Machine Learning · Cross-listed from econ.GN
A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite Horizon Dynamic Pricing
Lev Razumovskiy and Nikolay Karenin
This paper provides a systematic comparison between Fitted Dynamic Programming (DP), where demand is estimated from data, and Reinforcement Learning (RL) methods in finite-horizon dynamic pricing problems. We analyze their performance across environments of increasing structural complexity, ranging from a single typology
benchmark to multi-typology settings with heterogeneous demand and inter-temporal revenue constraints. Unlike simplified comparisons that restrict DP to low-dimensional settings, we apply dynamic programming in richer, multi-dimensional environments with multiple product types and constraints. We evaluate revenue performance, stability, constraint satisfaction behavior, and computational scaling, highlighting the trade-offs between explicit expectation-based optimization and trajectory-based learning.
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Machine Learning
$\pi$-Play: Multi-Agent Self-Play via Privileged Self-Distillation without External Data
Yaocheng Zhang, Yuanheng Zhu, Wenyue Chong et al. (+7 more)
Deep search
agents have emerged as a promising paradigm for addressing complex information-seeking tasks, but their training remains challenging due to sparse rewards, weak credit assignment, and limited labeled data. Self-play offers a scalable route to reduce data dependence, but conventional self-play optimizes students only through sparse outcome rewards, leading to low learning efficiency. In this work, we observe that self-play naturally produces a question construction path (QCP) during task generation, an intermediate artifact that captures the reverse solution process. This reveals a new source of privileged information: self-play can provide high-quality privileged information for the self-
distillation at low cost and at scale, without relying on human feedback or curated privileged information. Leveraging this insight, we propose Privileged Information Self-Play ($\pi$-Play), a novel multi-
agent self-
evolution framework combining self-play and self-
distillation. In $\pi$-Play, an examiner generates tasks together with QCPs, and a teacher employs QCP as privileged context to densely supervise a student via self-
distillation. This design transforms sparse-reward self-play into a dense-feedback co-
evolution. Extensive experiments show that data-free $\pi$-Play surpasses fully supervised search
agents and improves evolutionary efficiency by 2-3$\times$ over conventional self-play. Code is available at https://github.com/zhyaoch/pi-play.
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All authors Yaocheng Zhang, Yuanheng Zhu, Wenyue Chong, Songjun Tu, Qichao Zhang, Jiajun Chai, Xiaohan Wang, Wei Lin, Guojun Yin, Dongbin Zhao
Machine Learning
A Complete Symmetry Classification of Shallow ReLU Networks
Pranavkrishnan Ramakrishnan
Parameter space is not function space for neural network architectures. This fact, investigated as early as the 1990s under terms such as “reverse engineering," or “parameter identifiability", has led to the natural question of parameter space
symmetries\textemdash the study of distinct parameters in neural architectures which realize the same function. Indeed, the quotient space obtained by identifying parameters giving rise to the same function, called the neuromanifold, has been shown in some cases to have rich
geometric properties, impacting optimization dynamics. Thus far, techniques towards complete classifications have required the analyticity of the activation function, notably excising the important case of ReLU. Here, in contrast, we exploit the non-differentiability of the ReLU activation to provide a complete classification of the
symmetries in the shallow case.
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Machine Learning
First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs
Kavya Gupta, Nektarios Kalampalikis, Christoph Heitz et al. (+1 more)
Fairness in algorithmic decision-making is often defined in the predictive space, where predictive performance - used as a proxy for decision-maker (DM) utility - is traded off against prediction-based
fairness notions, such as demographic parity or equality of opportunity. This perspective, however, ignores how predictions translate into decisions and ultimately into utilities and welfare for both DM and decision subjects (DS), as well as their allocation across social-salient groups. In this paper, we propose a multi-stakeholder framework for fair algorithmic decision-making grounded in welfare economics and distributive justice, explicitly modeling the utilities of both the DM and DS, and defining
fairness via a social planner's utility that captures inequalities in DS utilities across groups under different justice-based
fairness notions (e.g., Egalitarian, Rawlsian). We formulate fair decision-making as a post-hoc multi-objective optimization problem, characterizing the achievable performance-
fairness trade-offs in the two-dimensional utility space of DM utility and the social planner's utility, under different decision policy classes (deterministic vs.
stochastic, shared vs. group-specific). Using the proposed framework, we then identify conditions (in terms of the stakeholders' utilities) under which
stochastic policies are more optimal than deterministic ones, and empirically demonstrate that simple
stochastic policies can yield superior performance-
fairness trade-offs by leveraging outcome uncertainty. Overall, we advocate a shift from prediction-centric
fairness to a transparent, justice-based, multi-stakeholder approach that supports the collaborative design of decision-making policies.
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All authors Kavya Gupta, Nektarios Kalampalikis, Christoph Heitz, Isabel Valera
Machine Learning · Cross-listed from cs.AI
Hierarchical Reinforcement Learning with Runtime Safety Shielding for Power Grid Operation
Gitesh Malik
Reinforcement learning has shown promise for automating power-grid operation tasks such as
topology control and congestion management. However, its deployment in real-world power systems remains limited by strict safety requirements, brittleness under rare disturbances, and poor generalization to unseen grid
topologies. In safety-critical infrastructure, catastrophic failures cannot be tolerated, and learning-based controllers must operate within hard physical constraints. This paper proposes a safety-constrained hierarchical control framework for power-grid operation that explicitly decouples long-horizon decision-making from real-time feasibility enforcement. A high-level reinforcement learning policy proposes abstract control actions, while a deterministic runtime safety shield filters unsafe actions using fast forward simulation. Safety is enforced as a runtime invariant, independent of policy quality or training distribution. The proposed framework is evaluated on the Grid2Op
benchmark suite under nominal conditions, forced line-outage stress tests, and zero-shot deployment on the ICAPS 2021 large-scale transmission grid without retraining. Results show that flat reinforcement learning policies are brittle under stress, while safety-only methods are overly conservative. In contrast, the proposed hierarchical and safety-aware approach achieves longer episode survival, lower peak line loading, and robust zero-shot generalization to unseen grids. These results indicate that safety and generalization in power-grid control are best achieved through architectural design rather than increasingly complex reward engineering, providing a practical path toward deployable learning-based controllers for real-world energy systems.
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Machine Learning · Cross-listed from math.OC
Stochastic Trust-Region Methods for Over-parameterized Models
Aike Yang and Hao Wang
Under interpolation-type assumptions such as the strong growth condition,
stochastic optimization methods can attain convergence rates comparable to full-batch methods, but their performance, particularly for SGD, remains highly sensitive to step-size selection. To address this issue, we propose a unified
stochastic trust-region framework that eliminates manual step-size tuning and extends naturally to equality-constrained problems. For unconstrained optimization, we develop a first-order
stochastic trust-region
algorithm and show that, under the strong growth condition, it achieves an iteration and
stochastic first-order oracle complexity of $O(\varepsilon^{-2} \log(1/\varepsilon))$ for finding an $\varepsilon$-stationary point. For equality-constrained problems, we introduce a quadratic-penalty-based
stochastic trust-region method with penalty parameter $\mu$, and establish an iteration and oracle complexity of $O(\varepsilon^{-4} \log(1/\varepsilon))$ to reach an $\varepsilon$-stationary point of the penalized problem, corresponding to an $O(\varepsilon)$-approximate KKT point of the original constrained problem. Numerical experiments on deep neural network training and orthogonally constrained subspace fitting demonstrate that the proposed methods achieve performance comparable to well-tuned
stochastic baselines, while exhibiting stable optimization behavior and effectively handling hard constraints without manual learning-rate scheduling.
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Machine Learning
MAny: Merge Anything for Multimodal Continual Instruction Tuning
Zijian Gao, Wangwang Jia, Xingxing Zhang et al. (+6 more)
Multimodal Continual Instruction Tuning (MCIT) is essential for sequential task adaptation of
Multimodal Large
Language Models (MLLMs) but is severely restricted by catastrophic forgetting. While existing literature focuses on the
reasoning language backbone, in this work, we expose a critical yet neglected dual-forgetting phenomenon across both perception drift in Cross-modal Projection Space and
reasoning collapse in Low-rank Parameter Space. To resolve this, we present MAny (Merge Anything), a framework that merges task-specific knowledge through Cross-modal Projection Merging (CPM) and Low-rank Parameter Merging (LPM). Specifically, CPM recovers perceptual
alignment by adaptively merging cross-modal visual representations via visual-prototype guidance, ensuring accurate feature recovery during inference. Simultaneously, LPM eliminates mutual interference among task-specific low-rank modules by recursively merging low-rank weight
matrices. By leveraging recursive least squares, LPM provides a closed-form solution that mathematically guarantees an optimal fusion trajectory for
reasoning stability. Notably, MAny operates as a training-free paradigm that achieves knowledge merging via efficient CPU-based algebraic operations, eliminating additional gradient-based optimization beyond initial tuning. Our extensive evaluations confirm the superior performance and robustness of MAny across multiple MLLMs and
benchmarks. Specifically, on the UCIT
benchmark, MAny achieves significant leads of up to 8.57% and 2.85% in final average accuracy over state-of-the-art methods across two different MLLMs, respectively.
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All authors Zijian Gao, Wangwang Jia, Xingxing Zhang, Pengfei Qian, Tao Sun, Bo Ding, Yong Dou, Huaimin Wang, Kele Xu
Machine Learning
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
Zekai Lin, Chao Xue, Di Liang et al. (+8 more)
Supervised Fine-Tuning (SFT) of large
language models often suffers from task interference and catastrophic forgetting. Recent approaches alleviate this issue by isolating task-critical parameters during training. However, these methods represent a static solution to a dynamic problem, assuming that parameter importance remains fixed once identified. In this work, we empirically demonstrate that parameter importance exhibits temporal drift over the course of training. To address this, we propose Evolving Parameter Isolation (EPI), a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance. Instead of freezing a fixed subset of parameters, EPI periodically updates isolation masks using gradient-based signals, enabling the model to protect emerging task-critical parameters while releasing outdated ones to recover plasticity. Experiments on diverse multi-task
benchmarks demonstrate that EPI consistently reduces interference and forgetting compared to static isolation and standard fine-tuning, while improving overall generalization. Our analysis highlights the necessity of synchronizing isolation mechanisms with the evolving dynamics of learning diverse abilities.
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All authors Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng
Machine Learning · Cross-listed from cs.CL
Davyd Naveriani, Albert Zeyer, Ralf Schlüter et al. (+1 more)
Diffusion language models have recently emerged as a leading alternative to standard
language models, due to their ability for bidirectional
attention and parallel text generation. In this work, we explore variants for their use in
speech recognition. Specifically, we introduce a comprehensive guide to incorporating masked
diffusion language models (MDLM) and uniform-state
diffusion models (USDMs) for rescoring ASR hypotheses. Additionally, we design a new joint-decoding method that combines CTC and USDM by integrating the framewise probability distributions derived from CTC with the labelwise probability distributions computed by USDM at each decoding step, thereby generating new candidates that combine strong
language knowledge from USDM and acoustic information from CTC. Our findings reveal that USDM, as well as MDLM, can significantly improve the accuracy of recognized text. We publish all our code and recipes.
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All authors Davyd Naveriani, Albert Zeyer, Ralf Schlüter, Hermann Ney
Machine Learning
Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
Mohammad Nooraiepour, Zezhang Song, Wei Li et al. (+1 more)
Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfer across data-scarce geological systems, and calibrated uncertainty estimates, capabilities that are rarely addressed together in existing frameworks. We present a physics-informed transfer learning framework that adapts a hydrogen sorption PINN to methane sorption prediction via Elastic Weight Consolidation, coal-specific feature engineering, and a three-phase curriculum that progressively balances transfer preservation with thermodynamic fine-tuning. Trained on 993
equilibrium measurements from 114 independent coal experiments spanning lignite to anthracite, the framework achieves R2 = 0.932 on held-out coal samples, a 227% improvement over pressure-only classical isotherms, while hydrogen pre-training delivers 18.9% lower RMSE and 19.4% faster convergence than random initialization. Five
Bayesian uncertainty quantification approaches reveal a systematic divergence in performance across physics-constrained architectures. Monte Carlo Dropout achieves well-calibrated uncertainty at minimal overhead, while deep ensembles, regardless of architectural diversity or initialization strategy, exhibit performance degradation because shared physics constraints narrow the admissible solution
manifold. SHAP and ALE analyses confirm that learned representations remain physically interpretable and aligned with established coal sorption mechanisms: moisture-volatile interactions are most influential, pressure-temperature coupling captures thermodynamic co-dependence, and features exhibit non-monotonic effects. These results identify Monte Carlo Dropout as the best-performing UQ method in this physics-constrained transfer learning framework, and demonstrate cross-gas transfer learning as a data-efficient strategy for geological material modeling.
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All authors Mohammad Nooraiepour, Zezhang Song, Wei Li, Sarah Perez
Machine Learning · Cross-listed from cs.CL
Adaptive Conformal Prediction for Improving Factuality of Generations by Large Language Models
Aleksandr Rubashevskii, Dzianis Piatrashyn, Preslav Nakov et al. (+1 more)
Large
language models (LLMs) are prone to generating factually incorrect outputs. Recent work has applied conformal prediction to provide uncertainty estimates and statistical guarantees for the factuality of LLM generations. However, existing approaches are typically not
prompt-adaptive, limiting their ability to capture input-dependent variability. As a result, they may filter out too few items (leading to over-coverage) or too many (under-coverage) for a given task or
prompt. We propose an adaptive conformal prediction approach that extends conformal score transformation methods to LLMs, with applications to long-form generation and multiple-choice question answering. This enables
prompt-dependent calibration, retaining marginal coverage guarantees while improving conditional coverage. In addition, the approach naturally supports selective prediction, allowing unreliable claims or answer choices to be filtered out in downstream applications. We evaluate our approach on multiple white-box models across diverse domains and show that it significantly outperforms existing baselines in terms of conditional coverage.
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All authors Aleksandr Rubashevskii, Dzianis Piatrashyn, Preslav Nakov, Maxim Panov
Machine Learning
Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realism
Mohammad Ahangarkiasari, Andreas Tind Damgaard, Casper Haurum et al. (+1 more)
Objective: Investigate whether hypnogram 'realism' can be used to guide an unsupervised method for handling arbitrary types of signal degradation in mobile sleep monitoring. Approach: Combining a pretrained, state-of-the-art 'u-sleep' model with a 'discriminator' network, we align features from a target domain with a feature space learned during pretraining. To test the approach, we distort the source domain with realistic signal degradations, to see how well the method can adapt to different types of degradation. We compare the performance of the resulting model with best-case models designed in a supervised manner for each type of transfer. Main Results: Depending on the type of distortion, we find that the unsupervised approach can increase Cohen's kappa with as little as 0.03 and up to 0.29, and that for all transfers, the method does not decrease performance. However, the approach never quite reaches the estimated theoretical optimal performance, and when tested on a real-life domain mismatch between two sleep studies, the benefit was insignificant. Significance: 'Discriminator-guided fine tuning' is an interesting approach to handling signal degradation for 'in the wild' sleep monitoring, with some promise. In particular, what it says about sleep data in general is interesting. However, more development will be necessary before using it 'in production'.
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All authors Mohammad Ahangarkiasari, Andreas Tind Damgaard, Casper Haurum, Kaare B. Mikkelsen
Machine Learning
PRiMeFlow: Capturing Complex Expression Heterogeneity in Perturbation Response Modelling
Zichao Yan, Yan Wu, Mica Xu Ji et al. (+10 more)
Predicting the effects of perturbations in-silico on
cell state can identify drivers of
cell behavior at scale and accelerate drug discovery. However, modeling challenges remain due to the inherent heterogeneity of single
cell gene expression and the complex, latent gene dependencies. Here, we present PRiMeFlow, an end-to-end flow matching based approach to directly model the effects of genetic and small molecule perturbations in the gene expression space. The distribution-fitting approach taken by PRiMeFlow enables it to accurately approximate the empirical distribution of single-
cell gene expression, which we demonstrate through extensive benchmarking inside PerturBench. Through ablation studies, we also validate important model design choices such as operating in gene expression space and parameterizing the velocity field with a U-Net architecture. Finally, by scaling PRiMeFlow to a broad perturbation data atlas spanning multiple
datasets and employing a carefully designed pretraining-finetuning strategy, we demonstrate its outstanding performance on the H1 human embryonic stem
cells from the ARC Virtual
Cell Challenge
benchmark.
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All authors Zichao Yan, Yan Wu, Mica Xu Ji, Chaitra Agrahar, Esther Wershof, Marcel Nassar, Mehrshad Sadria, Ridvan Eksi, Vladimir Trifonov, Ignacio Ibarra, Telmo Felgueira, Błażej Osiński, Rory Stark
Machine Learning
BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization
Jackie Rao, Ferran Gonzalez Hernandez, Leon Gerard et al. (+1 more)
Antibody lead optimization is inherently a multi-objective challenge in drug discovery. Achieving a balance between different drug-like properties is crucial for the development of viable candidates, and this search becomes exponentially challenging as desired properties grow. The ever-growing zoo of sophisticated in silico tools for predicting antibody properties calls for an efficient joint optimization procedure to overcome resource-intensive sequential filtering pipelines. We present BOAT, a versatile
Bayesian optimization framework for multi-property antibody engineering. Our `plug-and-play' framework couples uncertainty-aware surrogate modeling with a genetic
algorithm to jointly optimize various predicted antibody traits while enabling efficient exploration of sequence space. Through systematic benchmarking against genetic
algorithms and newer generative learning approaches, we demonstrate competitive performance with state-of-the-art methods for multi-objective
protein optimization. We identify clear regimes where surrogate-driven optimization outperforms expensive generative approaches and establish practical limits imposed by sequence dimensionality and oracle costs.
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All authors Jackie Rao, Ferran Gonzalez Hernandez, Leon Gerard, Alexandra Gessner
Machine Learning · Cross-listed from cs.CL
How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data
Joel Niklaus, Atsuki Yamaguchi, Michal Štefánik et al. (+9 more)
Synthetic data is a standard component in training large
language models, yet systematic comparisons across design dimensions, including rephrasing strategy, generator model, and source data, remain absent. We conduct extensive controlled experiments, generating over one trillion
tokens, to identify critical factors in rephrasing web text into synthetic pretraining data. Our results reveal that structured output formats, such as tables, math problems, FAQs, and tutorials, consistently outperform both curated web baselines and prior synthetic methods. Notably, increasing the size of the generator model beyond 1B parameters provides no additional benefit. Our analysis also demonstrates that the selection of the original data used for mixing substantially influences performance. By applying our findings, we develop \textbf{FinePhrase}, a 486-billion-
token open
dataset of rephrased web text. We show that FinePhrase outperforms all existing synthetic data baselines while reducing generation costs by up to 30 times. We provide the
dataset, all
prompts, and the generation framework to the research community.
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All authors Joel Niklaus, Atsuki Yamaguchi, Michal Štefánik, Guilherme Penedo, Hynek Kydlíček, Elie Bakouch, Lewis Tunstall, Edward Emanuel Beeching, Thibaud Frere, Colin Raffel, Leandro von Werra, Thomas Wolf
Machine Learning
Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation
Shangzhe Li and Weitong Zhang
We study value adaptation in offline-to-online reinforcement learning under general function approximation. Starting from an imperfect offline pretrained $Q$-function, the learner aims to adapt it to the target environment using only a limited amount of online interaction. We first characterize the difficulty of this setting by establishing a minimax lower bound, showing that even when the pretrained $Q$-function is close to optimal $Q^\star$, online adaptation can be no more efficient than pure online RL on certain hard instances. On the positive side, under a novel structural condition on the offline-pretrained value functions, we propose O2O-LSVI, an adaptation
algorithm with problem-dependent sample complexity that provably improves over pure online RL. Finally, we complement our theory with neural-network experiments that demonstrate the practical effectiveness of the proposed method.
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Machine Learning
HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark
Jiacheng Wang, Jinchang Hou, Fabian Wang et al. (+3 more)
Existing
agent-safety evaluation has focused mainly on externally induced risks. Yet
agents may still enter unsafe trajectories under benign conditions. We study this complementary but underexplored setting through the lens of intrinsic risk, where intrinsic failures remain latent, propagate across long-horizon execution, and eventually lead to high-consequence outcomes. To evaluate this setting, we introduce non-attack intrinsic risk
auditing, a guard-oriented safety evaluation task, and present HINTBench, a
benchmark of 596
agent trajectories, comprising 400 synthetic risky trajectories, 136 synthetic safe trajectories, 30 reconstructed real-world risky trajectories, and 30 reconstructed real-world safe trajectories, with an average length of 24.0 steps. HINTBench supports three tasks: risk detection, risk-step localization, and intrinsic failure-type identification, with annotations organized under a unified five-constraint taxonomy. Experiments reveal a substantial capability gap: strong LLMs perform well on trajectory-level risk detection, but the best model remains below 37 on fine-grained Strict-F1 for risk-step localization. Existing off-the-shelf guard models evaluated under their native
prompts transfer poorly to this setting. These findings establish intrinsic risk
auditing as an open challenge for
agent safety.
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All authors Jiacheng Wang, Jinchang Hou, Fabian Wang, Ping Jian, Chenfu Bao, Zhonghou Lv
Machine Learning
Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
Vojtěch Novák, Ivan Zelinka, Lenka Přibylová et al. (+3 more)
This study evaluates colorectal risk factors and compares classical models against
Quantum Neural Networks (QNNs) for anastomotic leak prediction. Analyzing clinical data with 14% leak prevalence, we tested ZZFeatureMap encodings with RealAmplitudes and EfficientSU2 ansatze under simulated noise. $F_\beta$-optimized
quantum configurations yielded significantly higher sensitivity (83.3%) than classical baselines (66.7%). This demonstrates that
quantum feature spaces better prioritize minority class identification, which is critical for low-prevalence clinical risk prediction. Our work explores various optimizers under noisy conditions, highlighting key trade-offs and future directions for hardware deployment.
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All authors Vojtěch Novák, Ivan Zelinka, Lenka Přibylová, Lubomír Martínek, Vladimír Benčurík, Martin Beseda
Machine Learning
Unsupervised Anomaly Detection in Process-Complex Industrial Time Series: A Real-World Case Study
Sergej Krasnikov, Lukas Meitz, Samineh Bagheri et al. (+3 more)
Industrial time-series data from real production environments exhibits substantially higher complexity than commonly used
benchmark datasets, primarily due to heterogeneous, multi-stage operational processes. As a result, anomaly detection methods validated under simplified conditions often fail to generalize to industrial settings. This work presents an empirical study on a unique
dataset collected from fully operational industrial machinery, explicitly capturing pronounced process-induced variability. We evaluate which model classes are capable of capturing this complexity, starting with a classical Isolation Forest baseline and extending to multiple autoencoder architectures. Experimental results show that Isolation Forest is insufficient for modeling the non-periodic, multi-scale dynamics present in the data, whereas autoencoders consistently perform better. Among them, temporal convolutional autoencoders achieve the most robust performance, while recurrent and variational variants require more careful tuning.
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All authors Sergej Krasnikov, Lukas Meitz, Samineh Bagheri, Michael Heider, Thorsten Schöler, Jörg Hähner
Machine Learning
ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection
Romain Hermary, Samet Hicsonmez, Dan Pineau et al. (+2 more)
Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data. This scarcity makes unsupervised approaches predominant, yet existing methods often rely on reconstruction or
forecasting, which struggle with complex data, or on
embedding-based approaches that require domain-specific anomaly synthesis and fixed distance metrics. We propose ASTER, a framework that generates pseudo-anomalies directly in the latent space, avoiding handcrafted anomaly injections and the need for domain expertise. A latent-space decoder produces tailored pseudo-anomalies to train a
Transformer-based anomaly classifier, while a pre-trained LLM enriches the temporal and contextual representations of this space. Experiments on three
benchmark datasets show that ASTER achieves state-of-the-art performance and sets a new standard for LLM-based TSAD.
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All authors Romain Hermary, Samet Hicsonmez, Dan Pineau, Abd El Rahman Shabayek, Djamila Aouada
Machine Learning · Cross-listed from physics.flu-dyn
Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in fires
Anran Jiao, Wengyao Jiang, Xiaoyi Lu et al. (+2 more)
Computational fluid dynamics (CFD) has become an essential tool for predicting fire behavior, yet maintaining both efficiency and accuracy remains challenging. A major source of computational cost in fire simulations is the modeling of radiation transfer, which is usually the dominant heat transfer mechanism in fires. Solving the high-dimensional radiative transfer equation (RTE) with traditional numerical methods can be a performance bottleneck. Here, we present a machine learning framework based on
Fourier-enhanced multiple-input neural operators (
Fourier-MIONet) as an efficient alternative to direct numerical integration of the RTE. We first investigate the performance of neural operator architectures for a small-scale 2D pool fire and find that
Fourier-MIONet provides the most accurate radiative solution predictions. The approach is then extended to 3D CFD fire simulations, where the computational mesh is locally refined across multiple levels. In these high-resolution settings, monolithic surrogate models for direct field-to-field mapping become difficult to train and computationally inefficient. To address this issue, a nested
Fourier-MIONet is proposed to predict radiation solutions across multiple mesh-refinement levels. We validate the approach on 3D McCaffrey pool fires simulated with FireFOAM, including fixed fire sizes and a unified model trained over a continuous range of heat release rates (HRRs). The proposed method achieves global relative errors of 2-4% for 3D varying-HRR scenarios while providing faster inference than the estimated cost of one finite-volume radiation solve in FireFOAM for the 16-solid-angle case. With fast and accurate inference, the surrogate makes higher-fidelity radiation treatments practical and enables the incorporation of more spectrally resolved radiation models into CFD fire simulations for engineering applications.
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All authors Anran Jiao, Wengyao Jiang, Xiaoyi Lu, Yi Wang, Lu Lu
Machine Learning
DiPO: Disentangled Perplexity Policy Optimization for Fine-grained Exploration-Exploitation Trade-Off
Xiaofan Li, Ming Yang, Zhiyuan Ma et al. (+9 more)
Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed significant advances in the
reasoning capabilities of Large
Language Models (LLMs). However, effectively managing the exploration and exploitation trade-off remains a critical challenge. In this paper, we fully analyze the exploration and exploitation dilemma of extremely hard and easy samples during the training and propose a new fine-grained trade-off mechanism. Concretely, we introduce a perplexity space disentangling strategy that divides the sample space into distinct exploration (high perplexity) and exploitation (low perplexity) subspaces, thereby mining fine-grained samples requiring exploration-exploitation trade-off. Subsequently, we propose a bidirectional reward allocation mechanism with a minimum impact on verification rewards to implement perplexity-guided exploration and exploitation, enabling more stable policy optimization. Finally, we have evaluated our method on two mainstream tasks: mathematical
reasoning and function calling, and experimental results demonstrate the superiority of the proposed method, confirming its effectiveness in enhancing LLM performance by fine-grained exploration-exploitation trade-off.
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All authors Xiaofan Li, Ming Yang, Zhiyuan Ma, Shichao Ma, Jintao Du, Yu Cheng, Weiqiang Wang, Zhizhong Zhang, Xin Tan, Yanyun Qu, Lizhuang Ma, Yuan Xie
Machine Learning
MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals
Adam Lahouari, Shen Ai, Jihye Han et al. (+16 more)
We present an open Molecular
Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs. The first release comprises fine-tuned MACE models for nine molecular
crystal systems—Benzamide, Benzoic acid, Coumarin, Durene, Isonicotinamide, Nicotinic acid , Niacinamide, Pyrazinamide, and Resorcinol—developed using the Automated Machine Learning Pipeline (AMLP), which streamlines the entire MLIP development workflow, from reference data generation to model training and validation, into a reproducible and user-friendly pipeline. Models are fine-tuned from the MACE-MH-1 foundation model omol head), yielding a mean energy MAE of 0.141 kJ/mol/atom and a mean force MAE of 0.648 kJ/mol/Angstrom across all systems. Benchmarked against three state-of-the-art foundation models on the DFT-labelled polymorph set, only the fine-tuned models resolve the polymorphic energy landscape. Dynamical stability and structural integrity, as assessed through energy conservation, P2 orientational order parameters, and radial distribution functions, are evaluated using molecular dynamics simulations. The released models and
datasets constitute a growing open database of validated MLIPs, ready for production MD simulations of molecular
crystal polymorphism across the polymorphic landscape of each target compound under different thermodynamic conditions.
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All authors Adam Lahouari, Shen Ai, Jihye Han, Jillian Hoffstadt, Philipp Hoellmer, Charlotte Infante, Pulkita Jain, Sangram Kadam, Maya M. Martirossyan, Amara McCune, Hypatia Newton, Shlok J. Paul, Willmor Pena, Jonathan Raghoonanan, Sumon Sahu, Oliver Tan, Andrea Vergara, Jutta Rogal, Mark E. Tuckerman
Machine Learning · Cross-listed from physics.soc-ph
Sandpile Economics: Theory, Identification, and Evidence
Diego Vallarino
Why do capitalist economies recurrently generate crises whose severity is disproportionate to the size of the triggering shock? This paper proposes a structural answer grounded in the evolutionary geometry of production networks. As economies evolve through specialization, integration, and competitive selection, their inter-sectoral linkages drift toward configurations of increasing
geometric fragility, eventually crossing a threshold beyond which small disturbances generate disproportionately large cascades. We introduce Sandpile Economics, a formal framework that interprets macroeconomic instability as an emergent property of disequilibrium production networks. The key state variable is the Forman–
Ricci curvature of the input–output graph, capturing local substitution possibilities when supply chains are disrupted. We show that when
curvature falls below an endogenous threshold, the distribution of cascade sizes follows a power law with tail index $\alpha \in (1,2)$, implying a regime of unbounded amplification. The underlying mechanism is evolutionary: specialization reduces input substitutability, pushing the economy toward criticality, while crisis episodes induce endogenous network reconfiguration and path dependence. These dynamics are inherently non-ergodic and cannot be captured by representative-
agent frameworks. Empirically, using global input–output data, we document that production networks operate in persistently negative
curvature regimes and that
curvature robustly predicts medium-run output dynamics. A one-standard-deviation increase in
curvature is associated with higher cumulative growth over three-year horizons, and
curvature systematically outperforms standard network metrics in explaining cross-country differences in resilience.
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Machine Learning · Cross-listed from cs.CV
Context Sensitivity Improves Human-Machine Visual Alignment
Frieda Born, Tom Neuhäuser, Lukas Muttenthaler et al. (+6 more)
Modern machine learning models typically represent inputs as fixed points in a high-dimensional
embedding space. While this approach has been proven powerful for a wide range of downstream tasks, it fundamentally differs from the way humans process information. Because humans are constantly adapting to their environment, they represent objects and their relationships in a highly context-sensitive manner. To address this gap, we propose a method for context-sensitive similarity computation from neural network
embeddings, applied to modeling a triplet odd-one-out task with an anchor image serving as simultaneous context. Modeling context enables us to achieve up to a 15% improvement in odd-one-out accuracy over a context-insensitive model. We find that this improvement is consistent across both original and "human-aligned"
vision foundation models.
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All authors Frieda Born, Tom Neuhäuser, Lukas Muttenthaler, Brett D. Roads, Bernhard Spitzer, Andrew K. Lampinen, Matt Jones, Klaus-Robert Müller, Michael C. Mozer
Machine Learning
Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Xuanyan Liu, Ignacio Cabrera Martin, Marcello Trovati et al. (+2 more)
The evaluation of supervised machine learning models is a critical stage in the development of reliable predictive systems. Despite the widespread availability of machine learning libraries and automated workflows, model assessment is often reduced to the reporting of a small set of aggregate metrics, which can lead to misleading conclusions about real-world performance. This paper examines the principles, challenges, and practical considerations involved in evaluating supervised learning
algorithms across classification and regression tasks. In particular, it discusses how evaluation outcomes are influenced by
dataset characteristics, validation design, class imbalance, asymmetric error costs, and the choice of performance metrics. Through a series of controlled experimental scenarios using diverse
benchmark datasets, the study highlights common pitfalls such as the accuracy paradox, data leakage, inappropriate metric selection, and overreliance on scalar summary measures. The paper also compares alternative validation strategies and emphasizes the importance of aligning model evaluation with the intended operational objective of the task. By presenting evaluation as a decision-oriented and context-dependent process, this work provides a structured foundation for selecting metrics and validation protocols that support statistically sound, robust, and trustworthy supervised machine learning systems.
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All authors Xuanyan Liu, Ignacio Cabrera Martin, Marcello Trovati, Xiaolong Xu, Nikolaos Polatidis
Machine Learning
Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety
Hossem Eddine Hafidi, Elisabetta De Giovanni, Teodoro Montanaro et al. (+3 more)
Driver drowsiness significantly impairs the ability to accurately judge safe braking distances and is estimated to contribute to 10%-20% of road accidents in Europe. Traditional driver-assistance systems lack adaptability to real-time physiological states such as drowsiness. This paper proposes a deep reinforcement learning-based autonomous braking system that integrates vehicle dynamics with driver physiological data. Drowsiness is detected from ECG signals using a Recurrent Neural Network (RNN), selected through an extensive
benchmark analysis of 2-minute windows with varying
segmentation and overlap configurations. The inferred drowsiness state is incorporated into the observable state space of a Double-Dueling Deep Q-Network (DQN)
agent, where driver impairment is modeled as an action delay. The system is implemented and evaluated in a high-fidelity CARLA simulation environment. Experimental results show that the proposed
agent achieves a 99.99% success rate in avoiding collisions under both drowsy and non-drowsy conditions. These findings demonstrate the effectiveness of physiology-aware control strategies for enhancing adaptive and intelligent driving safety systems.
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All authors Hossem Eddine Hafidi, Elisabetta De Giovanni, Teodoro Montanaro, Ilaria Sergi, Massimo De Vittorio, Luigi Patrono
Machine Learning
Hardware-Efficient Neuro-Symbolic Networks with the Exp-Minus-Log Operator
Eymen Ipek
Deep neural networks (DNNs) deliver state-of-the-art accuracy on regression and classification tasks, yet two structural deficits persistently obstruct their deployment in safety-critical, resource-constrained settings: (i) opacity of the learned function, which precludes formal verification, and (ii) reliance on heterogeneous, library-bound activation functions that inflate latency and silicon area on edge hardware. The recently introduced Exp-Minus-Log (EML) Sheffer operator, eml(x, y) = exp(x) - ln(y), was shown by Odrzywolek (2026) to be sufficient - together with the constant 1 - to express every standard elementary function as a binary tree of identical nodes. We propose to embed EML primitives inside conventional DNN architectures, yielding a hybrid DNN-EML model in which the trunk learns distributed representations and the head is a depth-bounded, weight-sparse EML tree whose snapped weights collapse to closed-form symbolic sub-expressions. We derive the forward equations, prove computational-cost bounds, analyse inference and training acceleration relative to multilayer perceptrons (MLPs) and physics-informed neural networks (PINNs), and quantify the trade-offs for FPGA/analog deployment. We argue that the DNN-EML pairing closes a literature gap: prior neuro-symbolic and equation-learner approaches (EQL, KAN, AI-Feynman) work with heterogeneous primitive sets and do not exploit a single hardware-realisable Sheffer element. A balanced assessment shows that EML is unlikely to accelerate training, and on commodity CPU/GPU it is also unlikely to accelerate inference; however, on a custom EML
cell (FPGA logic block or analog
circuit) the asymptotic latency advantage can reach an order of magnitude with simultaneous gain in interpretability and formal-verification tractability.
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Machine Learning · Cross-listed from math.OC
Gradient Descent's Last Iterate is Often (slightly) Suboptimal
Guy Kornowski and Ohad Shamir
We consider the well-studied setting of minimizing a convex Lipschitz function using either gradient descent (GD) or its
stochastic variant (SGD), and examine the last iterate convergence. We prove that $\Theta(\sqrt{\log(T)/T})$ is the optimal anytime rate attainable with a fixed stepsize schedule (that does not depend on the total number of steps $T$). The upper bound holds already for SGD, and improves on the $\log(T)/\sqrt{T}$ rate of Shamir and Zhang [2013], as well as the $\sqrt{\log(T)/T}$ rate of Zamani and Glineur [2023] which was proven only for deterministic GD. The matching lower bound holds already for GD, and confirms the
conjecture of Jain et al. [2019], namely that the optimal $1/\sqrt{T}$ rate for the last iterate cannot be obtained without prior knowledge of $T$.
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Machine Learning
Simulation-Based Optimisation of Batting Order and Bowling Plans in T20 Cricket
Tinniam V Ganesh
This paper develops a unified Markov Decision Process (MDP) framework for optimising two recurring in-match decisions in T20 cricket, namely batting order selection and bowling plan assignment, directly in terms of win and defend probability rather than expected runs. A three-phase player profile engine (Powerplay, Middle, Death) with James-Stein shrinkage (a technique that blends a player's individual statistics toward the league average when their phase-specific data is sparse) is estimated from 1,161 IPL ball-by-ball records (2008-2025). Win/defend probabilities are evaluated using vectorised Monte Carlo simulation over N = 50,000 innings trajectories. Batting orders are evaluated by comparing all feasible arrangements of the remaining players and selecting the one that maximises win probability. Bowling plans are optimised through a guided search over possible over assignments, progressively improving the allocation while respecting constraints such as the prohibition on consecutive overs by the same bowler. Applied to two 2026 IPL matches, the optimal batting order improves Mumbai Indians' win probability by 4.1 percentage points (52.4% to 56.5%), and the optimal Gujarat Titans bowling plan improves defend probability by 5.2 percentage points (39.1% to 44.3%). In both cases, the observed sub-optimality is consistent with phase-agnostic deployment: decisions that appear reasonable under aggregate metrics are shown to be costly when phase-specific profiles are applied.
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