Machine Learning · Cross-listed from eess.SP
Equation-Free Digital Twins for Nonlinear Structural Dynamics
Mohammad Mahdi Abaei, Ahmad BahooToroody, Arttu Polojärvi et al. (+4 more)
Monitoring floating offshore wind turbines (FOWTs) under
stochastic excitation and sensor failure requires computationally efficient and physically interpretable estimators. This paper introduces an equation-free, output-only digital twin framework combining Hankel dynamic mode decomposition (Hankel-DMD), sparse-sensor reconstruction, and a
stochastic Kalman extension. A sufficient identifiability condition is derived for modal amplitudes recovered from delay-embedded measurements, while Tikhonov regularization addresses ill-conditioned sensor layouts. Correlation-domain identification improves damping estimates under
stochastic forcing and distinguishes structural modes from rotor harmonics. The framework is validated using OpenFAST simulations of an NREL 5-MW spar turbine and experimental basin-test measurements from a 1:70-scale IEA 15-MW semi-submersible. In simulations, the identified tower modes agree with established operational modal analysis methods (MAC at least 0.99), while the Kalman estimator achieves R-squared values of 0.91 to 0.99 under severe sensor sparsity. Experimental results identify tower modes at 0.430 and 0.415 Hz with MAC of 1.00 against SSI-COV and reconstruct tower-base bending moments on unseen runs with R-squared values of 0.83 to 0.89 using three of four sensors. The framework provides a computationally efficient, sensor-failure-resilient structural-state estimation layer for digital twins.
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All authors Mohammad Mahdi Abaei, Ahmad BahooToroody, Arttu Polojärvi, Heikki Remes, Ulf Tyge Tygesen, Mikko Suominen, Michael Beer
Machine Learning · Cross-listed from cs.SE
PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generation
Gourisetty Venkata Sai Koushik, Dama Aditya, Mahankali Harish Sai et al. (+3 more)
Developing effective test cases capable of thoroughly exercising large-scale software systems is inherently difficult, especially if such systems have voluminous, complex, and deeply nested source codes. In this work, we present a novel approach for generating test cases using a reinforcement learning-driven agentic framework where Proximal Policy Optimization (PPO) is coupled with an LLM engine to guide
prompt selection during test generation. Our approach consists of two phases. In Phase I, the ToT-guided optimization
agent partitions and minimizes the source code by removing redundancies without changing the functional behavior of the source code. In Phase II, a PPO-based policy network is trained to solve the problem of selecting
prompts among eight different prompting techniques, such as Boundary Value Analysis, Random Fuzzing, etc., based on the inputted 11-dimensional state vector representing the source code complexity metrics and live coverage metrics to direct the LLM engine towards exploring unvisited paths in the program. The PPO
agent receives rewards based on a combination of increases in line and branch coverages, penalties for unexplored branches, and rewards for reducing source code length. From experiments conducted on twenty
benchmark programs, it is evident that the proposed approach, PPO-LLM, outperforms CBMC, kS-LLM, and kS-LLM++ in terms of branch and line coverage in almost all cases, for various loop bound values ranging from BOUND~1 to BOUND~2000. While at BOUND~1, the coverage of branches is 100% using PPO-LLM on the PALS suite, in comparison, it is around 86.8% using kS-LLM++. This confirms that adaptive
prompt selection driven by PPO substantially outperforms static prompting strategies on PALS type programs.
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All authors Gourisetty Venkata Sai Koushik, Dama Aditya, Mahankali Harish Sai, Peddi Siddarhta, Shadab Ahmad, Vivek Yelleti
Machine Learning
Uncertainty in a Single Pass: A Closed-Form Identity for One-Step Flow Matching
Jiarui Xing, Song Wang and Jian Wang
Flow matching provides a highly effective framework for generative modeling, yet estimating the uncertainty of its generated samples remains a fundamental challenge. Existing methods rely on auxiliary variance heads, model ensembles, or iterative covariance propagation, which invariably introduce accumulated errors and structural interference along the noise-to-image trajectory. We resolve these limitations by adapting Tweedie's formula to the linear flow matching interpolant, yielding an exact, closed-form identity for the posterior covariance. Because this identity relies exclusively on the divergence of the learned velocity field, it can be evaluated post hoc on pretrained models without any architectural modifications. This analytical formulation proves exceptionally powerful for single-step generative frameworks. By computing the end-to-end posterior covariance in one forward pass, our approach completely bypasses the compounding errors inherent to sequential integration. This mechanism fundamentally enhances robustness, providing highly accurate and stable uncertainty estimates while preventing the numerical degradation typical of multi-step approximations. We validate our framework across
benchmark datasets, including CIFAR-10 and real
brain MRI scans. The resulting divergence-based uncertainty maps are highly interpretable and tightly correlated with empirical reconstruction errors. Crucially, in medical imaging, this precise localization of structural ambiguity provides essential decision support for high-stakes clinical tasks such as tumor boundary delineation and neurosurgical planning.
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Machine Learning
Interpretable experiential learning based on state history and global feedback
Anton Kolonin
A new interpretable experiential learning model based on state history and global feedback is presented. It is capable of learning a behavioral model represented by a transition graph between sets of states, with transitions attributed with utility and evidence count. This model is expected to be suitable for solving reinforcement learning problem in resource-constrained environments. The model was thoroughly evaluated on the OpenAI Gym Atari Breakout
benchmark, demonstrating performance comparable to some known neural network-based solutions.
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Machine Learning
From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity
Yee Zhing Liew, Andrew Huey Ping Tan and Anwar P.P. Abdul Majeed
Traditional
hallucination detection fails on "Stubborn
Hallucinations" - errors where LLMs are confidently wrong. We propose a
geometric solution:
Embedding-Perturbed Gradient Sensitivity (EPGS). We hypothesize that while robust facts reside in flat minima, stubborn
hallucinations sit in sharp minima, supported by brittle memorization. EPGS detects this sharpness by perturbing input
embeddings with Gaussian noise and measuring the resulting spike in gradient magnitude. This acts as an efficient proxy for the Hessian spectrum, differentiating stable knowledge from unstable memorization. Our experiments show that EPGS significantly outperforms
entropy-based and representation-based baselines, providing a robust signal for detecting high-confidence factual errors.
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Machine Learning
Fusing Urban Structure and Semantics: A Conditional Diffusion Model for Cross-City OD Matrix Generation
Bin Chen, Zhuoya Meng, Fang Yang et al. (+5 more)
Accurate modeling of commuting flows is important for urban governance, traffic planning, and resource allocation. However, the combined influence of individual intentions, geographic constraints, and social dynamics leads to considerable heterogeneity in commuting patterns, making it difficult to develop generation models that generalize across cities. To address this issue, we propose SEDAN, a Structure-Enhanced
Diffusion model conditioned on Attributed Nodes for generalizable OD
matrix generation. SEDAN models a city as an attributed graph. Each region is treated as a node with demographic and point-of-interest features, and commuting flows are modeled as weighted edges. Adjacency and distance
matrices are incorporated to characterize spatial structure. Based on this representation, we design a fusion mechanism within SEDAN to jointly model semantic information and spatial information. Regional semantic attributes are used to model latent travel demand through graph-
transformer-based node interactions, while spatial structure is injected into the generation process as explicit constraints. The adjacency
matrix guides
attention weights to strengthen interactions between neighboring regions. Meanwhile, the distance
matrix serves as a
diffusion condition to capture spatial proximity and travel impedance. The fusion of urban semantics and spatial constraints enables SEDAN to generate OD
matrices that are both behaviorally plausible and geographically coherent. Experiments on real-world OD
datasets from U.S. cities show that SEDAN achieves a 7.38% improvement in RMSE over the state-of-the-art baseline, WEDAN. It also remains robust across heterogeneous urban scenarios and varying structural patterns. Our work provides an effective and generalizable solution for commuting OD
matrix generation. The code is available at https://anonymous.4open.science/r/SEDAN.
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All authors Bin Chen, Zhuoya Meng, Fang Yang, Runkang Guo, Jingtao Ding, Yin Zhang, Chuan Ai, Zhengqiu Zhu
Machine Learning · Cross-listed from physics.flu-dyn
An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction
Shihang Zhao, Martín Saravia, Haokui Jiang et al. (+2 more)
We propose an arbitrary Lagrangian-Eulerian (ALE)-consistent machine learning framework for long-term fluid-structure interaction (FSI) prediction on deforming unstructured meshes. Specifically, the fluid dynamics are modeled by a surrogate that combines a graph neural operator (GNO) with a
vision Transformer (ViT) for spatiotemporal prediction, while a lightweight long short-term
memory (LSTM) network predicts structural kinematics at the interface. The two surrogates are coupled through a standard partitioned procedure. Most importantly, kinematic compatibility at the moving interface is enforced via an ALE-consistent boundary-correction step that updates the fluid-side interface velocity with the predicted structural velocity at each coupling update, thereby improving near-interface accuracy and long-term rollout stability. To mitigate autoregressive error accumulation, a two-stage training strategy is adopted, consisting of single-step supervised pretraining followed by long-term autoregressive fine-tuning. The proposed framework is validated on the
benchmark problem of a flexible beam vibration in the wake of a cylinder. Results demonstrate accurate phase-consistent predictions over long rollouts and robust generalization under inlet-profile variations in both interpolation and extrapolation settings. Systematic ablation studies further assess the respective contributions of the ViT module, ALE-consistent boundary correction, and long-term training to predictive accuracy and rollout robustness.
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All authors Shihang Zhao, Martín Saravia, Haokui Jiang, Zhiyang Xue, Shunxiang Cao
Machine Learning
EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems
Luan Pham, Victor Nicolet, Joey Dodds et al. (+2 more)
Anomaly detection and localization (ADL) is critical for maintaining reliability and availability in cloud systems. Recent ADL developments focus on metric and log data, leaving event data unexplored. To address this gap, we propose EventADL, the first open-box event-based ADL framework for cloud-based service systems. To motivate the design of our framework, we conduct a systematic analysis on 520 real-world incidents, and provide insights into how anomalies and their root causes manifest through event data. EventADL has three phases: offline training, online anomaly detection, and root cause localization. During the training phase, EventADL first learns Event Semantic Patterns (ESPs), which capture normal interactions between system entities using historical event data, and then learns Event Frequency Patterns (EFPs), which capture the normal frequency of known ESPs. In the online anomaly detection phase, any data in the event stream that deviates significantly from either pattern is identified as anomalous. For localization, EventADL constructs an Intervention Graph that models the relationships between recent system interactions and the detected anomalies for automatic root cause localization. The framework is designed to operate efficiently with unlabeled data and to produce interpretable anomalies with their corresponding root causes. Our evaluation on three real cloud service systems and two real-world incidents demonstrates that EventADL outperforms existing methods, achieving F1-scores of at least 90% for anomaly detection and 100% top-3 accuracy in root cause localization.
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All authors Luan Pham, Victor Nicolet, Joey Dodds, Hui Guan, Daniel Kroening
Machine Learning
Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding
An Huang, Junggab Son and Zuobin Xiong
Diffusion models have become the foundation of modern generative systems, with most research focusing primarily on improving generation efficiency and output quality. The timestep
embedding component is a crucial part of the
diffusion pipeline, which provides a temporal conditioning signal to the denoising network, enabling it to adapt its predictions across different noise levels throughout the process. Despite their potential to contain substantial information, timestep
embeddings remain underexplored in current research, especially for security risks and reliable provenance. To fill this gap, we introduce Shadow Timestep
Embedding (STE), a novel mechanism that investigates the underutilized temporal space for malicious information injection into
diffusion models. In particular, when zooming in on the timestep
embedding space, we find that different timesteps exhibit distinct representational capabilities that can encode side-channel information. Moreover, such encoded information can be utilized for attack and defense purposes through the scheduler interface. We present a theoretical analysis of timestep
embeddings as position-encoding mappings and derive a mutual coherence evaluation that explains the separability of disjoint timestep intervals. Our findings reveal the
diffusion model's timestep as a powerful side channel for carrying dedicated information, motivating new directions for
adversarial generative modeling by understanding the temporal dimension.
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Machine Learning
Structured Analytic Coherent Point Drift for Non-Rigid Point Set Registration
Wei Feng and Haiyong Zheng
Coherent Point Drift (CPD) is a representative probabilistic framework for unsupervised non-rigid point set registration. Its standard non-rigid M-step, however, relies on a point-indexed Gaussian-kernel system whose size grows with the number of moving points, making deformation estimation computationally heavy for large point sets and difficult to control in complexity during registration. To address these limitations, we propose Analytic-CPD, a new unsupervised non-rigid registration framework that gives CPD a structured analytic reformulation. Analytic-CPD preserves the CPD posterior correspondence layer, but lifts the M-step from point-indexed kernel displacement estimation to structured analytic mapping estimation. By coupling the Gaussian-mixture posterior mechanism of CPD with Structured Analytic Mappings (SAM), the method obtains a deformation model whose coefficient dimension is governed by the ambient dimension and analytic order rather than by the number of moving points. More importantly, deformation estimation is organized over an interpretable hierarchy of analytic function spaces, so the analytic order can be increased progressively as posterior correspondences become more reliable. We implement this idea through an increasing-degree continuation strategy with decreasing stage lengths: low-order analytic maps first stabilize the posterior correspondence structure, while higher-order modes later refine nonlinear residual deformation. Experiments on controlled model-matched, smooth model-mismatch, and registered human-shape data demonstrate the effectiveness and favorable accuracy–efficiency performance of Analytic-CPD.
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Machine Learning
CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
Hada Melino Muhammad, Zechen Li, Flora Salim et al. (+1 more)
Continuous Glucose Monitoring (CGM) can detect early metabolic subphenotypes (insulin resistance, IR; $\beta$-
cell dysfunction), but population-scale deployment faces two coupled problems. First, the same physiological state appears through multiple views (CGM time series, venous OGTT, Glucodensity summaries), so single-view representations fail to transfer when deployment shifts the modality or setting. Second, baselines perform inconsistently across these shifts. Both problems point to one remedy: representations that abstract away from any single view to capture higher-level temporal and distributional structure. We propose CGM-JEPA, a self-supervised pretraining framework which predicts masked latent representations rather than raw values, yielding abstraction that transfers across modalities. X-CGM-JEPA adds a masked Glucodensity cross-view objective for complementary distributional information. We pretrain on $\sim$389k unlabeled CGM readings from 228 subjects and evaluate on two clinical cohorts ($N=27$ and $N=17$ public-release subsets) across three regimes (cohort generalization, venous-to-CGM transfer, home CGM) under 20-iteration $\times$ 2-fold cross-validation. X-CGM-JEPA ranks first or second on AUROC for both endpoints across all three regimes while no baseline does, exceeding the strongest baseline by up to $+6.5$ pp in cohort generalization and $+3.6$ pp in venous-to-CGM transfer (paired Wilcoxon, $p<0.001$). Under modality shift, it matches mean AUROC while redistributing toward weaker subgroups (ethnicity AUROC gap shrinks 25-54%); on sparse in-domain venous data, the distributional view lifts label-aware clustering (ARI $+39\%$, NMI $+40\%$). Code and weights: https://github.com/cruiseresearchgroup/CGM-JEPA
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All authors Hada Melino Muhammad, Zechen Li, Flora Salim, Ahmed A. Metwally
Machine Learning
Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design
Seyed Mohammad Azimi-Abarghouyi, Mehdi Bennis and Leandros Tassiulas
Federated learning (FL) is fundamentally a distributed optimization problem executed by communicating
agents with local data, local computation, and partial system visibility. Once FL is viewed through that lens, hierarchy is not merely a scalability mechanism. It becomes the natural place to rethink how distributed optimization should be organized over real multi-tier networks. This article argues that hierarchical
federated learning (HFL) should move beyond its common framing as a communication-saving protocol and instead be viewed as an architecture-aware design framework for networked AI. The framework is organized around three coupled design axes: architectural parameters, layer-wise optimization decomposition, and layer-wise communication realization. The first axis determines the coordination geometry of learning through hierarchy depth, layer asymmetry, and layered connectivity. The second determines how the global FL objective is decomposed across layers and highlights modular multi-layer optimization as a major opportunity beyond one dominant method everywhere. The third determines how the distributed optimization is physically realized under heterogeneous communication regimes, from interference-limited lower tiers to reliable upper tiers. A central message is that, in HFL, convergence becomes architecture-dependent: it is directly shaped by the chosen hierarchy, the assigned optimization roles, and the communication mechanisms that connect them. We develop this viewpoint using large-scale wireless edge intelligence as a flagship networked AI setting, then provide a comparative perspective on flat FL, two-tier HFL, and deep HFL together with a regime-oriented design map. The resulting perspective positions HFL as a practical methodology for designing future networked AI systems.
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Machine Learning · Cross-listed from cs.CE
HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs
Jinpai Zhao, Nishant Panda, Yen Ting Lin et al. (+3 more)
We introduce HyCOP, a modular framework that learns parametric PDE solution operators by composing simple modules (advection,
diffusion, learned closures, boundary handling) in a query-conditioned way. Rather than learning a monolithic map, HyCOP learns a policy over short programs - which module to apply and for how long - conditioned on regime features and state statistics. Modules may be numerical sub-solvers or learned components, enabling hybrid surrogates evaluated at arbitrary query times without autoregressive rollout. Across diverse PDE
benchmarks, HyCOP produces interpretable programs, delivers order-of-magnitude OOD improvements over monolithic neural operators, and supports modular transfer through dictionary updates (e.g., boundary swaps, residual enrichment). Our theory characterizes expressivity and gives an error decomposition that separates composition error from module error and doubles as a process-level diagnostic.
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All authors Jinpai Zhao, Nishant Panda, Yen Ting Lin, Eirik Valseth, Diane Oyen, Clint Dawson
Machine Learning
Generating Statistical Charts with Validation-Driven LLM Workflows
Pavlin G. Poličar, Andraž Pevcin and Blaž Zupan
Generating diverse, readable statistical charts from tabular data remains challenging for LLMs, as many failures become apparent after rendering and are not detectable from data or code alone. Existing chart
datasets also rarely provide fully aligned artifacts, such as executable code,
dataset context, and question-answer pairs. We present a structured LLM-based workflow that decomposes chart generation into
dataset screening, plot proposal, code synthesis, rendering, validation-driven refinement, description generation, and question-answer generation. By incorporating rendered-output validation, the workflow addresses visualization-specific failure modes such as readability and semantic mismatch. It treats chart generation as an inspectable process rather than a one-shot
prompt-to-code task, retaining each chart with its code,
dataset context, description, and question-answer pairs. Applied to UCI
datasets, the workflow produces 1,500 charts from 74
datasets, spanning 24 chart families and paired with 30,003 question-answer pairs. We evaluate 16
multimodal LLMs (MLLMs) on these chart-question pairs. The results show that chart-syntax questions are nearly saturated, while value extraction, comparison, and
reasoning remain more challenging, illustrating the workflow's utility for diagnostic studies of chart-grounded
multimodal reasoning.
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Machine Learning
RunAgent: Interpreting Natural-Language Plans with Constraint-Guided Execution
Arunabh Srivastava, Mohammad A. Khojastepour, Srimat Chakradhar et al. (+1 more)
Humans solve problems by executing targeted plans, yet large
language models (LLMs) remain unreliable for structured workflow execution. We propose RunAgent, a multi-
agent plan execution platform that interprets natural-
language plans while enforcing stepwise execution through constraints and rubrics. RunAgent bridges the expressiveness of natural
language with the determinism of programming via an agentic
language with explicit control constructs (e.g., IF, GOTO, FORALL). Beyond verifying syntactic and semantic verification of the step output, which is performed based on the specific instruction of each step, RunAgent autonomously derives and validates constraints based on the description of the task and its instance at each step. RunAgent also dynamically selects among LLM-based
reasoning, tool usage, and code generation and execution (e.g., in Python), and incorporates error correction mechanisms to ensure correctness. Finally, RunAgent filters the context history by retaining only relevant information during the execution of each step. Evaluations on Natural-plan and SciBench
Datasets demonstrate that RunAgent outperforms baseline LLMs and state-of-the-art PlanGEN methods.
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All authors Arunabh Srivastava, Mohammad A. Khojastepour, Srimat Chakradhar, Sennur Ulukus
Machine Learning · Cross-listed from cs.CV
Make Your LVLM KV Cache More Lightweight
Xihao Chen, Yangyang Guo and Roger Zimmermann
Key-Value (KV) cache has become a de facto component of modern Large
Vision-
Language Models (LVLMs) for inference. While it enhances decoding efficiency in Large
Language Models (LLMs), its direct adoption in LVLMs introduces substantial GPU
memory overhead due to the large number of
vision tokens processed during the prefill stage. To tackle this problem, we propose LightKV, a novel approach that reduces KV cache size by exploiting the redundancy among
vision-
token embeddings. Guided by text
prompts, LightKV employs cross-modality message passing to aggregate informative messages across
vision tokens and progressively compress them during prefill. This
prompt-aware guidance distinguishes our method from prior
vision-only compression strategies. We evaluate LightKV on eight open-source LVLMs across eight public
benchmark datasets, e.g., MME and SeedBench. Experimental results demonstrate that with only 55% of the original
vision tokens, LightKV (a) halves the
vision-
token KV cache size, (b) reduces computation by up to 40%, and (c) preserves general-purpose performance while significantly outperforming existing baselines.
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Machine Learning
SAVGO: Learning State-Action Value Geometry with Cosine Similarity for Continuous Control
Stavros Orfanoudakis and Pedro P. Vergara
While representation and similarity learning have improved the sample efficiency of Reinforcement Learning (RL), they are rarely used to shape policy updates directly in the action space. To bridge this gap, a geometry-aware RL
algorithm that explicitly incorporates value-based similarity into the policy update, State-Action Value Geometry Optimization (SAVGO), is proposed. In detail, SAVGO learns a joint state-action
embedding space in which pairs with similar action-value estimates exhibit high cosine similarity, while dissimilar pairs are mapped to distinct directions. This learned geometry enables the generation of a similarity kernel over candidate actions sampled at each update, allowing policy improvement to be guided directly toward higher-value regions beyond local gradient-based updates. As a result, representation learning, value estimation, and policy optimization are unified within a single geometry-consistent objective, while preserving the scalability of off-policy actor-critic training. The proposed method is evaluated on standard MuJoCo continuous-control
benchmarks, demonstrating improvements over strong baselines on challenging high-dimensional tasks. Ablation studies are done to analyze the contributions of value-geometry learning and similarity-based policy updates.
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Machine Learning
Observable Performance Does Not Fully Reflect Adaptive System Organization: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint
Jacques Raynal, Pierre Slangen, Elsa Raynal et al. (+1 more)
In biomechanical systems, observable performance is often used as a proxy for underlying organization, although similar outputs may arise from different adaptive configurations. This study considers the vertical dimension of occlusion (VDO) as a constraint applied to an adaptive neuromechanical system. A single-case design in a patient with Parkinson's disease enabled repeated intra-individual gait observations under six occlusal probes. Three complementary analytical levels were examined: (i) an aggregated scalar score of observable performance, (ii) a conceptual dynamical systems framework, and (iii) an exploratory UMAP representation of 55 standardized biomechanical variables from 270 M1 observations. The revised Level 1 analysis showed that the relative ranking of OC2.5 and OC3 depended on score construction, while their scalar distributions remained close. The Level 3
embedding showed substantial overlap among all six probes and did not identify independently separated condition-specific clusters. OC2.5 and OC3 displayed limited centroid displacement but broad observation-level overlap. The principal result is therefore representational non-identifiability: neither the aggregated score nor the selected low-dimensional
embedding uniquely identifies an occlusal-condition-specific system state. VDO is interpreted as a constraint parameter rather than a causal determinant. The findings are exploratory, model dependent, and non causal. They do not establish distinct physiological states, an optimal VDO, clinical thresholds, or diagnostic, predictive, mechanistic, or prescriptive validity.
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All authors Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit
Machine Learning
Meritocratic Fairness via $K$-Shapley Values in Budgeted Combinatorial Bandits with Full-Bandit Feedback
Shradha Sharma, Shweta Jain and Swapnil Dhamal
We study meritocratic
fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback, where a learner selects at most $K$ arms per time step and observes only the noisy aggregate reward of the selected set. To define merit under budgeted coalition constraints, we introduce the $K$-Shapley value, an adaptation of the classical Shapley value that measures marginal contributions using only coalitions of size at most $K$. We show that the $K$-Shapley value is the unique solution concept satisfying
symmetry, linearity, null player, and $K$-efficiency axioms. We then establish an $\Omega(T^{2/3})$ lower bound on
fairness regret for monotone submodular valuation functions. We show that an explore-then-commit
algorithm MURaS (Meritocratic Uniform Random Sampling) achieves $\tilde O(T^{2/3})$
fairness regret by exploring all arms uniformly in exploration phase. To improve empirical regret, we propose IW-KSVFair, a meritocratic full-bandit
algorithm that learns a selection policy whose arm marginals are proportional to the unknown $K$-Shapley values. To correct the bias induced by adaptive sampling, IW-KSVFair uses importance-weighted estimation and mixes the adaptive set distribution with a uniform distribution to keep importance weights bounded. We prove that IW-KSVFair achieves $\tilde O(T^{2/3})$
fairness regret, matching the lower bound up to logarithmic factors. Experiments on synthetic and real-world
datasets show that IW-KSVFair achieves low cumulative
fairness regret and closely aligns empirical selection frequencies with $K$-Shapley value-based merit.
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Machine Learning
Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries
Rodolphe Barlogis, Ferhat Tamssaouet, Quentin Falcoz et al. (+1 more)
This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network, the DeepONet framework. We consider a 2D square domain with an inclusion of arbitrary boundary geometry at its center. It acts as a scatterer for an incoming harmonic wave. The aim is to learn the operator linking the geometry of the scatterer to the resulting scattered field. A signed distance function to the boundary of the inner inclusion evaluated in several points on the domain is used to encode its geometry. It serves as input for the branch part of the DeepONet architecture and local information as the input for the trunk part. This approach enables the encoding of arbitrary geometries, whether they are parameterized or not. The evaluation of the model on unseen geometries was compared to its finite element method (FEM) equivalent to test its generalization capabilities. The trained network weights implicitly embed the local physics and their interaction with the domain geometry. If the training space sufficiently covers the target evaluation space, the model can generalize accordingly. Furthermore, it can be refined to extend to another region of interest without retraining from scratch. This framework also avoids the need to remesh the domain for each geometry. The proposed approach delivers a computationally lighter surrogate model than FEM alternatives and avoids relying on FEM generated training data.
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All authors Rodolphe Barlogis, Ferhat Tamssaouet, Quentin Falcoz, Stéphane Grieu
Machine Learning · Cross-listed from cs.SE
Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring
Indraneil Paul, Goran Glavaš and Iryna Gurevych
Reward models (RMs) have become an indispensable fixture of the
language model (LM) post-training playbook, enabling policy
alignment and test-time scaling. Research on the application of RMs in code generation, however, has been comparatively sparse, with existing work largely focusing on execution feedback. This choice constrains post-training to optimizing functional correctness over self-contained executable code. In this work, we examine the training and evaluation of
multilingual, multi-criteria code RMs. To this end, we first compile Themis-CodeRewardBench, a
benchmark to evaluate code RMs across five preference dimensions (i.e., criteria) and eight programming
languages, on which we profile 50+ code, math, and general-purpose RMs. Observing the limited proficiency of current RMs beyond scoring for functional correctness, we develop Themis-CodePreference, the largest open-source collection of code preferences to date (more than 350k preference pairs), and use it to train Themis-RM, a suite of
multilingual code reward models for flexible multi-criteria scoring, ranging in size from 600M to 32B parameters. Our experiments and ablations demonstrate positive scaling trends, strong cross-lingual transfer when training on diverse preferences, and the importance of multi-criteria training for reliable code reward modeling.
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Machine Learning
NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
Sizhe Tang, Zuyuan Zhang, Mahdi Imani et al. (+1 more)
Monte Carlo Tree Search (MCTS) scales poorly in cooperative multi-
agent domains because expansion must consider an exponentially large set of joint actions, severely limiting exploration under realistic search budgets. We propose NonZero, which keeps multi-
agent MCTS tractable by running surrogate-guided selection over a low-dimensional nonlinear representation using an interaction-guided proposal rule, instead of directly exploring the full joint-action space. Our exploration uses an interaction score: single-
agent deviations are ranked by predicted gain, while two-
agent deviations are scored by a mixed-difference measure that reveals coordination benefits even when no single
agent can improve alone. We formalize candidate proposal as a bandit problem over local deviations and derive a proposal rule, NonZero, with a sublinear local-regret guarantee for reaching approximate graph-local optima without enumerating the joint-action space. Empirically, NonZero improves sample efficiency and final performance on MatGame, SMAC, and SMACv2 relative to strong model-based and model-free baselines under matched search budgets.
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All authors Sizhe Tang, Zuyuan Zhang, Mahdi Imani, Tian Lan
Machine Learning · Cross-listed from quant-ph
Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks
Emma Andrews, Nahyeon Kim and Prabhat Mishra
Quantum machine learning is a promising field for efficiently learning features of a
dataset to perform a specified task, such as classification. Interval bound propagation (IBP) is a popular certified training method in classical machine learning, where the lower and upper bounds are tracked throughout the model. These bounds are used during training to ensure that the model is certified to predict the correct label even under
adversarial perturbations. While IBP is successful in classical domain, there are limited certified training efforts in
quantum domain. In this paper, we present
quantum interval bound propagation (QIBP) to establish a certified training routine for
quantum machine learning, certifying the accuracy of models under
adversarial perturbations. We implement QIBP using both interval and affine arithmetic to explore the tradeoffs between the two implementations in terms of accuracy and other design considerations. Extensive evaluation demonstrates that the resulting certified trained models have robust decision boundaries, guaranteed to predict the correct class for the samples within the trained
adversarial robustness bounds.
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Machine Learning · Cross-listed from cs.AI
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer et al. (+27 more)
LLMs excel at predictive tasks and complex
reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to consult, or how many resources to invest. While the usefulness and feasibility of
Bayesian approaches remain unclear for LLM inference, this position paper argues that the control layer of an agentic AI system (that orchestrates LLMs and tools) is a clear case where
Bayesian principles should shine.
Bayesian decision theory provides a framework for agentic systems that can help to maintain beliefs over task-relevant latent quantities, to update these beliefs from observed agentic and human-AI interactions, and to choose actions. Making LLMs themselves explicitly
Bayesian belief-updating engines remains computationally intensive and conceptually nontrivial as a general modeling target. In contrast, this paper argues that coherent decision-making requires
Bayesian principles at the orchestration level of the agentic system, not necessarily the LLM
agent parameters. This paper articulates practical properties for
Bayesian control that fit modern agentic AI systems and human-AI collaboration, and provides concrete examples and design patterns to illustrate how calibrated beliefs and utility-aware policies can improve agentic AI orchestration.
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All authors Theodore Papamarkou, Pierre Alquier, Matthias Bauer, Wray Buntine, Andrew Davison, Gintare Karolina Dziugaite, Maurizio Filippone, Andrew Y. K. Foong, Vincent Fortuin, Dimitris Fouskakis, Jes Frellsen, Eyke Hüllermeier, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Nikita Kotelevskii, Salem Lahlou, Yingzhen Li, Fang Liu, Clare Lyle, Thomas Möllenhoff, Konstantina Palla, Maxim Panov, Yusuf Sale, Kajetan Schweighofer, Artem Shelmanov, Siddharth Swaroop, Martin Trapp, Willem Waegeman, Andrew Gordon Wilson, Alexey Zaytsev
Machine Learning · Cross-listed from math.OC
Randomized Subspace Nesterov Accelerated Gradient
Gaku Omiya, Pierre-Louis Poirion and Akiko Takeda
Randomized-subspace methods reduce the cost of first-order optimization by using only low-dimensional projected-gradient information, a feature that is attractive in forward-mode automatic differentiation and communication-limited settings. While Nesterov acceleration is well understood for full-gradient and coordinate-based methods, obtaining accelerated methods for general subspace sketches that use only projected-gradient information and can improve over full-dimensional Nesterov acceleration in oracle complexity is technically nontrivial. We develop randomized-subspace Nesterov accelerated gradient methods for smooth convex and smooth strongly convex optimization under
matrix smoothness and generic sketch moment assumptions. The key technical ingredient is a three-sequence formulation tailored to
matrix smoothness, which recovers the corresponding classical Nesterov methods in the full-dimensional case. The resulting theory establishes accelerated oracle-complexity guarantees and makes explicit how
matrix smoothness and the sketch distribution enter the complexity. It also provides a unified basis for comparing sketch families and identifying when randomized-subspace acceleration improves over full-dimensional Nesterov acceleration in oracle complexity.
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Machine Learning
Temporal Data Requirement for Predicting Unplanned Hospital Readmissions
Ramin Mohammadi, Vahab vahdat, Sarthak Jain et al. (+3 more)
With the proliferation of Electronic Health Records (EHRs), a critical challenge in building predictive models is determining the optimal historical data time window to maximize accuracy. This study investigates the impact of various observation windows ranging from the day of surgery to three years prior on predicting 30-day readmission following hip and knee arthroplasties. The
dataset encompasses both structured encounter records (over 4 million) and unstructured clinical notes (80,000) from 7,174 patients. To extract meaning from the clinical notes, we employed a suite of non neural (BOW, count BOW, TF IDF, LDA) and neural encoders (BERT, 1D CNN, BiLSTM, Average). We subsequently evaluated models utilizing clinical notes alone, structured data alone, and a combination of both modalities. Our results demonstrate that the optimal time window for unstructured clinical notes is significantly shorter than for structured data, maximum predictive performance was achieved using notes from just three to six months prior to surgery. In contrast, performance using structured data improved as the time window lengthened, but strictly plateaued after twelve months. These modality-specific temporal patterns remained consistent regardless of model complexity or encoder type. Ultimately, these findings challenge the general assumption that more historical data inherently yields better machine learning predictions, establishing targeted time-window guidelines for optimizing readmission prediction models.
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All authors Ramin Mohammadi, Vahab vahdat, Sarthak Jain, Amir T. Namin, Ramya Palacholla, Sagar Kamarthi
Machine Learning · Cross-listed from cs.NI
Zihao Ding, Beining Wu and Jun Huang
Federated Multimodal Learning (FML) trains
multimodal models across decentralized clients while keeping their image-text pairs private. However, joint
embedding training entangles forgotten knowledge across both modalities and client gradient subspaces, hindering
federated unlearning. Previous
federated unlearning approaches neither sever the cross-modal reconstruction channel mediated by bilinear coupling nor separate forget-exclusive update directions from those shared with retained clients. We identify an Anchor Principle for
federated multimodal contrastive unlearning: forgotten
alignments persist through three residual anchors arising from bilinear cross-modal coupling, principal-angle subspace
entanglement, and continued
federated updates. At the modality level, we show that bilateral displacement of both visual and
language branches closes the cross-modal reconstruction channel. Correspondingly, our method addresses subspace
entanglement through Cosine–Sine decomposition of client-update subspaces, isolating forget-exclusive directions from retain support. Moreover, we propose a direction-selective Forget Lock that bounds residual drift across rounds. Combining these strategies, we present EASE, an
Entanglement-Aware Subspace Excision framework that closes all three anchor channels under a unified design. EASE demonstrates consistent superiority across multiple
datasets and unlearning scenarios, for instance, matching the retrain reference to within 0.2 and 4.2 R@1 points on the forget and retain sides under client unlearning on Flickr30K with CLIP-B/32.
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Machine Learning
Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networks
Jiawen Chen, Qi Shao, Zhiqiang Ge et al. (+2 more)
Topological neural networks have emerged as effective tools for modeling higher-order relational structures beyond pairwise graphs, including hypergraphs, simplicial complexes, and
cell complexes. However, existing Weisfeiler-Leman type expressivity analyses are typically developed on different structural domains and rely on domain-specific neighborhood systems, making their expressive powers difficult to compare within a common formalism. In this paper, we introduce the Combinatorial Complex Weisfeiler-Leman (CCWL) framework, a unified expressive power refinement defined on combinatorial complexes. By exploiting the ability of combinatorial complexes to represent both set-type relations and part-whole hierarchies, CCWL performs topological color refinement through four structural neighborhoods: boundary, co-boundary, lower adjacency, and upper adjacency. We show that, under specified lifting maps, CCWL can simulate several domain-specific WL-type refinements, thereby providing a common theoretical baseline for analyzing topological message passing. We further study the neighborhood sufficiency problem and prove that, under explicit coverage conditions, a reduced refinement using only lower- and upper-adjacent bridge information preserves the distinguishing power of the full four-neighborhood CCWL refinement. Guided by this theoretical result, we instantiate the reduced refinement as the Combinatorial Complex Isomorphism Network (CCIN). Experiments on synthetic and real-world
benchmarks demonstrate that CCIN achieves competitive performance against representative graph and topological neural network baselines. Ablation studies and resource-efficiency analyses further support the effectiveness of the proposed lower/upper-neighborhood design.
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All authors Jiawen Chen, Qi Shao, Zhiqiang Ge, Duxin Chen, Wenwu Yu
Machine Learning · Cross-listed from stat.ML
Decentralized Proximal Stochastic Gradient Langevin Dynamics
Mohammad Rafiqul Islam and Lingjiong Zhu
We propose Decentralized Proximal
Stochastic Gradient Langevin Dynamics (DE-PSGLD), a decentralized Markov chain Monte Carlo (MCMC)
algorithm for sampling from a log-concave probability distribution constrained to a convex domain. Constraints are enforced through a shared proximal regularization based on the Moreau-Yosida envelope, enabling unconstrained updates while preserving consistency with the target constrained posterior. We establish non-asymptotic convergence guarantees in the 2-Wasserstein distance for both individual
agent iterates and their network averages. Our analysis shows that DE-PSGLD converges to a regularized Gibbs distribution and quantifies the bias introduced by the proximal approximation. We evaluate DE-PSGLD for different sampling problems on synthetic and real
datasets. As the first decentralized approach for constrained domains, our
algorithm exhibits fast posterior concentration and high predictive accuracy.
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Machine Learning
Aitchison Embeddings for Learning Compositional Graph Representations
Nikolaos Nakis, Chrysoula Kosma, Panagiotis Promponas et al. (+2 more)
Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph
embeddings are hard to interpret, offering limited insight into how learned features relate to graph structure. Many networks naturally admit a role-mixture view, where nodes are best described as mixtures over latent archetypal factors. Motivated by this structure, we propose a compositional graph
embedding framework grounded in Aitchison geometry, the canonical geometry for comparing mixtures. Nodes are represented as simplex-valued compositions and embedded via isometric log-ratio (ILR) coordinates, which preserve Aitchison distances while enabling unconstrained optimization in Euclidean space. This yields intrinsically interpretable
embeddings whose geometry reflects relative trade-offs among archetypes and supports coherent behavior under component restriction; we consider both fixed and learnable ILR bases. Across node classification and link prediction, our method achieves competitive performance with strong baselines while providing explainability by construction rather than post-hoc. Finally, subcompositional coherence enables principled component restriction: removing and renormalizing subsets preserves a well-defined geometry, which we exploit via subcompositional dimensionality removal to probe how archetype groups influence representations and predictions.
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All authors Nikolaos Nakis, Chrysoula Kosma, Panagiotis Promponas, Michail Chatzianastasis, Giannis Nikolentzos