Numerical Analysis
On the well-posedness and efficient approximation for the classical Melan equation in suspension bridges
Jinxiang Wang
The classical Melan equation modeling suspension bridges is considered. We first study the explicit expression and global properties of the analytical solution for the simplified “less stiff” model, based on which we derive an a priori estimate for the original classical Melan equation and establish, under an explicit load condition, that it has a unique solution with nonnegative integral under downward live loads, thereby showing the uniqueness of the corresponding deflection curve in the engineering setting. We also develop an efficient iterative approximation method by taking the solution of the simplified “less stiff” model as the first iterate, and prove its
geometric convergence with explicit error estimates. The applicability and computational efficiency of the method are demonstrated through calculations for two actual bridges, which also quantify the influence of the nonlinear nonlocal term on the solution and clarify the relationship between the simplified and original models. Several engineering observations are verified and explained, and some related open problems are suggested.
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Human-Computer Interaction
To Stop or Not to Stop: Exploring the Intention-Behavior Gaps in Smartphone Usage
Jian Zheng and Eun Kyoung Choe
As smartphones become integral to daily life, researchers have sought to identify when the use becomes problematic. Previous studies have operationalized problematic smartphone usage (PSU) from either an intention or a behavior perspective. Both risk delivering interventions not welcomed by users. We propose a novel approach to operationalizing PSU as the intention-behavior gap (IBG). We collected self-reported data on intentions to stop phone usage, alongside usage behavior data, from 37 participants over two weeks. We calculated IBG, examined effects of demographic and contextual variables, and developed machine learning models to predict IBG in real time. We found that IBG was explained by gender, time, app, and input interactions, among other factors. Intention was predicted most accurately with only personal data, whereas behavior and IBG were predicted most accurately with both personal and global data. Our findings can inform the design of future intervention tools optimized for timing and adaptive intensity.
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Emerging Technologies
Sparse Walsh-Pauli Correlation Encoding for MaxCut: A Quantum-Inspired Formulation with Quantum-Hardware Validation
Cesar Augusto do Amaral, Nicolás Legnazzi, Marcos Vinicius Reballo et al. (+3 more)
We introduce a sparse Walsh-Pauli correlation encoding for MaxCut, in which graph variables are represented by diagonal Pauli-string expectation values computed classically as normalized XOR autocorrelations of Walsh coefficients. The complete Walsh basis can reproduce the diagonal correlators of arbitrary
quantum states, while a structured sparse support reduces the number of trainable coefficients to $m+1$ for $m$ graph variables. We evaluate the method on $1{,}400$ held-out random graphs with up to $7{,}000$ vertices, obtaining empirical approximation ratios near $0.98$ relative to MQLib heuristic references. On Gset instances G14, G23, G60, and G64, the sparse model achieves mean refined approximation ratios of $0.9828$, $0.9926$, $0.9781$, and $0.9369$, respectively, with $80.4\%$-$89.3\%$ fewer parameters than the complete Walsh representation. The trained coefficients are transferred without retraining to
quantum circuits. Statevector simulations of the four Gset instances achieve fidelities above $0.99994$ and preserve all correlator signs. Separately, a seven-
qubit experiment on a $15$-vertex graph executed on IBM
quantum hardware achieves a correlator-sign agreement of $0.927\pm0.049$ relative to the corresponding noiseless
circuit. These results demonstrate the potential of sparse Walsh-PCE for large-instance
quantum-inspired MaxCut optimization while validating the transfer of classically trained parameters to
quantum hardware.
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All authors Cesar Augusto do Amaral, Nicolás Legnazzi, Marcos Vinicius Reballo, Marcus Ritt, Alexsandro Santos da Rosa Júnior, Fernando Augusto Caletti de Barros
Robotics
Visible-Reachable Workspace for Perception-Aware Humanoid Design
Boxi Xia, Zijiang Yang, Ryan Shin et al. (+5 more)
Workspace analysis measures where a
robot can place its end effector. For visually guided manipulation, reachability alone is insufficient: a kinematically reachable target may not be visible in the specific pose required to reach it. The
robot must then redirect its sensing or move its body to acquire a view, turning a perception limitation into additional motion. Existing
humanoids largely inherit this limitation when copying human form factors. We introduce the visible-reachable workspace (VRW), a design-stage measure that conditions visibility on feasible reaching configurations and extends it to concurrent visibility of spatially separated work regions. We apply VRW by building a 31-DoF
humanoid with independently actuated RGB-D cameras. On the same
robot, camera articulation increases visible-reachable coverage from 38% to 97%. With actuated camera layouts, a second camera raises pairwise coverage from 0.45 to 0.95, while a third changes it only to 0.97. In a controlled two-target reach-and-grasp
benchmark, our dual-actuated design reduces mean completion time by 17% and mechanical energy by 19% relative to the same
robot with its cameras fixed. Hardware experiments demonstrate simultaneous observation and manipulation of front/back and left/right target pairs without torso reorientation. The results suggest that reachability becomes a more informative design quantity for perception-driven
humanoid manipulation when it is evaluated together with the sensing configurations that make the reachable space observable. We will open-source all software and the
humanoid hardware design. Our website is https://generalroboticslab.com/DukeHumanoidv2
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All authors Boxi Xia, Zijiang Yang, Ryan Shin, Bokuan Li, Eric Wun-Hao Lu, Jacob Lee, Jiaxun Liu, Boyuan Chen
Machine Learning
The BatchNorm Illusion: Diagnosing Normalization Artifacts in Machine Unlearning Evaluation
Aaryaman Kalani, Murari Mandal, Dhruv Kumar et al. (+2 more)
Approximate machine unlearning aims to remove the influence of specific training data from a trained model without retraining from scratch. We identify a previously undocumented confound in how unlearning is evaluated on BatchNorm-based architectures: a single forward pass over retain data, an operation that modifies no weight, can deterministically rewrite the model's normalization state and reverse the apparent surface-metric forgetting. We formalize this operation as a weight-preserving fixed-point operator and prove that any pre-versus-post gap it induces is provably attributable to BN running statistics rather than to any modification the unlearning method made to the weights. This attribution claim cleanly separates measurement failure (BN artifact) from encoder failure (residual weight-encoded information, recently documented in concurrent work), and the same operator framework yields a unique decomposition of linear-probe elevation into BN-measurement-bias and encoder-geometry components. Empirically, the artifact reverses headline forget accuracy by up to 78 pp across nine evaluated methods on standard
benchmarks; an attacker with as few as 10 unlabeled images recovers most of the masked accuracy; and a strict GroupNorm control reduces the artifact to zero across all methods. The tested membership-inference attacks change little under recalibration, locating the observed evaluation failure in forget accuracy and linear probing.
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All authors Aaryaman Kalani, Murari Mandal, Dhruv Kumar, Mohan Kankanhalli, Yash Sinha
Sound
From Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection
Mengzhe Geng, Yujia Lu, Manuela Kunz et al. (+1 more)
Speech deepfake detectors usually emit one score per utterance, but a borderline score does not reveal why two examples differ during retrospective error analysis. We ask whether a final score can be calibrated from component fields while keeping those fields visible for inspection. We build a decision record with a passive detector score and a score from a probe applied to a marked copy. It also includes
retrieval support held out of the evaluated family, a margin from a support-set profile, and raw neighbor closeness. A cross-fit calibrator combines these fields and two differences between raw scores into one final score. On matched ASVspoof development data, the calibrated record reduces equal error rate (EER) by 3.48 percentage points relative to the fixed
retrieval-augmented rule. It reaches 8.43% EER, whereas a passive WavLM baseline reaches 6.71% on the same subset. The record is therefore not the strongest detector in this comparison. Its value is to retain inspectable component fields while producing one scalar score for retrospective diagnosis.
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All authors Mengzhe Geng, Yujia Lu, Manuela Kunz, Patrick Littell
Emerging Technologies
OTTER - Two Transistor - One RRAM Architecture for Reliable In-Memory-Computing in 28 nm CMOS Technology
Yang Chen, Daniele Storelli, Xinyi Zhao et al. (+20 more)
This work presents OTTER, a 28 nm CMOS platform co-integrated with TaOx-based valence-change mechanism (VCM) RRAM, demonstrating a two-transistor-one-memristive-device (2T1R) architecture for reliable in-
memory computing. The 2T1R
cell combines a low-drive-current (LD) transistor and a high-drive-current (HD) transistor in parallel, providing dedicated bias paths for SET programming and RESET operation, respectively. Through systematic experimental and simulated comparison of various transistor-pairing configurations using the physical compact model JART VCM Rth, design guidelines for transistor sizing are derived, establishing the minimum RESET transistor W/L required for complete RESET as a function of the SET current compliance. The 2T1R
cell is further characterized under pulse-based programming, demonstrating multilevel analog conductance tuning with narrow, well separated conductance states across six programmable levels. An analog content-addressable
memory (aCAM) design based on the same 2T1R
cell is additionally analyzed at the
circuit level, evaluating trade-offs between top- and bottom-connected RRAM comparator configurations. A hardware implementation of compute-in-
memory (CIM) multiply-and-accumulate (MAC) operations is further demonstrated on a 15 x 15 2T1R crossbar array.
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All authors Yang Chen, Daniele Storelli, Xinyi Zhao, Ankit Bende, Paul-Philipp Manea, Oliver Artner, Arun Ashok, Kay Winterberg, Godwin Paul, Siyuan Jia, Christian Roth, Sabitha Kusuma, Michael Schiek, Vikas Rana, Dirk Wouters, Stephan Menzel, Andre Zambanini, Christian Grewing, Stefan Wiefels, Susanne Hoffmann-Eifert, John Paul Strachan, Stefan van Waasen, Regina Dittmann
Computation and Language
Evolution of Multimodal Question Answering: From Modality-Adaptive Extraction to Unified Language Representation
Abdullah Al Shafi
The rapid growth of
multimodal data has intensified the need for question answering (QA) systems capable of
reasoning across heterogeneous sources such as text, tables, and images. In this paper, we present a comprehensive methodological comparison of three influential frameworks, namely
Multimodal Adaptive Extraction (MAE),
Solar, and UniMMQA, tracing the
evolution of
multimodal question answering from modality-adaptive pipelines to fully unified architectures. We examine how each approach models cross-modal interactions, transforms heterogeneous inputs, and performs
reasoning, highlighting key design differences in modality representation,
reasoning, and answer generation. Our analysis demonstrates a clear shift from explicit modality-specific processing toward unified text-centric formulations enabled by pre-trained
language models (PLMs). Empirical comparisons across
benchmark datasets show that this transition leads to substantial improvements in both Exact Match (EM) and F1-Scores, with UniMMQA achieving the most consistent and scalable performance. Despite these advances, we identify persistent challenges, including information loss during modality transformation, error propagation in multi-stage pipelines, and limitations in capturing fine-grained cross-modal dependencies. Overall, this study provides a deeper understanding of current design trends and offers insights into the future direction of unified
multimodal reasoning systems.
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Human-Computer Interaction
Healthcare Utilization, Chronic Condition Management, and Workplace Functioning Among Users of a Purpose-Built Mental Health AI (Ash): Cross-Sectional Study
Kristen M. Van Swearingen, Thomas D. Hull, Jeffrey Swigert et al. (+1 more)
Mental health challenges can exacerbate physical symptoms and complicate management of chronic conditions. Purpose-built artificial intelligence (AI) tools may offer scalable support for co-occurring mental and physical health concerns. This cross-sectional study compared past-6-month healthcare utilization, chronic condition management, physical health behaviors, mental health change, and workplace functioning between active (n = 169) and non-users (n = 73) of a mental health AI (Ash). Participants had at least one chronic condition (e.g. hypertension, chronic pain). Binary outcomes were modeled as adjusted risk differences (RDs) using linear probability models and continuous outcomes were modeled with linear regression; all models were adjusted for hypertension. Relative to non-users, active users were more likely to report improved mental health (61.4% vs. 34.3%; RD = 0.27), higher medication adherence (91.7% vs. 76.4%, RD = 0.15), fewer skipped or delayed chronic-condition care activities (b = -0.44), and were less likely to report repeat urgent care visits (9.5% vs. 23.3%; RD = -0.15) and monthly-or-more absenteeism (24.2% vs. 45.2%; RD = -0.20, all ps < .05). Findings provide preliminary evidence that use of purpose-built AI may be associated with positive symptom-based and utilization outcomes for those managing co-occurring mental and physical concerns.
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All authors Kristen M. Van Swearingen, Thomas D. Hull, Jeffrey Swigert, Caitlin A. Stamatis
Information Theory
On Weighted Mathai-Haubold Entropy Measures
Oindrali Das and Siddhartha Chakraborty
In this paper, we propose weighted Mathai-Haubold
entropy along with their residual and past versions and study their properties. We develop aging classes based on the weighted Mathai-Haubold residual and past
entropy measures. Also, some inequalities related to the three proposed measures are discussed. Non-parametric
estimators of the proposed measures are introduced and their properties are investigated. Performance of this
estimator is evaluated by means of bias and mean squared error using Monte-Carlo simulations.
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Game Theory
Strengthening Proportionality in Participatory Budgeting with Additive Utilities
Tzeh Yuan Neoh and Nicholas Teh
Proportional representation is a central goal in participatory budgeting, where voters select public projects subject to a shared budget. Full justified representation (FJR) accounts for groups whose members may value different projects, but computing an FJR outcome is strongly NP-hard for additive utilities. We show that FJR up to one project can be achieved in
polynomial time for arbitrary nonnegative additive utilities and project costs. Our approach extends Residual-Budget Greedy and satisfies a stronger fractional axiom that can also be verified in
polynomial time. For approval utilities, this axiom coincides with FJR+, providing exact FJR in that setting. We further show that every feasible completion of the
algorithm's selected set preserves the representation guarantee, providing flexibility in allocating the remaining budget. In particular, we give a completion that ensures priceability at the original budget. For unit-cost projects, we strengthen the representation guarantee to the Droop quota and combine it with priceability whenever there are enough positively valued projects to fill the budget.
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Information Retrieval
Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems
Maximilian Schall, Sedigheh Eslami, Markus Krimmel et al. (+4 more)
Evaluating first-stage retrievers in large-scale production RAG requires a
benchmark that pairs a large-scale corpus with a large set of
agent-reformulated search queries based on real user queries and their conversation threads, and that labels many relevant documents per query. No existing public
benchmark evaluates this setting: large-scale collections typically provide only a small number of evaluation queries, whereas
benchmarks with many queries generally contain only millions of documents. Moreover, most
benchmarks assess human-written queries, while the first-stage retrievers in agentic RAG pipelines serve machine-written reformulations whose distribution differs from human search behavior. To overcome these evaluation gaps, we introduce Q2D-Web (Query2Doc-Web), a large-scale agentic
retrieval benchmark consisting of a 190M-document web corpus and 70k agentic search queries in ten
languages, reformulated from real-world user queries in production systems. Q2D-Web provides three sets of fixed relevance judgments:
agent citations, production rankings, and a combined set that unions both signals and adds LLM-based judgments of unlabeled pooled documents to reduce false negatives. We
benchmark 13 retrievers including lexical, dense, and late-interaction models and find that their relative ordering is largely insensitive to the choice of judgment set, while diverging substantially across topical domains, query
languages, and query types. To enable fast evaluation, we also study subcorpus sampling as an approximation to full-corpus evaluations. Retaining a third of the corpus, selected by reciprocal rank fusion over pooled retriever runs, preserves the full-corpus model ranking under the combined judgments while raising absolute Recall@1000 only by 4 to 7 points. The public leaderboard is accessible under: https://huggingface.co/spaces/perplexity-ai/q2d-web-leaderboard
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All authors Maximilian Schall, Sedigheh Eslami, Markus Krimmel, Antoine Chaffin, Louis Milliken, Bo Wang, Denis Bykov
Computer Vision
FRAME: Factored Retrieval via Attribute Readouts for Object-Centric Scene Memory
Woosang Jeon, Sanghyeok Choi, Minwoo Kim et al. (+2 more)
Language-guided
robots need persistent scene
memories to follow instructions, revisit objects, and resolve references to objects encountered over time. While much of
language-guided scene-
memory retrieval has emphasized spatial or relational references, many everyday object references specify objects by multiple persistent attributes, such as category, material, size, or surface appearance. We formalize this problem as attribute-compositional
retrieval, where a fixed object-centric scene
memory is queried with natural
language to retrieve the object satisfying the requested attributes. To investigate this capability directly, we introduce a controlled evaluation protocol with fixed scene
memories and attribute-defined targets, separating
retrieval from perception and annotation ambiguities. We then propose FRAME, which turns
language into query-relevant attribute weights, uses learned readouts to estimate per-attribute evidence from object
embeddings, and ranks objects by aggregating this evidence according to the query. Across held-out scenes and object assets, FRAME outperforms representative scene-
memory retrieval baselines while reducing post-decomposition object scoring to lightweight
matrix-vector computation. These results position attribute-compositional
retrieval as a complementary scene-
memory capability for
language-guided
robots, showing that persistent object attributes can be exposed as composable evidence for accurate and efficient multi-attribute
retrieval.
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All authors Woosang Jeon, Sanghyeok Choi, Minwoo Kim, Taehyun Jung, Taehyeong Kim
Discrete Mathematics · Cross-listed from math.CO
A $(\log n)^{1/4}$ Bound for the Komlós Problem
Eren Ercan
Let $A\in\mathbb{R}^{m\times n}$ have columns of Euclidean norm at most one. We prove that $\operatorname{disc}(A)\le2395\left(1+\log_+\frac n9\right)^{1/4}+2\sqrt2$. Here $\log_+t=\max\{0,\log t\}$. Building on Bansal and Jiang's affine spectral independence framework, we remove the $(\log\log n)^{7/4}$ factor from their bound. The fourth root comes from balancing the logarithmic decrease in the alive dimension against the fourth power of the row thresholds. Historical exponential sums control the covariance budget across size classes with summable thresholds. An exact threshold-sum certificate gives the coefficient $2395$, and rounding at most eight remaining fractional coordinates costs $2\sqrt2$. The finite construction also gives partial colourings from any prescribed starting point and at any prescribed depth, preserving existing signs. We formalize the partial- and full-colouring
theorems in Lean, including the finite trajectory, exact threshold sum and final rounding, with Bansal–Jiang
Theorem A.4 as the sole external research
theorem assumption.
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Game Theory
The Complexity of Membership, Uniqueness, and Counting for Optimal Proportional Approval Voting Committees
Yizhou Ai
Proportional Approval Voting (PAV) chooses committees that maximize a sum of harmonic utilities. We study the set of maximizing committees: whether a candidate belongs to some or all of them, whether the optimum is unique, and how many optima exist. When the committee size is part of the input, the three decision problems are $\Delta_2^\mathrm{P}$-complete. Uniqueness remains hard for instances with at most two optimal committees. Counting optimal committees is $\#\!\cdot\!\mathrm{OptP}$-complete under metric reductions: every function $f$ in this class reduces to an election with exactly $f(x)+1$ optimal committees. The reductions encode satisfying assignments directly as committees and use harmonic marginal rewards to realize binary objectives with polynomially many voters. Each satisfying assignment has a unique committee representation, and fixed clause ballots give these representations the same clause score. We also prove Turing equivalence with $\#\mathrm{SAT}$ and show that membership of the counting problem in $\#\mathrm{P}$ would imply $\\mathrm{NP}=\mathrm{coNP}$.
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Numerical Analysis
High-Order Discontinuous Cut Finite Element Methods for Scalar Hyperbolic Conservation Laws
Pei Fu, Gunilla Kreiss, Zelin Xin et al. (+1 more)
In this paper, we present a family of high-order cut finite element methods based on the discontinuous Galerkin (DG) framework for scalar
hyperbolic conservation laws on complex domains. Building on our previous work, we develop a multidimensional formulation that combines macro-element stabilization with flux limiting to obtain a scheme that preserves the maximum principle and remains robust with respect to arbitrary boundary cuts of the background mesh. The physical domain is embedded in a regular background mesh, which may produce arbitrarily small cut
cells. To avoid the severe time step restrictions typically associated with such
cells, ghost penalty stabilization terms are added on interior facets of macro-elements. The resulting method exhibits stability and accuracy properties similar to those of standard DG methods on fitted meshes. An $L^2$-stability result is derived for the semi-discrete scheme under both periodic and inflow-
outflow boundary conditions. To enforce the maximum principle and suppress nonphysical oscillations, we adapt limiter techniques from standard DG methods to the CutFEM setting by defining limiting parameters on macro-elements. In particular, we present a macro-element-based parameterized flux limiter together with adaptations of the Zhang-Shu bound-preserving limiter and the Barth-Jespersen slope limiter. Numerical experiments in two and three spatial dimensions demonstrate optimal convergence orders, preservation of the maximum principle, and accurate shock capturing without spurious oscillations, even for challenging cut configurations involving very small-cut
cell intersections.
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All authors Pei Fu, Gunilla Kreiss, Zelin Xin, Sara Zahedi
Computer Vision
Medical AI Encodes a "Feeling of Error": Verifying Cancer Segmentation via Internal Concepts
Mengmeng Ma, Yunxiang Peng, Tang Li et al. (+4 more)
Cancer
segmentation models can fail silently, generating plausible but incorrect masks that risk missed findings or unnecessary biopsies. A critical question arises: Do AI models "know" when they are wrong, and if so, can we use the signal to predict their own failures? Humans do have a "Feeling of Error" (FOE): a spontaneous sense of unease that flags a potential error during thinking. We investigate whether cancer
segmentation models exhibit an analogous internal signal. Unlike output-level cues (e.g., prediction confidence or uncertainty), which offer no insight into why a failure occurs and suffer from a sensitivity-quality tradeoff where high detection sensitivity could degrade overall
segmentation quality. We instead propose to capture the model's FOE from its inner workings. Using mechanistic interpretability tools, specifically Sparse Autoencoders, we decompose internal neural activations into a dictionary of human-interpretable concepts and show that failure cases exhibit a distinct latent signature: fewer active concepts with lower activation magnitudes compared to successful
segmentation. By training a classifier on these concept activations, we achieve accurate failure detection along with explanations for the model's mistakes. Experiments on prostate, pancreatic, and
brain cancer
segmentation demonstrate that our approach outperforms output-based methods in failure detection while preserving
segmentation quality.
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All authors Mengmeng Ma, Yunxiang Peng, Tang Li, Lu Lin, Binsheng Zhao, Oguz Akin, Xi Peng
Data Structures and Algorithms
High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression
Syamantak Kumar, Purnamrita Sarkar, Kevin Tian et al. (+1 more)
Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over the Hamming slice $\mathcal{X}_k^d:=\{\mathbf{x}\in\{\pm 1\}^d:|\{i:\mathbf{x}_i=1\}|=k\}$, in high-dimensional regimes where $k\ll d$ (i.e., where $\mathcal{X}_k^d$ is highly magnetized). We use our frameworks to design improved samplers for canonical problems in the study of Ising models and
Bayesian sparse linear regression. Our first main result considers the Sherrington–Kirkpatrick (SK) model restricted to fixed-magnetization slices $\mathcal{X}_k^d$. We give a
polynomial-time sampler for fixed-magnetization SK models at any inverse temperature $\beta>0$, under arbitrary external fields, provided that $k\le c_\beta d$ for an appropriate constant $c_\beta$. By combining this result with an annealing strategy for estimating normalizing constants, we obtain
polynomial-time samplers for the SK model at arbitrarily low temperatures under a sufficiently strong external field of strength $h$. In the large-$\beta$ limit, our framework permits sampling at field strengths within constant factors of the Almeida–Thouless line delineating the replica-symmetric and replica-
symmetry-breaking regions ([dAT78]), improving polynomially over the field strength $h(\beta)$ required by the recent work of [BAR26]. Our second main result concerns the measurement complexity of
polynomial-time
Bayesian sparse linear regression. Recent work by [KSTZ25] shows how to sample from the canonical Gaussian spike-and-slab posterior with expected sparsity $k$, at any signal-to-noise ratio, given $n\gtrsim k^3\log^3 d$ Gaussian measurements. We improve this requirement to $n\gtrsim k^{3/2}\log^2 d+k\log^3 d$, using a common sparsity-aware framework underlying both our results.
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All authors Syamantak Kumar, Purnamrita Sarkar, Kevin Tian, Yusong Zhu
Numerical Analysis
Strong convergence rates of tamed exponential Euler schemes for superlinear hyperbolic SPDEs
Katharina Klioba
In this paper, we prove pathwise uniform convergence at rates up to $1/2$ for tamed exponential Euler schemes for semilinear
hyperbolic stochastic evolution equations with superlinearly growing nonlinearities and multiplicative noise. We take the term
hyperbolic to mean that the leading operator generates a contractive $C_0$-
semigroup but no
parabolic smoothing occurs. Under local Lipschitz,
polynomial growth, coercivity, and monotonicity conditions on the nonlinearities, we establish pathwise uniform strong error estimates of the form \begin{equation*} \Big(\mathbb{E}\max_{0\le j \le N} \|U(t_j)-U^j\|_X^p\Big)^{1/p} \lesssim \sqrt{k} \end{equation*} on a
Hilbert space $X$ for $p\in [2,\infty)$. Here, $U$ is the mild solution and $U^j$ is the tamed exponential Euler approximation at time $t_j=jk$ with step size $k>0$. This extends previous convergence results for non-
parabolic SPDEs from globally to locally Lipschitz nonlinearities, allowing both drift and
diffusion to grow polynomially. In a
stochastic Kato framework, we further establish local and global well-posedness as well as uniform a priori estimates for the mild solution and its approximation. Applications to nonlinear
stochastic transport, Airy, wave-type, and dissipatively damped nonlinear Schrödinger equations are included, covering different nonlinearities-stopped and fractionally tamed schemes. For the Klein-Gordon equation with cubic velocity damping, this complements previous results obtained for additive noise.
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Cryptography and Security
Towards Standardized Evaluation of GPU Memory Safety with GMSBench
Saurabh Singh, Jaewon Lee, Seonjin Na et al. (+1 more)
As GPUs become increasingly integral to high-performance computing and machine learning, ensuring
memory safety in GPU programs has become crucial for reliable and secure execution. However, evaluating GPU
memory safety techniques remains challenging due to the lack of comprehensive and standardized
benchmarks. In this paper, we present GMSBench, a GPU
memory safety
benchmark designed to evaluate a broad range of
memory safety violations across different GPU
memory spaces and execution scenarios. GMSBench comprises 149 self-contained CUDA tests spanning spatial, temporal, and concurrency errors. The suite provides a standardized foundation for the evaluation and comparative analysis of GPU
memory safety mechanisms and helps expose gaps in their detection coverage. We demonstrate the utility of GMSBench by evaluating Compute Sanitizer, a widely used GPU
memory error detection tool across multiple GPU architectures.
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All authors Saurabh Singh, Jaewon Lee, Seonjin Na, Hyesoon Kim
Emerging Technologies
OntoKG-EQ: Evidence-boundary auditing for analytical knowledge graphs
Furqan Nasir, Muhammad Atif Saeed, Muhammad Ehsan et al. (+2 more)
An analytical knowledge-graph result can be supported in its ambient graph while its delivered explanation omits deciding observations. OntoKG-EQ connects competency-question requirements to an executable audit of ambient observations, declared finding support and delivered evidence. A bounded declarative interpreter distinguishes positive, false and unknown numerical status, with same-witness unit readiness reported separately. A public-data application reconstructs England's August 2026 provider-level referral-to-treatment statistical screen. Independent source recomputation reconciles 532 providers with the publisher workbook: 91 meet the inclusive 92% threshold and 441 do not. One additional roster entry remains unavailable rather than zero-filled. All 533 application frames agree with an independently implemented standards-based validation and availability baseline. Transfer requires a disclosed source adapter and arithmetic compilation, but no change to the core interpreter. Twenty-six constructed analytical cases yield 78 status/readiness agreements. Actual financial release replay reproduces two omitted-volume defects: support and delivery suffice for 19 of 21 original findings and all 21 repaired findings after interface adaptation. A separate replay agrees on 487 overlapping financial records. Source audits expose scaling errors and unresolved comparator and temporal assumptions, so financial examples do not establish authenticated
market effects. The contribution is a reproducible analytical audit interface and source-reconciled application, not new sufficiency logic or first evidence-boundary isolation. Historical ratings measure preference only; objective human audit benefits and independent production deployment remain untested.
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All authors Furqan Nasir, Muhammad Atif Saeed, Muhammad Ehsan, Sher Jeel Ahmad, Abdul Moiz Altaf
Computer Vision
Linnan Zhao, Xu Liu, Lingling Li et al. (+3 more)
Reasoning segmentation converts an implicit linguistic conclusion into a precise mask, requiring both semantic identification and spatial grounding. Existing MLLM-segmenter interfaces either use a special trigger or compress both signals into one context, although they receive different supervision and fail differently. This coupling obscures whether a failure arises from target interpretation or from localization. We present SeGDeP, an explicit what-where interface. A semantic
prompt branch and an independent
geometric projection path transform resolved MLLM states into semantic features and a DETR-predicted box, which jointly condition a SAM 3 mask decoder. Training first aligns this executable interface, then uses group reward-decoupled policy optimization (GDPO) to balance format, box-IoU, and mask-IoU feedback. SeGDeP-4B reaches 82.7 average cIoU over eight RefCOCO-family splits and 66.0/59.6 gIoU on ReasonSeg val/test while adapting only 0.38% of Qwen3-VL parameters through LoRA. Controlled stage-wise ablations, gradient diagnostics, and
prompt interventions further show that the two paths develop complementary semantic and
geometric specialization rather than duplicating the same evidence.
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All authors Linnan Zhao, Xu Liu, Lingling Li, Licheng Jiao, Fang Liu, Wenping Ma
Data Structures and Algorithms · Cross-listed from quant-ph
Distributed Quantum Property Testing with Quantum Carrier Pigeons
Kenny Chen, Mina Doosti, Ryan Sweke et al. (+1 more)
We introduce a framework for distributed
quantum inference under communication constraints. In our model, $m$ distributed nodes each receive one copy of an unknown $d$-dimensional
quantum state $\rho$, before communicating via a constrained one-way communication channel with a central node, which aims to infer some property of $\rho$. This framework generalizes the classical distributed inference framework introduced by Acharya, Canonne, and Tyagi [COLT 2019], by allowing
quantum resources such as
quantum communication and shared
entanglement. Within this setting, we focus on the fundamental problem of
quantum state certification: Given a complete description of some state $\sigma$, decide whether $\rho=\sigma$ or $\|\rho-\sigma\|_1\geq \epsilon$. Additionally, we focus on the case of limited communication between distributed nodes and the central node: we assume each communication channel is limited to only $n_c$ bits and $n_q$
qubits with $n_c + n_q \leq \log d$. When all nodes can make use of a shared source of randomness, we show that the copy complexity of distributed state certification is $\Theta(\frac{d^2}{2^{n_q} 2^{n_c/2}\epsilon^2})$. We further demonstrate that shared randomness is necessary to achieve the above complexity, by proving an $\Omega(\frac{d^3}{4^{n_q} 2^{n_c} \epsilon^2})$ lower bound in the $\textit{private-coin}$ setting. Moreover, we develop a private-coin
algorithm that matches this bound up to a $\sqrt{\log d}$ factor, showing this complexity is near-optimal. Together, our work establishes a general framework for distributed
quantum inference with communication constraints and characterizes the complexity of distributed state certification with limited communication.
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All authors Kenny Chen, Mina Doosti, Ryan Sweke, Chirag Wadhwa
Artificial Intelligence
API Benchmark Scores Do Not Reliably Transfer to Chatbot Interfaces
Jennifer Wang, Joachim Baumann, Daniel E. Ho et al. (+1 more)
Benchmark scores are a central currency in model releases: they inform purchasing decisions, shape public trust, and influence policy. Yet, a key assumption underlying
benchmark scores is that the model performance measured through APIs faithfully reflects the behavior of deployed systems. We challenge this assumption by
auditing ChatGPT, Claude, and Gemini across seven systems and nine
benchmarks spanning general capability, social bias, and sycophancy. We find systematic API–interface differences in both accuracy and consistency. On average, API evaluations score 3.4 percentage points higher in accuracy and 2.1 percentage points higher in test–retest agreement than corresponding interface evaluations. For ChatGPT, the performance difference between API and interface access rivals the API-only difference between GPT 5.3 and GPT 5.4. Put differently, switching access surfaces can degrade performance as much as downgrading a full model version. We further test whether exposed API controls can reproduce interface behavior by varying system
prompts, sampling parameters, and
reasoning settings. These controls shift behavior in some cases but do not reliably eliminate the gap. Our findings document a context-validity gap: measurements obtained through APIs do not necessarily generalize to corresponding deployed interfaces, complicating the use of API evaluations as proxies for deployed systems.
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All authors Jennifer Wang, Joachim Baumann, Daniel E. Ho, Sanmi Koyejo
Information Retrieval
REDSI: Addressing the Reproducibility and Evaluation Consistency of Differentiable Search Indexing for Document Retrieval
Vivien Nicolas, Hicham Randrianarivo, Pascale Sébillot et al. (+1 more)
The differentiable search index (DSI) framework (Tay et al., 2022) has become the de facto baseline for generative
retrieval. However, DSI is hard to reproduce: no public implementation covers all three original document identifier types (atomic, naive, semantic), reported results vary widely, and the ubiquitous NQ320K
dataset is built from Natural Questions through diverse and underspecified preprocessing. We introduce ReDSI, the first open-source DSI implementation supporting all three identifier types, together with a parameterizable and well-documented NQ320K construction pipeline. Experimentally, we achieve results that are competitive with or stronger than previous DSI baselines. Moreover, we conduct extensive experiments under model downscaling, covering
retrieval effectiveness, parameter efficiency, training methods and decoding strategies, opening novel directions for future research.
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All authors Vivien Nicolas, Hicham Randrianarivo, Pascale Sébillot, Caio Corro
Machine Learning
Earth System World Model for What-If Simulations: A Case Study for Terrestrial Ecosystems
Zhihao Wang, Ruichen Wang, Ruohan Li et al. (+6 more)
Machine learning emulators have become essential for accelerating expensive Earth-system simulations, but most existing approaches remain passive forecasters: they reproduce simulator trajectories under prescribed forcings without an explicit interaction mechanism for user-specified interventions. This limits their use in interactive scientific workflows and Earth-system digital twins, where users often need to explore how a system would respond if selected state components were changed. We propose an action-conditioned world-modeling framework for Earth-system emulation that reformulates simulator trajectories as supervision for controllable state-transition learning. The key idea is transition-action pretraining: naturally observed state changes are treated as label-free action supervision, allowing the model to learn both prescribed dynamics and action-conditioned responses without manually annotated interventions. We further introduce masked response learning to infer unobserved variables under partial state edits and learn coupled system dependencies. We test this framework on ecosystem dynamics across six global regions and multiple stand ages. Experiments show that the model preserves competitive long-horizon emulation accuracy while enabling controllable structural interventions and coherent responses in coupled ecosystem-cycle variables. These results suggest a practical route from passive Earth-system emulators toward interactive, intervention-aware scientific surrogates.
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All authors Zhihao Wang, Ruichen Wang, Ruohan Li, Lei Ma, George Hurtt, Xiaowei Jia, Gengchen Mai, Shaowen Wang, Yiqun Xie
Robotics
CAST: Alternating State-Value Targets and Expanded Policy Gradients for Model-Based Reinforcement Learning
Pietro Noah Crestaz, Mohamed Yassine Kabouri, Nicolas Mansard et al. (+1 more)
Model-based reinforcement learning (MBRL) is a family of RL methods that learn a model of the environment and use it for action selection, making it well suited to robotics due to its sample efficiency. Combining learned models with online planning can further improve action selection, as the planner can exploit the model to find better actions than the learned policy alone. Recent methods combining learned policies with online planning typically learn the value of the policy rather than the stronger planner-guided behavior. We present CAST (Critic with Alternating State-value Target), which uses planner-guided behavior to improve value learning while regularizing the value estimate with the current policy. CAST replaces the action-value critic with a state-value critic, trained using a target that combines a real planner-guided transition and an imagined transition under the current policy. The resulting value function corresponds to an alternating process between planner-guided behavior and the current policy, allowing it to benefit from the stronger planner behavior while being regularised by the policy being learned. We evaluate CAST on the DeepMind Control and HumanoidBench Suites against several state-of-the-art methods, and demonstrate successful transfer to a physical Unitree Go2 quadruped performing a dynamic handstand.
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All authors Pietro Noah Crestaz, Mohamed Yassine Kabouri, Nicolas Mansard, Andrea Del Prete
Machine Learning
Length Generalization for Transformers via Compression
Georg Zetzsche, Hongjian Jiang, Andy Yang et al. (+4 more)
Recent advancements in
transformer length generalization theory enable us to reliably predict when a
transformer can learn to solve a task. In particular, the C-RASP hypothesis (a formalized version of the so-called RASP-l
conjecture) posits that
transformers length-generalize on a task if and only if a solution is expressible in the C-RASP
language. While this hypothesis has strong empirical validation, theoretical problems arise from the fact that no computable length generalization bounds exist for C-RASP, alongside the discovery of seemingly contradictory experiments. To address these problems, we refine the C-RASP hypothesis utilizing the recently-proposed fragments C-RASP+ and C-RASP1. These fragments have computable length generalization bounds, though in the worst case requiring an extremely large (double exponential) sample size. It is an open question whether these sample size bounds are tight. In this paper, we resolve this open question by providing an exponentially tighter bound. In doing so, we show a
polynomial length generalization bound for
transformers if we adopt compressed strings, via a novel connection to power words. As an application, we show how this yields a fine-grained analysis of the C-RASP
conjecture that resolves contradicting experimental evidence against it.
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All authors Georg Zetzsche, Hongjian Jiang, Andy Yang, Pascal Bergsträßer, Marco Sälzer, David Chiang, Anthony W. Lin
Computer Vision
Matching in Context: Temporal Fusion and Query Adaptation for Training-Free Video Temporal Grounding
Zhuo Cao, Bingqing Zhang, Sen Wang et al. (+1 more)
Text-guided
Video Temporal Grounding (VTG) localizes moments in an untrimmed
video that correspond to a text query. Supervised approaches require costly temporal annotations and task-specific training, yet can generalize poorly under distribution shift. To reduce this dependence on task-specific supervision, training-free approaches use pretrained
vision-
language models to localize moments by measuring the similarity between
video and query features. However, independently encoded frames provide limited temporal context, while a fixed query
embedding can assign similar scores to different candidate moments within the
video. To address these limitations, we propose DSE-VTG, a \underline{D}ual-\underline{S}ide \underline{E}nhancement framework that combines visual evidence across temporal granularities with
video-specific query adaptation, without task-specific training. On the visual side, Multi-scale Similarity Fusion (MSF) combines frame- and clip-level similarities into a unified, temporally aware similarity profile. On the query side, Query-level Test-Time Adaptation (Q-TTA) uses pseudo-labels to optimize a lightweight additive offset for more discriminative matching within the
video, without fine-tuning the backbone or calling external large
language models. Experiments on three standard and two out-of-distribution (OOD)
benchmarks show gains over the prior training-free methods. On Charades-STA, DSE-VTG improves mIoU by 5.89 percentage points over the strongest compared baseline under the same backbone. Under distribution shift, DSE-VTG reaches 50.86 mIoU on Charades-CG Novel-Word, surpassing the strongest supervised baseline by 2.76 mIoU. Our code will be released upon acceptance.
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All authors Zhuo Cao, Bingqing Zhang, Sen Wang, Xue Li
Computer Vision
FIRE3D: Feed-forward Interactive 3D Scene Reconstruction Within A Minute
Hongchi Xia, Tianhang Cheng, Wei-Chiu Ma et al. (+1 more)
We present FIRE3D, a unified framework that transforms a single RGB image or casual
video into interactable 3D scene assets for games and interactive applications in under a minute for up to twelve textured instances including preprocessing. At the core of FIRE3D is a feed-forward inference pipeline that predicts a compositional scene representation from posed RGB-D observations estimated from the RGB capture, including the 6-DoF pose, bounding box, mesh, and texture for detected objects. By modeling the scene as a collection of discrete entities, FIRE3D produces amodal object assets that can be independently edited and used in interactive applications. Our framework requires no test-time optimization, runs substantially faster than the compared reconstruction systems, and provides object-level completeness beyond existing feed-forward 3D approaches. We demonstrate leading detection accuracy, strong geometry reconstruction, and competitive rendering quality on the evaluated
benchmarks, with substantial runtime gains.
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All authors Hongchi Xia, Tianhang Cheng, Wei-Chiu Ma, Shenlong Wang