OpenRaiser/Intern-Atlas huggingface dataset:
Tight bounds for the sensitivity of CDAWGs with left-end edits
Venue: arXiv cs.DS 2025
Year: 2025

Compact directed acyclic word graphs (CDAWGs) [Blumer et al. 1987] are a fundamental data structure on strings with applications in text pattern searching, data compression, and pattern discovery. Intuitively, the CDAWG of a string $T$ is obtained by merging isomorphic subtrees of the suffix tree [Weiner 1973] of the same string $T$, thus CDAWGs are a compact indexing structure. In this paper, we investigate the sensitivity of CDAWGs when a single character edit operation (insertion, deletion, or substitution) is performed at the left-end of the input string $T$, namely, we are interested in the worst-case increase in the size of the CDAWG after a left-end edit operation. We prove that if $e$ is the number of edges of the CDAWG for string $T$, then the number of new edges added to the CDAWG after a left-end edit operation on $T$ does not exceed $e$. Further, we present a matching lower bound on the sensitivity of CDAWGs for left-end insertions, and almost matching lower bounds for left-end deletions and substitutions. We then generalize our lower-bound instance for left-end insertions to leftward online construction of the CDAWG, and show that it requires $Ω(n^2)$ time for some string of length $n$.
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
Venue: arXiv cs.LG 2025
Year: 2025

Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes. Recently, neural operators have shown the ability to solve PDEs by learning the integral kernel directly in Fourier/Wavelet space, so the difficulty for solving the coupled PDEs depends on dealing with the coupled mappings between the functions. Towards this end, we propose a \textit{coupled multiwavelets neural operator} (CMWNO) learning scheme by decoupling the coupled integral kernels during the multiwavelet decomposition and reconstruction procedures in the Wavelet space. The proposed model achieves significantly higher accuracy compared to previous learning-based solvers in solving the coupled PDEs including Gray-Scott (GS) equations and the non-local mean field game (MFG) problem. According to our experimental results, the proposed model exhibits a $2\times \sim 4\times$ improvement relative $L$2 error compared to the best results from the state-of-the-art models.
What is Memory? A Homological Perspective
Venue: arXiv cs.LG 2025
Year: 2025

We introduce the delta-homology model of memory, a unified framework in which recall, learning, and prediction emerge from cycle closure, the completion of topologically constrained trajectories within the brain's latent manifold. A Dirac-like memory trace corresponds to a nontrivial homology generator, representing a sparse, irreducible attractor that reactivates only when inference trajectories close upon themselves. In this view, memory is not a static attractor landscape but a topological process of recurrence, where structure arises through the stabilization of closed loops. Building on this principle, we represent spike-timing dynamics as spatiotemporal complexes, in which temporally consistent transitions among neurons form chain complexes supporting persistent activation cycles. These cycles are organized into cell posets, compact causal representations that encode overlapping and compositional memory traces. Within this construction, learning and recall correspond to cycle closure under contextual modulation: inference trajectories stabilize into nontrivial homology classes when both local synchrony (context) and global recurrence (content) are satisfied. We formalize this mechanism through the Context-Content Uncertainty Principle (CCUP), which states that cognition minimizes joint uncertainty between a high-entropy context variable and a low-entropy content variable. Synchronization acts as a context filter selecting coherent subnetworks, while recurrence acts as a content filter validating nontrivial cycles.
Memory-adaptive Depth-wise Heterogeneous Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data. However, the presence of heterogeneous devices in federated learning, such as mobile phones and IoT devices with varying memory capabilities, would limit the scale and hence the performance of the model could be trained. The mainstream approaches to address memory limitations focus on width-slimming techniques, where different clients train subnetworks with reduced widths locally and then the server aggregates the subnetworks. The global model produced from these methods suffers from performance degradation due to the negative impact of the actions taken to handle the varying subnetwork widths in the aggregation phase. In this paper, we introduce a memory-adaptive depth-wise learning solution in FL called FeDepth, which adaptively decomposes the full model into blocks according to the memory budgets of each client and trains blocks sequentially to obtain a full inference model. Our method outperforms state-of-the-art approaches, achieving 5% and more than 10% improvements in top-1 accuracy on CIFAR-10 and CIFAR-100, respectively. We also demonstrate the effectiveness of depth-wise fine-tuning on ViT. Our findings highlight the importance of memory-aware techniques for federated learning with heterogeneous devices and the success of depth-wise training strategy in improving the global model's performance.
Toward A Dynamic Comfort Model for Human-Building Interaction in Grid-Interactive Efficient Buildings: Supported by Field Data
Venue: arXiv cs.CY 2025
Year: 2025

Controlling building electric loads could alleviate the increasing grid strain caused by the adoption of renewables and electrification. However, current approaches that automatically setback thermostats on the hottest day compromise their efficacy by neglecting human-building interaction (HBI). This study aims to define challenges and opportunities for developing engineering models of HBI to be used in the design of controls for grid-interactive efficient buildings (GEBs). Building system and measured and just-in-time surveyed psychophysiological data were collected from 41 participants in 20 homes from April-September. ASHRAE Standard 55 thermal comfort models for building design were evaluated with these data. Increased error bias was observed with increasing spatiotemporal temperature variations. Unsurprising, considering these models neglect such variance, but questioning their suitability for GEBs controlling thermostat setpoints, and given the observed 4°F intra-home spatial temperature variation. The results highlight opportunities for reducing these biases in GEBs through a paradigm shift to modeling discomfort instead of comfort, increasing use of low-cost sensors, and models that account for the observed dynamic occupant behavior: of the thermostat setpoint overrides made with 140-minutes of a previous setpoint change, 95% of small changes ( 2°F) were made with 120-minutes, while 95% of larger changes ( 10°F) were made within only 70-minutes.
Constrained Adversarial Learning for Automated Software Testing: a literature review
Venue: arXiv cs.SE 2025
Year: 2025

It is imperative to safeguard computer applications and information systems against the growing number of cyber-attacks. Automated software testing tools can be developed to quickly analyze many lines of code and detect vulnerabilities by generating function-specific testing data. This process draws similarities to the constrained adversarial examples generated by adversarial machine learning methods, so there could be significant benefits to the integration of these methods in testing tools to identify possible attack vectors. Therefore, this literature review is focused on the current state-of-the-art of constrained data generation approaches applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance their software testing tools with adversarial testing methods and improve the resilience and robustness of their information systems. The found approaches were systematized, and the advantages and limitations of those specific for white-box, grey-box, and black-box testing were analyzed, identifying research gaps and opportunities to automate the testing tools with data generated by adversarial attacks.
Optimal Volume-Sensitive Bounds for Polytope Approximation
Venue: arXiv cs.CG 2025
Year: 2025

Approximating convex bodies is a fundamental question in geometry, which has a wide variety of applications. Given a convex body $K$ in $\textbf{R}^d$ for fixed $d$, the objective is to minimize the number of facets of an approximating polytope for a given Hausdorff error $\varepsilon$. It is known that $O((\text{diam}(K)/\varepsilon)^{(d-1)/2})$ facets suffice and are necessary for many instances, such as the Euclidean ball. However, this bound is far from optimal for ``skinny'' convex bodies. A natural way to characterize the skinniness of a convex object is in terms of its relationship to the Euclidean ball. Given a convex body $K$, its \emph{volume diameter} $Δ_d(K)$ is defined to be the diameter of a Euclidean ball of the same volume as $K$. The \emph{surface diameter} $Δ_{d-1}(K)$ is defined analogously for surface area. It follows from generalizations of the isoperimetric inequality that $\text{diam}(K) \geq Δ_{d-1}(K) \geq Δ_d(K)$. Arya, da Fonseca, and Mount proved that the diameter-based bound could be made sensitive to the surface diameter, improving the above bound to $O((Δ_{d-1}(K)/\varepsilon)^{(d-1)/2})$. In this paper, we strengthen this by proving the existence of an approximation with $O((Δ_d(K)/\varepsilon)^{(d-1)/2})$ facets. As a function of volume alone, this bound is tight up to constant factors. Our improvements arise from a combination of new ideas. We exploit known properties of the original body and its polar dual. In order to obtain a volume-sensitive bound, we explore the problem of computing a low-complexity polytope that is sandwiched between two given convex bodies. We show that this problem can be reduced to a covering problem involving a natural intermediate body based on the harmonic mean. Our proof relies on a geometric analysis of a relative notion of fatness involving these bodies.
Confidence-aware 3D Gaze Estimation and Evaluation Metric
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning appearance-based 3D gaze estimation is gaining popularity due to its minimal hardware requirements and being free of constraint. Unreliable and overconfident inferences, however, still limit the adoption of this gaze estimation method. To address the unreliable and overconfident issues, we introduce a confidence-aware model that predicts uncertainties together with gaze angle estimations. We also introduce a novel effectiveness evaluation method based on the causality between eye feature degradation and the rise in inference uncertainty to assess the uncertainty estimation. Our confidence-aware model demonstrates reliable uncertainty estimations while providing angular estimation accuracies on par with the state-of-the-art. Compared with the existing statistical uncertainty-angular-error evaluation metric, the proposed effectiveness evaluation approach can more effectively judge inferred uncertainties' performance at each prediction.
Terminal Coalgebras in Countably Many Steps
Venue: arXiv cs.LO 2025
Year: 2025

We present a collection of results that imply that an endofunctor on a category has a terminal object obtainable as a countable limit of its terminal-coalgebra chain. This holds for finitary endofunctors preserving nonempty binary intersections on locally finitely presentable categories, assuming that the posets of strong quotients and subobjects of finitely presentable objects satisfy the descending chain condition. This allows one to adapt finiteness arguments that were originally advanced by Worrell concerning terminal coalgebras for finitary set functors. Examples include the categories of sets, posets, vector spaces, graphs, nominal sets, and presheaves on finite sets. Worrell also described, without proof, the terminal-coalgebra chain of the finite power-set functor. We provide a detailed proof following his ideas. We then turn to polynomial endofunctors on the categories of Hausdorff topological spaces and metric spaces. The Vietoris space of compact subsets of a given Hausdorff space yields an endofunctor $\mathscr{V}$ on the category of Hausdorff spaces. Vietoris polynomial endofunctors on that category are built from $\mathscr{V}$, the identity and constant functors by forming products, coproducts and compositions. We present an analogous class of endofunctors on the category of extended metric spaces, using in lieu of $\mathscr{V}$ the Hausdorff functor $\mathcal{H}$. We prove that the ensuing Hausdorff polynomial functors have terminal coalgebras and initial algebras. We show that every finitary endofunctor on the category of vector spaces over a fixed field again has a terminal coalgebra obtained in $ω+ω$ steps. Whereas the canonical constructions of terminal coalgebras for Vietoris polynomial functors takes $ω$ steps, one needs $ω+ ω$ steps in general for our other concrete settings.
Can AI-Generated Text be Reliably Detected?
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) perform impressively well in various applications. However, the potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their responsible use. Consequently, the reliable detection of AI-generated text has become a critical area of research. AI text detectors have shown to be effective under their specific settings. In this paper, we stress-test the robustness of these AI text detectors in the presence of an attacker. We introduce recursive paraphrasing attack to stress test a wide range of detection schemes, including the ones using the watermarking as well as neural network-based detectors, zero shot classifiers, and retrieval-based detectors. Our experiments conducted on passages, each approximately 300 tokens long, reveal the varying sensitivities of these detectors to our attacks. Our findings indicate that while our recursive paraphrasing method can significantly reduce detection rates, it only slightly degrades text quality in many cases, highlighting potential vulnerabilities in current detection systems in the presence of an attacker. Additionally, we investigate the susceptibility of watermarked LLMs to spoofing attacks aimed at misclassifying human-written text as AI-generated. We demonstrate that an attacker can infer hidden AI text signatures without white-box access to the detection method, potentially leading to reputational risks for LLM developers. Finally, we provide a theoretical framework connecting the AUROC of the best possible detector to the Total Variation distance between human and AI text distributions. This analysis offers insights into the fundamental challenges of reliable detection as language models continue to advance. Our code is publicly available at https://github.com/vinusankars/Reliability-of-AI-text-detectors.
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning
Venue: arXiv cs.LG 2025
Year: 2025

The goal of data-free meta-learning is to learn useful prior knowledge from a collection of pre-trained models without accessing their training data. However, existing works only solve the problem in parameter space, which (i) ignore the fruitful data knowledge contained in the pre-trained models; (ii) can not scale to large-scale pre-trained models; (iii) can only meta-learn pre-trained models with the same network architecture. To address those issues, we propose a unified framework, dubbed PURER, which contains: (1) ePisode cUrriculum inveRsion (ECI) during data-free meta training; and (2) invErsion calibRation following inner loop (ICFIL) during meta testing. During meta training, we propose ECI to perform pseudo episode training for learning to adapt fast to new unseen tasks. Specifically, we progressively synthesize a sequence of pseudo episodes by distilling the training data from each pre-trained model. The ECI adaptively increases the difficulty level of pseudo episodes according to the real-time feedback of the meta model. We formulate the optimization process of meta training with ECI as an adversarial form in an end-to-end manner. During meta testing, we further propose a simple plug-and-play supplement-ICFIL-only used during meta testing to narrow the gap between meta training and meta testing task distribution. Extensive experiments in various real-world scenarios show the superior performance of ours.
CELLO: Co-designing Schedule and Hybrid Implicit/Explicit Buffer for Complex Tensor Reuse
Venue: arXiv cs.DC 2025
Year: 2025

Tensor algebra accelerators have been gaining popularity for running high-performance computing (HPC) workloads. Identifying optimal schedules for individual tensor operations and designing hardware to run these schedules is an active area of research. Unfortunately, operators in HPC workloads such as Conjugate Gradient often have operators with skewed shapes, fundamentally limiting the reuse any schedule can leverage. Moreover, the operators form a complex DAG of dependencies, making it challenging to apply simple fusion/pipelining techniques to extract inter-operation reuse. To address these challenges, this work proposes an accelerator CELLO. CELLO uses a novel on-chip buffer mechanism called CHORD co-designed with a novel scheduler called SCORE, which together enables identifying and exploiting reuse over complex DAGs of tensor operations. CELLO provides 4x geomean speedup and 4x energy efficiency over state-of-the-art accelerators across HPC workloads.
Flying robots for a smarter life
Venue: arXiv cs.RO 2025
Year: 2025

Innovative ideas are continuously emerging to produce better life conditions where essential human needs are supposed to be fulfilled with perfect scenarios leading us to propose modern strategies drawing the future of smart cities. In this context, flying robots are increasingly exploited in many fields to improve the quality of our life. This paper illustrates new designs of flying robots that could be used to perform a variety of advanced missions like investigating the state of high-power lines and manipulating cabling maintenance procedures when failures are detected, evaluating the state of the outer edge of sidewalks to color their partially or wholly erased parts, and spraying pesticides to trees or crops that are affected by different diseases. Creating such smart devices demands developing many other partial designs relying on AI-based algorithms, computer vision techniques, and embedded systems. A variety of techniques that we have recently developed in this field are presented here.
GarmentTracking: Category-Level Garment Pose Tracking
Venue: arXiv cs.CV 2025
Year: 2025

Garments are important to humans. A visual system that can estimate and track the complete garment pose can be useful for many downstream tasks and real-world applications. In this work, we present a complete package to address the category-level garment pose tracking task: (1) A recording system VR-Garment, with which users can manipulate virtual garment models in simulation through a VR interface. (2) A large-scale dataset VR-Folding, with complex garment pose configurations in manipulation like flattening and folding. (3) An end-to-end online tracking framework GarmentTracking, which predicts complete garment pose both in canonical space and task space given a point cloud sequence. Extensive experiments demonstrate that the proposed GarmentTracking achieves great performance even when the garment has large non-rigid deformation. It outperforms the baseline approach on both speed and accuracy. We hope our proposed solution can serve as a platform for future research. Codes and datasets are available in https://garment-tracking.robotflow.ai.
Fast Rate Information-theoretic Bounds on Generalization Errors
Venue: arXiv cs.IT 2025
Year: 2025

The generalization error of a learning algorithm refers to the discrepancy between the loss of a learning algorithm on training data and that on unseen testing data. Various information-theoretic bounds on the generalization error have been derived in the literature, where the mutual information between the training data and the hypothesis (the output of the learning algorithm) plays an important role. Focusing on the individual sample mutual information bound by Bu et al., which itself is a tightened version of the first bound on the topic by Russo et al. and Xu et al., this paper investigates the tightness of these bounds, in terms of the dependence of their convergence rates on the sample size $n$. It has been recognized that these bounds are in general not tight, readily verified for the exemplary quadratic Gaussian mean estimation problem, where the individual sample mutual information bound scales as $O(\sqrt{1/n})$ while the true generalization error scales as $O(1/n)$. The first contribution of this paper is to show that the same bound can in fact be asymptotically tight if an appropriate assumption is made. In particular, we show that the fast rate can be recovered when the assumption is made on the excess risk instead of the loss function, which was usually done in existing literature. A theoretical justification is given for this choice. The second contribution of the paper is a new set of generalization error bounds based on the $(η, c)$-central condition, a condition relatively easy to verify and has the property that the mutual information term directly determines the convergence rate of the bound. Several analytical and numerical examples are given to show the effectiveness of these bounds.
Effect Algebras as Omega-categories
Venue: arXiv cs.LO 2025
Year: 2025

We show how an effect algebra $\mathcal{X}$ can be regarded as a category, where the morphisms $x \rightarrow y$ are the elements $f$ such that $x \leq f \leq y$. This gives an embedding $\mathbf{EA} \rightarrow \mathbf{Cat}$. The interval $[x,y]$ proves to be an effect algebra in its own right, so $\mathcal{X}$ is an $\mathbf{EA}$-enriched category. The construction can therefore be repeated, meaning that every effect algebra can be identified with a strict $ω$-category. We describe explicitly the strict $ω$-category structure for two classes of operators on a Hilbert space.
If At First You Don't Succeed: Test Time Re-ranking for Zero-shot, Cross-domain Retrieval
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we introduce a novel method for zero-shot, cross-domain image retrieval. Our key contribution is a test-time Iterative Cluster-free Re-ranking process that leverages gallery-gallery feature information to establish semantic links between query and gallery images. This enables the retrieval of relevant images even when they do not exhibit similar visual features but share underlying semantic concepts. This can be combined with any pre-existing cross-domain feature extraction backbone to improve retrieval performance. However, when combined with a carefully chosen Vision Transformer backbone and combination of zero-shot retrieval losses, our approach yields state-of-the-art results on the Sketchy, TU-Berlin and QuickDraw sketch-based retrieval benchmarks. We show that our re-ranking also improves performance with other backbones and outperforms other re-ranking methods applied with our backbone. Importantly, unlike many previous methods, none of the components in our approach are engineered specifically towards the sketch-based image retrieval task - it can be generally applied to any cross-domain, zero-shot retrieval task. We therefore also present new results on zero-shot cartoon-to-photo and art-to-product retrieval using the Office-Home dataset. Project page: finlay-hudson.github.io/icfrr, code available at: github.com/finlay-hudson/ICFRR