponent example illustrates the construction at a critical point, and the coefficient selection is shown to be canonical under the flux-based prescriptions rather than determined uniquely by profile reproduction alone.
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Computer Vision

Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation

Team Kandinsky, Julia Agafonova, Bulat Akhmatov et al. (+85 more)

We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video Lite (3B parameters) and Kandinsky 6.0 Video Pro (29B parameters). Both models generate 5-second video clips with synchronized 44 kHz audio, including lip-sync, in text-to-audio-video (T2AV) and image-to-audio-video (I2AV) modes; a built-in super-resolution model raises the output resolution to Full-HD (1920$\times$1080). Building on the video generation capabilities of Kandinsky 5.0, Kandinsky 6.0 Video employs a dual-stream CrossDiT architecture that connects a pretrained video stream and a newly trained audio stream through bidirectional cross-attention for temporal and semantic alignment. Our continuous pretraining strategy first trains the audio stream from scratch on large-scale audio corpora and then trains both streams jointly on paired audio-video data while preserving unimodal fidelity; pretraining is followed by supervised fine-tuning, reinforcement-learning-based post-training, and distillation. In side-by-side human evaluation, Kandinsky 6.0 Video Pro clearly outperforms its predecessor, Kandinsky 5.0 Video Pro, and remains competitive with leading audio-video generation models, particularly in speech quality. To accelerate open research and deployment in multimedia generation, we release the code, model checkpoints, and diffusers integration under the MIT license.
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All authors Team Kandinsky, Julia Agafonova, Bulat Akhmatov, Mikhail Aksyutin, Grigorii Alekseenko, Anastasia Aliaskina, Olga Androsova, Vladimir Arkhipkin, Anna Averchenkova, Alexander Belykh, Serafima Bocharova, Sofiya Bogakovskaya, Anton Bukashkin, Mark Bulygin, Kirill Buzygin, Irina Cheremnykh, Kirill Chernyshev, Mikhail Chernyshov, Vladimir Chernyy, David Chikovani, Georgy Daniltsev, Denis Dimitrov, Anna Dmitrienko, Vladimir Dokholyan, Sergey Emelyanov, Dmitry Ermilov, Georgii Fedorov, Polina Gavrilova, Nikolai Gerasimenko, Aleksandr Gordeev, Andrey Inozemtsev, Andrei Ivaniuta, Alexander Ivanov, Mikhail Karaev, Anastasiia Kargapoltseva, Ivan Kirillov, Nikita Kiselev, Valeria Kobenko, Yury Kolabushin, Denis Koposov, Anatoly Korobov, Vladimir Korviakov, Kirill Kozlov, Denis Krzhivokolskiy, Konstantin Kuklev, Alexander Kunitsyn, Sergey Kuzin, Vladislav Lakhtionov, Alexey Letunovskiy, Maxim Litvinov, Alexander Lyulkov, Georgy Makarov, Kirill Malakhov, Egor Malykh, Mikhail Mamaev, Dmitrii Mikhailov, Polina Mikhailova, Ivan Mikheev, Elizaveta Muromtseva, Nikolai Nazarkin, Tatiana Nikulina, Lev Novitskiy, Stanislav Onuchin, Nikita Osterov, Denis Parkhomenko, Anatoliy Parpara, Vladimir Polovnikov, Konstantin Reznikov, Azat Saginbaev, Nikita Samsonov, Alexander Sentsov, Nikita Shaimov, Artem Sherstyuk, Andrey Shutkin, Egor Silvestrov, Bulat Suleimanov, Matvey Suprunov, Sergey Taranov, Irina Tolstykh, Tatiana Trofimuk, Ilya Trushkin, Aleksandra Tsybina, Olga Varlashina, Viacheslav Vasilev, Ilya Vasiliev, Eugeny Vilisov, Sergey Yakubson, Konstantin Zakharov

Robotics

Emergency Obstacle Avoidance Maneuvers in Differential-Drive Mobile Robots

Kautik Mandve and Nilay Kant

Emergency obstacle avoidance requires a mobile robot to brake or change its heading within the available distance. This paper investigates the dependence of maneuver performance and odometric error on approach speed for a differential-drive robot. A footprint-clearance analysis and a normalized wheel-motion index provide a kinematic description of five braking and turning maneuvers. The principal experiment comprises 540 block-randomized trials on tile, of which 539 are retained, at commanded approach speeds of 45, 55, and 65 cm/s. An overhead camera provides an independent pose reference. When encoder, gyro, and camera heading changes are evaluated over complete motion records, the mean encoder discrepancy increases by 4.7-5.1 degrees for the two reverse-spin maneuvers between the lowest and highest speeds; the arc and brake-assisted pivot change by less than 0.4 degrees. Larger encoder discrepancy is associated with lower avoidance success after adjustment for distance, speed, maneuver, day, and turn direction. Gyro discrepancies remain approximately 3 degrees for the reverse spins. Estimated reaction distances for 90% success and their uncertainty quantify the maneuver trade-offs. The results support speed-specific empirical characterization of emergency maneuvers and distinguish encoder error from recovery motion and measurement-window mismatch.
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Human-Computer Interaction

Exploring the Effects of Personality in Human-Agent Interactions: A Study on User-Agent Synchrony with Human-based Vocalics

Kai Alexander Hackney, Jhonathan Sora-Cardenas, Aibek Musaev et al. (+1 more)

User trust is paramount in human-agent interactions, as it allows users to feel comfortable being themselves around an agent. The process of building user rapport starts in how an agent was designed, from its modality to the setting, to any of the many features and characteristics that can be tailored. All these aspects can affect whether users will be able to properly interact with the virtual agent and achieve the intended purpose. One such feature that is critical in human-human interactions is personality. A person's personality can strongly influence whether those that interact with them perceive them as trustworthy. This study used virtual agents generated from human voices with known personality traits to evaluate user perceptions. We found that extraverted agents were deemed to be more likeable by users, and that there was no significant effect of user-agent synchrony on user perceptions of the agent. In addition, it was found that user personality, without regard for agent personality, affected user perceptions of the agents. Our observations suggest that user perceptions may depend more on agent-topic synchrony than user-agent synchrony, and contribute to the broader community with considerations for the design of effective human-agent interactions.
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All authors Kai Alexander Hackney, Jhonathan Sora-Cardenas, Aibek Musaev, Pedro Guillermo Feijóo-García

Distributed Computing

Handling Missing Data in Performance Portability Studies

Ami Marowka

Missing data is a common challenge in many real-world datasets, often leading to biased results or reduced accuracy. Missing data is a pervasive problem in performance efficiency datasets, particularly in high-performance computing (HPC) performance portability studies. Performance portability scores rely on complete sets of performance efficiencies as inputs to their respective metrics; however, missing values can arise due to incomplete benchmarking, hardware constraints, or implementation gaps. Many established performance portability metrics lack mechanisms for handling missing inputs, which can result in incomplete analyses or the inability to compute scores altogether. In this study, we evaluate a range of imputation methods and algorithms for addressing missing performance efficiencies. We present illustrative examples for each approach and apply them to real-world HPC performance portability datasets. Our results highlight the trade-offs between accuracy, robustness, and computational cost, offering practical recommendations for researchers seeking to mitigate the impact of missing data in performance portability assessments.
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Robotics

Building A Multi-Sensor Platform For Autonomous Driving Research: Challenges and Lessons Learned

Paulo Ricardo Marques de Araujo, Eslam Mounier, Qamar Bader et al. (+2 more)

This paper reports on the challenges encountered and lessons learned during the development and deployment of a flexible multi-sensor platform for autonomous driving research. It aims to serve as a reference for researchers developing new multi-sensor systems. As the need for reliable, diverse datasets increases, novel environments and sensing configurations are essential to tackle real-world operational challenges. Consequently, many research groups create custom multi-sensory data collection platforms, where fundamental issues, such as mechanical design, sensor calibration, power management, and time synchronization, arise regardless of sensor types. We reflect on these challenges and share key insights to guide future platform designs, enhancing reproducibility and robustness in autonomous vehicle testing. We also summarize the mitigation strategies we adopted, for instance, system-wide time synchronization using GNSS timing and NTP protocols, custom calibration routines for different sensor configurations, and design practices to improve reliability and data integrity during field deployments.
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All authors Paulo Ricardo Marques de Araujo, Eslam Mounier, Qamar Bader, Emma Dawson, Aboelmagd Noureldin