Robotics
Measuring Language Transfer in Robot Policies: Adding Greek to a Cosmos3 Vision-Language-Action Policy
Ayoub Kirouane, Georgios Giaples and Christos Petrocheilos
Robot foundation models are trained and evaluated predominantly in English, and
robot demonstration corpora do not exist for most
languages. We study the addition of Greek to an open
vision-
language-action stack using only machine-rephrased instructions and no architecture changes. The main challenge is measurement rather than translation. Several plausible instruments produce false conclusions: a color-histogram metric rewards noise, a single-goal
benchmark scores 84.6% under correct Greek and 82.6% under deliberately wrong instructions, training loss fails to predict Greek success, and single-run comparisons are dominated by seed variation. On a discriminative ninety-task suite with three seeds per arm, a
multilingual text tower without Greek demonstrations remains at its wrong-instruction floor, while Greek-only training exceeds its control by at most 2.7 points. Bilingual training yields a consistent 6.7-7.1 point margin over its control and reaches about two fifths of English performance. The policy also overfits the translator's phrasing; training on seven phrasings per task approximately halves this penalty. Warm-starting from a
language-adapted world model and unfreezing the text tower both degrade performance. The results support two practical requirements for low-resource
robot-policy localization: build a guaranteed null before trusting a metric, and replicate low-resource-
language results across seeds.
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Computer Vision
When Superpixels Fail on Documents: A Study of Segmentation for LIME Explanations
Quentin Telnoff, Emanuela Boros, Mickaël Coustaty et al. (+3 more)
Post-hoc explanation methods are widely used to inspect image classifiers, but their reliability depends on design choices that are often treated as implementation details. We study this issue for LIME on document image classification, focusing on the
segmentation step that defines the interpretable units being perturbed. Standard image-based LIME typically relies on natural-image superpixels, which are poorly aligned with document structure such as text regions, layout blocks, and identification codes. Using RVL-CDIP, we compare Quickshift and SLIC with document-aware
segmentations based on OCR bounding boxes and regular grids. Our results show that
segmentation strongly affects explanation consistency, correctness, and local fidelity. Document-aware
segmentations produce more stable and faithful explanations, require fewer perturbations to converge, and expose shortcut behaviour based on document identification codes, a known RVL-CDIP bias that superpixel-based LIME often obscures. These findings show that reliable post-hoc explanation requires domain-aware interpretable representations, and that
segmentation should be treated as part of the explanation method rather than as neutral preprocessing.
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All authors Quentin Telnoff, Emanuela Boros, Mickaël Coustaty, Robin Jarry, Fabrice Crohas, Antoine Doucet
Machine Learning
Temporal-Causal Inference for Reinforcement Learning via Automata Learning
Jan Corazza, Daniil Kaminskyi, Simon Lutz et al. (+4 more)
We consider reinforcement learning in environments with dynamics that undergo an irreversible phase transition governed by a hidden temporal pattern. The
agent observes the base state but cannot observe the phase directly. We formalize this problem as a two-phase non-Markovian decision process and introduce Temporal-Causal Inference for Reinforcement Learning (TCIRL), a framework that jointly learns a control policy and infers the hidden temporal cause of the phase transition. TCIRL maintains a hypothesis deterministic finite automaton (DFA) to track what phase is active and refines it via counterexample-driven SAT-based synthesis. We prove that the hypothesis converges almost surely to a DFA whose
language induces the same dynamics as the cause
language, yielding an optimal policy for the original non-Markovian decision process. Experiments on a genetic therapy gridworld and a traffic signal environment show that TCIRL recovers the correct cause DFA and matches the full-information baseline in both domains.
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All authors Jan Corazza, Daniil Kaminskyi, Simon Lutz, Patrick Nossol, Hadi Partovi Aria, Zhe Xu, Daniel Neider
Computer Vision
Omar Todd, Sooha Kim, Raghav Mehta et al. (+5 more)
Uncertainty arising from inter-observer variability in medical image
segmentation plays an important role in developing treatment plans. Research in this area is inhibited by the lack of multiple annotations for large-scale medical
datasets, especially for volumetric data, which suffers from additional scaling and computational complexity challenges. Flow matching has emerged as a powerful framework for generative modelling and has also been demonstrated to maintain strong performance when working with latent representations of images. In this work, we introduce a latent-to-latent flow technique for
stochastic segmentation of medical volumes via encoded representations of both the image and label space. We evaluate our method on two challenging applications covering delineation uncertainty for radiotherapy planning and multiple organ structure
segmentation, improving efficiency up to 14x compared with full resolution models while maintaining clinically relevant performance.
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All authors Omar Todd, Sooha Kim, Raghav Mehta, Katherine Mackay, David Bernstein, Alexandra Taylor, Fabio De Sousa Ribeiro, Ben Glocker
Machine Learning · Cross-listed from cond-mat.dis-nn
Graph neural networks and the energetic cavity method for combinatorial optimization
Joe Bacchus George and George T. Cantwell
We study the use of graph neural networks (GNNs) for finding approximate ground states of Ising models. Efficiently finding these ground states is of broad significance because many combinatorial optimization problems can be formulated as an Ising model with the appropriate choice of couplings and fields. Exactly solving these problems is hard but there are many good heuristic methods. A lineage of these heuristics build from mean-field approximations: one approach uses the leading eigenvector of an appropriately defined
matrix, another is the min-sum
algorithm, also known as the energetic cavity method. Without modification, GNNs perform worse than both of these methods. We consider small modifications to the GNN to incorporate these heuristics and find that this considerably improves performance. While the modified approach is competitive against other deep-learning approaches, we still find that simulated annealing is reliably at least as good as deep learning methods for the same computational cost.
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Computer Vision
Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection
Sébastien Thuau, Amira Gran, Siba Haidar et al. (+1 more)
How can
vision-
language models help
video anomaly detection (VAD) when surveillance data remain distributed, weakly labeled, and resource-constrained? Most weakly supervised VAD methods assume centralized training; recent VLM-based extensions further rely on dense inference, generated explanations, or additional adaptation. We introduce a lightweight
federated MIL-VLM cascade in which only a compact MIL scorer is trained across clients, while a frozen VLM verifies high-scoring suspect segments post hoc. We study two VLM feedback interfaces: parsed text-generation decisions and a logit-based interface that extracts a continuous anomaly score from next-
token Yes/No probabilities. Experiments on UCF-Crime with InternVL3.5-2B and Qwen3-VL-2B-Instruct show that text-generation verification can improve frame-level AUC after diagnostic temporal post-processing, but remains sensitive to
prompts, parsers, model choice, and smoothing. In contrast, the logit interface provides a fixed parser-free signal that improves both frame-level AUC and frame-level AP over the MIL baseline across both VLMs, without temporal post-processing in its main configuration. Since suspect segments are updated independently once available, next-
token logit feedback provides a simple segment-local alternative to text-generation verification.
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All authors Sébastien Thuau, Amira Gran, Siba Haidar, Rachid Chelouah
Computation and Language
FramingQA: Does the Question Shape the Answer? Measuring the Compositional Framing Effect
Hazel H. Kim, Andrew M. Bean, Guilherme Affonso Ferreira de Camargo et al. (+10 more)
We introduce FramingQA, a
benchmark that measures the model sensitivity to question framing across law, medicine, finance, and robotic simulations. Large
language models (LLMs) often change their responses to subtle rephrasings that align with an implied stance by users. This can leave users with advice tainted by how they happened to phrase a question rather than by the underlying facts, and the consequences are highly costly in high-stakes domains. Because in the realistic scenarios, both expert practitioners and non-expert users frequently ask LLMs questions containing incomplete or misleading assumptions, models are highly susceptible to those framings. To test this, we inject the framing bias across three nested levels: a framing-biased question phrasing (root), an injected framing-biased premise prepended to a neutral question (propositional), and a premise paired with a framing-biased question (global). Evaluating nine open models (3.8B-70B) across four families, we find that strong per-variant accuracy does not guarantee the robustness across differently phrased questions under the fixed factual information.
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All authors Hazel H. Kim, Andrew M. Bean, Guilherme Affonso Ferreira de Camargo, Shanyu Chauhan, Felix Drinkall, Jade Kosché, Chenyang Ma, Glory Nwaugbala, Nabeel Seedat, Bradley Max Segal, Samuel Recht, Hinrich Schütze, Philip H.S. Torr
Computation and Language
An LLM-Associated Register Shift in Korean Journal Abstracts: A Morphology-Aware Excess-Vocabulary Study, 2018-2026
Aron Lee
Excess vocabulary, a word's frequency above its pre-2023 trend, is how the change in scholarly English after 2022 has been measured. We adapt it to Korean with morphological units on 398,296 KCI abstracts (2018-August 2026), with 47,165 Vietnamese abstracts for comparison. Placebo floors are 0.1-2.2 points for the single-word statistic and at most 2.9 for the re-selected split-half set statistic. Korean abstracts show nothing in 2023, onset in late 2024, a rise through 2025 flattening in mid-2026: sisahada "suggest" appears in 21.4% of 2026 abstracts against 5.3% expected; plain verbs like araboda "look into" fall to a quarter of trend. Under stated assumptions the single-word conditional lower bound on LLM-processed abstracts is 3.5%, 10.5% and 16.1% for 2024-2026 and a split-half set bound 7.8%, 20.6% and 33.0%. Holzwarth et al.'s
estimator under the same discipline gives 41.9% and 72.1% for 2025-2026. Subject-matter controls reduce but do not remove it: restricting the set to lemmas three
language-model annotators all call style leaves 14.7 of the 33.0 points, and pairing each 2026 abstract with its journal's closest base-period abstract leaves 34.1. Tested translation routes do not explain it: the surface marks of translated Korean fall as the markers rise. In the same articles' English abstracts the excess appears a year earlier; where the English side carries none, the Korean shift persists at 30 to 66% of the rate where it does. Control abstracts from three providers reproduce the rising words, with marker turnover consistent with model generations; implied prevalences are scenario-dependent.
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Robotics
SMaRT-Tug: Structured Multi-Agent Reinforcement Learning for Physics-Based Tugboat-Barge Collaborative Manipulation
Junkai Lu, Jiadong Zhao, Jiacheng Zhang et al. (+8 more)
Autonomous tugboating is central for automating maritime operations such as port logistics and vessel maneuvering, where multiple tugboats must cooperatively transport/manipulate a larger vessel. Collaborative pushing in this setting is challenging due to coupled hydrodynamics, low resistance, strong environmental disturbances, underactuated barge dynamics, and contact-rich interactions. Conventional control methods often rely on simplified models and fixed configurations, which limit their adaptability, while learning-based approaches are constrained by the lack of scalable and physically realistic training environments. We address these challenges by introducing a physics-based, GPU-accelerated simulation and learning framework for collaborative tugboat manipulation. Our simulator incorporates a customized buoyancy model, wave modeling, and hydrodynamic resistance, and supports large-scale multi-
agent training under marine dynamics. In this simulator, we train a decentralized MAPPO (Multi-
Agent PPO) policy augmented with a structured control prior (SCP) to improve training stability and maintain feasible pushing configurations. We evaluate our learned policy on straight-line transit, turning, and deceleration tasks, where we show that our decentralized framework yields more reliable and accurate maneuvering performance compared to a PID-based controller and a centralized PPO baseline. We further demonstrate zero-shot generalization to more challenging sea states and advanced maneuvers, as well as zero-shot scalability to larger teams of three and four tugboats despite training with only two
agents.
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All authors Junkai Lu, Jiadong Zhao, Jiacheng Zhang, Wenqi Zhao, Hao Gen Chia, Qun Shen Png, Germaine Ee, Chengyang He, Yifeng Zhang, Nathanael Tan, Guillaume Sartoretti
Machine Learning
TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning
Avik Bhatnagar, Federico Nicolas Peccia and Oliver Bringmann
The rise of
privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, where deep learning models are finetuned directly on edge hardware using local user data. However, this shift requires optimization of deep learning training on resource-constrained hardware to maximize throughput while maintaining predictive accuracy. This paper introduces a novel technique for on-device model training that incorporates an efficient
Bayesian optimization-based batch size tuning approach to maximize hardware throughput. To evaluate the impact of this hyperparameter on the learning dynamics, we investigated two distinct paradigms: standard supervised learning (SL) and online continual learning (CL). Experimental results across various edge devices demonstrate a throughput ceiling, beyond which increasing the batch size yields no additional throughput gains. The proposed tuning approach identifies the optimal batch size, which, when combined with gradient accumulation and linear learning rate scaling, achieves up to a 2X increase in training throughput on platforms such as Raspberry Pi 4 compared to maximum batch sizes, without compromising model accuracy. Furthermore, in the CL paradigm, we demonstrate that optimal batch sizes maintain the stability-plasticity balance required for incremental learning, effectively mitigating catastrophic forgetting while maximizing computational efficiency on edge-hardware.
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Machine Learning
TabBench-Bio: A Living Benchmark for Machine Learning on High-Dimensional Biomedical Tables
Jules Kreuer, Sofiane Ouaari, Julia Hellmig et al. (+2 more)
Biomedical tables often combine thousands of measured variables with only tens or hundreds of labelled samples, a regime that is poorly represented in general-purpose tabular
benchmarks. We introduce TabBench-Bio, a living and interactive
benchmark of 43 biomedical
datasets spanning multiple domains. Under a shared cross-validation protocol, we compare classical
estimators, neural networks, and tabular foundation models across 28 feature-by-sample operating points. At the reference
cell of 10,000 features and 100 training samples, RealTabPFN 2.5 has the highest point estimate, closely followed by TabPFN 3 and Logistic Regression. A paired bootstrap over the target pool separates RealTabPFN 2.5 from TabPFN 3 by 87 Elo (95% interval [43, 129]). Tabular foundation models generally occupy the leading ranks, while the strongest configuration depends on the operating point and biomedical modality. The AutoML framework AutoGluon, using its one-hour "extreme" preset, is configured as a separate resource-intensive reference and reported here at the reference and full
cell. Fold-level predictions, run status, and deterministic aggregations make every reported result reproducible and reusable. The
benchmark is open to contributions of new biomedical
datasets. The interactive leaderboard is available at https://tabbench-bio.eu.
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All authors Jules Kreuer, Sofiane Ouaari, Julia Hellmig, Julius Braitinger, Nico Pfeifer
Robotics
Open-Set Ego-Noise Separation for Legged-Robot Audition via Annotation-Free Adaptation and Pretrained-Model Transfer
Koki Shoda, Jun Younes Louhi Kasahara, Aoba Koyanagi et al. (+2 more)
This paper proposes an open-set ego-noise separation framework for legged-
robot audition via annotation-free adaptation and pretrained-model transfer. The framework removes
robot-specific ego-noise while preserving environmental sounds whose classes are not specified in advance. Acoustic sensing provides cues about a
robot's surroundings beyond the visual field, but walking-induced ego-noise from footstep impacts, joint-backlash rattling, and motor noise severely contaminates the recordings. The framework first uses RecurGraph to select ego-noise-dominant clips from the unlabeled recordings by aggregating clip
embeddings into an
embedding centroid and propagating scores over an audio-
embedding graph. The selected clips are mixed with diverse environmental sounds from a large-scale sound-event
dataset to provide paired mixture–target supervision for open-set separation. Transfer-DiT then adapts a general-purpose zero-shot neural separator to achieve high-fidelity open-set ego-noise separation for the target
robot. Experiments with bipedal and quadrupedal
robots show reliable clip selection and improvements in separation quality and downstream task performance over baseline separators. These results demonstrate the feasibility of annotation-free adaptation without separately recorded ego-noise-only data or manual clip-level annotations.
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All authors Koki Shoda, Jun Younes Louhi Kasahara, Aoba Koyanagi, Qi An, Atsushi Yamashita
Computer Vision
AFID: A Unified Open Framework for Automated Fingermark Identification, Quality Assessment and Feature Extraction
Tim Oblak, Rudolf Haraksim and Peter Peer
Automated fingermark identification is the foundation of forensic investigation, yet progress in the field is held back by fragmented, closed-source solutions trained on private or discontinued data. We present AFID, a unified open-source framework for friction ridge image processing that performs recognition, quality assessment, and feature extraction based on a single shared encoder, trained exclusively on publicly available data. At its core is a fixed-length representation learned for identity discrimination, trained under heavy augmentation. Despite applying essentially no preprocessing beyond resizing and padding at inference, AFID sets a new state of the art in fixed-length fingermark recognition, leading identification across NIST SD 27 (67.6% rank-1) , SD 302 (54.9% rank-1), and SD 303 (67.6% rank-1), surpassing a commercial matcher on fingermarks. From the same frozen backbone, a quality assessment module predicts recognition utility more accurately than any compared baseline and generalizes across independent matchers, while lightweight decoders recover minutiae, ridge orientation, and
segmentation competitive with dedicated methods. The framework proves that a single, efficiently trained encoder can support the full fingermark processing pipeline, from recognition through quality assessment all the way to feature extraction. To accelerate research on fingermark analysis even further, we release the code, models, and annotations to the community.
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Numerical Analysis
A Systematic Analysis of Automatic Differentiation versus Discretization-based Constraints for Physics-Informed PDE Solvers
Xing Guo, Hongwei Tang, Zewei Meng et al. (+3 more)
Physics-informed neural networks (PINNs) represent a growing frontier in using artificial intelligence to solve partial differential equations (PDEs). Automatic differentiation (AD) plays a central role in this paradigm, which is mesh-free and replaces traditional iterative solvers with gradient-based optimization in continuous space. However, the inherent limitations of AD, particularly in handling higher-order derivatives and discontinuous solutions, pose significant challenges for complex problems. This has motivated a growing number of researchers to explore discretization-based constraints as an alternative path. Yet, the respective applicability of these two paradigms remains largely unexplored. In this work, we conduct systematic experiments across a wide spectrum of problems, from simple linear Poisson to high-Mach hypersonic flows with strong discontinuities. Through a rigorous decomposition of approximation, optimization, and truncation errors, we systematically elucidate the fundamental trade-offs and error-governing mechanisms of both paradigms, as well as two representative network architectures: multi-layer perceptron (MLP) and graph neural network (GNN). Our results reveal a consistent trend: as nonlinearity strengthens, the accuracy advantage of discretization-based constraints becomes increasingly pronounced, with smaller optimization errors compensating for the truncation errors. Moreover, the more complex the nonlinearity and boundary conditions, the greater the advantage of GNN over MLP. These insights offer a robust practical guideline for configuring neural PDE solvers in demanding engineering applications. Our source data and code are available at https://github.com/guoxing0809/neuropde_analysis.
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All authors Xing Guo, Hongwei Tang, Zewei Meng, Yidong Zhang, Shaoqiu Xiao, Feng Liu
Numerical Analysis · Cross-listed from math.AP
Fully discrete stochastic maximal regularity and $H^\infty$-calculus for second-order elliptic operators
Foivos Evangelopoulos-Ntemiris
This paper establishes the fully discrete
stochastic maximal $L^p$-regularity and the accompanying sharp maximal estimate for numerical approximations of
parabolic stochastic partial differential equations. We consider the spatial finite element discretization $A_h$ of a general second-order
elliptic operator $A=-\nabla \cdot a\nabla +b\cdot \nabla +c$ with Dirichlet boundary conditions on a smooth, bounded, convex domain in $\mathbb{R}^3$, coupled with a broad class of temporal schemes, including rational approximations and the exponential Euler method. To obtain these optimal discrete regularity results, we establish a bounded $H^\infty$-calculus for the discrete spatial operator $A_h$, uniformly in the mesh size $h$. As a direct byproduct, we also establish the discrete-in-space
stochastic maximal regularity for the corresponding spatial semi-discretizations. As an application, we establish the pathwise uniform convergence of a fully discrete approximation of the three-dimensional
stochastic Allen–Cahn equation with variable
diffusion coefficients and multiplicative noise.
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Artificial Intelligence
CIT-CAD: Constraint Intent Tree-based CAD Code Generation and Verification
Yali Du, Hui Sun, San-Zhuo Xi et al. (+1 more)
Natural-
language Computer-Aided Design (CAD) code generation aims to turn design intent into executable and editable parametric programs. Large
language models (LLMs) make this goal increasingly practical, but useful systems must preserve the construction process behind the rendered geometry. Existing
benchmarks and methods mostly focus on how closely the generated CAD model matches the reference geometry, often using metrics such as Intersection over Union (IoU). Such metrics can miss errors in part decomposition, construction hierarchy, Boolean operations, sketch structure, and
geometric relations. This gap calls for a representation that makes design intent explicit and lets a system check generated code against that intent. We propose CIT-CAD, a framework that infers a Constraint Intent Tree (CIT) from the input description to represent the intended entities, hierarchy, operations, and relations. The tree has two roles: it guides CAD code generation and defines expected constraints for verification. The framework extracts actual constraints from the generated program, compares them with the expected constraints, and uses mismatches to localize and repair design violations. Experiments show that the framework improves CAD generation performance, with larger gains on more complex multi-entity designs. By turning design intent into an explicit and checkable object, this work is the first attempt to move text-to-CAD generation beyond rendered-geometry matching toward construction-aware synthesis, verification, and repair.
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All authors Yali Du, Hui Sun, San-Zhuo Xi, Ming Li
Systems and Control
Stochastic Model Predictive Control under AC Power-Flow Constraints Using Generative Learning
Jie Zhu, Yinliang Xu and Guan Wang
This paper proposes an end-to-end generative framework for efficiently solving multi-period and multi-scenario
stochastic model predictive control (SMPC) problems under nonlinear AC power-flow constraints. Conventional deterministic neural surrogates rely on a single-shot prediction, which makes reliable feasibility difficult to achieve because one dispatch trajectory must simultaneously satisfy nonlinear constraints across all scenarios and time periods. To address this limitation, a conditional
stochastic neural generator (CSNG) is developed to produce multiple candidate dispatch trajectories for each uncertainty instance, enabling the recovery of a feasible and economical solution through candidate selection. A feasibility-aware self-supervised distribution-shaping scheme is further introduced to promote constraint satisfaction, candidate diversity, and operating economy without requiring computationally expensive SMPC solution labels, while mitigating candidate collapse in generative ACOPF learning. To support efficient end-to-end training, a constraint-aware differentiable architecture is introduced. It employs a projection mechanism to exactly enforce box and ramping constraints while preserving informative gradients near active bounds, together with a differentiable equality-completion surrogate for efficient AC power-flow reconstruction. Case studies on the IEEE 14- and 118-bus systems demonstrate $100\%$ feasibility, optimality gaps below $2\%$, and computational efficiency suitable for intraday dispatch. The implementation is publicly available at https://github.com/JieZhu6/Generative_SMPC.
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Machine Learning
Revisiting Thinning Methods for Kernel Learning Problems
Blanca Cano-Camarero, Yago R. Aguado-Carrillo-de-Albornoz, Ángela Fernández-Pascual et al. (+1 more)
Kernel methods are widely used because of their strong theoretical guarantees and empirical performance. However, their high computational cost limits their applicability to large-scale
datasets. To address this shortcoming, several approaches use Maximum Mean Discrepancy to construct representative subsets that preserve the properties of the full
dataset in a Reproducing Kernel
Hilbert Space. We introduce Backward Kernel Herding, an
algorithm that addresses this problem by iteratively removing points from the
dataset, achieving results comparable to current state-of-the-art approaches while accelerating the subsampling process in realistic scenarios where the reduced size is less than half of the
dataset. Moreover, we overcome a limitation of Kernel Thinning by proposing an extension that enables the construction of subsets of arbitrary size rather that restricting to successive halvings. Finally, we conduct an extensive experimental comparison focusing on the most relevant kernel learning procedures: Gaussian Processes and Kernel Support Vector Machines. The results show that Backward Kernel Herding consistently achieves competitive performance with the most favorable training-time efficiency, while the proposed Flexible Kernel Thinning frequently achieves the best predictive performance. These gains become especially pronounced for moderate compression ratios, highlighting the benefits of incorporating supervised information into the thinning process. In terms of
memory consumption, Flexible Kernel Thinning is also competitive, whereas Backward Kernel Herding remains an alternative when computational efficiency is the primary objective. Overall, no single method dominates across all scenarios, underscoring the importance of selecting the reduction strategy according to the desired trade-off between predictive performance, training cost, and
memory requirements.
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All authors Blanca Cano-Camarero, Yago R. Aguado-Carrillo-de-Albornoz, Ángela Fernández-Pascual, José R. Dorronsoro
Robotics
CALM: Configuration-Aware Human Intervention Boundaries During Robot Approach
Xinting Gao, Sipu Zhu and Weimin Zhuang
How
robot body configuration shapes human intervention during approach remains underexplored. We conducted a within-participants study with 41 participants, measuring final stopping distance, subjective comfort, and exploratory eye-tracking responses across four
humanoid arm configurations and two spatial scales. Full forward arm extension increased stopping distance by approximately 31-36 cm relative to arms-down. Spatial scale primarily affected comfort and pupil responses without a detectable stopping-distance shift. We introduce the Configuration-Aware Limit Model (CALM), which translates stopping-distance distributions into configuration-dependent population-coverage boundaries. Estimated boundaries at 80% coverage ranged from 0.88 to 1.47 m. In an illustrative one-dimensional planning analysis, reconfiguration enabled a 1.10 m approach goal that was unreachable with arms remaining fully extended under the same nominal pointwise 20% intervention-probability constraint. These findings support treating body configuration as a planning variable while distinguishing physical safety, behavioral intervention, and subjective cost.
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Data Structures and Algorithms
Algorithms for Finite Group Epimorphism Testing
Joshua A. Grochow, Pranjal Srivastava and Dhara Thakkar
The Group Epimorphism Problem (GpEpi) asks, given two finite groups $G_1$ and $G_2$, whether there exists a surjective group homomorphism, or epimorphism, from $G_1$ to $G_2$. When the input groups are given by their multiplication (Cayley) tables, the problem admits a quasipolynomial-time
algorithm in general, but little is known about its complexity for structured classes of finite groups. In this paper, we study the computational complexity of GpEpi for several well-studied classes of finite groups. Our main results are
polynomial-time epimorphism tests for several classes of groups for which
polynomial-time isomorphism testing was previously known: Groups with Abelian normal
Hall subgroups with cyclic complement; Groups with (product of) elementary Abelian normal
Hall subgroup with elementary Abelian complement; and Groups with some constraints on their Abelian chief factors.
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Machine Learning · Cross-listed from eess.SP
Masking Radar Cognition under Adversarial Surveillance: A Distributional Privacy Framework
Sreedevi K, Nandhini K, Anup Aprem et al. (+1 more)
In this article, we propose an online electronic counter-countermeasure (ECCM) framework designed to conceal the strategic decision-making processes of a cognitive radar (CR) operating under
adversarial surveillance. We model the CR under two distinct decision paradigms: a static constrained utility-maximizing behavior and a dynamic expected utility-maximizing behavior. The radar's utility function is modeled via a von Mises–Fisher (vMF) distribution, with the distributional parameter constituting the private information to be protected from
adversarial inference. We adopt a distribution
privacy framework to conceal this private information and provide formal distribution
privacy guarantees for cognition masking. In this work, we develop cognition-hiding
algorithms for both static constrained utility maximization (WDPCH-SU), and dynamic expected utility maximization (WDPCH-DU). Through rigorous mathematical analysis, we show that both WDPCH-SU and WDPCH-DU satisfy $\epsilon$-distribution
privacy ($\epsilon$-DistP) against inference-based
adversarial attacks and present the
privacy–performance trade-off bounds, quantifying utility loss (in static setting) and expected utility deviation (in dynamic setting) as functions of $\epsilon$. Numerical results show that WDPCH-SU gives about 15% improvement in utility loss at maximum
privacy compared to the existing methodology while WDPCH-DU achieves a greater reduction in
adversarial Fisher information without requiring explicit Fisher information constraints, at a moderate, analytically bounded utility deviation. These results are highly promising in many 6G communication scenarios such as network slicing for automated driving and swarm
UAV coordination, where it is essential to keep the resource allocation policy robust against
privacy attacks.
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All authors Sreedevi K, Nandhini K, Anup Aprem, Deepthi P P
Computer Vision
Unified Vision-Centric Pedestrian Crossing Action Prediction via Adaptive Patch Projection and Proactive Spatial Rectification
Yao Tian, Le Yang and Binglu Wang
Vision cues are available and informative for pedestrian action prediction, but obtaining stable target-centric representations from
video frames remains challenging without frame-level external perception cues. Thus, most methods rely on additional perception modules or multi-source information fusion, leaving the reliability of
vision-centric setting an open question. To this end, we propose ViCross, a
vision-centric pedestrian crossing action prediction framework powered by
multimodal large
language models, which maintains target-centric
reasoning from
video frames without additional perception modules beyond first-frame target initialization. While
multimodal large
language models exhibit strong visual understanding, applying them directly to
vision-centric action prediction faces two challenges. First, accurately perceiving target pedestrians often requires high resolution inputs and dense visual tokenization, making full-frame encoding computationally prohibitive. ViCross tackles this with Variable Resolution Patch Mapping module for efficient
token allocation while preserving key pedestrian details. Second, missing spatiotemporal priors hinder consistent cross frame
reasoning. ViCross mitigates this with a Spatial Constraint Enhancement Strategy that captures past motion, future locations, and action semantics for training-time proactive spatial rectification. Extensive experiments show that ViCross delivers clear gains in
vision-centric prediction settings and is competitive with multi-source fusion approaches in several settings. Code is available at https://github.com/2tianyao1/ViCross.git.
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Neural and Evolutionary Computing
Photonic reservoir computing with dimensionally compressed readout
Gerald Kobi, Mohab Abdalla, Miguel C. Soriano et al. (+1 more)
This work addresses a hardware constraint in reservoir computing: the limited size of the readout layer imposed by systems with a physical readout. We investigate a strategy to accommodate this constraint based on random projection, which compresses high-dimensional reservoir states into a lower-dimensional subspace while preserving key properties of the source space and information- processing capabilities. To evaluate this approach, we compare a small, standalone time delay reservoir against a larger configuration whose output is projected down to match the same restricted readout dimension. Using task-independent metrics, we demonstrate that the distribution of information-processing capacities may differ between the two configurations, even at identical readout sizes. Furthermore, we perform a comprehensive hyperparameter scan to assess how both systems behave under varying physical regimes. Finally, we
benchmark this approach on the standard NARMA10 task, showing that the random projection framework can yield superior performance compared to a standalone constrained reservoir, within specific compression range. These results provide a scalable pathway to bypass physical readout bottlenecks in hardware-based reservoir computing.
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All authors Gerald Kobi, Mohab Abdalla, Miguel C. Soriano, Damien Rontani
Computer Vision
Self-Supervised Multi-View 3D Gaze Target Estimation via Probabilistic Ray Marching
Keqi Chen, Vinkle Srivastav and Nicolas Padoy
We present a self-supervised approach, Self-MVGTE, for estimating 3D gaze targets from multiple camera views. Unlike existing methods that independently estimate 2D gaze targets per camera view, Self-MVGTE predicts gaze targets directly in 3D space for the first time. Moreover, it does not require any ground-truth annotations from the target scene and uses only the multi-view input images from a calibrated camera setup, pseudo 2D gaze target labels from a
monocular gaze target estimation model, and 3D gaze vectors from a
monocular 3D gaze estimation model. A key challenge is that these pseudo labels are inherently noisy and multi-view inconsistent. To address this, we propose a probabilistic ray marching framework, which models the uncertainty of these pseudo labels and exploits 3D gaze vectors as
geometric priors. Specifically, these gaze vectors are first integrated into the
monocular gaze target estimation model to improve its generalization to unseen scenes, producing higher-quality pseudo labels. Then, for 3D gaze target estimation, we construct a 3D gaze cone by casting a bundle of rays from the eye position around the gaze vector to strictly constrain the solution space. Within this cone, we propose a depth-guided feature sampling strategy using off-the-shelf DINOv2 and Depth-Anything-3 models, and estimate a spatial likelihood distribution of the gaze target. Finally, we convert the pseudo gaze target labels into a target distribution and softly optimize the network. Extensive experiments on the MVGT
dataset show that Self-MVGTE achieves state-of-the-art performance, surpassing existing fully-supervised baselines.
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Computer Vision
RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting
Hejun Wang, Jinxi Li, Junwei Jiang et al. (+4 more)
Image relighting is traditionally tackled via complex inverse rendering pipelines, which suffer from ill-posed optimization, or single-image generative models that ignore crucial multi-view cues necessary for understanding 3D geometry and material interactions. To address these limitations, we introduce a feed-forward generative
Transformer for direct single- and multi-view image relighting that entirely bypasses explicit intrinsic property estimation. Adapted from a
video foundation model, our architecture features a latent illumination module that dynamically injects target environment maps into spatial features via cross-
attention. Furthermore, we employ permutation-invariant positional encodings to symmetrically process unordered multi-view inputs without sequential bias. To train this robust data-driven model, we construct the massive Laval Objaverse
Dataset (LOD), comprising 90K objects and 39K unique illuminations. Extensive experiments demonstrate state-of-the-art visual quality, photorealistic relighting quality, and strong zero-shot generalization across single-view, multi-view, and novel-view relighting tasks.
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All authors Hejun Wang, Jinxi Li, Junwei Jiang, Shiwei Mao, Hu Cheng, Shouwang Huang, Bo Yang
Computer Vision
Multi-label versus multi-class classification of blood cells and their aggregates in microfluidic channels
Igor Zingman, Shada Abuhattum, Sara Kaliman et al. (+8 more)
Deformability cytometry (DC) is a type of imaging flow cytometry, which uses a camera-equipped device to measure cellular stiffness in addition to other cellular properties at high throughput. Cellular properties such as area and elongation can identify
cell types, but this requires prior knowledge of distinguishing properties and cannot be applied to clinically important
cell aggregates. Using DC data, we evaluated conventional multi-class (MC) classification and introduced a multi-label (ML) approach for identifying blood
cells and their aggregates. In particular, an ML classifier can simultaneously assign multiple
cell-type labels to a single imaged event. We show that, unlike MC classification, ML classification can identify
cell aggregates not represented in the training data. It also avoids the need for exhaustive, strictly defined aggregate labels, thereby simplifying and speeding up annotation. Since automated blood analyzers do not reliably analyze
cell aggregates, our approach may help address this clinical gap.
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All authors Igor Zingman, Shada Abuhattum, Sara Kaliman, Maximilian Schlögel, Paul Müller, Markéta Kubánková, Nadine Ströhlein, Manuela Hauke, Lena Schnörer, Martin Kräter, Jochen Guck
Artificial Intelligence
RAFM-SER++: A Lightweight Multimodal Emotion Recognition Framework for Real-Time Behavioral Monitoring in Surveillance Systems
Ngo Truong Dinh, Tung-Lam Bui, Chi-Trung Duong et al. (+3 more)
Recent
multimodal Speech Emotion Recognition (SER) systems achieve high accuracy through interaction-heavy cross-modal
transformers, but their computational cost limits deployment in latency-sensitive and resource-constrained surveillance systems. To address this challenge, we propose RAFM_SER++, a lightweight
multimodal SER framework featuring an asymmetric Residual
Attention Fusion Mechanism (RAFM). Rather than relying on computationally expensive bidirectional interactions, RAFM injects affective
speech cues into semantic text representations through a one-directional residual
attention pathway. Combined with a BYOL-inspired cross-modal
alignment objective and
attention-guided pooling, the proposed framework improves
multimodal representation learning while maintaining low computational overhead. Experiments on the IEMOCAP and ESD
benchmarks demonstrate that RAFM_SER++ consistently outperforms the HuBERT-Base baseline and achieves a superior accuracy-efficiency trade-off compared with the state-of-the-art MemoCMT. Specifically, RAFM_SER++ reduces trainable parameters by more than 60%, achieves faster inference (79.60 it/s), and attains BACC scores of 81.10% on IEMOCAP and 95.39% on ESD. These results indicate that lightweight asymmetric
multimodal fusion is an effective alternative to interaction-heavy cross-modal
transformers for real-time surveillance applications.
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All authors Ngo Truong Dinh, Tung-Lam Bui, Chi-Trung Duong, Vien Nguyen Thi, Viet-Anh Nguyen, Phuc-Lu Le
Machine Learning
Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson's disease classification
Sameer Bhat and Piotr Szczuko
This study investigates the classification of individuals as healthy or at risk of Parkinson's disease using machine learning (ML) models, focusing on the impact of
dataset size and preprocessing techniques on model performance. Four
datasets are created from an original
dataset: DS_0, (normal
dataset), DS_1 (DS_O subjected to Canny edge detection and Hessian filtering), DS_2 (augmented DS_0), and DS_3 (augmented DS_1). We evaluate a range of ML models-Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), XGBoost (XBG), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost (AdB)-on these
datasets, analyzing prediction accuracy, model size, and prediction latency. The results show that while larger
datasets lead to increased model
memory footprints and prediction latencies, the Canny edge detection preprocessing supplemented by Hessian filtering (used in DS_1 and DS_3) degrades the performance of most models. In our experiment, we observe that Random Forest (RF) maintains a stable
memory footprint of 61 KB across all
datasets, while models like KNN and SVM show significant increases in
memory usage, from 5.7-7 KB on DS_0 to 102-220 KB on DS_2, and similar increases in prediction time. Logistic Regression, Decision Tree, and Naive Bayes show stable
memory footprints and fast prediction times across all
datasets. XGBoost's prediction time increases from 180-200 ms on DS_0 to 700-3000 ms on DS_2 (truncated)
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Machine Learning
Think Wider: Mitigating Latent Rank Collapse in Implicit Chain-of-Thought Reasoning
Yuwen Hao and Menglin Yang
Chain-of-thought (CoT)
reasoning improves the
reasoning ability of large
language models by introducing intermediate computation, but explicit rationales increase decoding length, latency, and context cost. Implicit CoT offers a more efficient alternative by moving intermediate
reasoning into continuous latent states. However, latent
reasoning can be unstable: successive latent states may become overly similar and collapse toward a shared dominant direction, reducing the diversity of the
reasoning trajectory. In this work, we identify $\textit{latent rank collapse}$ and propose $\textbf{WIDER}$, a lightweight spectral regularizer for implicit CoT. During training, WIDER estimates the shared direction of each latent trajectory and penalizes projections onto this direction, encouraging latent states to span a broader representational subspace. The method is plug-and-play and leaves the backbone model, latent schedule, and inference-time decoding procedure unchanged. We further formulate this collapse as a
geometric bottleneck in implicit
reasoning, casting its mitigation as a training-time regularization problem rather than an inference-time decoding change. Extensive experiments show that WIDER improves matched implicit CoT baselines, while mechanistic analyses reveal higher effective rank, lower dominant-direction energy, and reduced redundancy among latent steps. These results highlight latent subspace utilization as an important factor for efficient continuous
reasoning, providing a
geometric perspective for analyzing and improving implicit CoT. Code is available at https://github.com/whitesweater/WIDER.
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Computer Vision
Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification
Côme-Alexis Puech, Sébastien Thuau, Amira Gran et al. (+3 more)
Privacy-sensitive surveillance systems could benefit from large
vision-
language models (VLMs), but such models typically require centralized access to raw
video. In
federated learning settings, this challenge is amplified by non-independent and identically distributed (non-IID) client data, which can make direct multiclass anomaly classification unstable, especially for rare categories. We propose a hybrid two-stage architecture that combines a
federated binary convolutional neural network (CNN) gate with server-side zero-shot VLM inference. The lightweight LiteCNN3D gate performs local anomaly screening and forwards only flagged
videos to Qwen3-VL-8B, which assigns them to four anomaly metaclasses. We evaluate this design on UCF-Crime grouped into five coarse metaclasses and implement the
federated stage in a real three-node heterogeneous deployment. In the studied setting, direct
federated multiclass training collapses, whereas the proposed decomposition yields a better trade-off between classification quality and raw-
video transmission. With fixed-threshold routing, the
federated hybrid pipeline preserves nearly the same macro-averaged F1 score (F1-macro) as its centralized CNN+VLM counterpart while reducing the fraction of transmitted
videos to 51.4%, although with a lower proxy macro receiver operating characteristic area under the curve (ROC AUC) than the centralized hybrid system. A complementary sensitivity-oriented routing operating point increases macro ROC AUC from 0.673 to 0.692 and reduces the false negative rate from 29.3% to 22.9%, but decreases F1-macro from 0.503 to 0.485 while increasing transmission from 51.4% to 57.9%. These results suggest that federation is better suited to coarse local screening, while routing rules can be adjusted to trade server-side VLM usage for higher anomaly sensitivity.
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All authors Côme-Alexis Puech, Sébastien Thuau, Amira Gran, Arthur Mennessier, Siba Haidar, Rachid Chelouah