Machine Learning
Universal Sample-Based Embeddings of Higher-Order Objects
Radovan Haluška, Christopher Solinas, Nathan R. Sturtevant et al. (+2 more)
Neural networks operate naturally on finite-dimensional vectors, yet many problems require
reasoning about higher-order objects such as distributions, functions, and graphs. Such objects capture relationships within a ground set and rarely admit an explicit compact representation. Moreover, we typically want a compact representation not of a single higher-order object but of an entire family of such objects, including members unseen during training. While a few approaches exist for particular object types, none is designed to work across object types. We propose a unified framework that represents any higher-order object as a fixed-size
embedding vector learned over a family of such objects. An encoder maps an object, seen only through a finite set of samples, to an
embedding, and a domain-specific decoder supplies the training signal by modeling the object conditioned on that
embedding. Since the encoder works only from samples, one architecture serves different object types. Because the decoder is a model already suited to single instances of the object type, the framework inherits that model's native operations and inductive biases. We instantiate the framework on distributions, functions, and graphs. In each case we show that a single trained model represents an entire family and generalizes to members unseen during training. Furthermore, we demonstrate the downstream utility of these
embeddings: interpolating distributions in
embedding space, solving differential equations in
embedding space, and predicting unseen edges from a graph's
embedding.
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All authors Radovan Haluška, Christopher Solinas, Nathan R. Sturtevant, Martin Schmid, Michael Bowling
Cryptography and Security
Bilinear Compressive Security with Finitely Many Samples
Axel Flinth, Semira Einsele, Matilda Kylberg et al. (+2 more)
Bilinear Compressive Security (BCS) is a lightweight encryption scheme that combines compressed sensing with a fresh random convolution filter to improve resistance against key-recovery attacks under key reuse. Previous work derived information-theoretic guarantees for BCS by relating key identifiability to phase
retrieval under the assumption that an adversary has access to the full ciphertext distribution induced by the random filter. In practice, however, an adversary only observes finitely many encryptions, leaving the security of BCS in the finite-sample regime largely unexplored. In this paper, we study key recovery from only finitely many observations. We first develop a covariance-based attack that estimates the linear measurements from repeated encryptions of the same plaintext and subsequently recovers the secret measurement
matrix. We analyze the estimation procedure and derive finite-sample guarantees together with corresponding sample-complexity bounds. We further investigate the complete attack experimentally and compare its observed recovery behavior with the previously established information-theoretic identifiability results. Our experiments show two qualitatively different recovery regimes on the structure of the measurement
matrix. For dense
matrices, including Gaussian
matrices, the number of measurements required for successful recovery appears to grow linearly with the signal dimension. This differs from the worst-case behavior suggested by information theory. In contrast, for
matrices with sparse rows, we observe a quadratic growth consistent with the theoretical bound. These results suggest that the sparsity structure of the measurement
matrix plays an important role in the security of BCS and should be taken into account when selecting parameters and designing practical instances of the scheme.
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All authors Axel Flinth, Semira Einsele, Matilda Kylberg, Hubert Orlicki, Gerhard Wunder
Robotics
Active Perception of Upper-Limb Functional Mobility through Human-Robot Handovers
Francesco Semeraro, Marta Lagomarsino, Alberto Ranavolo et al. (+1 more)
The reintegration of people in the workplace after an upper-limb injury is often a long process. Collaborative
robots and humanoids operating in shared workspaces must adapt their behavior to the physical capabilities of their human partners. Achieving this requires
robots to autonomously estimate task-relevant human mobility during interaction, without relying on external clinical assessments. In this work, we formulate the estimation of upper-limb functional mobility as an active perception problem. We model task-available upper-limb functional ranges of motion through impairment coefficients that parameterize reductions of healthy joint limits, and integrate this representation into a human model used by the
robot during interaction. Based on this formulation, we propose an online active perception pipeline in which the
robot actively probes the user through repeated handover requests while progressively updating a
Bayesian belief over the impairment coefficients. This enables the
robot to continuously refine its internal model of the user's joint-level capabilities through interaction rather than passive observation. The proposed framework was evaluated through simulation studies and a proof-of-concept physical validation with a robotic platform and enacted human impairments, demonstrating that the pipeline can accurately recover the impairment coefficients during real human-
robot handovers.
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All authors Francesco Semeraro, Marta Lagomarsino, Alberto Ranavolo, Arash Ajoudani
Software Engineering
Systematic Detection of Energy Regressions in Software Version Histories
Jérôme Maquoi, François Bechet, Luís Cruz et al. (+2 more)
Context: Green software engineering is emerging as a crucial response to the rising energy impact of information technology, especially in continuous development. However, challenges remain in devising automated methods to efficiently identify energy regressions across commits. In particular, little effort has been put into automatically detecting regressions at the commit level by identifying statistically significant changes in energy consumption. Objectives: This paper presents EnergyTrackr, an approach for detecting energy regressions across multiple commits. Methods: We empirically evaluated EnergyTrackr on 1,932 commits from five Java projects, assessing its ability to identify significant energy changes. We also compared a straightforward linear
algorithm that analyzes the entire commit history with a more efficient dichotomous search
algorithm. Results: The evaluation demonstrates the effectiveness of EnergyTrackr in identifying significant energy changes across commits. Our findings highlight the efficiency of the dichotomous
algorithm in uncovering overall software energy trends, achieving results comparable to the linear approach while reducing resource consumption and processing time. Conclusion: We expect EnergyTrackr to help developers accurately monitor energy regressions and improvements in their projects and optimize their source code to reduce software energy consumption.
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All authors Jérôme Maquoi, François Bechet, Luís Cruz, Benoît Vanderose, Xavier Devroey
Artificial Intelligence · Cross-listed from eess.SP
DeepPV-MRF: End-to-End Partial-Volume Fraction Estimation from MR Fingerprinting Time Series
Tianyi Ding, Yang Gao, Zhuang Xiong et al. (+4 more)
Background: Estimating subvoxel tissue fractions from
magnetic resonance fingerprinting (MRF) is challenging. Conventional dictionary matching and segmentation pipelines can propagate errors from intermediate parametric maps into tissue fraction estimates. Purpose: We introduce DeepPV-MRF, a deep learning framework that directly estimates voxel-wise tissue fractions from raw MRF signals. Methods: A cascaded spatial-temporal network was trained on subject-derived Bloch simulations with radial k-space undersampling. The network combines temporal encoding and spatial U-Net refinement to estimate cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) fractions without intermediate T1/T2 maps. We compared DeepPV-MRF with partial-volume dictionary matching (PV-DM) and pseudoinverse estimation (PV-Pinv) using simulated and in vivo data. Results: DeepPV-MRF outperformed both methods across acquisition settings. In single-shot simulations, CSF/WM/GM mean absolute errors were 0.0418/0.0744/0.0757, versus 0.1281/0.2443/0.2958 for PV-DM and 0.2021/0.2793/0.2630 for PV-Pinv. Corresponding structural similarity values were 0.960/0.916/0.922. In vivo results showed sharper anatomical boundaries and smoother tissue transitions, while cross-shot analysis indicated improved consistency across volunteers. Conclusions: DeepPV-MRF enables accurate and efficient direct tissue fraction estimation from MRF signals, particularly with limited shots. Further validation is needed before clinical deployment.
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All authors Tianyi Ding, Yang Gao, Zhuang Xiong, Feng Liu, Martijn A. Cloos, Jin Jin, Hongfu Sun
Sound
Few-Shot Adaptation for Anomalous Sound Detection at Low False-Positive Rate
Simone Lugani, Christof Pichler, Hannes Wegleiter et al. (+3 more)
Reliable fault detection is crucial for industrial machinery in large-scale facilities, where unexpected failures can cause costly downtime and production interruptions. Although acoustic monitoring provides a scalable solution for machine condition monitoring, its deployment is often constrained by high false-positive rates. We propose a two-step approach to automatic anomalous sound detection: first, when only normal operating audio samples are available, a regularized unsupervised model is deployed; then, after the first machine fault, a few-shot adaptation strategy exploits the anomalous samples acquired during the fault to improve the system. This adaptation increases the area under the precision-recall curve (AUPR) from 23.0 to 64.0 and improves detection in the low false-positive-rate region, with the partial area under the receiver operating characteristic curve (pAUROC) increasing from 39.6 to 72.8. We also show that covariance regularization improves both prediction accuracy and training stability in the unsupervised model. These results provide a practical strategy for acoustic anomaly detection systems, enabling improved accuracy and reduced false alarms.
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All authors Simone Lugani, Christof Pichler, Hannes Wegleiter, Christoph Feilmayr, Rodolfo Zunino, Edoardo Ragusa
Machine Learning
On the Limits of Representation-Based Test-Time Adaptation for Multimodal LLMs
Mikołaj Piórczyński, Wojciech Masarczyk, Damian Sójka et al. (+2 more)
Vision-
language models suffer significant accuracy drops under common corruptions. While test-time adaptation (TTA) successfully mitigates these shifts in CLIP-like contrastive models, its application to generative
multimodal large
language models (MLLMs) remains underexplored and uniquely challenging due to their sequential
token generation. In this work, we demonstrate that representation-based TTA fails to recover performance in MLLMs. Our experiments reveal that even when violating standard TTA assumptions by leveraging target data offline, sample-blind feature
alignment recovers only marginal accuracy. We attribute this failure to the nature of corruption shifts: unlike CLIP-like architectures, which benefit from a shared domain-shift direction within a single pooled classification
embedding, MLLM visual
token sequence shifts are nearly orthogonal and span the full
embedding space.These findings bound what any sample-blind, position-shared correction at a single encoder site can achieve, establishing that representation-space interventions alone might be insufficient to ensure corruption robustness in MLLMs.
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All authors Mikołaj Piórczyński, Wojciech Masarczyk, Damian Sójka, Sebastian Cygert, Bartłomiej Twardowski
Computation and Language
Frustratingly Easy Majority Voting Using Length Conditioning
Fiorenzo Parascandolo, Enver Sangineto, Xiaoyi Yu et al. (+4 more)
Majority Voting (MV) is a simple and widely used strategy which improves the
reasoning accuracy of Large
Language Models (LLMs) by allocating additional inference-time computation. Specifically, in MV multiple Chain-of-Thoughts (CoTs) are independently sampled and the most frequent final answer is selected. In this work, we propose Length-Conditioned Majority Voting (LCMV), a Test-Time Training approach which improves MV leveraging an empirical observation that has recently been established in several studies: longer CoTs tend to be associated with a lower probability of correctness. We formulate this inductive bias as a length-conditioned prior probability, which is modeled by a simple logistic function. Moreover, we adapt this prior at test time using only unlabeled testing data and without fine-tuning the LLM. Finally, the adapted prior is used to weight the vote of each sampled answer. Extensive experiments across multiple LLMs and
reasoning benchmarks show that LCMV consistently improves over both standard MV and existing length-based and confidence-based aggregation strategies, while retaining the simplicity and model-agnostic nature of MV. Our code is available at: https://github.com/aimagelab/LCMV.
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All authors Fiorenzo Parascandolo, Enver Sangineto, Xiaoyi Yu, Wenhui Tan, Jipei He, Ruihua Song, Rita Cucchiara
Numerical Analysis
Second-Order Flows for Approaching Stationary Points via the SAV-E Approach
Haifan Chen, Wei Liu, Ziqing Xie et al. (+1 more)
Second-order flows represent an emerging computational framework for approaching stationary points of variational energies, characterized by damped
hyperbolic partial differential equations. In this paper, we develop a scalar auxiliary variable (SAV) method for this class of flows, referred to as SAV-E. The resulting scheme is linear and decoupled, and we prove unconditional modified-energy stability. We further establish subsequential convergence to stationary points for the Ginzburg-Landau and conserved Swift-Hohenberg models under suitable assumptions. Numerical experiments support the analysis and demonstrate robustness and efficiency compared with representative baselines and the corresponding gradient flows.
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All authors Haifan Chen, Wei Liu, Ziqing Xie, Yifang Yuan
Machine Learning
Occupancy Modeling via Drifting: One-Step Generation Enables Direct Policy Extraction
Robin Uhrich, Marco Bagatella, Núria Armengol Urpí et al. (+2 more)
The growing interest in behavior foundation models, combined with recent advancements in generative modeling, has led to an appealing recipe for reinforcement learning from unlabeled data: pre-train a generative model to model occupancies, i.e., discounted state visitations, on reward-free trajectories, such that a policy can be easily extracted once a small reward-labeled
dataset becomes available. We decompose this recipe along three axes: (i) the degree to which the multi-modal structure of occupancies must be captured, (ii) the class of generative model, and (iii) the method used for policy extraction. Along each axis, we consider existing choices, propose simpler alternatives, and validate each decision experimentally. This analysis culminates in a novel, streamlined method that leverages drifting, a modern one-step generative sampler, to model the occupancy. We show that this enables policy extraction via direct differentiation through the occupancy model, and bypasses the need for critic
distillation. Overall, we observe that this nimble setup leads to improvements in performance on standard
benchmarks.
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All authors Robin Uhrich, Marco Bagatella, Núria Armengol Urpí, Pavel Kolev, Georg Martius
Computer Vision
BluePixel: Toward Native 4K Agentic Image Editing
Shanyan Guan, Shiyu Li, Xin Li et al. (+17 more)
Instruction-driven image editing has reached native 4K, yet how such editors are trained remains undisclosed, and pretraining one from scratch is beyond most teams. The task itself compounds the difficulty: a single end-to-end editor cannot make precise parametric adjustments, match specialized experts, or interpret the vague instructions users actually write. We argue that 4K editing is a system problem rather than a single-model problem, and present BluePixel, which couples a native-4K editing model with an
agent harness; the complete training recipe is disclosed in this paper. The model makes 4K training practical: it turns a 1K-pretrained editor into a native-4K one through progressive VAE adaptation that keeps 4K training as affordable as 1K, absorbs a long tail of vertical editing skills through continual learning, and refines the aesthetic-fidelity trade-off with reinforcement learning.
Distillation via source-anchored target projection (SATP) then compresses 50 denoising steps to 4, completing a 4K edit in 8.7 s on a single Kunlunxin P800 XPU while preserving task success rate and background consistency. The harness, BluePixel
Agent, makes editing controllable: it decomposes user instructions into specialist and general operations, verifies every result with the same reward model that guided training, and retries within bounded attempts. On VINS-4KEval++ (3,305 native-4K cases), BluePixel ranks second among seven models, behind only GPT Image 2 in non-edited-region consistency.
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All authors Shanyan Guan, Shiyu Li, Xin Li, Zhizhou Chen, Yunzhe Xu, Yuehao Liu, Ruixue Shen, Shiju Zhang, Min Jin, Qiang Liu, Junbo Liu, Penggang Shi, Sheng Sun, Xiao Wang, Yong Zhang, Zhongang Qi, Wei Li, Chao Ma, Ying Tai, Yanhao Ge
Computer Vision
CoDy: Codec Dynamics as Complementary Priors for Unsupervised Temporal Action Localization
Tianyu Yan, Qingyi Yang, Qin Yang et al. (+5 more)
Unsupervised temporal action localization (UTAL) aims to identify action categories and localize their temporal boundaries without annotation supervision. Existing methods typically derive supervision for both tasks solely from RGB and optical-flow representations, often resulting in noisy category assignments and inaccurate temporal boundaries. In this work, we reveal an overlooked property of
video codecs: group-of-pictures quantization patterns preserve category-discriminative dynamics, while motion-vector and residual variations exhibit pronounced responses around action transitions. Based on this insight, we propose CoDy, a codec dynamics-guided framework that exploits the codec as complementary information to the visual view for UTAL. CoDy integrates a codec-enhanced view with RGB and flow through confidence-aware consensus clustering, producing reliable soft pseudo-labels. It further converts codec dynamics into a class-agnostic boundary prior, which supervises visual boundary learning and guides proposal initialization and iterative refinement. The codec branches are only used during the training stage and introduce no extra codec-side computation at inference. Extensive experiments on THUMOS14 and ActivityNet v1.3 demonstrate that CoDy consistently outperforms existing UTAL methods and achieves state-of-the-art localization performance. These results establish codec signals as effective annotation-free priors for jointly inferring which actions occur and when they occur.
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All authors Tianyu Yan, Qingyi Yang, Qin Yang, Xufei Guo, Yuan Tang, Rui Wang, Yixue Hao, Long Hu
Machine Learning
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference
Amartya Banerjee, Harlin Lee and Caroline Moosmüller
Population dynamics are often observed only as unpaired snapshots, which leave the
evolution between measurements underdetermined. Second-order regularization can constrain it, but a penalty on the accelerations of sampled particles is a prior on joint particle paths, and different particle representations of the same marginal curve can have different acceleration costs. We therefore introduce Neural Wasserstein Splines (NWSP), a sample-based method that learns a continuous population curve under a Wasserstein smoothness prior. A neural network maps latent variables to the control points of a cubic spline in time, so samples at any time come from the spline basis without numerical integration. We project the generator's velocity onto gradients of a finite dictionary of scalar potentials and differentiate this measure-dependent projection in time, obtaining a finite-dimensional surrogate for the intrinsic Wasserstein acceleration, trained jointly with distributional fit losses. For a fixed dictionary and ridge parameter, we prove that the population penalty depends only on the marginal curve and bound its sensitivity to moment-estimation errors. Learned potentials (NWSP-N) sharpen acceleration estimates. On various synthetic and single-
cell benchmarks, NWSP is highly competitive with transport and flow-based methods, ranking in the top two in 11 of 16 settings. The penalty improves on an unregularized spline in 13 of 16 settings and, while competitive with a particle-acceleration penalty, adds provable representation invariance.
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Machine Learning
SPARS: A Stability-Plasticity-Aware Region Selection Framework for Continual Learning
Teif Aldaajani, Huda Hakami and Maha A. Thafar
Continual learning aims to acquire new knowledge while retaining previously learned knowledge. However, updating shared parameters when learning new tasks often overwrites existing knowledge, causing catastrophic forgetting.
Memory replay methods mitigate catastrophic forgetting but rely on previous samples, raising
privacy,
memory, and scalability concerns. On the other hand, parameter isolation-based methods preserve learned knowledge but may limit knowledge transfer, whereas regularization promotes adaptability but remains susceptible to interference between old and new tasks. To maintain the stability-plasticity trade-off, this paper proposes Stability-Plasticity-Aware Region Selection (SPARS), a replay-free hybrid framework combining global parameter isolation with localized parameter regularization. SPARS estimates parameter importance using either the Fisher Information
Matrix (FIM) or a novel Confidence Score (CS)
estimator that leverages logit sensitivity and achieves comparable performance to FIM without requiring labelled data. Based on parameter importance, SPARS partitions parameters into three importance-aware regions namely, winning, neighboring, and non-important parameter regions. Each region receives a different adaptation strategy: selective freezing, soft regularization, or unrestricted learning based on its role in the stability-plasticity trade-off. To mitigate classifier bias in class-incremental learning, we employ Quadratic Discriminant Analysis to improve performance on previously learned classes .Experiments on CIFAR-10 and TinyImageNet demonstrate consistent improvement in average incremental accuracy over existing isolation- and regularization-based methods across task- and class-incremental learning. On TinyImageNet, SPARS achieves the best performance with 6% improvement for class-incremental learning while retaining over 60% of parameters for future tasks.
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Computation and Language
RCCM: Diagnosing Gains and Harms in Reasoning-Prompt Transfer
Yuqi Zhang and Di Zhang
A
reasoning prompt can improve one deployment and degrade another. RCCM (
Reasoning-Control Capability Mismatch) examines these reversals by pairing answers to the same questions with records of how generation ends. Across five open-weight deployments and nine strategies, aggregate accuracy hides substantial question-level gains and harms; a common-budget MATH500 evaluation examines transfer on a separate sample. On 60 additional questions, concise
reasoning improves Phi's automatic answer matching at a 2,048-
token cap across three collections, but sharply reduces Qwen's. Author-reviewed final-answer commitments give a smaller, statistically unresolved Phi gain. Reserving an answer stage also improves matching, with additional input and call costs. These results show why a
prompt benefit must be interpreted alongside its execution and evaluation: matching a reference answer need not mean delivering it, and an apparent compression benefit may admit another explanation. RCCM records this evidence for reproducible transfer comparisons.
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Computation and Language
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning
Mingyeong Kim, Gyusang Cho, Juho Lee et al. (+1 more)
Sampling multiple chain-of-thought rationales from a
language model can uncover correct solutions missed by a single attempt. However, these rationales are typically lengthy, making repeated autoregressive generation costly. Continuous latent
reasoning offers a compact alternative, but point-valued methods condition repeated answer sampling on a single rationale, which can limit exploration of diverse paths and answer coverage. We thus propose rationale distribution
distillation (RDD), training a conditional flow over fixed-length latent sequences by distilling from multiple textual
reasoning paths. Furthermore, we expand the
distillation targets through within-query mixup, interpolating between complete latent rationales for the same query. The flow nonautoregressively generates the entire latent sequence, and a frozen
language model autoregressively decodes answers from sampled rationale contexts. Empirically, the resulting distributional reasoner achieves higher answer coverage than the evaluated point-valued latent
reasoning baselines, particularly on advanced mathematical tasks.
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All authors Mingyeong Kim, Gyusang Cho, Juho Lee, Giung Nam
Sound
SEA-HELM Audio: Evaluating Multilingual ASR-LLM Pipeline Performance Across SEA-HELM Subsets
Yixian Tan, Aulia Adila, Jonathan Heng et al. (+3 more)
We introduce SEA-HELM Audio, a Southeast Asian (SEA) multilingual evaluation framework that extends the text-based SEA-HELM
benchmark to assess performance degradation in cascaded automatic
speech recognition and large
language model (ASR-LLM) pipelines. Using synthesized
speech, we compare
speech-input and text-input performance across five
languages (Chinese, English, Malay, Indonesian, and Tamil) on multiple-choice
reasoning and instruction-following tasks. We evaluate open-weight and commercial ASR-LLM systems using identical verifiers and quantify the
speech gap as the difference between text and
speech accuracy. Results show substantial degradation, with accuracy drops of 9.00 to 18.50 percentage points for multiple-choice
reasoning and 20.00 to 42.86 points for instruction following. Tamil exhibits the largest degradation, exceeding 40 percentage points. Our findings highlight the limitations of text-only
benchmarks in predicting voice-
agent reliability and demonstrate the value of SEA-HELM Audio for evaluating multilingual voice-
agent systems.
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All authors Yixian Tan, Aulia Adila, Jonathan Heng, Kunat Pipatanakul, Donghang Wu, Anocha Sutaveephamochanon
Artificial Intelligence
Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards
Tianyu Liu, Davide Belli, Bence Major et al. (+2 more)
Training LLM
agents on long-horizon interactive tasks with reinforcement learning typically relies on human-annotated tasks and reliable verification, both expensive to construct for stateful environments. Self-play offers an alternative, but must generate achievable, compositional tasks and provide feedback beyond binary outcomes. We propose Explorer-Zero, a self-play framework that jointly generates tasks and fine-grained, verifiable rewards through environment interaction. A learned proposer explores the environment, constructs tasks, and records intermediate and final states as verification targets. These checkpoints reward partial completion, while alternating proposer–solver training adapts the generated curriculum. We show theoretically that subtask rewards reduce collapse among unsuccessful rollouts when they complete different numbers of subtasks. Without human-annotated training tasks, Explorer-Zero outperforms the evaluated self-play baselines across ALFWorld, ScienceWorld, and AppWorld; on AppWorld, our 30B
agent reaches 59.67% task success, approaching 62.65% for RL on human-annotated tasks. Further analyses support task-relevant composition and benefits on complex tasks, while self-play initialization improves subsequent human-data RL across all three
benchmarks.
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All authors Tianyu Liu, Davide Belli, Bence Major, Arash Behboodi, Tribhuvanesh Orekondy
Social and Information Networks
From Content Sharing to Opinion Shaping: A Theoretical Framework for Cascade-Driven Opinion Dynamics
Elisabetta Biondi, Chiara Boldrini, Andrea Passarella et al. (+1 more)
Online social networks (OSNs) tightly couple information
diffusion and opinion formation, yet most existing models treat these processes separately. Building on the Friedkin-Johnsen on Cascades (FJC) model, which integrates opinion updating within
stochastic information cascades, we advance its analytical characterization and systematic validation. We derive an exact conditional layer-wise recursion that reveals the statistical coupling between
diffusion realizations and opinion
evolution, showing that a closed-form recursion in unconditional expectations is not available. To overcome this limitation, we introduce a mean-field decoupling approximation (MFA) that yields a tractable linear recursion for the expected opinion trajectory. We also develop scalable tree-based reductions that drastically reduce computational complexity while preserving structural
diffusion properties, and validate the analytical recursion and its approximations through extensive simulations. Using
Stochastic Block Model networks with controlled initial opinion configurations, we show that, unlike the classical Friedkin-Johnsen model, which typically leads to global homogenization, the FJC dynamics generate richer regimes of bounded assimilation and sustained polarization. Overall, this work bridges
diffusion and influence modeling, providing a theoretical foundation for understanding how cascade-driven interactions reshape collective opinion landscapes in structured social networks.
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All authors Elisabetta Biondi, Chiara Boldrini, Andrea Passarella, Marco Conti
Data Structures and Algorithms
Optimal Online Stochastic Matching
Paul Duetting, Michal Feldman and Mathieu Molina
We resolve the problem of unweighted online
stochastic matching with a known arrival law by establishing its optimal competitive ratio. For Poisson arrivals, we achieve $1-e/e^e\approx0.8206$, matching the upper bound of Chierichetti, Giacchini, Panconesi, and Vattani [ICALP 2025]. For exactly $n$ i.i.d.~arrivals, we obtain the same guarantee up to an additive $O(n^{-1/2})$ loss. Our key technical contribution is an exact correspondence between Poisson matching and exponential scheduling, preserving both online performance and the expected offline optimum. To prove the latter, we show that the distribution of the maximum of a random supermodular set function is determined by its joint distributions along chains of nested sets, a result of independent interest. This reduces the analysis to chains, where classical Poisson–exponential duality applies. The offline equivalence extends to arbitrary nonnegative edge weights. We then design a posterior allocation policy for exponential scheduling and use the correspondence to transfer its optimal competitive guarantee to online matching. Additionally, we show that the optimal competitive ratio under an unknown arrival law is strictly lower than $1-e/e^e$, establishing a strict separation between the known and unknown arrival models.
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Artificial Intelligence
Euler: Towards Human-Centered Multi-Agent Mathematical Discovery
Qiuyu Chen, Qi Wang, Liang Xu et al. (+3 more)
Mathematical research drives scientific discovery, and recent advances in AI for mathematics have shown that
language-model
agents can solve challenging problems and develop substantial mathematical proofs. Yet much of today's AI-for-mathematics work centers on obtaining a final answer. This emphasis leaves the exploration behind a result difficult for researchers to understand, while long-running investigations face fragmented context and the risk of unsupported claims propagating across
reasoning steps. We present Euler, a human-centered mathematical research system built around a shared, structured representation of the research process. Euler organizes mathematical statements, assumptions, proof dependencies, alternative approaches, failed attempts, and verification evidence into connected research graphs. This representation allows researchers to trace how results are developed and supplies
agents with persistent, task-relevant knowledge for planning, collaboration, and verification across sessions. Researchers can also select a graph node linked to a saved team state, revisit the
agents' earlier
reasoning, and explore alternative directions through continued interaction. We illustrate Euler's capabilities through three mathematical research case studies and describe the implemented system and its software validation.
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All authors Qiuyu Chen, Qi Wang, Liang Xu, Xin Jin, Baao Xie, Wenjun Zeng
Multiagent Systems
When Extremists Enter LLM Societies: Investigating Extreme Behavior in Multi-Agent Systems
Guo Chen, Huaijin Xie, Ziwen Li et al. (+5 more)
Large
language model (LLM)
agents increasingly serve as computational actors in multi-
agent social simulations. While existing work emphasizes representative personas and average-case behavior, social science shows that persistent tail actors can disproportionately shape collective dynamics. We study this regularity in controlled LLM-based multi-
agent systems (LLM-MAS). In these systems, such actors cannot be reduced to fixed scalar states:
prompts, natural-
language histories, and backbone generation mediate their behavior and influence. We first analyzed three properties of extremists in sociology and model the LLM extremist based on LLM-native perspective. We introduce EXAM, an EXtremist
Agent Modeling framework that capable of configuring extremist
agents for specific tasks and precisely controlling their population proportion, spatial distribution, interaction protocols, and measurement interfaces. Across four scenarios: congressional election, financial crisis, epidemic public opinion, and iterated prisoner's dilemma, we observe that small persistent tail groups produce measurable, backbone- and scenario-dependent collective shifts. EXAM shows how this social-science regularity manifests through
language-mediated mechanisms specific to LLM-based social simulation.
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All authors Guo Chen, Huaijin Xie, Ziwen Li, Reed Li, Yu Lu, Haibo Shi, Bingbing Xu, Junjie Huang
Machine Learning · Cross-listed from stat.ML
On the Optimisation Landscape of Overparameterised Weighted MMD
Tharrshann Jayan Logarajah, Jeremias Knoblauch and Louis Sharrock
We study the optimisation landscape of the maximum mean discrepancy (MMD) over weighted atomic measures. For the one-dimensional Gaussian kernel, we show that, for any compactly supported target distribution, sufficiently many particles ensure that every local minimum has arbitrarily small MMD. Moreover, any stationary point whose MMD remains bounded away from zero is unstable under an unweighted gradient flow over the particle locations. These results hold both when the weights are constrained to the simplex, and when they are unconstrained but optimised at each step. When the target is itself finitely supported, we further strengthen this result: beyond an explicit particle threshold, there are no spurious local minima, and every spurious stationary point is unstable. To our knowledge, these are the first finite-particle overparameterisation guarantees of their kind for weighted MMD optimisation.
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Machine Learning · Cross-listed from stat.ML
Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance
Kristoffer Hjelmen Nesland, Bjarne Grimstad, Antorweep Chakravorty et al. (+1 more)
In sectors such as energy, aquaculture and chemical processing, the effectiveness of both mechanistic and data-driven models is frequently limited by poor-quality training data, which obscures underlying physical laws and degrades predictive accuracy. Such quality issues commonly manifest as anomalies in the
dataset, ranging from sensor faults to process upsets and incorrect operator inputs. While traditional rule-based filtering can remove a subset of the anomalies, it is unable to leverage contextual information stored as unstructured text, such as engineer comments and operational logs that explain under what conditions the data was collected. We propose a text-informed data filtering method that leverages large
language models (LLMs) to integrate unstructured operational data with tabular sensor data. The method consists of two steps: (1) contextual anomaly detection by
reasoning over both data modalities, and (2) generation of a binary mask that is applied to filter the training
dataset for model development. To investigate the potential benefit, a steady-state soft-sensor regression model, namely a virtual flow meter that estimates well flow rates, is used as the downstream evaluation target. Experiments on a synthetic oil and gas
dataset show improved model performance compared to traditional rule-based filtering. Ablation studies show that textual operator comments can provide filtering-relevant signals beyond sensor data alone, with the
multimodal approach reducing downstream MAPE from 7.1% to 5.4% relative to sensor-only filtering. This work demonstrates that integrating unstructured operational knowledge with sensor data through LLMs can improve data quality for industrial machine learning applications, offering a scalable alternative to manual data curation.
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All authors Kristoffer Hjelmen Nesland, Bjarne Grimstad, Antorweep Chakravorty, Dan Sui
Systems and Control
Actuation-Aware Pinching-Antenna Placement Under Blockage and User Mobility
Javad Bagherivand
Existing placement studies for pinching-
antenna systems (PASS) usually assume that
antenna movement is instantaneous and free. In practice, actuators move at finite speeds and must pause radiation while repositioning. This makes performance depend on consecutive
antenna positions, turning placement into an edge-weighted longest-path problem on a temporal trellis. We also show that an obstacle blocks a wider range of
antenna positions as it approaches the user, causing sudden performance drops that make multi-step lookahead essential, and that once obstacle height is included every blockage event shadows at least a fixed minimum length of waveguide. We give an exact offline solution with complexity O(TMS) and bound the per-slot loss of a W-step receding-horizon policy by O(1/W). Measurements show that the required lookahead window is set by shadow width rather than by waveguide length. Across 20 random seeds, the exact policy delivers 17.7% higher max-min throughput than per-slot optimization under blockage, and matches it when no blockers are present.
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Computer Vision
OmniPoser: Flexible Human Motion Recovery in the Wild with Masked Flow Matching
Minghao Liu and Tutian Tang
Recovering 3D human motion from sparse wearable sensors is challenging due to the inherent noise, occlusion, and instability of real-world tracking signals. Existing approaches are restricted to fixed sensor configurations and, when built on
diffusion models, are computationally prohibitive for real-time deployment. We propose OmniPoser, a universal framework that recovers full-body SMPL motion from a flexible combination of heterogeneous inputs, such as 3D keypoints, sparse IMU rotations, head-mounted 6-DoF poses, or any subset thereof, within a single model. OmniPoser introduces a cross-modal masking mechanism paired with a Masked Conditioning Encoder, enabling one architecture to incorporate diverse sensor configurations through a masked design. A decoupled dual-stream generative backbone generates complete motion via Conditional Flow Matching, requiring only a single ODE integration step at inference. Across three settings and seven
benchmarks, including in-the-wild Nymeria and Ego-Exo4D
datasets, OmniPoser performs on par with state-of-the-art methods while running at over 700 FPS. The project page is available at https://jamesbridgewater51.github.io/omniposer-eccv/.
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Cryptography and Security
Lost in the Fallback: How Reliably Do Typed Decision Models Use Fallback Options?
Yuanbo Xie, Mingli Zhu, Tianyun Liu et al. (+3 more)
Typed decision models (TDMs) are increasingly used in applications such as request routing and model selection, making reliable decisions critical to system performance. Fallback options accommodate categories outside regular choices, enabling deferral to human experts or more capable models. While recent works have examined
adversarial vulnerabilities in TDMs, their ability to follow fallback rules in non-
adversarial settings remains underexplored. We introduce the Paired Fallback Robustness (PFR) Test to evaluate two aspects of fallback reliability: whether models correctly switch between regular and fallback options, and whether they make consistent decisions under logically equivalent definitions of the same rule. We evaluate open-source decision models on three
benchmarks, with controlled comparisons across architectures and parameter counts. We find that correct recognition does not reliably transfer to correct fallback options, and errors persist even when the correct category is explicitly supplied.Moreover, logically equivalent formulations of the same fallback rule can also produce substantial disagreement in model decisions. These findings reveal a crucial reliability gap in current TDMs: fallback mechanisms designed to improve reliability can instead introduce errors. It highlights faithful execution of fallback rules and consistency under equivalent definitions as essential requirements for reliable typed decision-making.
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All authors Yuanbo Xie, Mingli Zhu, Tianyun Liu, Yingjie Zhang, Haixiao Xiong, Tingwen Liu
Machine Learning
Energy Consumption Analysis of Discrete Diffusion and Autoregressive Language Models
Boris Ruf and Marcin Detyniecki
As Large
Language Models (LLMs) continue to scale, their energy consumption during inference has become a critical concern. While Autoregressive (AR) models are the industry standard,
Diffusion Language Models (DLLMs) have emerged as a promising alternative capable of parallel
token generation. In this paper, we perform a comparative energy
benchmark of
diffusion models (LLaDA-8B, Dream-7B) against AR counterparts (Llama-3.1-8B, Qwen-2.5-7B). We analyze the trade-offs between model architecture, KV-cache optimization, and Joules-per-
token efficiency. Our results show that at longer sequences (200–600
tokens),
diffusion models achieve 2.5–2.7 J/
token, approximately 2$\times$ more efficient than AR models with KV-cache (5.3–5.7 J/
token). KV-cache provides substantial benefits for AR models at longer sequences (50–70% energy reduction).
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Information Theory
Global and Local Entropy Tests for STL Files Under IEEE-754 Validity Constraints
Tonislav Troev and Borislav Stoyanov
Shannon
entropy is a standard measure of ciphertext randomness, evaluated either globally over the byte histogram of the entire ciphertext or locally as the average over non-overlapping blocks of ?xed size. While the global statistic captures only the overall byte distribution, the local statistic is also sensitive to non-randomness that is con?ned to parts of the ciphertext. Both measures are commonly assessed against reference values derived under the assumption of independent uniform bytes. However, STL encodes most numerical data as single-precision IEEE-754 ?oats, and format validity requires them to be ?nite, causing nonuniform byte probabilities and dependence between the two bytes of each exponent pair. We derive the exact mean and variance of
entropy under the valid-STL (VSTL) distribution, which assigns equal probability to every admissible sequence of triangle records, and provide second-order expansions that keep the global reference values practical to compute at large ?le sizes. Using these expansions, we establish a two-sided hypothesis test for the global
entropy statistic, whose limiting value is lowered from 8 to 7.99991479609 bits. The format constraints also change the local
entropy reference: applying the capacity energy criterion to triangle records selects a block size of 1950 bytes instead of 1936 bytes, with an exact reference mean of 7.903106732975 rather than 7.902469316647 bits. Although small, these shifts are su?cient to place the VSTL reference means outside the conventional 95% acceptance intervals at practical ?le sizes, making unadjusted
entropy tests prone to falsely rejecting ideal VSTL ciphertexts. Monte-Carlo simulations con?rm all reference values within sampling error. We therefore recommend using the format-speci?c reference values derived here to evaluate STL ciphers.
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Data Structures and Algorithms
Output-linear time enumeration of potential maximal cliques
Pierre Bergé, Vincent Limouzy and Nicolas Schivre
First defined by Bouchitté and Todinca, Potential Maximal Cliques (PMC) are a well studied notion in graph theory due to its numerous applications. The PMCs of an undirected graph $G$ are defined as the maximal cliques of the minimal triangulations of $G$. Their number can be exponential in the size of the graph. PMCs were crucial in the design of a
polynomial-time
algorithm which solves Maximum Weight Independent Set on $P_6$-free graphs (Grzesik, Klimošová, Pilipczuk, Pilipczuk, 2020). They also play a central role in the proposal of a
polynomial-time
algorithm to solve Largest Induced Subgraph of treewidth $\le t$ (Fomin, Todinca, Villanger, 2013). Last but not least, certainly one of the main application of PMCs is the existence of an exact dynamic-programming
algorithm to determine the treewidth of a graph (Fomin, Kratsch, Todinca, Villanger, 2008) which uses its list of PMCs as an input. In fact, the enumeration of PMCs stands as the bottleneck for the computation of treewidth with this method: the PMC-enumeration step takes output-quadratic time (Bouchitté, Todinca, 2002) while the dynamic-programming one takes a time linear in the number of PMCs. This two-step procedure was proven to be among the most performing approaches in practice to determine treewidth. Answering a longstanding open question, we propose an
algorithm to enumerate PMCs in output-linear time, i.e. a running time linear in the number of PMCs. This offers nice perspectives not only from the theoretical viewpoint - better running time for the PMC-based methods determining treewidth, for Minimum Fill-In and Largest Induced Subgraph of treewidth $\le t$ - but also from the practical side, with the possibility to propose top-performance implementation.
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