Machine Learning
Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation
Georgios I. Orfanidis, Dimitris A. Pados, George Sklivanitis et al. (+1 more)
We consider the problems of computing the optimal rank-1 Hankel and Toeplitz-structured approximation of arbitrary
matrices under L2 and L1-norm error. Such problems arise naturally in engineered systems, including the basic few-shot signal Direction-of-Arrival (DoA) estimation problem that is of importance to modern autonomous systems applications. We develop accurate and computationally efficient structured
matrix decomposition
algorithms for both formulations and then derive analytically grounded small-sample-support DoA
estimators for practical sensing system deployments. The resulting
estimators under the L2 and L1 norms are formally shown to be maximum-likelihood optimal under white Gaussian and Laplace noise, respectively. The
estimators are further validated through extensive simulation studies and real-world data experiments in few-shot DoA inference.
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All authors Georgios I. Orfanidis, Dimitris A. Pados, George Sklivanitis, Elizabeth Serena Bentley
Machine Learning
Accelerating RL Post-Training Rollouts via System-Integrated Speculative Decoding
Hayate Iso, Tiyasa Mitra, Sudipta Mondal et al. (+15 more)
RL post-training of frontier
language models is increasingly bottlenecked by autoregressive rollout generation, making rollout acceleration a central systems challenge. Many existing efficiency methods improve throughput by changing the rollout or optimization regime, for example, through off-policy execution, replay, or lower-precision generation. We study speculative decoding as a lossless acceleration primitive for RL rollouts that preserves the target model's output distribution. We implement speculative decoding in NeMo-RL with a vLLM backend, supporting both synchronous and asynchronous pipelines and enabling speculation during RL rollouts. This benefit is realizable across speculation mechanisms, such as pretrained MTP heads, small external draft models or even techniques such as Eagle3, which are traditionally applied after RL phase. This yields a deployment path for state-of-the-art speculative decoding inside RL training. In a
reasoning post-training workload at 8B scale under synchronous RL, speculative decoding improves rollout throughput by 1.8x. Using a high-fidelity performance simulator, we project that combining speculative decoding with asynchronous RL yields up to 2.5x end-to-end training speedup at 235B scale.
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All authors Hayate Iso, Tiyasa Mitra, Sudipta Mondal, Rasoul Shafipour, Venmugil Elango, Terry Kong, Yuki Huang, Seonjin Na, Izzy Putterman, Benjamin Chislett, Maor Ashkenazi, Joseph Guman, Gerald Shen, Tugrul Konuk, Ashwath Aithal, Ritika Borkar, Ran Zilberstein, Bita Rouhani
Machine Learning · Cross-listed from cs.CL
Domain-Adapted Small Language Models for Reliable Clinical Triage
Manar Aljohani, Brandon Ho, Kenneth McKinley et al. (+2 more)
Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly variable free-text triage documentation contributes to mistriage and workflow inefficiencies. This study evaluates whether open-source small
language models (SLMs) can serve as reliable,
privacy-preserving decision-support tools for clinical triage. We systematically compared multiple SLMs across diverse prompting pipelines and found that clinical vignettes, concise summaries of triage narratives, yielded the most accurate predictions. The SLM, Qwen2.5-7B, demonstrated the strongest balance of accuracy, stability, and computational efficiency. Through large-scale domain adaptation using expert-curated and silver-standard pediatric triage data, fine-tuned Qwen2.5-7B models substantially reduced discordance and clinically significant errors, outperforming all baseline SLMs and advanced proprietary large
language models (LLMs, e.g., GPT-4o). These findings highlight the feasibility of institution-specific SLMs for reliable,
privacy-preserving ESI decision support and underscore the importance of targeted fine-tuning over more complex inference strategies.
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All authors Manar Aljohani, Brandon Ho, Kenneth McKinley, Dennis Ren, Xuan Wang
Machine Learning
Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
Zhangzhi Xiong, Haoyi Wu, You Wu et al. (+3 more)
The Probabilistic
Transformer (PT) establishes that the
Transformer's self-
attention plus its feed-forward block is mathematically equivalent to Mean-Field Variational Inference (MFVI) on a Conditional Random Field (CRF). Under this equivalence the
Transformer ceases to be a black-box neural network and becomes a programmable factor graph: graph
topology, factor potentials, and the message-passing schedule are all explicit and inspectable primitives that can be engineered. PT was originally developed for natural
language and in this report we investigate its potential for time series. We first lift PT into the Spatial-Temporal Probabilistic
Transformer (ST-PT) to repair PT's missing channel axis and weak per-step semantics, and adopt ST-PT as a shared cornerstone backbone. We then identify three distinct properties that PT/ST-PT offers as a factor-graph model and derive three Research Questions, one per property, that probe how each property can be exploited in time series: RQ1. The graph
topology and potentials are direct programmable primitives. Can this be used to inject symbolic time-series priors into ST-PT through structural graph modifications, especially under data scarcity and noise? RQ2. The CRF's factor
matrices are the operator's potentials. Can an external condition program these factor
matrices on a per-sample basis, so that conditional generation becomes structural rather than feature-level modulation of a fixed one? RQ3. Each MFVI iteration is a
Bayesian posterior update on the factor graph. Can this turn the latent transition of latent-space AutoRegressive (AR)
forecasting from an opaque MLP into a principled posterior update, and can a CRF teacher distill its latents into the AR student to counter cumulative error? We give one empirical study per question. Together, these three studies position ST-PT as a programmable framework for time-series modeling.
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All authors Zhangzhi Xiong, Haoyi Wu, You Wu, Shuqi Gu, Kan Ren, Kewei Tu
Machine Learning · Cross-listed from cs.AI
FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards
Zhixin Han, Yanzhi Zhang, Chuyang Wei et al. (+11 more)
Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using large
language model-based
agent systems, and it is important for building
agents that can continually learn from the real world. It can provide a large number of prediction questions grounded in diverse real-world events, while preventing answer leakage. To leverage the advantages of future prediction, we present FutureWorld, a live agentic reinforcement learning environment that closes the training loop between prediction, outcome realization, and parameter updates. Specifically, we modify and extend verl-tool, resulting in a new framework that we call verl-tool-future. Unlike standard reinforcement learning training frameworks that rely on immediate rewards, verl-tool-future stores prediction-time rollouts, backfills rewards after real-world outcomes become available, and then replays the completed trajectories for policy update. Across three open-source
agents, successive FutureWorld training rounds lead to consistent improvements in prediction accuracy, probabilistic scoring, and calibration, demonstrating that delayed real-world outcome feedback can serve as an effective reinforcement learning signal.
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All authors Zhixin Han, Yanzhi Zhang, Chuyang Wei, Maohang Gao, Xiawei Yue, Kefei Chen, Yu Zhuang, Haoxiang Guan, Jiyan He, Jian Li, Yitong Duan, Yu Shi, Mengting Hu, Shuxin Zheng
Machine Learning · Cross-listed from cs.CV
CurEvo: Curriculum-Guided Self-Evolution for Video Understanding
Guiyi Zeng, Junqing Yu, Yi-Ping Phoebe Chen et al. (+3 more)
Recent self-
evolution frameworks for
video understanding have demonstrated the potential for autonomous improvement from unlabeled
videos. However, existing approaches often suffer from weakly controlled optimization and capability progression because they lack structured guidance for generating, evaluating, and reusing supervision throughout the iterative learning process. We present CurEvo, a curriculum-guided self-
evolution framework that organizes this loop as a competence-aware progression. Curriculum-Guided Self-
Evolution maintains a competence-aware curriculum state and cumulative training pool across iterations. Within this loop, Multi-Dimensional Question Generation supplies complementary supervision, from directly verifiable structural questions for perception to type-conditioned questions for recognition and understanding. Type-Adaptive Evaluation separately assesses question quality and answer reliability with criteria matched to each type's evidence; its scores determine which pairs enter the pool and how the next curriculum state is updated. Across seven backbones and four VideoQA
benchmarks, CurEvo consistently improves
benchmark accuracy and
benchmark semantic score. For Qwen2.5-VL(7B), accuracy rises from 67.84% to 69.91% on ActivityNet-QA and from 76.94% to 79.61% on NExT-QA. CurEvo thus establishes self-generated supervision as a stable route to autonomous
video understanding without task-specific human QA annotations during self-
evolution.
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All authors Guiyi Zeng, Junqing Yu, Yi-Ping Phoebe Chen, Chen Xu, Wei Yang, Zikai Song
Machine Learning · Cross-listed from quant-ph
Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification
Ralntion Komini, Aikaterini Mandilara, Georgios Maragkopoulos et al. (+1 more)
We investigate variational
quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the
quantum circuit defines a nonlinear data
embedding while the readout determines how this representation is exploited. Using the EuroSAT-MS
dataset, we perform a systematic one-vs-one evaluation across all class pairs under a controlled experimental protocol, comparing classical baselines (logistic regression, SVMs, neural networks) with VQCs employing both linear readout and
quantum-kernel SVM strategies. Our results show that, while VQCs with linear readout do not outperform strong classical baselines such as RBF-SVM, the same trained
quantum feature map can significantly improve performance when reused within a kernel-based decision framework. A
qubit-count sweep further reveals saturation effects consistent with the mismatch between exponential
Hilbert space dimension and linear parameter scaling. Overall, our findings highlight that the effectiveness of
quantum models depends critically on the interplay between representation and readout, and that meaningful gains may arise from combining learned
quantum feature maps with classical decision mechanisms rather than seeking direct replacement of classical models.
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All authors Ralntion Komini, Aikaterini Mandilara, Georgios Maragkopoulos, Dimitris Syvridis
Machine Learning · Cross-listed from stat.ML
Laplace Approximation for Bayesian Tensor Network Kernel Machines
Albert Saiapin and Kim Batselier
Uncertainty estimation is essential for robust decision-making in the presence of ambiguous or out-of-distribution inputs. Gaussian Processes (GPs) are classical kernel-based models that offer principled uncertainty quantification and perform well on small- to medium-scale
datasets. Alternatively, formulating the weight space learning problem under tensor network assumptions yields scalable tensor network kernel machines. However, these assumptions break Gaussianity, complicating standard probabilistic inference. This raises a fundamental question: how can tensor network kernel machines provide principled uncertainty estimates? We propose a novel
Bayesian Tensor Network Kernel Machine (LA-TNKM) that employs a (linearized) Laplace approximation for
Bayesian inference. A comprehensive set of numerical experiments shows that the proposed method consistently matches or surpasses Gaussian Processes and
Bayesian Neural Networks (BNNs) across diverse UCI regression
benchmarks, highlighting both its effectiveness and practical relevance.
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Machine Learning · Cross-listed from cs.NE
Evolutionary feature selection for spiking neural network pattern classifiers
Michal Valko, Nuno C. Marques and Marco Castelani
This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model is presented as an alternative to the basic multi-layer perceptron model. An evolutionary procedure previously applied to the simultaneous solution of feature selection and neural network training on standard multi-layer perceptrons is extended with JASTAP model. Preliminary results on IRIS standard data set give evidence that this extension allows the use of smaller neural networks that can handle noisier data without any degradation in classification accuracy.
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Machine Learning · Cross-listed from cs.IR
The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems
Pedro R. Pires, Gregorio F. Azevedo, Rafael T. Sereicikas et al. (+2 more)
With the increasing availability of online information, recommender systems have become an important tool for many web-based systems. Due to the continuous aspect of recommendation environments, these systems increasingly rely on contextual multi-armed bandits (CMAB) to deliver personalized and real-time suggestions. A critical yet underexplored component in these systems is the representation of user state, which typically encapsulates the user's interaction history and is deeply correlated with the model's decisions and learning. In this paper, we investigate the impact of different
embedding-based state representations derived from
matrix factorization models on the performance of traditional CMAB
algorithms. Our large-scale experiments reveal that variations in state representation can lead to improvements greater than those achieved by changing the bandit
algorithm itself. Furthermore, no single
embedding or aggregation strategy consistently dominates across
datasets, underscoring the need for domain-specific evaluation. These results expose a substantial gap in the literature and emphasize that advancing bandit-based recommender systems requires a holistic approach that prioritizes
embedding quality and state construction alongside algorithmic innovation. The source code for our experiments is publicly available on https://github.com/UFSCar-LaSID/bandits_blind_spot.
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All authors Pedro R. Pires, Gregorio F. Azevedo, Rafael T. Sereicikas, Pietro L. Campos, Tiago A. Almeida
Machine Learning · Cross-listed from cs.AI
SciHorizon-DataEVA: An Agentic System for AI-Readiness Evaluation of Heterogeneous Scientific Data
Dianyu Liu, Chuan Qin, Xi Chen et al. (+6 more)
AI-for-Science (AI4Science) is increasingly transforming scientific discovery by
embedding machine learning models into prediction, simulation, and hypothesis generation workflows across domains. However, the effectiveness of these models is fundamentally constrained by the AI-readiness of scientific data, for which no scalable and systematic evaluation mechanism currently exists. In this work, we propose SciHorizon-DataEVA, a novel agentic system to scalable AI-readiness evaluation of heterogeneous scientific data. At the evaluation-criteria level, we introduce the Sci-TQA2 principles, which organize AI-readiness into four complementary dimensions: Governance Trustworthiness, Data Quality, AI Compatibility, and Scientific Adaptability. Each dimension is decomposed into measurable atomic elements that enable fine-grained and executable assessment. To operationalize these principles at scale, we develop Sci-TQA2-Eval, a hierarchical multi-
agent evaluation approach orchestrated through a directed, cyclic workflow. Our Sci-TQA2-Eval dynamically constructs
dataset-aware evaluation specifications by combining lightweight
dataset profiling, applicability-aware metric activation, and knowledge-augmented planning grounded in domain constraints and
dataset-paper signals. These specifications are executed through an adaptive, tool-centric evaluation mechanism with built-in verification and self-correction, enabling scalable and reliable assessment across heterogeneous scientific data. Extensive experiments on scientific
datasets spanning multiple domains demonstrate the effectiveness and generality of SciHorizon-DataEVA for principled AI-readiness evaluation.
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All authors Dianyu Liu, Chuan Qin, Xi Chen, Xiaohan Li, Wenxi Xu, Yuyang Wang, Xin Chen, Yuanchun Zhou, Hengshu Zhu
Machine Learning
Electricity price forecasting across Norway's five bidding zones in the post-crisis era
My Thi Diem Phan, Trung Tuyen Truong, Hoai Phuong Ha et al. (+1 more)
Norway's electricity
market is heavily dominated by hydropower, but the 2021-2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of
forecasting models calibrated on historical data. Despite the critical need for updated models, a unified
benchmark evaluating feature contributions across all structurally diverse Norwegian bidding zones remains lacking. Here we present a comprehensive evaluation of one-step-ahead
forecasting of the Nord Pool
market across all five Norwegian bidding zones. We constructed a
multimodal hourly
dataset spanning 2019-2025 and evaluated eight
forecasting model families, including Light Gradient Boosting Machine (LightGBM), autoregressive models with exogenous variables, and advanced deep learning architectures, using a strictly causal test set. We implemented robust rolling-origin backtesting, leave-one-group-out feature ablation, and conditional regime analysis to dissect model performance and feature utility. Our results show that LightGBM achieves the best performance in every zone, with mean absolute error ranging from 1.60 to 5.58 euros per megawatt-hour, while a ridge-regularized autoregressive model with exogenous variables remains a highly competitive linear
benchmark in northern zones. Feature ablation reveals that models relying solely on lagged prices and calendar variables achieve high accuracy and often match or closely approach the performance of the full
multimodal model. However, conditional regime analysis demonstrates that external features like reservoir levels and gas prices remain crucial to stratify forecast errors, which consistently increase under stressed
market regimes. This highlights the practical value of model interpretability and regime awareness for decision makers facing structural changes in
market dynamics.
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All authors My Thi Diem Phan, Trung Tuyen Truong, Hoai Phuong Ha, Dat Thanh Nguyen
Machine Learning · Cross-listed from nlin.CD
Inferring bifurcation diagrams of two distinct chaotic systems by a single machine
Jianmin Guo, Yao Du, Yizhen Yu et al. (+2 more)
We propose a dual-channel reservoir-computing scheme for inferring the dynamics of two distinct chaotic systems with a single machine. By augmenting a standard reservoir with a system-label channel and a parameter-control channel, the machine can be trained from time series collected from a few sampled states of the two systems. We show that the trained machine not only predicts the short-time
evolution of the sampled states, but also reproduces the long-term statistical properties of unseen states, thereby enabling reconstruction of the bifurcation diagrams of both systems from partial observations. The effectiveness of the scheme is demonstrated for the Lorenz and Rössler systems in numerical simulations and for the Chua and Rossler
circuits in experiments. Functional-network analysis further shows that the two target systems are encoded by distinct dynamical patterns in the reservoir. These results extend multifunctional and parameter-aware reservoir computing, and provide a route to data-driven inference of multiple nonlinear systems using a single machine.
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All authors Jianmin Guo, Yao Du, Yizhen Yu, Yong Zou, Xingang Wang
Machine Learning
Who Trains Matters: Federated Learning under Enrollment and Participation Selection Biases
Gota Morishita
Federated learning (FL) trains a shared model from updates contributed by distributed clients, often implicitly assuming that contributing clients are representative of the target population. In practice, this representativeness assumption can fail at two distinct stages, inducing selection bias. First, eligibility rules such as device constraints, software requirements, or user consent determine which clients are ever enrolled and reachable for training, inducing enrollment bias. Second, among enrolled clients, user and system factors such as battery state, network status, and local time determine which clients participate in each communication round, inducing participation bias. Although existing work has largely addressed round-level participation bias, it has paid far less
attention to population-level enrollment bias, which can induce a persistent mismatch between the training objective and the target-population objective. We formalize FL under a two-stage selection model and derive FedIPW, an inverse-probability-weighted aggregation scheme that recovers the target-population mean update under standard ignorability and positivity assumptions. Because client-level
covariates are often unavailable for non-enrolled clients, we also introduce a limited-information aggregate-calibration extension that uses known target-population summaries to reweight the enrolled sample, partially correcting enrollment bias. We further provide an
algorithm-agnostic optimization analysis under residual weighting error and show that incomplete selection correction can induce a non-vanishing bias floor. Finally, experiments on synthetic
federated logistic regression validate the predicted objective mismatch and show that enrollment correction reduces target-population error under two-stage selection.
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Machine Learning
PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertainty
Marcus Haywood-Alexander, Gregory Duthé and Eleni Chatzi
Digital twins provide a powerful paradigm for diagnostic and prognostic tasks in the monitoring and control of engineered systems; however, their deployment for complex structures remains challenged by model-form uncertainty, arising from unknown nonlinear dynamics, and by sparse sensing. These limitations hinder reliable online state estimation using either purely physics-based or purely data-driven approaches. This work introduces the Physics-Guided Graph Neural ODE (PiGGO) framework, a physics-informed, graph-based
Bayesian state estimation approach in which a learned graph neural ordinary differential equation (GNODE) serves as the continuous-time state-transition model within an extended Kalman filter. The graph representation explicitly defines the system state-space, while physics-guided inductive biases encode known structural relationships and constrain the learning of nonlinear dynamics. By integrating graph-native learned dynamics with recursive
Bayesian filtering, the proposed PiGGO framework enables online virtual sensing and uncertainty-aware state estimation for nonlinear systems with unknown model form, while maintaining generalisation across topologically similar structures. Numerical case studies demonstrate improved robustness to model uncertainty and measurement noise, outperforming both open-loop graph neural models and conventional filtering approaches in online prediction tasks.
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Machine Learning
PAINT: Partial-Solution Adaptive Interpolated Training for Self-Distilled Reasoners
Zhiquan Tan and Yinrong Hong
Improving large
language model (LLM)
reasoning requires supervision that is both aligned with the model's own test-time states and informative at the
token level. Reinforcement learning with verifiable rewards provides on-policy exploration but offers sparse, high-variance credit; supervised fine-tuning and
distillation provide dense targets but often train on fixed trajectories or rely on stronger teachers. Recent privileged on-policy self-
distillation explores a middle ground by scoring student rollouts with the same model under verified solution context. We revisit this setting through a contextual re-scoring lens: for
reasoning, the important choices are not only whether privileged context is available, but how much of it should be revealed and where its distribution should shape the student. We propose PAINT (Partial-solution Adaptive INterpolated Training), which masks the verified solution according to rollout-reference overlap and applies a small energy-space interpolation on a sparse set of
entropy-mismatch
token positions. Across competition-level math
benchmarks, PAINT consistently improves over a strong prior on-policy self-
distillation baseline at all three Qwen3 scales. On Qwen3-8B, it raises macro Avg@12 by 2.1 points over this prior baseline and 2.9 points over GRPO.
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Machine Learning
Advancing multi-site emission control: A physics-informed transfer learning framework with mixture of experts for carbon-pollutant synergy
Yuxuan Ying, Hanqing Yang, Kaige Wang et al. (+6 more)
Municipal solid waste incineration (MSWI) converts urban waste to energy but simultaneously emits carbon dioxide, carbon monoxide and multiple regulated air pollutants whose formation is tightly coupled within a single combustion system. Controlling these emissions across a network of diverse facilities poses a fundamentally different challenge from optimising a single plant: data-driven models trained at one site capture local statistical patterns that rarely survive transfer to another, because they lack the physical constraints and regime-level structure needed to generalise. Here we show that shared emission-control relationships can be identified across heterogeneous MSWI plants when physical conservation laws, operating-regime heterogeneity and carbon-pollutant coupling are treated jointly. We develop a carbon-pollutant mixture-of-experts (CPMoE) model that routes process observations through regime-specific expert networks under conservation-based regularisation, and combine it with physics-informed transfer learning to adapt a reference model to new facilities. Across 13 plants, CPMoE predicts six major pollutants and a composite system-level risk index with source-domain R2 of 0.668-0.904 and 0.666-0.970, respectively; after transfer to 12 target plants these values remain 0.661-0.842 and 0.610-0.841. Expert-utilisation patterns show that adaptation proceeds through structured regime re-weighting rather than re-learning from scratch.
Embedding the transferred model in an offline digital twin and screening candidate operating adjustments against historical process records yields consistent risk-index reductions of 3.6-6.3% with simultaneous pollutant co-reductions in 94-100% of evaluated samples. These findings suggest a practical route toward transferable, system-level decision support for carbon-pollutant co-control in heterogeneous waste-to-energy networks.
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All authors Yuxuan Ying, Hanqing Yang, Kaige Wang, Yu Hu, Zhiming Zheng, Yunliang Jiang, Xiaoqing Lin, Xiaodong Li, Jun Chen
Machine Learning · Cross-listed from eess.SY
Learning to Route Electric Trucks Under Operational Uncertainty
Stavros Orfanoudakis, Ziyan Li, Ruixiao Yang et al. (+4 more)
Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumption, and competition for shared charging infrastructure. These features make electric truck routing a coupled logistics and energy problem, limiting the practicality of heuristics-based methods and rendering them computationally infeasible at scale. This paper proposes a learning-based framework for the
stochastic electric truck routing under charging constraints and operational uncertainty. The problem, solved by Reinforcement Learning, is formulated as an event-driven semi-Markov decision process with shared charging resources,
stochastic travel and energy requirements, and realistic nonlinear fast-charging behavior. To support learning in this setting, a graph-based representation of system state and feasible decisions is introduced, together with a rule-based action mask that restricts policies to operationally admissible actions; thus, improving training efficiency. Building on this formulation, an event-driven simulation environment is developed that supports both Reinforcement Learning and benchmarking against heuristic and mathematical programming baselines. Computational experiments across a range of fleet sizes show that the proposed learning-based
algorithm consistently outperforms baselines and attains performance close to optimization
benchmarks in many settings, while preserving high success rates under charging congestion and uncertainty.
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All authors Stavros Orfanoudakis, Ziyan Li, Ruixiao Yang, Nikolay Aristov, Pedro P. Vergara, Chuchu Fan, Elenna Dugundji
Machine Learning · Cross-listed from stat.ML
Deep-testing: the case of dependence detection
Gery Geenens, Pierre Lafaye de Micheaux and Ivan Muyun Zou
Deep learning methods have proved highly effective for classification and image recognition problems. In this paper, we ask whether this success can be transferred to hypothesis testing: if a neural network can distinguish, for example, an image of a handwritten digit from another, can it also distinguish an "image of a sample" (such as a scatter plot) generated under a given statistical model from one generated outside that model? Motivated by this idea, we propose a novel procedure called deep-testing, which approaches the classical inferential problem of hypothesis testing through deep learning. More specifically, the test statistic is a classification map learned by a deep neural network from simulated data satisfying the null and alternative hypotheses, leveraging its strong discriminating power to construct a highly powerful test. As a proof of concept, we apply deep-testing to the problem of independence testing, arguably one of the most important problems in statistics. In a large-scale simulation study, deep-testing achieves the highest overall power against nineteen competing methods across a broad range of complex dependence structures, confirming the viability of the proposed approach.
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Machine Learning · Cross-listed from cs.DC
FloatSOM: GPU-Accelerated, Distributed, Topology-Flexible Self-Organizing Maps
Tony Xu, Sarah Klamt, Katherine Turner et al. (+3 more)
GPU-accelerated Self-Organizing Map (SOM) implementations are among the most competitive options for large-scale SOM analysis, but growing
dataset sizes increasingly challenge their practical use because workloads no longer fit cleanly within device-
memory limits. We introduce FloatSOM, a SOM framework for scalable training and deployment that supports multi-GPU execution, out-of-
memory disk-backed streaming, and novel
topologies beyond regular
lattices. We evaluate FloatSOM on 14 synthetic and real
benchmark datasets together with controlled speed scaling
benchmarks, and show that these improved
topologies, combined with
topology-aware hyperparameter fine-tuning, yield lower quantization error than current state-of-the-art SOM baselines. FloatSOM also sustains this performance at large scale with high-throughput distributed execution; in the largest
benchmark, it trains a 1024-node SOM network on 1,000,000,000 samples with 50 features in 6.16 minutes on 8 GPUs across two separate high-performance-computing nodes.
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All authors Tony Xu, Sarah Klamt, Katherine Turner, Anne Brustle, Felix Marsh-Wakefield, Givanna Putri
Machine Learning · Cross-listed from cs.CL
TLPO: Token-Level Policy Optimization for Mitigating Language Confusion in Large Language Models
Jinho Choo, JunSeung Lee, Jimyeong Kim et al. (+3 more)
Large
language models (LLMs) demonstrate strong
multilingual capabilities, yet often fail to consistently generate responses in the intended
language, exhibiting a phenomenon known as
language confusion. Prior mitigation approaches based on sequence-level fine-tuning, such as DPO, ORPO, and GRPO, operate at the level of entire responses and can lead to unintended degradation of general model capabilities, motivating the need for more fine-grained alternatives. To address this, we introduce
Token-Level Policy Optimization (TLPO), a fine-tuning framework designed to mitigate
language confusion through localized,
token-level updates. TLPO identifies error-prone positions, explores alternative candidate
tokens, and updates the policy using a tailored objective to suppress error-inducing outputs at a granular level. This selective intervention enables effective mitigation of
language confusion without compromising the model's general abilities. Experiments on multiple
multilingual LLMs across diverse
languages demonstrate that TLPO significantly outperforms baselines in improving
language consistency while preserving downstream task accuracy.
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All authors Jinho Choo, JunSeung Lee, Jimyeong Kim, Yeeho Song, S. K. Hong, Yeong-Dae Kwon
Machine Learning
Large-scale semi-supervised learning with online spectral graph sparsification
Daniele Calandriello, Alessandro Lazaric and Michal Valko
We introduce Sparse-HFS, a scalable
algorithm that can compute solutions to SSL problems using only O(n polylog(n)) space and O(m polylog(n)) time.
Machine Learning · Cross-listed from cs.AI
AGEL-Comp: A Neuro-Symbolic Framework for Compositional Generalization in Interactive Agents
Mahnoor Shahid and Hannes Rothe
Large
Language Model (LLM)-based
agents exhibit systemic failures in compositional generalization, limiting their robustness in interactive environments. This work introduces AGEL-Comp, a neuro-symbolic AI
agent architecture designed to address this challenge by grounding actions of the
agent. AGEL-Comp integrates three core innovations: (1) a dynamic Causal Program Graph (CPG) as a world model, representing procedural and causal knowledge as a directed hypergraph; (2) an Inductive Logic Programming (ILP) engine that synthesizes new Horn clauses from experiential feedback, grounding symbolic knowledge through interaction; and (3) a hybrid
reasoning core where an LLM proposes a set of candidate sub-goals that are verified for logical consistency by a Neural
Theorem Prover (NTP). Together, these components operationalize a deduction–abduction learning cycle: enabling the
agent to deduce plans and abductively expand its symbolic world model, while a neural adaptation phase keeps its
reasoning engine aligned with new knowledge. We propose an evaluation protocol within the Retro Quest simulation environment to probe for compositional generalization scenarios to evaluate our AGEL
agent. Our findings clearly indicate the better performance of our AGEL model over pure LLM-based models. Our framework presents a principled path toward
agents that build an explicit, interpretable, and compositionally structured understanding of their world.
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Machine Learning · Cross-listed from cs.AI
Grounding vs. Compositionality: On the Non-Complementarity of Reasoning in Neuro-Symbolic Systems
Mahnoor Shahid and Hannes Rothe
Compositional generalization remains a foundational weakness of modern neural networks, limiting their robustness and applicability in domains requiring out-of-distribution
reasoning. A central, yet unverified, assumption in neuro-symbolic AI is that compositional
reasoning will emerge as a byproduct of successful symbol grounding. This work presents the first systematic empirical analysis to challenge this assumption by disentangling the contributions of grounding and
reasoning. To operationalize this investigation, we introduce the Iterative Logic Tensor Network ($i$LTN), a fully differentiable architecture designed for multi-step deduction. Using a formal taxonomy of generalization – probing for novel entities, unseen relations, and complex rule compositions – we demonstrate that a model trained solely on a grounding objective fails to generalize. In contrast, our full $i$LTN, trained jointly on perceptual grounding and multi-step
reasoning, achieves high zero-shot accuracy across all tasks. Our findings provide conclusive evidence that symbol grounding, while necessary, is insufficient for generalization, establishing that
reasoning is not an emergent property but a distinct capability that requires an explicit learning objective.
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Machine Learning
Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning
Seungyub Han, Hyungjin Kim and Jungwoo Lee
Offline reinforcement learning (RL)
agents often fail when deployed, as the gap between training
datasets and real environments leads to unsafe behavior. To address this, we present SAS (Self-
Alignment for Safety), a
transformer-based framework that enables test-time adaptation in offline safe RL without retraining. In SAS, the main mechanism is self-
alignment: at test time, the pretrained
agent generates several imagined trajectories and selects those satisfying the Lyapunov condition. These feasible segments are then recycled as in-context
prompts, allowing the
agent to realign its behavior toward safety while avoiding parameter updates. In effect, SAS turns Lyapunov-guided imagination into control-invariant
prompts, and its
transformer architecture admits a hierarchical RL interpretation where prompting functions as
Bayesian inference over latent skills. Across Safety Gymnasium and MuJoCo
benchmarks, SAS consistently reduces cost and failure while maintaining or improving return.
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Machine Learning
Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models
Cyril Shih-Huan Hsu, Wig Yuan-Cheng Cheng and Chrysa Papagianni
Deploying
Vision-
Language Models (VLMs) on edge devices remains challenging due to their substantial computational and
memory demands, which exceed the capabilities of resource-constrained embedded platforms. Conversely, fully offloading inference to the cloud is often impractical in bandwidth-limited environments, where transmitting raw visual data introduces substantial latency overhead. While recent edge-cloud collaborative architectures attempt to partition VLM workloads across devices, they typically rely on transmitting fixed-size representations, lacking adaptability to dynamic network conditions and failing to fully exploit semantic redundancy. In this paper, we propose a progressive semantic communication framework for edge-cloud VLM inference, using a Meta AutoEncoder that compresses visual
tokens into adaptive, progressively refinable representations, enabling plug-and-play deployment with off-the-shelf VLMs without additional fine-tuning. This design allows flexible transmission at different information levels, providing a controllable trade-off between communication cost and semantic fidelity. We implement a full end-to-end edge-cloud system comprising an embedded NXP i.MX95 platform and a GPU server, communicating over bandwidth-constrained networks. Experimental results show that, at 1 Mbps uplink, the proposed progressive scheme significantly reduces network latency compared to full-edge and full-cloud solutions, while maintaining high semantic consistency even under high compression. The implementation code will be released upon publication at https://github.com/open-ep/ProSemComVLM.
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Machine Learning · Cross-listed from cs.CR
Quantamination: Dynamic Quantization Leaks Your Data Across the Batch
Hanna Foerster, Ilia Shumailov, Cheng Zhang et al. (+3 more)
Dynamic quantization emerged as a practical approach to increase the utilization and efficiency of the machine learning serving flow. Unlike static quantization, which applies quantization offline, dynamic quantization operates on tensors at run-time, adapting its parameters to the actual input data. Today's mainstream machine learning frameworks, including ML compilers and inference engines, frequently recommend dynamic quantization as an initial step for optimizing model serving. This is because dynamic quantization can significantly reduce
memory usage and computational load, leading to faster
token generation and improved model serving efficiency without substantial loss in model accuracy. In this paper, we reveal a critical vulnerability in dynamic quantization: an adversary can exploit such quantization strategy to steal sensitive user data placed in the same batch as the adversary's input. Our analysis demonstrates that dynamic quantization, when improperly implemented or configured, can create side channels that expose information about other inputs within the same batch. We call this phenomenon Quantamination, describing contamination from quantization. Specifically, we show that at least 4 of the most popular ML frameworks in use today either default to or can use configurations that leak data across the batch boundary. This data leakage, in theory, allows attackers to partially or even fully recover other users' batched input data, representing a serious
privacy risk for existing ML serving frameworks.
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All authors Hanna Foerster, Ilia Shumailov, Cheng Zhang, Yiren Zhao, Jamie Hayes, Robert Mullins
Machine Learning
Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction
Jinjiang Guo and Sheng Ding
The rapid growth of molecular foundation models and large
language models (LLMs) has encouraged a scale centred view of AI in drug discovery, in which larger pretrained models are expected to supersede compact cheminformatics models. We test this assumption across 26 ADME, toxicity and bioactivity endpoints, covering 165,541 endpoint level compound label records. The
benchmark contains 78 endpoint and split entries evaluated under random, Murcko scaffold and structure separated 5-fold cross validation protocols, representing increasing chemical generalization difficulty. Across 156 task and metric comparisons, classical machine learning (ML) provides the largest share of best performing entries (47.4%), followed by pretrained molecular sequence models (28.8%), graph neural networks (21.8%) and LLM based SAR baselines (1.9%). Classical ML dominates random split interpolation and remains the largest winner family overall. GNN and sequence models are competitive in selected harder splits, but their strict winner shares decrease under a fixed final-window readout, indicating sensitivity to training settings and model selection. Paired bootstrap analyses show that small numerical differences between individual models should not be read as decisive victories. SAR knowledge from training folds improves GPT5.5-SAR and Opus4.7-SAR metrics but does not make rule based
reasoning a universal substitute for supervised predictors. Compact specialized models remain highly effective, and predictive performance depends on the fit among model, task and validation scenario, not on scale alone.
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Machine Learning
Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective
Jiancheng Wang, Mingjia Yin, Hao Wang et al. (+1 more)
DNNs have gained widespread adoption in feature interaction recommendation models. However, there has been a longstanding debate on their roles. On one hand, some works claim that DNNs possess the ability to implicitly capture high-order feature interactions. Conversely, recent studies have highlighted the limitations of DNNs in effectively learning dot products, specifically second-order interactions, let alone higher-order interactions. In this paper, we present a novel perspective to understand the effectiveness of DNNs: their impact on the dimensional robustness of the representations. In particular, we conduct extensive experiments involving both parallel DNNs and stacked DNNs. Our evaluation encompasses an overall study of complete DNN on two feature interaction models, alongside a fine-grained ablation analysis of components within DNNs. Experimental results demonstrate that both parallel and stacked DNNs can effectively mitigate the dimensional collapse of
embeddings. Furthermore, a gradient-based theoretical analysis, supported by empirical evidence, uncovers the underlying mechanisms of dimensional collapse.
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All authors Jiancheng Wang, Mingjia Yin, Hao Wang, Enhong Chen
Machine Learning · Cross-listed from cs.CV
Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners
Nikita Araslanov, Martin Sundermeyer, Hidenobu Matsuki et al. (+2 more)
One of the most exciting applications of
vision models involve pixel-level
reasoning. Despite the abundance of
vision foundation models, we still lack representations that effectively embed spatio-temporal properties of visual scenes at the pixel level. Existing frameworks either train on image-based pretext tasks, which do not account for dynamic elements, or on
video sequences for action-level
reasoning, which does not scale to dense pixel-level prediction. We present a framework that learns pixel-accurate feature descriptors from
videos, LILA. The core element of our training framework is linear in-context learning. LILA leverages spatio-temporal cue maps – depth and motion – estimated with off-the-shelf networks. Despite the noisy nature of those cues, LILA trains effectively on uncurated
video datasets,
embedding semantic and
geometric properties in a temporally consistent manner. We demonstrate compelling empirical benefits of the learned representation across a diverse suite of
vision tasks:
video object
segmentation, surface normal estimation and semantic
segmentation.
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All authors Nikita Araslanov, Martin Sundermeyer, Hidenobu Matsuki, David Joseph Tan, Federico Tombari