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版本更新 · v2arXiv:2605.23420

Naturalistic measure of social norms alignment

Yevhen Kostiuk, Kenneth Enevoldsen, Peter Bjerregaard Vahlstrup, Márton Kardos, Kristoffer Nielbo

原文摘要

Social norms reflect shared expectations on acceptable behavior. Measuring social norms alignment remains challenging, with existing approaches typically relying on artificial closed-form evaluations such as multiple-choice questionnaires or measuring agreement with predefined statements. In the context of this work, social norms alignment refers to measuring an agreement between solutions with respect to the social problem or dilemma. We propose a framework for measuring social norm alignment in naturalistic, free-form settings through solution matching. The framework enables us to measure alignment between any two dilemma responses e.g., LLMs to a human, LLMs to LLMs, or human to human. We introduce two metrics: stated and explicit agreement accuracy, and construct a dataset of 3k non-trivial social dilemmas in Danish. All dilemmas are assigned reference solutions derived from three panelists, who serve as culturally grounded judges. We evaluate the agreement of several LLMs and human responses in an interaction setup that resembles natural user-model conversations. Our results show that the proposed metrics produce consistent model rankings and reveal variation in agreement across different types of dilemmas, with higher agreement observed for topics such as neighbor conflicts and shared living situations. Overall, our work introduces a dataset and evaluation framework for studying culturally grounded social reasoning in naturalistic open-ended conversations.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35130

TUON: A Tensor-Aware Optimizer Utilizing Tensor-SVD and Polarization for Large Language Model Pretraining

Anda Tang, Xuanlin Yang, Junjie Wang, Zhouchen Lin

原文摘要

Large scale language model~(LLM) pretraining is increasingly bottlenecked by optimizer efficiency, driving optimizer design from vector-aware methods such as AdamW to matrix-aware methods. Matrix-aware optimizers such as Muon exploit matrix structure but treat layers independently, neglecting cross-layer correlations. Tensor-aware optimizers are therefore needed, yet existing ones rely on a matricization-based tensor algebra and remain restricted to shallow stacking and specific modules. In this paper, we propose \textbf{TUON}, a novel optimizer built on a transform-based tensor algebra derived from the Tensor-SVD (T-SVD). Unlike methods that polarize a globally unfolded matrix, TUON mixes layers via a unitary transform $\boldsymbol{Q}$ and then polarizes each transformed slice, and inverts the transform to recover the update. This mix-then-separate strategy captures cross-layer correlations while preserving per-layer diversity, avoiding the structural dimension imbalance and cross-layer misalignment of unfolding. Thus it successfully scales to more layers and diverse modules beyond QKV. Theoretically, we prove that TUON performs steepest descent under a $\boldsymbol{Q}$-transformed tensor spectral norm, and identify its update as the tensor polar factor of the T-SVD. We further establish transform non-redundancy, norm and smoothness comparisons with Muon and TEON, and a convergence guarantee. Empirically, TUON improves consistently over Muon and TEON on GPT-124M/350M and Qwen-0.6B/4B, with ablations confirming the transformation mechanism, cross-layer structure beyond two-layer stacking, adaptability beyond QKV, and robustness to the polarization method.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.21893

WADBERT: Dual-Channel Web Attack Detection via Permutation-Invariant Parameter Modeling

Kangqiang Luo, Yi Xie, Shiqian Zhao, Jing Pan

原文摘要

Web attack detection is the first line of defense for securing web applications, designed to preemptively identify malicious activities. Deep learning-based approaches are increasingly popular for their advantages: automatically learning complex patterns and extracting semantic features from HTTP requests to achieve superior detection performance. However, existing methods are less effective at representing irregular HTTP requests and introduce unnecessary sensitivity to payload parameter order. To tackle this issue, in this paper, we propose WADBERT, a structure-aware dual-channel framework for web attack detection built on two complementary designs. First, we introduce \textit{Hybrid Granularity Embedding (HGE)}, which combines WordPiece embeddings with token-aligned character representations to capture fine-grained patterns in symbol-dense URLs and payload parameters. Second, we develop \textit{structure-aware feature extraction}, with separate branches for the sequential URL and the unordered collection of payload parameters. Each parameter is encoded independently, and position-free multi-head attention followed by mean pooling produces a payload representation invariant to parameter reordering. The URL and payload features are fused for request classification, while the learned parameter-level attention weights support inspection of potentially malicious parameters. Experiments on CSIC2010 and SR-BH2020 validate the effectiveness of our method: WADBERT achieves F1-scores of 99.64\% and 99.59\%, respectively, outperforming the evaluated state-of-the-art baselines.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35125

When Fusion Order Matters: Insights on Progressive Multimodal Representation Learning for Cross-modal Retrieval

Haoxiang Zhao, Bin Wang, Yaofei Wang, Xiao Lan, Yunfeng Diao, Xu Wang

原文摘要

Multimodal video retrieval combines visual, acoustic, and speech information, yet how their fusion order should depend on the data and the retrieval query remains unclear. We introduce Unified Progressive Representation Learning for Order Analysis, or UniPRO, to investigate this question through configurable fusion schedules with matched model components and training settings. Starting from text-to-video retrieval, we find that the tested one-by-one schedules outperform stacked fusion, while changing the order within one-by-one fusion also changes performance. To guide order selection, we examine two complementary considerations: learning difficulty under unequal modality availability and preservation of information relevant to the query. Complete-modality controls show that missing inputs alone cannot explain order sensitivity. At R@1, text and audio retrieval favor schedules that introduce the modality matching the query last. Controlled speech missingness during training and testing reverses the preferred order in both R@1 and SumR, with different crossover points. These findings motivate balancing the two considerations when selecting a fusion path. We then use them to predict a visual-last schedule for image queries, which achieves 97.9 R@1, the highest among the evaluated orders on MSR-VTT-9k. Main experiments on four text-to-video benchmarks establish the effectiveness of UniPRO, with component ablations assessing its supporting design. Together, the results identify fusion order as a task-dependent choice that should account jointly for modality availability and the retrieval query.

cs.AI · cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2204.07867

Analytical benchmark problems and methodological framework for the assessment and comparison of multifidelity optimization methods

L. Mainini, A. Serani, H. Pehlivan, F. Di Fiore, M. P. Rumpfkeil, E. Minisci, D. Quagliarella, S. Yildiz 等 15 位作者

原文摘要

This paper introduces a comprehensive benchmarking framework that includes: (i) a suite of analytical benchmark optimization problems designed to stress-test and validate multifidelity methods; (ii) a set of assessment metrics for quantifying and comparing performance over measurable objectives; and (iii) the classification, evaluation, and comparison of several families of multifidelity optimization methods and frameworks using the proposed benchmarks to identify their respective strengths and weaknesses in real-world scenarios. The proposed benchmark problems are analytically defined functions carefully selected to capture mathematical challenges commonly encountered in real-world applications, including high dimensionality, multimodality, discontinuities, and noise. Their closed-form nature ensures computational efficiency, high reproducibility, and a clear separation of algorithmic behavior from numerical artifacts. The accompanying performance metrics support the systematic evaluation of multifidelity methods, measuring both optimization effectiveness and global approximation accuracy. By providing a rigorous, reproducible, and accessible benchmarking framework, this work aims to enable the broader community to understand, compare, and advance multifidelity optimization methods for complex problems in science and engineering.

math.OC · cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35123

Generative AI in Turbulence: Foundations, Applications, Validations and Benchmarks

Luca Biferale, Michele Buzzicotti

原文摘要

Generative AI offers new approaches to turbulence problems in which incomplete observations or unresolved d.o.f. admit multiple physically plausible solutions. Potential applications include stochastic closures, super-resolution, data assimilation, uncertainty quantification, and the generation of Eulerian and Lagrangian flow ensembles. However, turbulence is not a conventional image-generation problem. Its multiscale dynamics, intermittency, extreme events, rough velocity fields and intrinsically stochastic transport make visual realism, pointwise error, and low-order spectral agreement insufficient measures of physical fidelity. We examine where generative AI provide novel capabilities with emphasis on diffusion models, score-based methods, and related probability-transport formulations. We discuss their use in unconditional generation, probabilistic reconstruction, reduced-order modeling, temporal evolution, and mesh-free representations. We argue that validation must address turbulence properties: energy transfer, structure functions, anomalous scaling, pdf tails, coherent structures, conservation laws, temporal consistency, ensemble diversity and generalization. Finally, we discuss potential limitations in validation, baseline, data availability and reproducibility, and the need for a benchmark ecosystem combining open data, standardized challenges, hidden tests, physics-aware metrics, and documented failures. This paper serves as a bridge between two communities. For AI practitioners, we provide a fair and accessible introduction to turbulence, essential for appreciating the challenges posed by the field and for assessing the physical relevance of generative models. For turbulent practitioners, we provide an introduction to the key technological aspects of Generative AI, clarifying what these tools actually do, and where their potential and current limitations lie.

cs.AI · cs.LG · nlin.CD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35121

Revisiting entropy and entropy-related magnitudes of a symbolic sequence and their estimation

Efren Aragon-Perez, Ania Mesa-Rodriguez, Karel Garcia-Medina, Daniel Estevez-Moya, Amanda Mollineda-Rodriguez, Ernesto Estevez-Rams

原文摘要

This article revisits entropy and related magnitudes (entropy density, effective measure complexity, and informational distance) for the analysis of sequential data. We present their mathematical foundations within information theory and ergodic theory, establishing connections between probabilistic Shannon entropy and algorithmic Kolmogorov complexity. A central result, Zivs theorem, demonstrates the asymptotic equivalence between the normalised number of distinct words and entropy density, providing the theoretical basis for estimation via Lempel-Ziv factorisation. We examine the convergence properties of these magnitudes and introduce estimation procedures for finite sequences, including a method for the effective measure complexity and the transfer entropy rate. An open-source software library implementing parallel algorithms for efficient Lempel-Ziv factorisation is presented. The practical utility of the framework is illustrated through three case studies: functional brain connectivity analysis from fMRI data, characterisation of spatiotemporal dynamics in coupled nonlinear oscillators, and electrocardiogram-based classification of atrial fibrillation, demonstrating how entropic measures capture meaningful structural and dynamical properties across diverse domains.

nlin.CD · cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.25320

MMS Allocation for Chores with Item Disposals

Shinuo Ma, Xiaowei Wu

原文摘要

Web platforms organize collective work, including content review and community maintenance, that imposes heterogeneous effort costs on participants. Fairly distributing these burdens while leaving as few tasks unassigned as possible is a natural objective for the design of such platforms. We study this through the maximin share (MMS) allocation problem with chore disposals under nonnegative additive costs. Our goal is to minimize the number of unassigned chores while guaranteeing every agent her MMS benchmark from the original instance. We prove that, for every instance with $n \ge 4$ agents, at most $n-3$ chore disposals suffice. We improve the bound of $2m/11+n$ disposals established by Barman et al. (2025), where $m$ is the number of chores, and eliminate the dependence on $m$. For four agents, we obtain a tight bound: one disposal always suffices, whereas known instances admit no complete MMS allocation (Ezra and Garbuz, 2026). We also prove the same result for goods: $n-3$ duplications suffice to ensure MMS.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.19746

One-shot Human Video-Guided Adaptation for Vision-Language-Action Models

Qi Chen, Yuwei Wu, Quan Chen, Kunpeng Gao, Jin Chen, Che Sun, Zhi Gao, Yunde Jia

原文摘要

Vision-language-action (VLA) models have recently achieved remarkable progress by leveraging large-scale robot datasets and foundation models for general-purpose robot manipulation. However, their ability to rapidly adapt to novel manipulation tasks beyond the training distribution remains limited. Existing approaches often require task-specific robot demonstrations and additional training, which are labor- and compute-intensive, limiting the scalability of VLA adaptation to open-world scenarios. To reduce this dependence on target-task robot data, we propose CapPro, a framework that enables a VLA policy to adapt to previously unseen manipulation tasks using only a single human demonstration video. CapPro consists of two components. First, a phase-aware human video representation module constructs a compact video representation through learned visual token selection and learns task-relevant query representations under phase-caption supervision. Second, a video-conditioned adaptation module jointly incorporates the compact video and query-derived representations into the action-conditioning context of the pretrained VLA policy. % This design introduces task-specific guidance while largely preserving the original VLA action-generation backbone. In this way, the human demonstration provides task-specific execution guidance rather than an embodiment-aligned trajectory to imitate, without requiring target-task robot demonstrations or target-task fine-tuning. Experiments across source and unseen target tasks show that CapPro improves average performance across unseen target groups while maintaining comparable performance on the source tasks. These results demonstrate the effectiveness of human-video guidance for cross-task VLA adaptation.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35114

Quality over Quantity: Rethinking Data and Model Assumptions for Bengali Coreference Resolution

Zenith Biswas, Andrew Thomas Dyer

原文摘要

Multilingual transformer models are often assumed to generalise across languages, but their effectiveness for coreference resolution in low-resource settings remains unverified. We investigate this gap in Bengali, a language with over 250 million speakers but limited annotated coreference resources. We evaluate transformer-based pre-trained encoders under multiple settings, including zero-shot, fine-tuning, data augmentation, and cross-lingual transfer. Our results challenge several common assumptions about model generalisation and data efficiency in low-resource NLP. We demonstrate that a small amount of high-quality annotated data significantly outperforms larger mixed-quality datasets (p < 0.01). We find that data augmentation, particularly back-translation, does not improve performance and can degrade results in coreference resolution. Cross-lingual transfer is also inconsistent: transfer from a distant language like English reduces performance, and transfer from the related language Hindi provides no consistent benefit across models. Several widely used multilingual models exhibit collapsed embedding spaces, rendering them unsuitable for similarity-based clustering tasks. These findings highlight the importance of annotation quality over data quantity and provide empirical guidance for developing coreference resolution systems in low-resource languages.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35113

UTA: Universal Training Adaptive Learning Rate Decay

Anda Tang, Junjie Wang, Xuanlin Yang, Cong Fang, Zhouchen Lin

原文摘要

Learning rate decay (LRD) is critical for training high-performance models, yet most designs remain empirically driven. Although the Universal Budget Aware (UBA) LRD offers a theoretical framework, its reliance on SGD dynamics limits its applicability to modern Adam-type optimizers and leaves its key `optimization difficulty parameter' opaque. To bridge this gap, we propose the Universal Training Adaptive (UTA) LRD. Unlike UBA's fixed offline trajectory anchored solely to iterations, UTA treats LRD as a closed-loop shape-and-scale problem. By continuous-time formulation, UTA is training-state adaptive with segment-wise optimal shape. This adaptivity links external iterations with internal training states, enabling a near-optimal path in both progression shape and learning rate (LR) scale, a property unattainable by other LRDs, including UBA. Empirically, we demonstrate consistent improvements across language models (LLaMA 60M to 1B) and vision models (ResNet50, ViT-B) on ImageNet with plug-and-play ease and negligible overhead. Extensive ablation studies further confirm UTA's robustness across dimensions, including peak LR, final decay ratio, its parameter $\varphi_0$, component efficacy and generalization to fine-tuning scenarios, underscoring its practical implementability.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35111

Rethinking Evaluation for Principled Generalization Using a Kripkean Challenge to Meaning as Use

Niyar R Barman, Omar Naim, Nicholas Asher

原文摘要

Large language models operationalize an intuitive, view of meaning as use. We argue that it faces a rule-following challenge. Using Kripke's interpretation of Wittgenstein, we argue that finite histories of use do not determine a unique rule for future extension. This reframes the debate about OOD generalization but also about NLP evaluation; benchmarks that measure performance on fixed use cases or local robustness, we argue, do not by themselves establish semantic competence. To evaluate whether models preserve rules and concepts under principled domain extension, we propose to evaluate two capacities, central to this ability: abstraction and recursion; and we offer an experimental paradigm for testing both.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.13453

A Machine Learning API for Earth Observation Data Cubes Based on openEO

Brian Pondi, Jonas Hurst, Rolf Simoes, Jonas Starke, Marius Appel, Edzer Pebesma

原文摘要

Earth Observation (EO) data are increasingly organized as spatio-temporal data cubes, while machine learning (ML) methods operate on tabular feature matrices or structured tensor inputs. This mismatch forces platform-specific transformations that are difficult to reproduce or transfer across cloud infrastructures. The openEO specification provides a unified interface for EO data access and processing across heterogeneous backends, but lacks a standardized approach for ML integration. We propose a process-level ML specification for openEO structured into three stages: model initialization, model actions (training, tuning, inference, validation), and model management. It supports classical algorithms such as Random Forest and SVM, as well as deep learning architectures for time-series and spatial patch-based modeling, including TempCNN, Temporal Attention Encoders, and foundation model inference. Three prototype implementations in R and Python demonstrate feasibility across diverse technology stacks. A crop type mapping use case demonstrates cross-backend interoperability by submitting an identical process graph to independent R and Python backends and comparing predictions and evaluation metrics. Two further use cases demonstrate deep learning on time series and foundation model inference, each executed on a dedicated backend. The prototypes reveal, however, that full cross-backend portability requires deeper harmonization of serialization formats and execution semantics than the process level alone can enforce; backend library versions and preprocessing conventions outside the specification's boundary also affect reproducibility. Addressing both through explicit backend conformance profiles represents the most important near-term direction. The specification advances the reproducibility, portability, and accessibility of ML workflows on EO data cubes across cloud platforms.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.28166

Reasoning as a Distribution: Generative Plan Latents for Vision-Language-Action Models

Matteo Cercola, Katie Z Luo, Milan Ganai, John Irvin Alora, Simone Formentin, Marco Pavone

原文摘要

Vision-Language-Action (VLA) policies increasingly reason before acting, but existing reasoning models compress this intermediate decision into a single representation. When several plans are valid, this commitment hides alternatives that could otherwise be selected or revised. We introduce the Generative Plan Latent (GPL), which represents the reasoning state as a low-dimensional conditional distribution over plans, such as which object, grasp, or route to use. This makes the reasoning state addressable: without retraining the VLA, plans can be sampled (coverage), selected on request (steering), and reweighted by deployment outcomes (outcome adaptation). We test all three on SimplerEnv manipulation with $π_0$, and extend the analysis to RoboCasa, LIBERO, and a maze domain. Steering the plan raises the fraction of executions that follow the request from 0.48 to 0.94, while the same guidance on the native action distribution shows no reliable gain. Finally, we show that an explicit plan is most useful when it carries decision information not already determined by the observation.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35108

FlowGenesis: Representation-Driven Self-Organization of Neural Architectures

Maksim A Kazanskii

原文摘要

Deep neural networks typically rely on architectures specified before training, despite the continuous evolution of their internal representations. We introduce FlowGenesis, a framework in which neural architectures adapt through neuron birth and pruning guided by Representation Flux, a local statistic capturing changes in minibatch mean activations. Neurons exhibiting persistently high Flux undergo splitting designed to approximately preserve the network function, while neurons with low Flux and low activity are pruned. This mechanism allows network capacity to evolve without prescribing a target architecture or performing a separate architecture search. Experiments on image and text classi- fication show that networks initialized with different sizes approach similar final neuron-count ranges within each dataset while maintaining comparable predictive performance. We further extend FlowGenesis to continual learning, where representation-guided structural adaptation reduces the neurons and parameters allocated to successive tasks while maintaining accuracy comparable to fixed-width Progressive Neural Networks in a task-incremental learning setting. These findings demonstrate that neural architectures can adapt through feedback between representation learning and structural change, suggesting an alternative to prescribing network capacity before training.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.32545

Single-Machine Guarantees on Unrelated Machines and Beyond: Proportional Fairness for Nonclairvoyant Polytope Scheduling

Sven Jäger, Alexander Lindermayr, Bart Zondervan

原文摘要

The Polytope Scheduling Problem (PSP) is a general framework for preemptive scheduling in which feasible instantaneous job allocations form a packing polytope. In the nonclairvoyant online setting, Proportional Fairness (PF), a practical allocation rule rooted in market theory, is the leading general-purpose algorithm. On a single machine, PF coincides with weighted Round-Robin, which is $2$-competitive for weighted completion time with uniform release dates (optimal for nonclairvoyant algorithms) and $(2+\varepsilon)$-speed $O(1/\varepsilon)$-competitive for weighted flow time. Known guarantees beyond a single machine are weaker. This raises a central question: Are these heterogeneous environments intrinsically harder than a single machine under nonclairvoyance? We show that they are not: PF retains both single-machine guarantees on the novel class of support-submodular polytopes, which includes unrelated machines and polymatroids. On unrelated machines, this refutes a belief (JACM'18, SODA'25) that PF is not $O(1)$-speed $O(1)$-competitive for weighted flow time. Our main technical contribution is an equivalent characterization: a polytope is support submodular if and only if, for all job weights, the optimal weighted completion time is concave in the processing requirements. This structural insight underlies both guarantees. For general PSP, the picture changes: we give the first lower bound above $2$, showing that every randomized nonclairvoyant algorithm has a competitive ratio of at least $2+10^{-5}$ for weighted completion time, even with uniform release dates. On the positive side, we substantially improve the best known guarantee for weighted completion time from $27$ (SICOMP'26) to $4$ via a new dual-fitting analysis of PF. For weighted flow time, we show that PF achieves the first $O(1)$-speed $O(1)$-competitive nonclairvoyant guarantee for graph scheduling.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35106

Unbelievably Complex

Hans van Ditmarsch, Djanira Gomes, Rojo Randrianomentsoa, Thomas Studer

原文摘要

We present the directed simplicial complex: this is an extension of a simplicial complex with a direction on its faces. This enables us to model a local notion of belief in a three-valued semantics, in which formulas may be true, false, or undefined. Of particular interest is a class of directed simplicial complexes called cups, in which any maximal set of vertices of different colors is a facet. Cups permit a more concise relational representation. We give examples and applications of our logical semantics. We then provide a sound and complete axiomatization for the class of directed simplicial complexes and extend it to an axiomatization for the class of cups. We include a detailed technical comparison with other works on belief on complexes. Our work generalizes a prior axiomatization for knowledge on (undirected) impure simplicial complexes.

cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35105

New families of additive non-RS MDS codes and ACD codes from twisted Reed-Solomon codes

Xia Li, Yu Wang, Yansheng Wu

原文摘要

Additive codes generalize linear codes and play an important role in the construc?tion of quantum codes. As generalizations of linear MDS codes, additive MDS codes have received much research attention. As an important subclass of additive codes, ACD codes are widely used for quantum error correction. In this paper, we study single-twist additive generalized twisted Reed-Solomon (GTRS) codes over finite fields. We derive necessary and sufficient conditions for these codes to be MDS for arbitrary hook and twist parameters. Then we employ Schur squares to prove that such additive MDS codes are not monomially equivalent to additive Reed-Solomon codes. We also identify a class of additive GTRS codes whose duals remain additive GTRS codes, and show that, for all sufficiently large prime powers q whose characteristic avoids a finite set, such GTRS codes yield ACD codes.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.21673

Hierarchical Joint Source-Channel Coding with Constrained Information Leakage

Yiqi Chen, Holger Boche, Marc Geitz

原文摘要

This paper studies the hierarchical joint source-channel coding with information leakage constraint in the first-phase reconstruction and distortion constraints. The receiver's access to the data varies and is evaluated by the quality of the side information. Due to the consideration of channel capacity limitation or the efficiency of the system performance, the encoder may send some additional information in Phase 1 that can only be decoded in Phase 2 with higher-quality side information. While this can optimize the overall performance, the additional information causes excessive information leakage. We provide general inner and outer bounds for the conditions such that a given distortion-leakage pair $(D_1,D_2,L)$ is achievable, together with a capacity-achieving condition.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35103

The Language of Deformation: Semantic Failure Analysis as a Prerequisite for Dexterous Manipulation of Deformable Objects

Ignacio Cuiral-Zueco, David Hardman, Michele Pierallini, Erfan Shahriari

原文摘要

Dexterous manipulation of rigid objects has reached a level of maturity where grasp stability, control, and planning strategies are successfully applied in industry and logistics. In contrast, manipulating deformable objects (cables, plastic pieces, cloth) remains far more challenging, as rigid-object methods do not account for their additional complexity. This paper identifies semantic failure analysis as a key step toward mature deformable-object manipulation and supports this claim with proof-of-concept experiments. Unlike rigid-object manipulation, where goals are well-defined in terms of criteria such as force balance and slippage, deformable-object manipulation involves a broader variety of failure modes arising from differences in materials, geometry, and behaviour. Typical failures include buckling, plastic deformation, pressure variations, and tearing. These diverse and often hard-to-detect events require distinct corrective strategies, making semantic failure analysis necessary. We define its key components and challenges and present experimental results supporting its potential as a research direction.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35102

Useful Diversity or Hallucinated Variety? Persona-Conditioned Synthetic Query Generation for E-Commerce Search

Filippos Ventirozos, Tom Armitage, Mahmoud Oshagh, Peter A. Appleby, Matthew Shardlow

原文摘要

Synthetic supervision can bootstrap semantic parsers in new search domains, but prompt-induced diversity is useful only when generations are faithful to the structured intent. We explore this for cold-start e-commerce search: 118,098 synthetic pairs produced by 6 LLMs with 9 persona-conditioned prompt families on a 47-field UK automotive schema, against 1,214 human-annotated and 15,304 production queries. We show that the IPIP personality bundle improves parser F1 in all four matched contrasts (Holm-corrected $p<.001$), and all three bundles have a positive main effect over the complete $2^3$ grid. Syntax conditioning is the axis that moves generations towards real traffic. It also induces lexical diversity. Alignment sKL, the divergence between von Mises-Fisher distributions fitted to synthetic and human-annotated queries, is an unlabelled proxy for parser F1 ($ρ=-0.917$ against human queries written for simulated shopper scenarios, lower is closer). An LLM committee, checked against a blinded author pass, triages the 927 queries missed by every parser and labels 37% as hallucinated. Dropped constraints dominate. The four syntax-bearing families have the largest hallucinated shares, 38 to 42%, while IPIP alone has the lowest at 28%.

cs.CL · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35978

Testing Agentic Service Systems: An Agentic Living Lab Methodology Across the Physical-Digital Continuum

Zhongjie Wang, Xiang He, Xiao Wang, Mingyi Liu, Shuang Yu, Hanchuan Xu

原文摘要

Task success is insufficient for testing agents that act for principals, change persistent business state, and affect physical processes: a useful result may be unauthorized, leave obligations unresolved, or depend on the execution substrate. We propose an Agentic Living Lab methodology organized around three questions: what is the test unit, who and what participate, and under what reality is the test executed? The unit is a service episode containing delegated roles, obligations, world state, and evidence. The method separates agents under test from environment actors and distinguishes real humans, human traces, rule surrogates, and agent-based proxies. Realization Profiles, capability-declared Reality Substrate bindings, Grounding Profiles, and Semantic Delta disclosures make changes across software, enterprise systems, simulation, and physical settings explicit. A four-plane reference architecture separates experiment control, participants, world realization, and evidence evaluation, with cross-cutting authority and governance controls. Runtime semantics distinguish intent, normative admission, and factual reconciliation. Real stakeholder grounding, participant calibration or validation, and governed feedback are required for the Living Lab claim, but not synchronous human participation in every run. Released JSON Schemas, deterministic fixtures, and a limited reference runner make selected rules machine-checkable; they are not a complete platform or empirical validation. Constructed travel and industrial designs illustrate authorization and fulfillment failures, proxy sensitivity, and substrate changes. Agent quality, proxy validity, and cross-substrate effect stability remain empirical questions.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.22361

Ownership Refinement Types for Pointer Arithmetic and Nested Arrays

Yusuke Fujiwara, Yusuke Matsushita, Kohei Suenaga, Atsushi Igarashi

原文摘要

Tanaka et al. proposed a type system for verifying functional correctness properties of programs that use arrays and pointer arithmetic. Their system extends ConSORT--a type system combining fractional ownership and refinement types for imperative program verification--with support for pointer arithmetic. Their idea was to extend fractional ownership so that it can depend on an array index. Their formulation, however, does not handle nested arrays, which are essential for representing practical data structures such as matrices. We extend Tanaka et al.'s type system to support nested arrays by allowing ownership to depend on the indices of enclosing arrays. We formalize the extended calculus in Rocq using a mixed embedding and provide a machine-checked proof of type soundness. We have implemented a verifier based on the proposed type system and demonstrated that it can verify the correctness of programs that manipulate nested arrays, which were beyond the reach of Tanaka et al.

cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35101

Spherical Brownian Diffusion for Language Modeling

Andrea Miele, Ilija Bogunovic

原文摘要

Continuous diffusion language models involve two coupled design choices: how to corrupt continuous token representations and how to invert that corruption at generation time. Recent work has explored language generation on the unit sphere through geodesic and von Mises--Fisher (vMF) paths, raising the question of which forward process to use on this geometry and how much that choice matters relative to inference. We introduce Spherical Brownian Diffusion, using Brownian motion as the canonical isotropic diffusion on the sphere. Its heat semigroup provides a principled forward process, an additive dimension-free noise clock, and an exact ancestral bridge, while a clean-token posterior learned with cross-entropy supports multiple samplers. To separate corruption from inference, we derive high-dimensional approximation rates between Brownian and moment-matched vMF corruption and use this relationship for controlled substitutions. On Sudoku, Brownian and moment-matched vMF give nearly identical results across four samplers and five budgets under otherwise identical settings. In contrast, with the corruption process fixed, sampler choice produces a 25.0-point spread at a sampler budget of 180 calls. Using the ancestral bridge, we split this gain into a memory effect from re-noising the state, present on Sudoku and GSM8K, and a token-selection effect on GSM8K and largest at small budgets. Across reasoning tasks, categorical refinement improves performance, whereas unconditional text generation benefits from stochastic updates that preserve diversity.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.22335

Illumination-Aware Active Perception for Spacecraft Inspection

Sagarika Rao Valluri, Benjamin Riviere

原文摘要

Harsh and rapidly varying orbital illumination introduces failure modes during spacecraft inspection. Existing planners rely on line-of-sight inspection metrics which perform occlusion checks against a known target and conflate geometric visibility with task-relevant information. These metrics overlook illumination effects including eclipse, self-shadowing, and specular reflection from spacecraft materials and tend to declare components as inspected despite the corresponding images lacking sufficient information. We propose an inspection-planning framework that replaces geometric visibility with a learned measure of camera information derived from a classifier variational autoencoder (VAE) that operates directly on the captured image. The encoder model is trained to classify spacecraft surface components from rendered patches, and its total latent variance is used to learn the varying illumination of said components, replacing binary visibility checks with a continuous, image-grounded quantity. We evaluate the approach by implementing a Guidance, Navigation, and Control architecture in a photorealistic physics simulator, and vary lighting and viewing geometries with multiple orbits. We show that, although all approaches successfully maximize their own objective, our approach results in better illumination: a mean brightness of $0.57$, with $81.6\%$ of observations above $0.25$, compared with $0.02$ ($3.0\%$) for geometric planning with an illumination band and $0.04$ ($6.0\%$) for purely geometric planning.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31743

Not Every Agent Needs the Strongest Model: Unified Prompt-Model Optimization for Heterogeneous LLM-based Multi-Agent Systems

Mingxiu Cai, Haoyang Xian, Xinyu Ma, Yutong Wang, Zhenxi Song, Jing Li, Xuebo Liu

原文摘要

Large language model (LLM)-based multi-agent systems (MAS) rely on role-specific prompts and execution models to solve complex tasks collaboratively. However, prompt optimization typically keeps model assignments fixed, leaving differences in role requirements and their associated inference costs outside the search. We present UniMASO, a Unified framework for heterogeneous Multi-Agent System Optimization through the joint adaptation of prompts and model assignments. UniMASO evaluates candidate updates through local, downstream, and global feedback, using cost-aware Pareto search to retain useful quality-cost trade-offs. Topology-aware co-evolution then coordinates these updates according to agent roles and dependencies. Cross-level trajectory memory further supplies historical successes and failures to guide subsequent optimization. Experiments on five benchmarks and five execution topologies show that UniMASO achieves competitive task performance at substantially lower deployment cost than the strongest homogeneous configurations. Under centralized execution, it improves the average score from 69.79 to 70.40 while reducing token-weighted deployment cost by 85.2% relative to DeepSeek-V4-Pro with MASPO. These results support joint prompt-model adaptation as an effective approach to efficient multi-agent collaboration. The code is available at https://github.com/Achilles008/UniMASO_main.

cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.17713

Certified bounds on optimization problems in quantum theory

Younes Naceur, Jie Wang, Victor Magron, Antonio Acín

原文摘要

Semidefinite relaxations of polynomial optimization have become a central tool for addressing the non-convex optimization problems over non-commutative operators that are ubiquitous in quantum information theory and, more in general, quantum physics. Yet, as these global relaxation methods are solved numerically, the bounds issued by the semidefinite solver can - and often do - exceed the global optimum, undermining their certifiability. To counter this issue, we introduce a framework for extracting rigorous certified bounds on non-commutative optimization problems from inexact numerical data, and apply it to several paradigmatic problems in quantum information theory. An extension to sparsity and symmetry-adapted semidefinite relaxations is also provided and compared to the general dense scheme. Our results establish rational post-processing as a practical route to reliable certification, pushing semidefinite optimization toward a certifiable standard for quantum information science.

quant-ph · cs.SC · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35098

Interpreting LLM Scores in Peer Review: Alignment and Divergence Across Prompts, Domains, and Manuscript Features

Wenqiang Qin, Renlong Jie, Yanjun Qian, Salma Srifi

原文摘要

Large language model (LLM) scores are being studied as information for scholarly evaluation. Interpreting these scores requires evidence about agreement with human judgments and its variation across evaluation conditions. Guided by an argument-based approach to validation, we compare four LLMs on 7,083 public ICLR 2024 submissions under a standard review condition and examine ten prompt conditions using DeepSeek-V3.2. We distinguish average score divergence, paper-level absolute error, and rank correlation, then examine domain differences and adjusted manuscript-feature associations. For DeepSeek-V3.2, Area Chair guidance reduces mean rating divergence from 1.460 to 0.199 points and mean absolute error from 1.577 to 0.942, while rank correlation changes from 0.377 to 0.387. Historical references combined with downward scoring instructions yield mean divergence of 0.052 and rank correlation of 0.321. Domain differences remain after global calibration. Longer titles have positive adjusted associations with rating divergence, and formula density has negative associations across all four configurations. These findings connect score interpretation to the scoring procedure, evaluation context, and manuscript information represented in the comparison. They show why aggregate alignment, manuscript ordering, and correspondence within manuscript groups require distinct evidence when evaluating LLM-generated scholarly assessment information.

cs.DL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35094

RAGing Against Misogyny: Improving Implicit Misogyny Explanations

Ivana Crescenzi, Arianna Muti, Maarten Sap, Tommaso Caselli, Debora Nozza

原文摘要

Implicit misogyny often requires background knowledge to be understood. To address this challenge, we propose a Retrieval-Augmented Generation (RAG) framework for detecting and explaining implicit misogyny in English and Italian, leveraging encyclopedic, slang and feminist knowledge sources. Using the ImplicIT-Mis and SBIC+ datasets, we evaluate the contribution of each knowledge source to detection and explanation quality, investigating also whether RAG addresses known LLM failure modes in implicit misogyny. Results show that RAG improves recall on ImplicIT-Mis, but does not improve detection performance on SBIC+. However, human evaluation shows that RAG-based explanations are preferred over no-RAG explanations in both languages. Feminist literature emerges as the most consistently useful source for detection and explanation.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35093

Improving Instruction Hierarchy by Learning from Goal-Goal Conflicts

Sizhe Chen, Sanjay Kariyappa, Liwei Jiang, Chong Xiang, Jihye Choi, Chaowei Xiao, G. Edward Suh

原文摘要

The recent incident in which OpenAI agents autonomously compromised Hugging Face's infrastructure illustrates the importance of instruction hierarchy (IH): an LLM should follow lower-priority instructions, such as user requests, only when they do not conflict with higher-priority instructions, such as system policies. IH can be effectively improved by fine-tuning LLMs on simulated examples of instruction conflicts, whose diversity is key to teaching LLMs to properly handle conflicts at test time. However, our decomposition of a task into goals and constraints reveals a conflict type -- goal-goal conflicts -- that is absent from existing IH training sets from OpenAI and NVIDIA. We show the importance of this conflict type: fine-tuning Qwen3.5-9B and Gemma4-12B-it on examples of this conflict type significantly improves IH on a wide range of real-world tasks unseen in training; adding these examples to existing IH training sets for fine-tuning also gives better IH adherence. The IH gains come with trivial mean utility changes on no-conflict tasks across eight benchmarks. Our work advocates including this new conflict type in IH training, even if these examples do not reflect typical LLM use.

cs.AI

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arXiv ↗PDF ↗