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版本更新 · v2arXiv:2512.10817

Extrapolation of Periodic Functions Using Binary Encoding of Continuous Numerical Values

Brian P. Powell, Jordan A. Caraballo-Vega, Mark L. Carroll, Thomas Maxwell, Andrew Ptak, Greg Olmschenk, Jorge Martinez-Palomera

原文摘要

We introduce Normalized Base-2 Encoding (NB2E) as a method for encoding continuous numerical values and demonstrate that, using this input encoding, vanilla multi-layer perceptrons (MLP) successfully extrapolate four periodic waveforms without prior knowledge of their functional form. We contrast this finding with fixed Fourier encoding, which extrapolates well when the number of signal cycles over the normalized domain is an even integer and loses coherence at most other values, whereas NB2E degrades gradually away from those values and, except near odd cycle counts, remains substantially more accurate. These findings are limited to a single MLP architecture trained on one synthetic, exactly periodic, one-dimensional signal at a time, with extrapolation extending only to the end of the normalized domain. We do not propose a mechanism for this phenomenon. Rather, we present empirical observations which merit further study.

cs.LG · cs.AI · cs.CV · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.05871

Retain-Neutral Surrogates for Min-Max Unlearning

Junhao Cai, Dohun Kim, Dowon Kim, Sung Il Choi, Chengjun Jin, Juhyun Park, Changhee Joo

原文摘要

Machine unlearning aims to make a model forget designated training data while retaining performance on the rest. In min-max unlearning, an inner adversary perturbs the model to maximize the forget loss, and the model is updated to minimize the retain loss at the resulting surrogate point. When the forget and retain gradients are positively coupled, the standard perturbation also moves toward higher retain loss, and the retain step begins by recovering that loss. We propose Retain-Orthogonal Surrogate Unlearning (ROSU), which redefines the adversary to admit only perturbations orthogonal to the retain gradient. The one that raises the forget loss fastest has a closed form, and ROSU uses it to set the surrogate point and amplifies it into the forgetting step. Under positive coupling the ROSU surrogate avoids the retain cost that coupling induces in the standard surrogate, and the difference shrinks as the gradients approach orthogonality. ROSU has the smallest accuracy gap to retraining in every vision setting (CIFAR-10, CIFAR-100, Tiny-ImageNet) and is competitive with the baselines on language benchmarks (TOFU, WMDP). The redefinition helps most under high positive coupling. Code is available at https://github.com/unlearn-ml/machine-unlearning.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.03857

Optimized Minimal 4D Gaussian Splatting for Efficient Dynamic Scene Representation

Minseo Lee, Byeonghyeon Lee, Lucas Yunkyu Lee, Eunsoo Lee, JunYoung Jeong, Sangmin Kim, Seunghyeon Song, Joo Chan Lee 等 11 位作者

原文摘要

4D Gaussian Splatting has emerged as a new paradigm for dynamic scene representation, enabling real-time rendering of scenes with complex motions. However, it faces a major challenge of storage overhead, as millions of Gaussians are required for high-fidelity reconstruction. Compressing explicit 4D Gaussians is challenging because they exhibit heterogeneous redundancy across static and dynamic regions, and their temporal attributes make direct quantization unstable. In this work, we present OMG4 (Optimized Minimal 4D Gaussian Splatting), a framework that constructs a compact set of salient Gaussians capable of faithfully representing 4D Gaussian models. Our method progressively reduces Gaussians in three stages: (1) Gaussian Sampling to identify primitives critical to reconstruction fidelity, (2) Gaussian Pruning to remove redundancies, and (3) Gaussian Merging to fuse primitives with similar characteristics. In addition, we integrate implicit appearance compression and extend Sub-Vector Quantization (SVQ) to 4D representations with a staged quantization scheme, further reducing storage while preserving quality. Extensive experiments on standard benchmark datasets demonstrate that OMG4 achieves a favorable rate-distortion trade-off over recent state-of-the-art methods, reducing model sizes by over 60% while maintaining reconstruction quality. These results demonstrate the effectiveness of OMG4 as a practical framework for compact and high-fidelity 4D scene representation. Our source code is available at https://minshirley.github.io/OMG4/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15824

Rule-Sensitive Sensor Participation in Distributed Detection with Prescribed Boolean Fusion Rules

Ahmad Tanha

原文摘要

We study distributed binary detection when the fusion node must implement a prescribed Boolean decision rule. Under conditionally independent Bernoulli observations, we derive a signed-influence path identity valid for arbitrary Boolean rules; for monotone rules, it reduces to an exact Russo--Margulis representation in terms of coordinate influences. The identity yields rule-sensitive necessary conditions for a target Bayes error and an influence-weighted Kullback--Leibler feasibility bound. We then connect Boolean relevance to exact rule implementation and derive lower bounds on the number of sensors that must participate, including a heterogeneous bound based on the ordered per-sensor hypothesis separations. Numerical and Monte Carlo results illustrate both the tightening provided by the heterogeneous participation bound and the performance penalty that can arise when a prescribed fusion rule is retained instead of optimal likelihood-ratio fusion.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15822

A Faster Scaling Algorithm for Maximum Weight Matroid Intersection

Alina Ene, Huy L. Nguyen

原文摘要

We consider the maximum weight matroid intersection problem. In this problem, we are given two matroids $M_{1}=(E,\mathcal{I}_{1})$ and $M_{2}=(E,\mathcal{I}_{2})$ and integer weights $w(e)$ on the elements $e\in E$, and the goal is to find a maximum-weight common independent set. We give a randomized algorithm that constructs an optimal solution using $\widetilde{O}(nr\log W)$ queries to the independence oracles of the matroids, where $n$ is the size of the ground set $E$, $r$ is the minimum rank of the two matroids, and $W$ is the maximum weight.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.25403

A Faster Strongly Polynomial Time Algorithm for Submodular Function Minimization

Alina Ene, Huy L. Nguyen

原文摘要

We give a strongly polynomial time algorithm for submodular function minimization with overall running time $\widetilde{O}(n^{2}\mathsf{EO}+n^{3})$, where $\mathsf{EO}$ is the running time of a single evaluation of the function. Our algorithm improves upon the previously fastest strongly polynomial algorithms of Lee, Sidford, and Wong (FOCS 2015), Dadush, Végh, and Zambelli (SODA 2018), and Jiang, Lee, Song, and Zhang (SODA 2024) that run in time $\widetilde{O}(n^{3}\mathsf{EO}+n^{4})$. Similarly to prior works, our work combines a cutting plane algorithm with the combinatorial arc-finding framework of Iwata, Fleischer, and Fujishige (JACM 2001). We achieve our faster running time by developing an algorithm that runs the cutting plane method end to end, without restarting after discovering arcs and contracting strongly connected components.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15817

Tree Decomposition of Product and Nest Graphs for Assortment Optimization under the Generalized Nested Logit Model

Yuheng Wang, Paat Rusmevichientong, Huseyin Topaloglu

原文摘要

The generalized nested logit model extends the nested logit model by allowing each product to belong to multiple nests. The overlapping nests in the generalized nested logit model can capture complex substitution patterns, and the model has found applications in many areas. We study the assortment optimization problem under the generalized nested logit model and prove that it is NP-hard when there are only two nests or when any two distinct nests have at most one product in common. Motivated by these complexity results, we introduce novel concepts of product and nest graphs that capture which products are included in which nests. We use the tree decompositions of the graphs to characterize when the assortment optimization problem admits efficient solution methods. When the treewidth of a product graph is bounded, the assortment optimization problem can be solved in polynomial time. If the treewidth of the product graph is unbounded but the treewidth of the nest graph is bounded, then there exists a fully polynomial-time approximation scheme. These results represent the first polynomial-time algorithms for assortment optimization under the generalized nested logit model. Our numerical experiments on synthetic and real datasets show that the generalized nested logit model provides a better fit to the data than many existing models in the literature.

cs.DS · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15816

Global-Local and Window-Concatenated Functional Random Forests: Early and Late Fusion of Window-Specific Functional Principal Components

Fabrizio Maturo, Annamaria Porreca

原文摘要

Functional random forests built on a single functional principal component decomposition of the whole domain may dilute features confined to short intervals, whose location is rarely known in advance. We propose two random forests based on window-specific functional principal components. The global-local functional random forest (GL-FRF) is a late-fusion method that stacks a tuned full-curve forest and one forest per window with penalised convex weights; it contains the full-curve forest as a feasible solution, and its weights indicate which parts of the domain drive the prediction. The window-concatenated functional random forest (WC-FRF) is an early-fusion method that trains a single tuned forest on the concatenated window-specific scores. On twenty UCR datasets, GL-FRF improved on a tuned full-curve forest with the same number of trees on 16, with Brier score reductions of up to 0.21, significantly after Holm adjustment. No method was best on more than six datasets. WC-FRF was more accurate than GL-FRF on 13 datasets but failed when the signal required the full curve, whereas GL-FRF had the smallest mean and maximum excess over the best method across all 32 simulated and real settings, which makes it the safer choice when the location of the signal is unknown.

stat.ML · cs.LG · stat.ME

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.11632

Characterizing Scanpath Decoding Pipelines with Perturbation Profiles

Saleh Mozaffari, Abolfazl Nadi

原文摘要

When a scanpath descriptor supports a classification, it is rarely clear what the classification rests on. We propose perturbation profiles. Five transformations each target one stated property of every scanpath (fixation order, the coupling of durations to positions, the duration distribution, orientation and handedness, and fine spatial structure) while preserving a listed set of invariants. The classifier is refit, and the change in participant-held-out AUC is the pipeline's sensitivity to that property. Intervals come from a participant bootstrap that refits the classifier; in simulations they were conservative (99.7% coverage; standard errors about 1.8 times the empirical spread). We profiled nine descriptors on thirteen binary problems from three public datasets, with a within-fold AUC and class-balanced folds and a stated rule for which sensitivities to interpret. Of 565 intervals, 95 excluded zero; 80 were robust to the pooled AUC, 20 fold partitions and longer runs (36 of them within +/-0.05 AUC), and 45 also held with boosted trees. Most came from GazeBase (322 participants). Permuting fixation order raised the AUC of a descriptor based on mean saccade amplitude, because permuted amplitude has as its expectation the mean pairwise distance, a measure of spread. A "comprehension" contrast pooled over two programs was a contrast between their layouts: it vanished among trials of the same program, and synthetic gaze drawn from the layout alone reproduced the program contrast beneath it. For native versus non-native readers, spatial jitter at the scale of text lines reduced recurrence's discrimination. A Python package is released.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15814

Large Payments Model

Natraj Raman, Abhenandhan Manoharan, Vishwasreddy Thuniki, Dhvani Kansara, Anirudh Som, Hassan Firouzi, Kostis Gourgoulias, Laukik Hoshing 等 15 位作者

原文摘要

Payment applications such as fraud detection, authorization prediction, and payment optimization draw on overlapping transaction information. This motivates a reusable payments intelligence layer that shares learned representations and predictive signals, rather than developing predictive models independently for each task. Realizing this shared capability requires handling heterogeneous transaction fields, high-cardinality identifiers, lifecycle-dependent attributes, long histories, and rare outcomes while meeting latency and cost constraints. We present Large Payments Model (LPM), a domain-specific foundation model designed for operational use and trained from scratch on large-scale structured transaction data to learn reusable representations for payment prediction tasks. LPM employs field-specific encodings and lifecycle-aware self-supervision in two complementary architectures: the Independent variant uses hybrid masking and supports efficient per-transaction inference, while the Sequence variant incorporates entity-linked histories through compact latent representations for richer temporal context. Optional distillation enables resource-constrained deployment. We pretrain LPM on up to 400 billion tokens and study scaling, downstream performance, and representation reuse. LPM improves over strong task-specific baselines across multiple prediction tasks in Merchant and Treasury Services, demonstrating applicability in distinct payment domains. Its representations also facilitate adaptation to a target not observed during pretraining through compact task-specific components. Together with production deployment, these results position LPM as a practical basis for reusable payments intelligence.

cs.LG · cs.AI · cs.CE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.05163

What Makes a Good Response? An Empirical Analysis of Quality in Qualitative Interviews

Jonathan Ivey, Anjalie Field, Ziang Xiao

原文摘要

Qualitative interviews provide essential insights into human experiences when they elicit high-quality responses. While qualitative and NLP researchers have proposed various measures of interview quality, these measures lack validation that high-scoring responses actually contribute to the study's goals. In this work, we identify, implement, and evaluate 10 proposed measures of interview response quality to determine which are actually predictive of a response's contribution to the study findings. To conduct our analysis, we introduce the Qualitative Interview Corpus, a newly constructed dataset of 343 interview transcripts with 16,940 participant responses from 14 real research projects. We find that direct relevance to a key research question is the strongest predictor of response quality. We additionally find that two measures commonly used to evaluate NLP interview systems, clarity and surprisal-based informativeness, are not predictive of response quality. Our work provides analytic insights and grounded, scalable metrics to inform the design of qualitative studies and the evaluation of automated interview systems.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.02653

Prophecy Variables, Program Analysis, Binding Times, and Staged Compilation in the BuildIt System

Ajay Brahmakshatriya, Saman Amarasinghe, Martin Rinard

原文摘要

BuildIt is a lightweight C++ library system for implementing domain specific languages (DSLs) and compilers through multi-stage execution. It uses partial evaluation to generate optimized code without requiring parsers, explicit intermediate representations (IRs), or custom code generators. History variables and forward execution support analyses of past computation, but optimizations such as data movement, operator fusion, and binding-time analysis also require information about future execution. We introduce prophecy variables into BuildIt to predict future program behavior and guide code generation. Repeated first-stage execution refines predictions until they satisfy the program's checks, extending BuildIt's existing reexecution machinery with little additional complexity. This approach obtains information typically computed by backwards dataflow analyses over an explicit IR while preserving BuildIt's lightweight implementation model. The cost of first-stage reexecution can be amortized over repeated executions of the generated code. We formalize the mechanism and mechanize its guarantees in Lean 4. We demonstrate prophecy variables in three applications: selective CPU-GPU data movement, neural-network operator fusion, and automatic binding-time analysis. These applications achieve performance improvements of up to 5.5x while simplifying the programming interface.

cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15810

Sparse PCA with Overlapping Supports: NP-Hardness and an FPTAS

Alberto Del Pia, Dekun Zhou

原文摘要

In sparse principal component analysis (SPCA) with several components, each must be sparse and all must be pairwise orthogonal. When the supports of the components overlap, these two requirements interact. For a covariance matrix of fixed rank, polynomial-time algorithms with optimality or approximation guarantees are known for one component, and for several components with disjoint supports or with a common support. We study the general model, with individual sparsity constants and arbitrary support overlaps. We are given a rational $n\times r$ matrix $R$ and $d$ sparsity constants $s_i \le n$, and the goal is to find an $n\times d$ matrix $X$ with orthonormal columns that maximizes $\|R^\top X\|_F^2$, such that column $i$ has at most $s_i$ non-zero entries. Here $RR^\top$ plays the role of the covariance matrix and has rank at most $r$. We show that SPCA is NP-hard even when $r = d = 2$, but admits a fully polynomial-time approximation scheme (FPTAS) when $r$ and $d$ are fixed. The returned solution has exactly orthonormal columns, and its entries are real algebraic numbers in an exact representation. For fixed $ε$, the number of steps of the algorithm is linear in $n$ up to a polylogarithmic factor. Our approach reduces SPCA to a sparse version of the orthogonal Procrustes problem, that is, to maximizing a linear function over the feasible region of SPCA. We show that this problem is NP-hard even for $d = 2$, and we give an FPTAS for it when $d$ is fixed. This FPTAS combines approximate dynamic programming over the rows, two-column updates that restore exact orthogonality, and an exact computation of the few variables for which these updates are not available.

cs.LG · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15809

Fine-Grained Generative Hallucination Detection and Correction for Aerial Image Captioning

Debolena Basak, Somsubhra De, Maunendra Sankar Desarkar

原文摘要

Aerial imagery captured by unmanned aerial vehicles (UAVs) plays a vital role in applications such as surveillance, disaster response, and large-scale environmental monitoring. Automatic generation of textual descriptions for such imagery is essential for reducing manual inspection overhead and enabling better downstream decision-making. However, Multimodal Large Language Models (MLLMs), despite their impressive captioning capabilities, are prone to hallucination, producing descriptions that are inconsistent with the visual input. This problem is significantly amplified in aerial imagery due to its unique challenges such as extreme viewpoint variation, small objects, class imbalance, and ambiguous visual entities. Existing hallucination detection approaches either rely on computationally expensive large external models such as GPT-4, which perform poorly on aerial domains, or treat the problem as a rigid binary classification task, ignoring the inherent uncertainty in ambiguous cases. To address these limitations, we propose a lightweight, reference-free framework for hallucination detection and correction in free-form aerial image captioning. Our method leverages state-of-the-art MLLMs, specifically InternVL3 and Qwen2.5-VL, to detect hallucinated content in generated captions with minimal computational overhead. Detected hallucinations are corrected through a dedicated refinement module that improves the factual consistency of the original captions. Extensive experiments on the Labelled Illusion Dataset (LID) demonstrate that our framework significantly reduces hallucination rates while remaining practical for real-time deployment in resource-constrained environments. Code is available at: https://github.com/Debolena7/Hal_Detect.

cs.CV · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15804

Computational Explanations of Semantic Universals

Shane Steinert-Threlkeld, Jakub Szymanik

原文摘要

Despite their differences, the languages of the world share many deep features when it comes to how they express meaning. These semantic universals are likely to reflect shared aspects of human cognitive, communicative, and social structure. Recent advances in cross-linguistic semantics and computational linguistics allow us to develop explanations of the sources of such properties. This Element presents a body of work doing just that: we explicate a framework for providing computational explanations of semantic universals and then describe several such explanations using principles of ease of learning and efficient communication. Empirical domains include quantifiers, color terms, modals, and indefinites. After providing these explanations, we provide a discussion of future directions for advancing the explanatory power of computational methods in semantic typology.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15803

Statistical-to-computational gaps in central limit theorems for high-dimensional tensors

Debsurya De, Dmitriy Kunisky

原文摘要

We study high-dimensional central limit theorems (CLTs) as hypothesis testing problems, comparing the number of summands needed for statistical and computational indistinguishability of the normalized sum from the limiting Gaussian distribution as the dimension and number of summands grow together. For the off-diagonal entries of sums of i.i.d. rank-one tensors formed from standard Gaussian vectors (equivalently, the problem of distinguishing Wigner from Wishart tensors), we prove total variation bounds sharpening previous estimates of Mikulincer (2020) and give matching polynomial-time tests. This shows that the statistical and computational thresholds coincide for this CLT, contrary to a proposal by Kunisky, Moore, and Wein (2024). On the other hand, we also show that, for tensors of order at least three, sufficient Gaussian dilution of the summands by adding independent Gaussian noise and rescaling gives a CLT model with a statistical-to-computational gap (in the above setting and ones with more general rank-one summand distributions). To the best of our knowledge, such a phenomenon has not been observed before in any high-dimensional CLT. In particular, in the relevant parameter regimes, we show that a brute-force search achieves strong detection, while polynomials of degree up to $n^{Ω(1)}$ cannot achieve strong separation. The latter lower bounds control low-degree $χ^2$-divergences by a novel method based on cumulant generating functions, and we further illustrate the use of these tools by applying them to masked tensor CLT models where only a prescribed subset of tensor entries is revealed.

math.ST · cs.CC · math.PR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.13630

Minimum central circles: an effective characterization of the global asymptotic constant

Maurizio Falconi

原文摘要

Let $R^\ast(n)$ be the minimum radius of a central circle externally tangent to nonoverlapping circles of radii $1,\ldots,n$. We prove that $R^\ast(n)=C_\ast n^2+o(n^2)$ and give finite linear programs that enclose $C_\ast$ with prescribed error. An exact angular comparison reduces the problem to marked points on a line with separation at least the geometric mean of each pair of marks. Concatenation with bounded joining cost and reassignment to the prescribed normalized radii prove existence of the limit. Balanced finite-word programs give a bracket of width $(1/k+1/r)/π$, before directed arithmetic error, using $k$ types and words of length $r$. We also obtain explicit geometric bounds. For the upper bound, we recover finite and countable reflected-block constructions by actual permutations, retaining all pair constraints. A coupled terminal-subset argument gives an explicit lower bound strictly above $1/8$. Together these give $C_{\rm term}+η_{\rm width}\le C_\ast\le U_4$. The lower endpoint comes from the terminal-subset argument, and the upper endpoint from the four-block construction; neither is asserted sharp. The finite-word scheme establishes computability; efficient high-precision evaluation and the structure of global optimizers remain open.

math.MG · cs.CG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.28654

Minimum central circles: chain order, seam obstructions, and exact finite certification

Maurizio Falconi

原文摘要

We minimize the radius of a central circle externally tangent to pairwise non-overlapping circles of radii $1,2,\ldots,n$. We first study the adjacent-chain relaxation, which imposes only the constraints between consecutive circles. Its angular kernel has strict anti-Monge structure, so Supnick's classical theorem supplies a fixed minimizing cyclic order at every central radius. A monotonicity argument transfers this order to the chain-radius problem. Full geometric feasibility requires every pairwise angular constraint. We give a self-contained criterion for feasibility of the canonical Supnick necklace on $\{k,\ldots,n\}$ at its chain root, expressed by one seam deficit. For every fixed $k$ the seam eventually fails and its failure persists. We prove the complete classifications for $k=1,2,3$, whose first failures occur at $n=8,13,17$, respectively. For the global infimum, we certify rational brackets $L_n<R^*(n)\le U_n$ of exact width $10^{-11}$ for every $3\le n\le14$. The lower proof combines independently enclosed angles, integer dynamic programming, and complete cyclic-order coverage; the upper proof uses exact rational Cartesian witnesses. The certificate does not depend on floating-point pruning in the numerical search. We distinguish these finite brackets and fixed-order theorems from numerical descriptions of floating circles and from unresolved claims about all optimal placements.

cs.CG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15802

RUMI: A Robotiq-Matched Universal Manipulation Interface for Robot Learning with Augmented Reality and Haptic Feedback

Kevin Avdiu, Seongjin Bien, Yannik Blei, Philine Meister, Johannes Hechtl, Philipp Schmitt, Kai Wurm, Georg von Wichert 等 9 位作者

原文摘要

Many manipulation tasks require carefully controlled contact forces for successful execution, yet large-scale, force-aware datasets remain scarce. Handheld interfaces can help by transmitting interaction forces directly through the operator's arm, but most existing designs are tailored to simple linear grippers and do not match the nonlinear mechanics of widely used robotic grippers. We present RUMI, a handheld universal manipulation interface matched to the Robotiq 2F-85 with exchangeable fingertip sensors that preserves the target gripper geometry while enabling natural input before contact and force measurement after contact. By integrating the tracking system into a virtual reality headset, our method provides real-time augmented reality feedback about the kinematic feasibility of motion on the target robot platform. We further introduce a haptic feedback mechanism that supports force-aware teleoperation for simulated and real robots. A user study shows fewer episodes containing joint-limit violations with live robot visualization and pose initialization (3/50) than without augmented reality (16/50). We additionally train imitation policies from demonstrations, collected using two different fingertip modalities enabled by the gripper-matched hardware, and assess the haptic interface's capability for collecting force-aware teleoperation datasets in simulation.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.02879

Direct Informed Sampling on Riemannian Manifolds via Loewner Order Lower Bounds

Phone Thiha Kyaw, Jonathan Kelly

原文摘要

Informed sampling techniques accelerate sampling-based motion planners by focusing the search on promising regions of the state space, yet most existing methods rely on Euclidean heuristics that become inadmissible under configuration-dependent Riemannian metrics. While scalar eigenvalue bounds restore admissibility by uniformly scaling the Euclidean distance, they discard the directional structure of the metric, producing overly conservative informed sets. We propose a matrix-valued admissible heuristic that exploits the Loewner order on symmetric positive definite matrices to compute the tightest constant lower bound on the metric tensor while preserving its full directional structure. The Cholesky factorization of this bound defines a linear map to an isotropic Euclidean space in which the Riemannian informed set reduces to a standard prolate hyperspheroid, enabling direct, rejection-free sampling using existing algorithms. Experiments on manipulation tasks with a 6-DoF UR5, 7-DoF Franka, and 14-DoF PR2 under three distinct Riemannian metrics show that our heuristic produces consistently tighter informed sets than both the Euclidean and scalar eigenvalue bounds, accelerating convergence across multiple state-of-the-art asymptotically optimal planners.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.04792

Do More Modalities Always Help? A Geometric Perspective on Missing-Modality Robustness

Songyuan Sui, Zhen Tan, Mohan Zhang, Rana Muhammad Shahroz Khan, Xia Hu, Tianlong Chen

原文摘要

Missing modality remains a longstanding challenge in multimodal learning. Existing methods typically address this issue through modality recovery or adaptive strategies. However, they overlook models' internal cross-modal dependencies formed during multimodal training, which later impair robustness. We systematically characterize a counterintuitive deployment-time failure mode: models trained on full modalities can underperform unimodal models when one modality is missing at inference time. This pattern appears across diverse architectures, such as fusion models, CLIP-style two-tower models, and vision-language models. We show that such degradation is closely associated with learned cross-modal dependencies in the principal parameter subspaces. Multimodal training induces structured rotations of these subspaces, particularly in cross-modal interaction layers. These rotations are associated with reduced task-aligned margins and larger task-aware representation harm under missing-modality inputs. We propose Geodesic Unlearning (GU), a lightweight parameter-editing method that leverages Grassmannian subspace geometry for structured subspace correction to improve missing-modality robustness. It rotates the principal input subspace toward a unimodal reference along a geodesic path. We prove that this correction minimizes the distance to the reference within a fixed subspace-distance budget. Experiments across architectures and datasets show that GU improves performance under missing-modality inference while preserving full-modality accuracy, outperforming strong missing-modality robustness baselines. These findings support a geometric view of deployment-time missing-modality degradation and suggest localized subspace editing as a practical route for robustness correction.

cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15798

Bag of concepts can enhance other text representations for medical systematic review screening

Radosław Pytlak, Paweł Cichosz, Bartłomiej Fajdek, Bogdan Jastrzębski

原文摘要

The paper examines the choice of text representation for semi-automated systematic literature review (SLR) systems based on active learning. Motivated by the insufficient attention that the bag of concepts representation has received so far, the paper discusses the implementation and experimental evaluation of several variations thereof. The experimental study was performed on medical SLR case studies, with results reported for random forest classification, cluster initialization and uncertainty sampling. The utility of bag of concepts features obtained using different ontologies is evaluated by applying them within the active learning framework to sequentially create classification models based on training data iteratively expanded with expert annotations. Systematic experiments compare BoW, BoC, PubMedBERT, Jina, Gemma and Qwen3 representations, together with combinations of BoC and each of the other representations. The evaluation uses workload savings and performance profiles adapted from optimization benchmarking. The results suggest that concept-based features can provide complementary information, with the BoC-augmented representations achieving higher mean workload savings on the DERP cases than their corresponding base representations. The Qwen3+BoC representation gives the best average performance in this comparison. The effects of augmentation vary across case studies and base representations. The paper also concentrates on the impact of ontology choice on document classification outcomes. In particular, the paper compares the predictive value of bag of concepts representations based on chosen ontologies with that of representations obtained using MedCAT with a UMLS-based dictionary.

cs.IR

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新论文 · v1arXiv:2611.15797

Early Reflections on How Large Language Models are Reshaping Software Engineering Research

David Williams, Michail Kyrkos, Max Hort, Maria Kechagia, Justyna Petke, Federica Sarro

原文摘要

Large language models (LLMs) are radically reshaping software engineering (SE) research. Yet, their steep financial and computational costs raise questions about who participates in that change, and who does not. By analysing all 1,918 papers published in the ICSE 2019-2026 Research and SEIP tracks, we uncover a shifting and highly concentrated research landscape. China and the US dominate LLM-based SE research, appearing in 74% of LLM-based publications between 2023-2026, yet they diverge sharply in their use of commercial versus open-weight models. Beneath this national dominance lies extreme institutional concentration: just 5 institutions in China and 7 in the US appear in more than half of their respective nations' LLM-based output. In the US, only half of the unique institutions active in 2023-2026 participated in LLM-based research, whereas Singapore displays broader usage, with 11 of 15 active institutions participating in LLM-based work. Furthermore, across 2023-2026 Research-track papers, industry involvement is more prevalent in LLM-based work (23.4% vs 16.9%), and academia-only papers make up a smaller share (63.4% vs 70.9%). Crucially, we expose a gap in model usage across industry and academia. SEIP track papers lean strongly towards commercial LLMs, with 77.4% featuring them and 40.5% using them exclusively. In contrast, only 21.2% of Research-track work relies strictly on commercial models, making greater use of open-weight models that typically support more sustainable and reproducible experiments. However, this divide warrants reflection on the extent to which SE research aligns with industry practice.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2305.01420

An Improved Subquadratic Bound for Online Bisection

Marcin Bienkowski, Stefan Schmid

原文摘要

The online bisection problem is a natural dynamic variant of the classic bisection problem, where one has to dynamically maintain a partition of $n$ elements into two clusters of cardinality $n/2$. During runtime, an online algorithm is given a sequence of requests, each being a pair of elements: an inter-cluster request costs one unit while an intra-cluster one is free. The algorithm may change the partition, paying a unit cost for each element that changes its cluster. This natural problem admits a simple deterministic $O(n^2)$-competitive algorithm [Avin et al., DISC 2016]. While several significant improvements over this result have been obtained since the original work, all of them either limit the generality of the input or assume some form of resource augmentation (e.g., larger clusters). Moreover, the algorithm of Avin et al. achieves the best known competitive ratio even if randomization is allowed. In this paper, we present the first randomized online algorithm that breaks this natural quadratic barrier and achieves a competitive ratio of $\tilde{O}(n^{7/4})$ without resource augmentation and for an arbitrary sequence of requests.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15794

On Multi-Constrained Path Finding utilizing Grover Search and Decoded Quantum Interferometry

Dmitrii Chemodanov

原文摘要

Finding a path whose totals under several additive metrics all fall inside prescribed ranges is weakly NP-complete. We ask what two quantum algorithms can do for it, against strong classical baselines. For directed acyclic graphs (DAGs) we give a Grover search whose state preparation builds the uniform superposition over all source-to-destination paths with one qubit per arc and no ancilla. We prove it correct, give its exact gate count, and show that measured strings form an error- detecting code whose distance is a girth-like graph parameter. We complete the oracle and count its gates: one search on our largest instance, 136 arc qubits and 262,144 paths, needs about 4.4 x 10^5 Toffoli gates, while classical dynamic programming decides the same instance with 18,754 labels. Grover search needs O(sqrt(N/M)) oracle calls for M good paths among N . Classical meet-in-the-middle matches this exponent in the worst case, and known quantum algorithms with quantum memory beat it. For a staged fabric with node-independent linear weights, maximizing the number of satisfied metrics is max-LINSAT, and with power- of-index weights it is Optimal Polynomial Intersection, where no known polynomial-time classical algorithm matches decoded quantum interferometry (DQI). We verify this translation in simulation and run two small binary instances on IBM Heron processors, which keep 67 % and 15 % of the ideal gain over uniform sampling.

cs.NI · quant-ph

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版本更新 · v2arXiv:2610.12537

Market Impact Estimation via Causal Foundation Models

Dennis Thumm, Ying Chen

原文摘要

When a trader executes a large order, the relevant question is not \emph{what will the price be?} but \emph{what would the price have been had I not traded?} Market impact is thus a causal quantity, yet classical models (Almgren--Chriss, propagators) regress price on flow, which is biased whenever informed flow moves prices through information as well as trading. We present a proof-of-concept \emph{Causal Market Simulator}: a continuous-time causal prior-data fitted network (PFN) trained on a synthetic market prior in which a hidden informational flow confounds the flow--price relation by construction, with confounding strength an exact per-episode knob. On this prior, the naive estimator's bias follows a closed form we confirm empirically (up to 68\% overestimation), while the PFN tracks the unbiased true-parameter oracle at high confounding. Its \emph{do-ablated twin}, the same model with the intervention inputs removed, collapses onto the pre-window mean. Zero-shot, the model responds at the ground-truth scale to metaorders in an ABIDES limit-order book (+0.38 pre-onset standard deviations against seed-replay counterfactuals), and its intervention response replicates on about 2,300 detected flow bursts in BTC/ETH/SOL perpetuals and 3,800 on Nasdaq equities, evidence consistent with, though not proof of, causal transfer. Off-prior the response degrades gracefully, and split-conformal recalibration repairs interval calibration. Transfer hinges on encoding the metaorder as added flow rather than a clamped price level, not on more training. A recurrent state-space variant closes the remaining shape gap: its impact accrues over execution duration where the transformer's stays flat, raising the correlation with measured metaorder impact from +0.68 to between +0.92 and +0.96, at the cost of weaker on-prior deconfounding that traces to the training recipe rather than to recurrence.

cs.LG · cs.CE · q-fin.TR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15792

Bounded Knapsack in Truly Subquadratic Time

Huanyi Zhou

原文摘要

Bounded Knapsack asks how many copies of each item to take to maximize profit under a capacity constraint. Given $n$ records with binary-encoded multiplicities, we return optimal counts in randomized time with a fixed power saving in the maximum item weight $w_{\max}$ or maximum item profit $p_{\max}$. Fix $1<ρ<2$ and suppose integer $(\min,+)$-convolution on length-$m$ arrays can be computed with failure probability at most $η$ in $\widetilde O(m^ρ)$ time, up to polynomial factors in the entry bit length and $\log(1/η)$. Put $f(ρ)=4(3-ρ)/(8-3ρ).$ For any fixed $ε>0$, on inputs whose integers have magnitude polynomial in $n+\min(w_{\max},p_{\max})$, our algorithm runs in time $$\widetilde O\bigl(n\min(w_{\max},p_{\max})^{f(ρ)+ε}\bigr).$$ It returns optimal counts with high probability. If convolution saves $δ=2-ρ$ in its exponent, knapsack retains $δ/(1+3δ/2)$. With the integer convolution exponent $1.9601397040925$, a separate parameter choice gives $\widetilde O(n+\min(w_{\max},p_{\max})^{1.961})$, including for 0-1 Knapsack. We keep the copies compressed. Witnessed convolutions generate residual queries, and local linear programs filter them by cost. Resilient lattice cores allow recursive states to coalesce. Progression reserves support rounding, and a thin affordable fiber yields a lift that reduces the normalized column volume. This volume bounds the number of LP regions a grid lookup examines.

cs.DS · cs.CG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.09028

Diagnosing and Mitigating Domain Shift in Permission-Based Android Malware Detection

Md Rafid Islam

原文摘要

Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perform poorly on applications from another. This paper presents a comprehensive study on the generalizability and interpretability of permission-based detectors under cross-domain conditions. Using two complementary datasets (PerMalDroid and NATICUSdroid) and five ensemble classifiers, we first establish an intra-domain baseline, where models achieve over 92% accuracy, and then quantify a severe asymmetric performance drop. While models trained on PerMalDroid generalize well to NATICUSdroid (86% accuracy), the reverse direction sees a drastic drop to 73% accuracy. Explainable AI analysis reveals bimodal feature distributions and shows that feature importance is highly unstable, with key permissions losing or gaining influence across domains. The predictive feature sets for different domains are fundamentally mismatched, as models rely on different, dataset-specific permissions. Most importantly, an ablation study demonstrates that for most models, training on a noisy feature set leads to poor generalization, confirming that domain-specific artifacts are a greater obstacle than missing features. To mitigate this, we validate a hybrid training strategy based on the intersection of common features and successfully recover cross-domain performance, achieving 88% accuracy on PerMalDroid and maintaining 97% on NATICUSdroid. These findings highlight the importance of explainable, cross-domain-robust malware detection systems and provide a practical pathway toward improving real-world deployment of permission-based Android malware detectors.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15789

VibePrevisBench: Evaluating Agentic Coding for 4D Scene Generation in Blender

Yuzhe Chen, Muxi Diao, Lele Yang, Wenbin Xu, Xuyang Liu, Qile Su, Changyi Liu, Jiankang Chen 等 22 位作者

原文摘要

Controllable real-world video generation models can condition on a rendered graybox proxy video, which fixes layout, motion and camera. Coding agents can build such videos by reconstructing a 4D scene in Blender, yet no benchmark checks both these videos and the long, multi-shot scenes behind them. We introduce VibePrevisBench, a benchmark for 4D scene reconstruction: an agent turns a brief into an executable 4D scene and renders its proxy video. Its 242 tasks span four input kinds, including scripts, photos, continuations, and reference videos. Each delivery is scored along two evaluation dimensions. Rule-based Scene Check re-runs the code and compares the exported 4D scene with hidden ground truth facts in nine dimensions. VLM-based Screen Check asks a VLM judge whether the facts that pass can be recognized in the proxy video. We analyze the evaluation results and trajectories of 11 frontier coding agents and identify three key observations. First, multimodal understanding separates the agents, especially in understanding space and camera motion. Second, all agents seem to already master basic camera work and staging, and fail at challenging requests. Third, agents inspect their own renders but fail to correct their errors. Agents thus need better visual understanding of space and camera, explicit checks for challenging requests, and item-by-item verification of renders against the brief.

cs.CV · cs.AI

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新论文 · v1arXiv:2611.15784

Small Maximum Likelihood on Fixed Cavender-Farris-Neyman Trees: An Approximation Scheme and Limits to Fully Polynomial Approximation

Mohammad-Hadi Foroughmand-Araabi, Sama Goliaei

原文摘要

Small Maximum Likelihood fits the edge substitution probabilities of a given phylogenetic tree to an observed alignment, summing over the unobserved ancestral states. For the binary Cavender--Farris--Neyman model, we assume a uniform root and an edge-specific substitution probability $p_e\in[0,\tfrac12]$ for each edge $e$, shared across all alignment sites. We give a deterministic polynomial-time approximation scheme. The algorithm achieves a factor of $1+\varepsilon$ for both the negative log likelihood and the loss after subtracting the uniform-root contribution. Its main step fixes ancestral sequences at one periodically spaced class of depths while summing over all remaining ancestors. A posterior entropy bound controls the cost of this restriction, and dynamic programming optimizes the resulting components over a finite parameter grid. We also give a randomized reduction from Clique showing that an FPTAS returning feasible rational edge probabilities would imply $RP=NP$. These results separate fixed-accuracy approximation from fully polynomial approximation for the same fixed-tree model.

cs.DS · cs.CC

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