Machine Learning
Explore Beyond the Boundary Using Entropic Information
Bumgeun Park and Donghwan Lee
In reinforcement learning, exploration with sparse and delayed rewards presents a significant challenge due to the limited feedback available for guiding the learning process. Addressing this issue requires extensive exploration in the state space to discover valuable reward signals. In this paper, we propose Entropic Information for Exploration (ENTINEX), a novel method that enhances exploration by incentivizing
agents to explore beyond the boundaries of the state distribution. ENTINEX achieves this by assigning intrinsic rewards to these boundaries, leveraging entropic information to identify them effectively. Through extensive experimentation, we demonstrate that ENTINEX consistently improves exploration performance in environments characterized by sparse and delayed rewards. Our experimental results show that ENTINEX outperforms existing exploration methods, highlighting its effectiveness in both sparse and delayed reward scenarios.
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Machine Learning
ALIVE: Warnings Before Exclusion in Budgeted Multi-Source Learning
Xiyang Zhang, Hongzhi Wang and Yuanhe Tian
A routing decision can be revised at the next transaction, but a latched source exclusion persists across later decisions. We ask what evidence should authorize these unequal-persistence actions when finite-population
auditing and learning share a budget. ALIVE (Action-Layered Intervention via Evidence) is an auditable control layer: one randomized without-replacement prefix supplies cached evidence, heuristic warnings drive non-latching floor-bounded routing, and only two fresh simultaneous certificate separations may latch an exclusion request subject to capacity-feasible activation. Conditional on fixed support and labels under an ideal uniform audit permutation, any predictable controller preserving this interface inherits an anytime familywise bound of \delta on acting against a source that fails the pre-fixed absolute or relative strict-majority-disagreement predicate. With a published known-size, all-strict-majority PPR engine, median evidence count fell from 304 to 96 identities in e40 and from 171 to 62 in e60, while both engines used 48 in e80. In the matched CIFAR controller, the persistent-action layer added +0.1935 accuracy-AUBC percentage points over routing-only in all ten paired seed clusters. The +0.1954-point full-system contrast against CBR was also positive but did not meet the predeclared multiplicity-adjusted criterion (conditional Holm-adjusted sign-flip reference value =.097656). On a fixed natural panel, exploratory PPR used a median closure prefix of 95 rather than 105 for exploratory Serfling/FPC, but still exposed 88.0% of the panel and had no downstream task. Together these results map a restraint–power–cost–utility boundary: the action contract controls a defined persistent decision, while net value depends on evidence margin, audit cost, and budget regime.
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Machine Learning
OnlineCache: Learning Dynamic Caching Policies with Error Correction for Efficient Diffusion Inference
Zhikang Xie, Xichen Ye, Yifan Wu et al. (+5 more)
Diffusion models have revolutionized generative tasks but incur high latency due to iterative denoising. While cache-based strategies accelerate inference by reusing intermediate features, they largely rely on static, sample-agnostic schedules. We argue that this
rigidity overlooks two facts empirically validated in this paper: (i) generation difficulty varies across
prompts, requiring adaptive resource allocation–complex inputs demand more computation while simpler ones require less; (ii) error sensitivity fluctuates across timesteps, where static policies may cache high-error steps or waste computation on low-error ones. We therefore propose OnlineCache, a dynamic caching framework that jointly learns when to cache and how to correct approximation errors. We leverage policy gradient to train a lightweight network for adaptive speed-quality trade-offs, and incorporate a learnable corrector to mitigate caching-induced errors. Both modules are jointly optimized under a bilevel optimization framework, with the policy targeting global generation quality and the corrector minimizing local errors. Our method automatically allocates computational resources across both samples and timesteps, improving overall generation quality. Extensive experiments demonstrate clear superiority. On FLUX.1-dev model, OnlineCache achieves nearly 3 speedup while preserving generation fidelity. On DiT and CogVideoX, it similarly delivers competitive acceleration without compromising quality; across all scenarios, it consistently outperforms existing cache-based acceleration baselines.
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All authors Zhikang Xie, Xichen Ye, Yifan Wu, Haoshen Yu, Li chenan, Peizhu Gong, Weizhong Zhang, Cheng Jin
Machine Learning
Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies
Jan Marius Stürmer, Jascha Knack, Tobias Koch et al. (+1 more)
Large
language models (LLMs) have demonstrated a strong ability to generate syntactically correct code from natural-
language specifications. In this study, we explore how LLMs can be harnessed to automatically translate a neutral graph representation of fluid system models into executable code for two widely adopted simulation environments: the Python library WNTR and the Modelica Standard Library. We conduct a systematic comparison of ten state-of-the-art LLMs and six prompting strategies that differ in the contextual information supplied (e.g., code or documentation). For each configuration we assess the generated code using a suite of software-quality metrics and we validate the functional fidelity of the resulting simulation models by reproducing
benchmark fluid system scenarios. Our findings offer concrete guidance for researchers and engineers seeking to integrate LLM-driven code synthesis into model-based design pipelines. While the best-performing configurations achieve acceptable syntactic quality, we observe substantial gaps remain in simulation fidelity.
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All authors Jan Marius Stürmer, Jascha Knack, Tobias Koch, Andreas Weinmann
Machine Learning
Exploring Block Anomaly Detection In HDFS Log Data Analysis
WenYang Zhong and Tutut Herawan
In recent years, with the development of big data technology, increasingly more companies use HDFS for data processing and storage. As a result, the maintenance of distributed file systems has become an extremely important part of data management. As the function of server systems is becoming increasingly diversified and their services are becoming complex, the logs, recording real-time events make it easier for system operators to locate the failures and errors that happened in the server systems to make server always available. HDFS, a distributed file system, which contains large data sets, will record a large number of logs. Moreover, the logs are not always structured data, they are not stable as well. However, to detect the problems that occur in the system by checking one log by one log, it's complicated and boring work for the system operators. Using machine learning techniques and natural
language processing techniques to detect the HDFS block anomaly will help the system operators to locate and fix the anomaly rapidly and accurately. This paper proposes a streaming HDFS log block anomaly workflow. It helps maintenance practitioners to use parallel computing network in processing historical log, and construct LLM-BiLSTM hybrid deep learning model to detect anomaly block in HDFS, then build streaming log pipeline based on Kafka to give one real-time HDFS log block anomaly detection solution.
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Machine Learning · Cross-listed from cs.CL
PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction
Pirzada Suhail, Nagasai Saketh Naidu, Atanu R Sinha et al. (+1 more)
Large
language models (LLMs) generate text by auto-regressively sampling the next
token. This inherently leads to a many-to-many mapping between
prompts and responses, complicating the task of inferring
prompts from observed outputs. Prior work on LLM inversion frames
prompt recovery as a semantic reconstruction task. They rely on fine-tuning pretrained sequence-to-sequence models on large external
datasets–and requiring access to model weights or logits–to generate semantically plausible
prompts. In contrast, we present a functional approach to inverting a given LLM in a black-box setting, without auxiliary aids. We train an explicit inverse
language model entirely from scratch on data synthetically generated from the target LLM itself. Analogous to forward next-
token prediction, our inverse model is trained using previous-
token prediction, establishing a generative link between the forward and inverse processes that enables faithful
prompt reconstruction. Moreover, it naturally supports diverse
prompt reconstructions through sampling, whereby all such
prompts induce similar responses under the forward, target LLM. Our approach generalises across
datasets and exhibits transferability in reconstructing
prompts from responses generated by different LLMs. Further, across the set of
token based evaluation metrics for
prompt and response reconstructions, our approach outperforms prior work.
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All authors Pirzada Suhail, Nagasai Saketh Naidu, Atanu R Sinha, Amit Sethi
Machine Learning · Cross-listed from stat.ML
The Greedy Advantage in Finite-Horizon Bandits
Kai Zhou, Michael Lingzhi Li and Kai Wang
Organizations increasingly rely on sequential experimentation to improve decision-making. While the multi-armed bandit literature has developed
algorithms with strong asymptotic regret guarantees, many practical applications operate over finite and externally imposed horizons. Motivated by the finite-horizon setting, we develop a class of regularized greedy
algorithms for multi-armed Bernoulli bandits. We derive the first finite-horizon regret envelopes for regularized greedy bandits, showing that finite-horizon regret decomposes into transient exploration costs and a suboptimal convergence term that decays exponentially with the regularization strength. This characterization yields principled calibration rules for the regularization parameters and, as a limiting case, sharper regret guarantees for the classical greedy policy. Across extensive numerical experiments, calibrated regularized greedy policies consistently match or outperform state-of-the-art
algorithms. These results suggest that regularized greedy policies can provide an effective approach for finite-horizon bandit problems.
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Machine Learning
Cross-Resolution Semantic Learning for Graph Domain Adaptation
Yingxu Wang, Haoze Huang, Zhongkai Zheng et al. (+1 more)
Graph Domain Adaptation (GDA) transfers predictive knowledge from labeled source graphs to unlabeled target graphs under distribution shift. Existing methods align representations or regularize graph structures, but do not explicitly model how class-discriminative knowledge learned at different source neighborhood ranges should be routed across target ranges. We call the neighborhood range encoded by a graph representation its propagation resolution and define semantic resolution shift as a cross-domain change in the propagation resolutions at which class-discriminative evidence is strongest. Such shifts can make fixed same-resolution pairing suboptimal and increase the risk of negative transfer. To address this issue, we propose Cross-Resolution Semantic Learning (CReSL), a GDA method that learns soft sourceto-target resolution correspondence from cross-domain class structure. First, CReSL constructs a multi-resolution representation bank using a shared Graph Neural Network and learnable resolution
embeddings, with a resolution-indexed expert for each source resolution. Second, CReSL introduces Cross-Resolution Prototype Transport, which constructs class-resolution prototypes from source labels and soft target posteriors and converts cross-domain prototype discrepancies into expert-specific routing over target resolutions. Third, CReSL introduces Cross-Resolution Target Grafting, which constructs posterior-weighted target-to-source prototype displacements and enforces correspondence-weighted prediction consistency for instance-level adaptation under class uncertainty. Extensive experiments on graph
benchmarks under diverse domain shifts show that CReSL outperforms strong representative baselines across most settings.
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All authors Yingxu Wang, Haoze Huang, Zhongkai Zheng, Shangsong Liang
Machine Learning · Cross-listed from eess.AS
Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens
Yi Luo, Rongzhi Gu and Jixun Yao
Balancing sequence length, representational capacity, and long-horizon stability is a central problem in autoregressive (AR)
speech and audio generation. Representations with higher frame rates or greater capacity can preserve more signal detail, but they also make streaming generation more vulnerable to distribution drift and AR error accumulation. Conversely, shorter and more compressed representations simplify AR modeling, but their limited bandwidth may discard important components and constrain the upper bound of reconstruction fidelity and generation quality. We ask whether a low-frame-rate, high-dimensional, high-bandwidth continuous representation can be co-designed with a streaming generation framework to support robust high-fidelity reconstruction, strong single-
token predictability, and superior long-horizon stability. We decompose this goal into two coupled problems: what
geometric and statistical properties a high-dimensional representation space should have, and how an AR continuous-
token generator should be structured to resist error accumulation. Accordingly, we propose Locodec, a locally encoded codec that shapes its representation space to improve the interpolatability of a lower-dimensional core
manifold and the identifiability of the native high-dimensional coordinates, thereby improving the predictability of high-dimensional high-bandwidth
tokens. We also propose MP-ELD, a single-
token AR flow-matching framework that uses multi-path information routing and residual classifier-free guidance to mitigate error accumulation. Experiments with 8-Hz, 768-dimensional
tokens show that our design preserves reconstruction quality, improves single-
token predictability, achieves competitive WER, and maintains stable long-form synthesis, without using external SSL/ASR models, pretrained text
language models, or post-training stages.
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Machine Learning
Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning
Douwe den Blanken, Martin Lefebvre and Charlotte Frenkel
With the ever-increasing pervasiveness of smart edge devices, the demand is growing for applications that can be tailored to users (e.g., custom keyword spotting) or patients (e.g., adaptive health monitoring). Yet, most edge devices rely on fixed inference
algorithms and thus cannot learn on-device to personalize predictions. When they can, devices typically support only a specific learning scenario, such as few-shot learning (FSL): going beyond this requires resorting either to another specialized device or to cloud-based retraining, which implies significant energy and latency overheads, a lack of real-time capabilities, and
privacy concerns. In this work, we introduce embedder-centric learning (ECL), a framework that unifies four different online learning scenarios: FSL for on-the-fly customization, continual learning (CL) for knowledge accumulation, zero-shot learning (ZSL) for leveraging semantic data, and in-context learning (ICL) for adapting beyond classification. We demonstrate in silicon that ECL can be deployed on resource-constrained devices across four real-world use cases representative of the aforementioned learning scenarios. Our approach establishes a new state-of-the-art performance for FSL character recognition (Omniglot: 96.8% for 5-way 1-shot, 83.3% for 32-way 1-shot), and the first hardware baseline for CL in keyword spotting (NeuroBench keyword FSCIL: 71.8% for 200-way 5-shot). Moreover, we present the first hardware demonstrations of ZSL with semantic data (60.6% for 5-way spoken sentence classification) and ICL (46.2% at the 500th
token of RegBench) operating at micro-to-milliwatt power budgets. Therefore, by unifying multiple learning scenarios, we pave the way for smart and versatile devices that can adapt right at the edge, without reliance on the cloud.
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Machine Learning
Analysing User Reviews to Identify User Concerns Around Permissions in AI Apps
Babar Shah, Faheem Ullah, Myles Watkinson et al. (+3 more)
Artificial intelligence is increasingly embedded in everyday software, making its integration into mobile apps inevitable. However, AI mobile app developers are not always versed in security and
privacy best practices, leaving users to monitor their own security and understand how apps use their data. App reviews capture real user experiences, helping others make informed decisions before downloading. This paper presents a machine learning model for classifying AI app reviews into permission-related categories. Because user reviews are unstructured, assembling a conventional labeled training set is difficult. To address this, AI-generated security and permission reviews are used to identify relevant training examples from a large corpus of human-written reviews, eliminating the need for manual annotation. The proposed approach classified permission reviews with an accuracy of 82%. Analysis shows that users organise their concerns by sentiment toward the requesting app rather than specific permission types, with implications for users, developers, and platform administrators.
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All authors Babar Shah, Faheem Ullah, Myles Watkinson, Muhammad Moiz Khalid, Tehmina Karamat Khan, Muhammad Junaid
Machine Learning
Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification
Anders Jonsson, Emilie Kaufmann, Gianmarco Tedeschi et al. (+1 more)
We present HBPI-UCRL, a model-based
algorithm for hierarchical reinforcement learning (HRL) that learns high-level and low-level policies in parallel. HBPI-UCRL exploits the fact that a high-level transition corresponds to a multi-step transition at the low level. We introduce two conditions on the low-level dynamics that are sufficient to make parallel HRL learnable. When these conditions hold, we prove that HBPI-UCRL has a
polynomial sample complexity in the problem parameters. In the sparse-reward, goal-directed setting, our sample complexity upper bound for HBPI-UCRL is strictly lower than that of its non-hierarchical counterpart, providing theoretical justification for the empirical success of HRL.
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All authors Anders Jonsson, Emilie Kaufmann, Gianmarco Tedeschi, Lorenzo Steccanella
Machine Learning
Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels
Burak Karabulut, Olayiwola Arowolo, Carlo Manna et al. (+2 more)
Accurate fault location is critical for distribution network reliability. However, increasing distributed energy resource (DER) penetration complicates fault location due to intermittent generation and bidirectional power flows that reshape fault signatures. Spatio-Temporal Graph Neural Networks (STGNNs) have shown promise by jointly modeling spatial and temporal dependencies, but their behavior under increasing DER penetration has not been studied rigorously. In this paper, we (i) systematically
benchmark spatio-temporal graph
attention network (STGATv2) against purely temporal (gated recurrent unit, GRU), purely spatial (GATv2) and traditional machine learning baselines, and (ii) evaluate how well models generalize across increasing DER penetration levels (10%, 25%, 50%) on a reconfigured IEEE 123-bus feeder with multiple DER injection points and moderate-to-high impedance faults. Results show that STGATv2 consistently outperforms neural baselines, achieving 92-94% macro F1 in-distribution. Notably, generalization across penetration levels is asymmetric: training at 50% penetration retains near in-distribution F1 score at lower levels, whereas training at 10% degrades considerably at 50% - with STGATv2 retaining 81-84% F1 under these drastic shifts, substantially higher than GATv2 and GRU which drop to 69-74% F1 and 73-75% F1 respectively. Under realistic measurement noise, STGATv2 maintains > 85% F1, while GRU drops as low as 33.5% F1, highlighting the critical role of topological awareness for robust fault location in active distribution networks.
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All authors Burak Karabulut, Olayiwola Arowolo, Carlo Manna, Chris Develder, Jochen L. Cremer
Machine Learning · Cross-listed from cs.AR
RTLCurator: Label-Efficient Data Curation for RTL Generation
Siyang Cai, Cangyuan Li, Wenjing Chang et al. (+4 more)
Training large
language models (LLMs) to write register-transfer level (RTL) requires large corpora of paired specifications and code, and such data is scarce enough that most public corpora are now synthesized. Synthesis provides scale but not correctness, and in two widely used RTL
datasets only 24.4% and 53.5% of pairs pass generated functional tests. This raises the question of how much of such a corpus to keep and which part of it. Correctness alone is a poor answer. A pair that misbehaves in one corner case still shows valid syntax and interface conventions, and complex sequential designs are both harder to generate and harder to validate, so filtering by correctness leaves a corpus of short and simple modules. Correctness is also hard to obtain, since behavior leaves little trace on the surface in RTL, and validating an entire corpus only sorts pairs into passed and failed. We present RTLCurator, which learns a behavior-aware compatibility prior by contrasting each specification with implementations that fail simulation, and calibrates it to a new corpus using a small number of validated pairs. It then constructs the retained subset by balancing
alignment, representation coverage, and RTL structural richness. On CodeV and RTLCoder, keeping 80% of the corpus this way improves on training with the full corpus across all reported metrics while validating only 10% of the pool, whereas ranking by the score alone falls below random selection and filtering the whole pool by simulation does no better.
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All authors Siyang Cai, Cangyuan Li, Wenjing Chang, Kun Wang, Haoyu Gao, Yinhe Han, Ying Wang
Machine Learning
UniPolymer: A Unified Framework for Property Prediction, Structure Recommendation, and Evaluation in Polyimide Design
Junquan Hu, Zhihui Wang, Peng Xu et al. (+4 more)
Designing polyimide structures with specific glass transition temperatures (Tg) is highly challenging. Existing methods primarily focus on target-conditioned generation, lacking an assessment of the consistency between the generated structure and the target properties. This leads to low-quality candidates deviating from the design objective entering subsequent processes, increasing invalid experiments and prolonging the development cycle. To address this issue, we propose UniPolymer, a unified framework for property prediction, target-conditioned generation, candidate evaluation, and structure recommendation in polyimide design and a
dataset containing 10066 deduplicated polyimide repeating units with Tg tags (PITg-Curated) was constructed. To improve the consistency between generated candidate structures and the target Tg, UniPolymer first establishes a reliable structure-property relationship mapping through self-supervised chemical semantic learning, structural consistency enhancement, and multi-scale information fusion. Subsequently, the model employs a continuous-discrete joint Tg representation to guide the autoregressive generation of SELFIES. The generated candidate structures are further evaluated using a frozen property predictor and polyimide-specific structural constraints, and ranked according to their deviation from the target Tg, thereby preventing structures deviating from the target from entering the subsequent validation stage. Experimental results show that UniPolymer achieved a property prediction accuracy of R^2=0.93 and a candidate structure evaluation pass rate of 73.79%, which are 2% and 1.21% higher than the best baseline, respectively. Meanwhile, the predicted Tg values of the recommended candidates are in high agreement with the results of molecular dynamics simulations, thereby reducing the number of candidates that enter the high-cost experimental stage.
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All authors Junquan Hu, Zhihui Wang, Peng Xu, Xinru Guo, Xintong Li, Kun Lu, Ben Fei
Machine Learning · Cross-listed from cs.CL
CalibratedRubric: Task-Adaptive Rubric Banks for Open-Ended LLM Evaluation
Mengting Chen, Yanshu Sun, Wanting Liang et al. (+5 more)
Reliable evaluation of open-ended LLM outputs requires fine-grained rubrics, yet expert curation is costly and difficult to scale. Existing automated pipelines rely on strict judge unanimity and binary variance filters, which cannot distinguish measurable rubrics from informative ones. We introduce CalibratedRubric, a task-adaptive framework that combines type-specific scoring,
Bayesian rubric-measurability filtering, and item response theory (IRT)-based bank assembly. CalibratedRubric estimates each rubric's measurability with a Beta–Bernoulli agreement posterior and uses a submodular information-coverage objective to construct compact rubric banks over the observed capability range. Across financial, healthcare, general, and legal
benchmarks, measurability filtering improves human-gold agreement on JudgmentBench from $\kappa=0.604$ to $0.743$. IRT-based greedy selection improves cross-fitted rank fidelity over random selection across all six evaluated response blocks and requires only 49 rather than 131 rubrics to reach the target correlation on FinResearchBench decision-support tasks. Task-label perturbations further reduce system separation, confirming the practical relevance of task-adaptive scoring. These results support CalibratedRubric as an efficient, uncertainty-aware approach to open-ended LLM evaluation, with calibration gains depending on sufficient judge redundancy.
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All authors Mengting Chen, Yanshu Sun, Wanting Liang, Beidi Luan, Rui Sun, Dezhi Chen, Jing Li, Zuo Bai
Machine Learning · Cross-listed from stat.ML
Simple-regret rates and minimax optimality of fixed-prior expected improvement in Matérn and squared-exponential RKHSs
Emmanuel Vazquez and Sébastien Petit
We study expected improvement (EI) for minimizing a deterministic function $f$ in the RKHS $\mathcal H_k$ of a continuous positive-semidefinite kernel $k$ on a nonempty compact set $\mathcal X\subset\mathbb R^d$. Function values are observed exactly, and EI is computed from a fixed zero-mean Gaussian-process model with covariance $\sigma^2k$, $\sigma>0$. A weak-EI policy queries a point whose EI is at least a fixed positive fraction of its maximum. We introduce a notion of sequential separation radius relating ranked selected-point innovation norms to Kolmogorov widths, drawing on greedy approximation. Standard power-function estimates from scattered-data approximation and a finite-budget regret argument yield the rates. After $N$ post-initial queries, every weak-EI policy has simple regret $O(N^{-\nu/d})$ for isotropic Matérn kernels of smoothness $\nu>0$ and $O(\exp[-c_1\min\{N,N^{1/d}\log(eN)\}])$ for the isotropic squared-exponential kernel, with $c_1>0$. For $d=1$, the sharper bound $O(\exp[-c_2N\log(eN)])$ holds for exact EI, with $c_2>0$. These bounds are uniform over each fixed RKHS ball. If $\mathcal X$ has nonempty interior and $B>0$, the exact EI policy is minimax-rate optimal over the RKHS ball of radius $B$ for Matérn kernels, even among randomized strategies whose final recommendation need not be a query point. For the squared-exponential kernel, it is minimax-rate optimal up to constants in the exponent among deterministic methods whose final recommendation may be any point of $\mathcal X$.
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Machine Learning · Cross-listed from cs.CV
TAVI-TEC: An AI-Based Tool for Procedural Planning of Transcatheter Aortic Valve Implantation
Alessandra Zerillo, Stefano Cannata, Diego Bellavia et al. (+4 more)
Computed
tomography angiography (CTA) is crucial for preprocedural TAVI planning, providing the anatomical information required for prosthesis sizing and vascular access assessment. As the volume of TAVI procedure increases, improving efficiency and standardizing annotations is becoming essential in clinical practice. This study presents TAVI-TEC, a fully automated artificial intelligence-based framework integrated into a web based DICOM viewer for routine preoperative TAVI planning. Pre-procedural CTA scans from patients undergoing TAVI with SAPIEN 3 Ultra (S3U) prostheses were processed using a fully automated pipeline. Deep learning-based
segmentation of cardiovascular structures, calcification detection, centerline extraction, landmark identification, and annular plane definition was implemented to quantify key annular and aortic root measurements and color-coded maps of lumen reduction and vessel diameter for vascular access. A multilayer perceptron classifier was trained to predict prosthesis size prior to the TAVI procedure. Results revealed that TAVI-TEC enabled pre-procedural measurements in approximately 2-6 min. Strong agreement with clinician-derived measurements was observed for annular area (coefficient of concordance, CCC = 0.934; interclass correlation coefficient, ICC = 0.935; R^2 = 0.881) and perimeter (CCC = 0.909; ICC = 0.909; R^2 = 0.854). The valve-size prediction model achieved 82% overall accuracy, with most misclassifications occurring between adjacent prosthesis sizes. Though further multicenter validation and extension to additional measurements and valve platforms are required, the TAVI-TEC methodology may reduce operator variability in pre-TAVI measurements and streamline the preoperative workflows of the Heart Team for decision-making.
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All authors Alessandra Zerillo, Stefano Cannata, Diego Bellavia, Daniele Ciriello, Simone Manini, Salvatore Pasta, Caterina Gandolfo
Machine Learning
Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery
Panagiotis Krokidas, Christoforos Rekatsinas, Vassilis Sioros et al. (+3 more)
Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside optimization performance. Here we present a systematic, compute-aware study of BO that evaluates surrogate models along two axes: optimization quality and computational frugality. Across eight
benchmark functions and nine real-world
datasets spanning materials science, mechanics, robotics, chemistry, and machine learning, we
benchmark four surrogate models: Gaussian Processes, Random Forests, NGBoost, and
Bayesian Adaptive Spline Surfaces. We show that Gaussian Process-based BO consistently incurs the highest time and
memory overhead without delivering superior optimization or sample efficiency. In contrast, scalable alternatives achieve equal or better performance at a fraction of the computational cost. Motivated by these findings, we introduce a surrogate-recommendation framework that predicts the most suitable BO surrogate from inexpensive
dataset characteristics. Together, these results establish FruBO as a reproducible, compute-aware baseline for
Bayesian Optimization and provide practical guidance for surrogate selection under limited computational and experimental budgets.
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All authors Panagiotis Krokidas, Christoforos Rekatsinas, Vassilis Sioros, Grigorios M. Chatziathanasiou, Efi-Maria Papia, George Giannakopoulos
Machine Learning · Cross-listed from cs.IR
GALA: Generative Aligned Learning for Adaptive Multimodal Representation in the Taobao Shangou Recommender System
Jiping Liu, Zhongmin Zhang, Zisen Sang et al. (+7 more)
Modern recommender systems in food delivery increasingly leverage
multimodal signals, including images, text, and user interaction histories, to enhance user experience, yet effective fusion of these heterogeneous modalities remains challenging, hindering both the joint modeling of
multimodal signals and adaptation to evolving user intent. In mainstream two-stage approaches, the separation between content-semantic pretraining of image-text encoders and behavior-driven ranking models limits
alignment between semantic understanding and user behavior patterns. To address these issues, we present GALA, a three-stage pipeline whose core innovation lies in an intermediate "generative RL
alignment" stage that constructs
multimodal pretraining data from user behavior and refines it via conversion-based rewards, effectively bridging the pretraining-fine-tuning gap to align with downstream objectives. GALA comprises three stages: first, behavior-aware triplet pretraining on query-image-text pairs from search logs to early capture user intent and content preferences; second, a novel intermediate stage that refines
multimodal embeddings through reward-driven optimization (GRPO) to dynamically align them with user behavior and bridge the pretraining-fine-tuning gap; and finally, integration of
multimodal and ID
embeddings via adaptive gating with a hybrid loss, preserving
multimodal contributions under long-term ID-dominant training. GALA has been deployed in the production environment at Taobao Shangou, serving over 200 million daily active users. Compared with state-of-the-art (SOTA) methods, it delivers consistent offline gains of +0.12/+0.20 AUC along with better PCOC metrics. Large-scale online A/B tests further report a 0.55 percent increase in order volume, confirming GALA's effectiveness at industrial scale and its robustness across diverse demand patterns.
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All authors Jiping Liu, Zhongmin Zhang, Zisen Sang, Zhijia Fang, Tao Ouyang, Ma Jiang, Shaopeng Liang, Zeyang Hou, Guodong Cao, Jia Jia
Machine Learning
SAF-OPD: Stable Advantage Fusion for On-Policy Distillation
Yifan Ding, Xincheng Wei, Yoshua Y. Li et al. (+7 more)
Reinforcement learning with verifiable rewards (RLVR) broadcasts a single response-level reward to every
token, while on-policy
distillation (OPD) scores each
token against a stronger teacher for a dense advantage but caps performance at teacher quality and discourages exploration beyond it. Their complementarity makes combining RLVR and OPD promising, but we find that fusing the two advantages with a fixed coefficient triggers
entropy collapse from two miscalibrations: a magnitude mismatch, where
token-level OPD advantages can spike far beyond the bounded RLVR advantage and erase its signal, and a temporal mismatch, where sustained full-strength OPD keeps pulling the student toward the teacher and limits exploration needed to surpass it. We propose SAF, a Stable Advantage Fusion framework that resolves both issues via a lightweight, four-stage pipeline applied only to the OPD advantage: a sparsify-then-compress mechanism for magnitude control paired with a warm-up-then-anneal mechanism for temporal control, with each stage independently switchable and adding negligible overhead. Instantiating RLVR with GRPO, we evaluate SAF across seven mathematical
reasoning and code generation
benchmarks with Qwen3-1.7B/4B/8B: SAF avoids
entropy collapse and consistently outperforms fixed-coefficient GRPO+OPD fusion, improving the aggregate score by 0.51-2.70% across all six model-domain settings while achieving more stable training.
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All authors Yifan Ding, Xincheng Wei, Yoshua Y. Li, Ziheng Li, Yuquan Lu, Siyu Zhang, Dongsheng Ma, Rongxiang Weng, Xunliang Cai, Yun Chen
Machine Learning · Cross-listed from eess.IV
Few-shot Deep Learning for Phase-Amplitude Aberration Correction in Transcranial Focused Ultrasound
Minju Seol, Minjee Seo, Seonaeng Cho et al. (+1 more)
Transcranial focused ultrasound (tFUS) is a non-invasive technique that delivers focused acoustic energy through the skull for neuromodulation and therapeutic applications. However, the heterogeneous structure of the skull induces complex, patient-specific phase and amplitude aberrations that distort the acoustic focus and deviate it from the intended target, compromising therapeutic efficacy and safety. Conventional time-reversal (TR) simulations can correct these aberrations but rely on computationally expensive full-wave solvers, making them impractical for real-time use and iterative treatment planning. We propose a few-shot deep surrogate framework that predicts per-element phase and amplitude corrections for a 96-element 3D phased-array transducer from patient CT images. A geometry-aware encoder extracts skull-path features shared across dedicated phase classification and amplitude regression branches, where phase periodicity is handled via circular expectation decoding. The framework is pretrained on diverse skull geometries and fine-tuned with only ten target points, enabling rapid adaptation to unseen patients without full patient-specific simulation. Evaluated via leave-one-out cross-validation across 12 skulls, it achieves a mean phase CMAE of 0.155 rad and amplitude rMAE of 9.089%, a focal centroid error of 0.467 mm, Dice score of 94.422%, and peak pressure ratio of 92.332%, with an approximately 2,535 times speedup over TR simulation. The code is available at https://github.com/Minju-Seol/fewshot-tfus-correction.
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All authors Minju Seol, Minjee Seo, Seonaeng Cho, Kyungho Yoon
Machine Learning
SERUM: State Extraction and Refinement for User Modeling
Andy J. Phu, Karin de Langis, James Mooney et al. (+2 more)
Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow. However, building these models from raw, unstructured screen activity remains an open challenge. We present SERUM, a multi-pass framework that extracts finite-state behavioral models directly from unstructured
egocentric video using hierarchical VLM annotation. Processing screen recordings through a sliding window, SERUM alternates between activity-recognition and intent-inference passes, with each pass refining labels using accumulated prior context to reduce
hallucination and temporal conflation seen in single-pass annotation. Synonymous states are then merged via sentence
embeddings and human-calibrated thresholds into a compact, coherent taxonomy. We evaluate behavioral structure by fitting first-order Markov models over the resulting label sequences (both actions and intents) and measuring predictive accuracy against frequency baselines. Across 61
egocentric videos in four domains (coding, cooking, physical activities, and daily life), we find: (1) iterative label refinement converges to a stable state vocabulary, which we term schematic
equilibrium, after several passes; (2) normalized Markov models achieve substantially lower perplexity and higher action predictions than frequency baselines, with the largest gains on structured tasks like coding; and (3) human annotators rate final-pass labels as accurate and meaningfully improved over first-pass labels. To our knowledge, SERUM is the first system to produce interpretable process models from unstructured
egocentric screen
video without manual annotation, opening a scalable pathway for user modeling and behavioral understanding in the wild. Our demo, code, and results are publicly available
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All authors Andy J. Phu, Karin de Langis, James Mooney, Khanh Chi Le, Dongyeop Kang
Machine Learning
MBDiff: Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation
Rongchao Xu, Lin Jiang, Dahai Yu et al. (+2 more)
Utility data (e.g., electricity, water, and gas consumption), collected by ubiquitous sensors and embedded devices, often contains substantial missing values due to various factors such as device failures and data transmission issues. The data missingness can severely impact utility billing accuracy, hinder demand
forecasting, and disrupt efficient utility supply management. As a result, utility data imputation has attracted much interest from both industry and academia. While many studies have attempted to address this issue, most of them rely on aggregated
datasets for training, overlooking rich user behavior information, which could provide valuable insights for more accurate imputation. However, learning comprehensive user behavior from long-term, diverse, and incomplete utility data remains a significant challenge. Moreover, leveraging user behavior information to guide imputation is nontrivial due to the indirect nature of the correlations. To address these challenges, we propose MBDiff, a Multi-view Behavior-aware
Diffusion Model for Probabilistic Utility Data Imputation. MBDiff incorporates two key technical components: (i) a multi-view User Behavior Extraction module that learns comprehensive user behavior from multiple perspectives, including global, local, and instance-level views; and (ii) a behavior-aware conditional
diffusion model consisting of a reference selection module and a conditional attentional denoising network to impute utility data in a computationally efficient manner. We implement and evaluate MBDiff by collaborating with one of the largest municipal utility providers in Florida. Experimental results demonstrate our proposed MBDiff effectively outperforms state-of-the-art baselines, e.g., it improves 7.04% and 29.1% on the electricity and water usage
datasets for block missingness imputation, respectively.
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All authors Rongchao Xu, Lin Jiang, Dahai Yu, Ximiao Li, Guang Wang
Machine Learning
Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations
Johannes Maeß, Leon Werner, J. Thorben Frank et al. (+5 more)
We introduce implicit machine learning force fields (I-MLFFs), which replace explicit stacks of neural network layers with self-consistent fixed-point equations. In molecular simulations, this formulation enables intermediate representations to be reused across successive timesteps, thereby warm-starting force evaluation. The resulting models effectively combine the computational footprint of a shallow, single-layer MLFF with the representational capacity and accuracy of a deep neural network. Our approach unlocks architecture-agnostic efficiency gains that are inaccessible when force prediction and trajectory integration are considered separately. We demonstrate this across three major classes of graph neural networks: invariant, equivariant Cartesian tensor, and SO(3)-equivariant spherical-tensor architectures. Each yields a two- to five-fold reduction in compute and
memory footprint. Crucially, these gains are achieved while retaining full atomistic resolution and the original integration timestep, avoiding spatial or temporal coarse graining. Our contribution therefore advances the scaling frontier of
quantum-mechanically faithful molecular simulation, enabling longer trajectories and larger atomistic systems within fixed GPU
memory and compute budgets, and thereby opening access to new insights across biomolecular and material systems.
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All authors Johannes Maeß, Leon Werner, J. Thorben Frank, Winfried Ripken, Martin Michajlow, Joshua Futterer, Klaus-Robert Müller, Stefan Chmiela
Machine Learning · Cross-listed from quant-ph
Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization
Piotr Malkowski, Domenik Eichhorn, Joshua Ammermann et al. (+4 more)
Quantum software engineering is an emerging research field focusing on efficiently
embedding the
quantum programming paradigm into existing software ecosystems. A key aspect of this field is the realization of
quantum algorithms using gate-based programming and the subsequent low-level optimization of the resulting
quantum circuits, a process that is commonly performed by so-called transpilation pipelines. One significant challenge in these pipelines is determining which optimizations to apply to a given
circuit. This decision is usually based on fixed default configurations that are uniformly applied to all
circuits, frequently resulting in missed opportunities for more aggressive
circuit optimization. In this work, we tackle this challenge by applying autotuning with supervised machine learning to develop an automated method for selection of transpiler passes. To train our machine-learning models, we employ feature-model based sampling to generate a representative
dataset that examines how different combinations of Qiskit transpiler passes perform across thousands of
circuits drawn from the state-of-the-art benchmarking suite MQT Bench. Using these data, we build a predictive model extension for the Qiskit transpilation pipeline that uses a machine learning model to automatically select combinations of transpiler passes aiming to achieve a maximum reduction in two-
qubit gates. Our empirical evaluation shows that the combinations selected by our model are never outperformed by Qiskit's optimization levels, achieve on average an additional 19.1$\%$ - 32.4$\%$ reduction in two-
qubit gates, and for some
circuits finds reductions of up to $95.8\%$ in cases where Qiskit achieves no reduction at all.
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All authors Piotr Malkowski, Domenik Eichhorn, Joshua Ammermann, Rinor Kelmendi, Nick Poser, Patrick Hopf, Ina Schaefer
Machine Learning · Cross-listed from cs.CV
Have I Seen You? Embedding Behavior Signals Synthetic Face Dataset Membership
Paweł Borsukiewicz, Daniele Lunghi, Wendkûuni C. Ouédraogo et al. (+2 more)
Synthetic face
datasets are increasingly used to reduce
privacy exposure and data access constraints in biometric recognition. Yet the generators that produce these
datasets are trained on real faces, so synthetic data may still reveal their real source data. We study this risk through a
dataset-level membership inference attack that first identifies the synthetic
dataset used to train a face recognizer and then infers the real
dataset used to train the generator. Across 11 face recognition models, 11 synthetic
datasets, and 7 real
datasets, the attack recovers the synthetic training
dataset in 100% of cases and identifies the generator's source
dataset in 54.5% of cases. These results show that synthetic data can retain
dataset-level traces of real training data and that
privacy-preserving deployment requires stronger leakage mitigation.
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All authors Paweł Borsukiewicz, Daniele Lunghi, Wendkûuni C. Ouédraogo, Jacques Klein, Tegawendé F. Bissyandé
Machine Learning
HERO: History-Enriched Rollout Training for Long-Horizon Autoregressive Neural Operators
Jiaquan Zhang, Shuxu Chen, Haifan Meng et al. (+6 more)
Neural operators provide fast surrogates for time-dependent partial differential equations (PDEs) by applying a learned
evolution operator recursively to its own predictions, but this autoregressive rollout feeds every prediction error back as input, so local errors accumulate. Existing rollout-training strategies reduce the mismatch between training inputs and self-generated states, yet their supervision still measures only the absolute discrepancy from the ground-truth trajectory. Such supervision is therefore uninformative about whether the operator has overcome the long-horizon failure behaviors it exhibited earlier during optimization. We propose history-enriched rollout training (HERO), which augments conventional absolute trajectory supervision with relative supervision derived from the model's optimization history. HERO ranks detached candidate rollouts from a periodically refreshed lagged operator, the current model, and a perturbed input by rollout error, spectral discrepancy, energy drift, and error growth, and selects the strongest failure trajectory as reference. This reference enters a margin-based objective as a fixed comparison baseline, inducing a bounded, sample-dependent reweighting of the ground-truth rollout gradient rather than an independent gradient direction, which we further analyze theoretically. Experiments on nine PDE
benchmarks with spectral and
attention-based backbones show that HERO consistently improves long-horizon accuracy, stable rollout length, and out-of-distribution robustness at no inference-time cost. These results indicate that history-enriched relative supervision is effective for stabilizing long-horizon autoregressive prediction.
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All authors Jiaquan Zhang, Shuxu Chen, Haifan Meng, Yi Lu, Zhihan Lyu, Fan Mo, Wei Dong, Yang Yang, Chaoning Zhang
Machine Learning
PluRel-to-RDB-PFN: Schema-Guided Synthetic Relational Pretraining
Mohammad Sadeq Abolhasani and Viswanath Ganapathy
Relational Foundation Models (RFMs) require large-scale synthetic relational databases for pretraining, but existing approaches tightly couple data generation with the model training pipeline. We study whether PluRel, a general-purpose synthetic relational database generator, can serve as an external data source for RDB-PFN, a relational in-context learner originally pretrained with a 600K-task single-table warm-up followed by an approximately 1.8M-task adaptation stage. We build a conversion pipeline that maps PluRel-generated databases, including externally constructed binary prediction tasks, into the RDB-PFN training format and evaluate three curriculum strategies: SCHEMA-GUIDED FIRST (real-world schema then fully synthetic), FULLY SYNTHETIC (diverse synthetic schemas throughout), and SCHEMA-GUIDED LAST (fully synthetic then real-world schema). Using only approximately 5,500 relational databases (approximately 33K tasks), roughly 55x fewer tasks than the original protocol, and no single-table warm-up, our best curriculum (SCHEMA-GUIDED FIRST) achieves 0.6346 average ROC-AUC across 19 real
benchmark tasks at 1024-shot context, recovering 87.6% of the published RDB-PFN performance (0.7245). At 64-shot context, the gap narrows to 93.8% (0.6116 vs. 0.6517). Our results demonstrate that external synthetic generators can provide useful pretraining signals for RFMs when combined with appropriate curriculum design and that exposure to a real-world schema early in training is substantially more effective than late-stage schema adaptation.
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Machine Learning · Cross-listed from cs.CV
SciFigPlag-Bench: A Benchmark for Provenance-Aware Scientific Figure Plagiarism Detection
Zhiying Cui, Minghao Yang, Linlin Gao et al. (+2 more)
Scientific figures often encode the visual evidence behind scientific findings, yet figure plagiarism remains underexplored as a benchmarked
multimodal evaluation problem. We present SciFigPlag-Bench, a
benchmark for provenance-aware
reasoning over scientific figures in scholarly documents. Unlike general image-similarity or image-forensics
benchmarks, SciFigPlag-Bench evaluates whether a suspicious figure reuses evidence from a specific source figure, how the reused content has been transformed, and where the reused evidence appears. We introduce a factorized taxonomy that separates what is reused from how it is transformed, covering material-preserving reuse, such as full-figure and subfigure reuse, as well as abstract-content reuse, such as data re-expression and structural redraw. Guided by this taxonomy, we construct a hybrid
benchmark with 2,582 positive pairs and 2,541 negative pairs, combining documented real-world cases, taxonomy-guided synthetic examples, and visually similar negatives. The
benchmark supports four diagnostic tasks: pairwise detection, source attribution, hierarchical reuse-type classification, and reuse correspondence localization. Experiments with diverse
vision-
language models establish initial baselines and reveal persistent challenges in fine-grained provenance
reasoning, reuse-type understanding, and spatial evidence grounding.
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All authors Zhiying Cui, Minghao Yang, Linlin Gao, Jie Liu, Pengyuan Li