Artificial Intelligence · Cross-listed from cs.LG
ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment
Xiaocheng Fang, Haoyu Wang, Jieyi Cai et al. (+14 more)
Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly those digitized from ECG images, remain incomplete because of short display formats, incomplete waveform digitization, lead loss, or signal corruption. We developed ImputeECG, a mask-conditioned one-dimensional
Transformer autoencoder that completes 12-lead, 10-s ECGs while retaining all observed samples. The model was trained on PTB-XL and evaluated on PTB-XL and CPSC2018 under simulated incomplete settings, with additional real-world validation in a 43,633-record Kailuan clinical cohort after ECG image digitization. Metrics were computed over originally missing regions, with analyses of morphology and downstream diagnostic utility. On PTB-XL, ImputeECG reduced missing-region MAE by 41.7-51.0% and MSE by 54.0-63.7% versus the strongest baseline, with lower errors in R-peak timing, RR interval, QRS duration, QT interval, and P-wave, QRS-complex, and T-wave reconstruction. On CPSC2018, ImputeECG reduced MAE by 49.7-51.9%, supporting external generalization. In downstream multi-label classification, ImputeECG restored performance to 92.28% AUROC and 33.88% AUPRC in the most incomplete PTB-XL setting, approaching complete-ECG performance. On CPSC2018, completed ECGs achieved 94.75-95.89% AUROC and 78.83-81.86% AUPRC across settings. In Kailuan, ECG completion improved zero-shot sex prediction AUROC from 82.6% to 85.8% and reduced age prediction MAE from 10.72 to 9.87 years after image-based ECG digitization. These findings support ECG completion as a practical strategy for converting incomplete ECG records into AI-ready 12-lead, 10-s digital signals and extending the usable scope of ECG archives for digital cardiac assessment.
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All authors Xiaocheng Fang, Haoyu Wang, Jieyi Cai, Qinghao Zhao, Jun Li, Shanwei Zhang, Guangkun Nie, Yujie Xiao, Shun Huang, Jiarui Jin, Hongmin Liu, Guodong Wang, Shuohua Chen, Liming Lin, Shouling Wu, Hongyan Li, Shenda Hong
Artificial Intelligence · Cross-listed from cs.CV
Comparison of Loss Functions for Robust Deep Learning-based Echocardiography Segmentation when Learning with Partially Labelled Data from Multiple Domains
Iman Islam, Esther Puyol-Antón, Bram Ruijsink et al. (+2 more)
Echocardiography is the first imaging modality used for assessing cardiac function, and accurate
segmentation of cardiac structures is essential for deriving biomarkers. However, the development of effective automated
segmentation models for multiple cardiac structures is challenged by the difficulty of training on
datasets from different sources that are often partially-labelled. This study aims to address this challenge by evaluating the performance of three loss functions - adaptive categorical cross
entropy (aCCE) loss, marginal loss, and the adaptive binary cross
entropy (aBCE) loss - in handling partially-labelled data. We conduct a comprehensive comparison of these loss functions across multiple scenarios and network architectures: intra-domain and inter-domain tasks, with both single and multiple partial-labels, and varying proportions of fully-labelled to partially-labelled data. Our experiments reveal that all three loss functions exhibit strong performance in intra-domain
segmentation tasks, effectively handling label variations within the same domain. For inter-domain tasks, where models are trained on
datasets with a domain shift, the aBCE and marginal losses show superior performance when dealing with the case of one label being missing from some training examples. In scenarios involving more than one label being missing, marginal loss outperforms the other methods, demonstrating its robustness in such complex conditions. These results highlight the strengths of each loss function depending on the labelling scenario, emphasizing the importance of selecting the appropriate loss function to optimize model performance. This study represents the first investigation of techniques for handling partially-labelled data from multiple different domains in echocardiography
segmentation and provides a comprehensive comparison of loss-based solutions.
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All authors Iman Islam, Esther Puyol-Antón, Bram Ruijsink, Andrew J. Reader, Andrew P. King
Artificial Intelligence
Quantum-Inspired Harmonic Decision Models: A Computational Framework for Music Generation
Josef Pavlíček, Petra Pavlíčková and Martin Molhanec
This paper introduces a
quantum-inspired computational framework for harmonic decision-making in music. The proposed approach formulates harmonization as an optimization problem within a structured combinatorial space, where multiple candidate chord sequences are evaluated under interacting musical constraints. The model combines an interference-based harmonization stage with a classical optimization procedure grounded in tonal harmony. The
quantum-inspired component enables the parallel consideration of multiple harmonic alternatives, while the classical stage refines the resulting sequences to ensure structural coherence and stylistic plausibility. The framework is evaluated on selected musical examples, including Autumn Leaves and It's a Long Way to Tipperary. Quantitative analysis shows that the optimization stage significantly reduces chord density, increases harmonic stability, and improves functional organization. At the same time, expert evaluation highlights the importance of stylistic context, demonstrating that increased harmonic complexity is not always perceived as more natural. The results suggest that harmonic generation can be interpreted as a structured decision-making process in a constrained search space. The proposed approach provides a computational model that integrates domain-specific knowledge with an interference-based search mechanism. Although preliminary, this work indicates that
quantum-inspired methods may offer a useful framework for modeling complex decision processes in creative domains such as music. The proposed framework contributes to ongoing research on
quantum-inspired models of cognition and decision-making in complex biological and creative systems.
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Artificial Intelligence · Cross-listed from cs.CR
TACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction
Mouhamed Amine Bouchiha and Gregory Blanc
Cyber Threat Intelligence (CTI) reports are predominantly unstructured, heterogeneous, and noisy, which limits their direct usability for automated analysis and
reasoning. Cybersecurity Knowledge Graphs (CSKGs) provide a structured representation of
adversarial entities, actions, and relations, but constructing such graphs from free-text CTI remains a challenge. Recent approaches rely on monolithic Large
Language Models (LLMs) to perform end-to-end extraction and completion, leading to high cost, limited controllability, and unstable performance. This paper introduces TACTIC-KG, an agentic framework for CSKG construction that decomposes the task into modular, specialized LLM
agents responsible for extraction, typing, verification, and curation. Using lightweight models (3B–8B), TACTIC-KG improves stability, recall, and graph consistency while reducing deployment cost. We implement and evaluate TACTIC-KG against recent state-of-the-art systems. Experiments on human-annotated CTI reports show that
agent specialization consistently outperforms larger monolithic in-context-learning (ICL) baselines in extraction F1-score, typing accuracy, and structural graph similarity.
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Artificial Intelligence · Cross-listed from cs.LG
The Map Behind the Flow: Finite-Step Gradient Descent as a Dynamical System
Thomas Hofmann
Many phenomena of deep learning are dynamical: they concern not only which minima exist, but how gradient descent reaches, avoids, or selects among them. Edge-of-stability behavior, sharpness oscillations, catapult phases, balancing, and movement toward flatter representations are effects of the training map itself, and are poorly captured by the small-step gradient-flow limit. This paper studies fixed-step gradient descent as a discrete dynamical system in a hierarchy of exactly solvable models retaining basic structures of deep learning: depth, factorization, width, data coupling, activation, and stochasticity. The starting point is the balanced scalar reduction of a deep linear chain, giving a quartic loss and a cubic gradient map whose post-edge behavior is explicit. Under the natural large-depth scaling, this dynamics converges to a universal Ricker-type map. The edge of stability is therefore not a breakdown of optimization, but the first bifurcation of the training map.
Embedding the scalar dynamics back into factored models turns these regimes into learning phenomena. Finite steps break conservation laws of gradient flow and contract factorization imbalance; residual oscillations move parameters toward flatter, more balanced representations. Wider linear networks produce a ladder of spectral edges, so the optimal learning rate can lie beyond the first edge. Data coupling, nonlinear activations, and
stochastic targets preserve the same organizing principle: finite-step oscillations drive
alignment, balancing, and representation selection. Thus the learning rate is not merely a numerical stability parameter. It is a structural parameter of the training dynamics, determining its attractors and shaping the representations gradient descent selects.
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Artificial Intelligence · Cross-listed from cs.NE
LLM for the development of FCM
Alexis Kafantaris
This article is about the development of a fuzzy cognitive map using a local large
language model. In the light of recent advances it is evident that large
language models, and even local large
language models are capable of extracting quantities from textual data. In other words, a local LLM like Qwen2.5-32B, or probably larger, can accept entities as
prompt input and determine relevant quantitative data as the model output. In turn, this output can be utilized for the construction of a data driven fuzzy cognitive map. Hence, this implementation is achieved and then the model is thoroughly tested; Qwen2.5-32B is used and the data is extracted from hotel reviews from TripAdvisor. Furthermore, the extracted documents pass through the model unfiltered and then a fuzzy cognitive map is trained and evaluated. A case is made about Greek reviews where a
star topology FCM is formed that indicates the preferences of the reviewers. Finally, external validation is performed to establish whether the fuzzy cognitive map can correlate the
star rating of the review -an outcome outside the model's inference scope -with its predicted satisfaction.
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Artificial Intelligence · Cross-listed from physics.geo-ph
Joint Velocity Slope Diffusion Prior for Structurally Constrained Velocity Model Building
Francesco Brandolin and Tariq Alkhalifah
High-resolution velocity models are crucial for reservoir characterization and subsurface delineation. However, the band limited nature of our surface recorded data limits resolution. Utilizing well measurements to enhance the resolution of our subsurface models is an important objective. To this end, we present a
diffusion-guided framework for structurally preconditioned velocity-model reconstruction from sparse well-log information. The proposed approach combines plane-wave PDE regularization, structurally preconditioned inversion, and measurement-guided
diffusion posterior sampling within a unified formulation. Local structural slopes estimated through plane-wave destruction are used both to propagate well information along geological dip directions and to guide the
diffusion sampling process through a joint velocity–slope generative prior. Numerical experiments on the Volve synthetic model and the Viking Graben field
dataset demonstrate that the proposed framework improves structural continuity, lateral consistency, and geological realism compared with conventional structurally preconditioned inversion approaches while maintaining computationally practical inference through DDIM sampling.
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Artificial Intelligence · Cross-listed from cs.RO
Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales
Yang Li, Feng Xue, Fan Mo et al. (+6 more)
Deploying
robot teams in the real world requires simultaneous adaptation to unseen environments, unknown partners, and varying team sizes, yet existing approaches often address these challenges in isolation under the closed-world assumption of fixed teammates. We formalize this as open adaptive multi-
robot teaming and propose a hypergraphic-form game formulation that captures team-level cooperative relationships beyond pairwise interactions, providing a principled foundation for coordination structure inference when team composition changes dynamically within episodes. Unlike graph neural network architectures, this is a game-theoretic construct for modeling strategic interactions and payoff structures among
agents. Building on this formulation, we develop the Hypergraphic Open-ended Learning
Algorithm (HOLA), which progressively expands partner and environment diversity during training rather than optimizing for fixed configurations. Evaluated on cooperative pursuit with multi-drone and multi-quadruped platforms, HOLA outperforms all baselines across all three adaptability dimensions. Learned policies transfer directly to physical hardware without fine-tuning, with successful deployments on Crazyflie and Zsibot L1 platforms confirming robust real-world coordination in novel environments with unseen teammates.
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All authors Yang Li, Feng Xue, Fan Mo, Yunhao Liu, Jianhong Wang, Ying Wen, Qingrui Zhang, Shaoshuai Mou, Wei Pan
Artificial Intelligence
STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training
Qiuyi Qi, Tian Liang, Mutian Bao et al. (+8 more)
Reinforcement Learning (RL) is the dominant paradigm for training Large
Language Model (LLM)
agents on long-horizon tasks. However, sparse and delayed rewards often lead to trajectory neglect, in which
agents lose focus on the task goal and interaction history at intermediate steps. Prior work has explored step-level supervision using Shannon-
entropy-based uncertainty signals, which conflate inherent state complexity with
agent confidence and therefore provide unreliable estimates of decision reliability. To address this issue, we propose normalized
entropy, which measures confidence deviations relative to an
agent's average behavior under a given state, thereby strengthening the association between low-quality actions and trajectory neglect. Building on this insight, we introduce Selective Trajectory-Aware Policy Optimization (STAPO), a hierarchical group-based RL framework. STAPO leverages normalized
entropy to locate outlier steps associated with trajectory neglect and optimizes them via a joint mechanism of trajectory-aware reward and trajectory-independent penalty, enhancing trajectory awareness while preserving training stability. Extensive experiments on ALFWorld, WebShop, and Search-Augmented QA demonstrate that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect, validating its effectiveness and robustness for agentic tasks.
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All authors Qiuyi Qi, Tian Liang, Mutian Bao, Jinjian Zhang, Dongnan Liu, Wei Zhou, Linjian Mo, Ming Kong, Jie Liu, Feng Zhang, Qiang Zhu
Artificial Intelligence · Cross-listed from cs.SE
Using Process Mining to Generate AI Agents from Software Engineering Process Records
Saimir Bala, Fabiana Fournier, Lior Limonad et al. (+1 more)
Integrating AI
agents into Software Engineering (SE) raises an important challenge: how can we specify and realize AI
agents that work effectively alongside humans in hybrid SE teams? Determining the right granularity and separation of concerns for such
agents is non-trivial. Coarse-grained
agents may introduce unmanageable complexity, whereas micro-
agents may create severe coordination overhead. Moreover, existing multi-
agent SE frameworks typically rely on predefined role structures and do not account for project-specific characteristics or process adaptations. We address this by combining object-centric, imperative, and declarative process mining. Using event logs extracted from software repositories, our approach discovers project-specific
agent roles using a predefined SE role vocabulary grounded in repository behavior and generates matching
agent specifications and implementations. As proof-of-concept, we applied our approach to a well-established open-source project. We performed functional tests and an exploratory user study to determine how well the generated AI
agent specifications are aligned with human expectations.
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All authors Saimir Bala, Fabiana Fournier, Lior Limonad, Andreas Metzger
Artificial Intelligence · Cross-listed from cs.CV
MemPose: Category-level Object Pose Estimation with Memory
Xiao Lin, Minghao Zhu, Yun Peng et al. (+4 more)
In the pursuit of robust and generalizable category-level object pose estimation, most existing methods adopt parametric formulations that learn effective representations from data, yet they primarily encode category-level patterns into fixed shape priors or static parameter weights, which limits their scalability to highly diverse instances. In this paper, we rethink category-level pose estimation from a
memory-centric perspective and present MemPose, a
memory-augmented framework that explicitly incorporates category-level
geometric memory into the pose estimation pipeline. We introduce an external
memory buffer that stores and dynamically updates structural representations from previously observed instances, enabling the model to leverage accumulated experience to support current perception. Extensive experiments on four challenging
benchmarks (REAL275, CAMERA25, Housecat6D and Wild6D) demonstrate the superiority of our proposed method over previous state-of-the-art approaches.
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All authors Xiao Lin, Minghao Zhu, Yun Peng, Liuyi Wang, Qiyi Wang, Chengju Liu, Qijun Chen
Artificial Intelligence · Cross-listed from cs.RO
DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation
Jian Zhu, Jianjun Zhang, Taiyi Su et al. (+10 more)
World Action Models (WAMs) provide a promising alternative to
Vision-
Language-Action (VLA) policies by using
video-based world modeling as dense supervision for
robot action learning. Existing WAMs excel at physically grounded execution, but typically lack the explicit
language-level planning interface in VLM-based VLAs for decomposing coarse instructions. Such decomposition becomes important when household tasks involve complex multi-step goals, where coarse user commands need to be converted into sequences of fine-grained executable subtasks. Meanwhile, the field still lacks a fair real-
robot comparison between VLA and WAM execution capabilities, since existing systems often differ in data,
robot embodiments, and task protocols. To address both the decomposition gap and the need for a controlled WAM-VLA comparison, we introduce DSWAM, a Dual-System World Action Foundation Model for fine-grained
robot manipulation. DSWAM keeps a System 1 WAM executor as the default control path and optionally activates a System 2
vision-
language subtask planner only when task decomposition is useful. The planner predicts executable subtasks from short-term visual history and a global task
prompt, while the WAM executor performs world-aware action generation for each instruction or subtask. The executor is trained with action prediction and
video co-training, but inference directly predicts action chunks without explicit future
video generation. To make this execution path practical on real
robots, we further integrate TensorRT acceleration, asynchronous execution, and real-time chunking (RTC) so that policy queries do not block
robot control. To provide a fair real-
robot comparison with VLA policies, we build and evaluate DSWAM under the DeMaVLA real-world deformable manipulation setting with matched
robot platform, pretraining data, post-training data, and evaluation criteria.
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All authors Jian Zhu, Jianjun Zhang, Taiyi Su, Tianbin Liu, Zhangyuan Wang, Kai Xie, Zitai Huang, Chong Ma, Youzhang He, Tianjian Wang, Hanyang Wang, Weihao Ding, Yi Xu
Artificial Intelligence · Cross-listed from cs.LG
Input Pathways Shape Few-Shot, Not Zero-Shot, Binding in Tiny Transformers: A Fully-Enumerable Study
Yoshiyuki Ootani
How does the way information reaches a
transformer – as symbolic
tokens, a clean per-factor "oracle" code, or an entangled perceptual vector – shape whether it binds that information compositionally? We study ~6-10K-parameter
transformers on finite factored worlds enumerated exhaustively, so every measurement covers the whole input space (zero sampling variance) and the informative routes are information-matched (exact Bayes ceiling 1.0). We report four findings. (1) Endpoint invariance: on held-out binding queries no informative route reaches converged zero-shot composition – each ends at or below chance despite a ceiling of 1.0, so within a bounded sweep the failure reflects inductive bias under a lookup-sufficient objective, not missing information. (2) A two-factor account of few-shot binding: sample efficiency is best explained by input-pathway parameter sharing and code readability; a dimension-matched control and a graded readability sweep isolate readability from input dimension, and the clean oracle is not the most sample-efficient readable route. (3) A double dissociation: early in training, distributed – but not index-like – codes pass through a transient above-chance phase (tracking code format), while few-shot efficiency tracks pathway sharing. (4) Failure anatomy: symbolic routes lose the answer at the readout; index routes mis-bind (the answer stays decodable, yet an input intervention shows the output tracks the wrong slot); entangled routes inherit their input's readability. The central claim is the two-factor account; the endpoint and anatomy results are diagnostic constraints. All code, manifests, and per-seed logs are released for exact reproduction.
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Artificial Intelligence · Cross-listed from cs.CV
Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing
Manish Kolachalam and Rani Malhotra
Deep learning
algorithms are notorious for their high carbon footprint and computational demands that limit their deployment on edge devices and raise concerns about their long-term sustainability. Neuromorphic computing and Spiking Neural Networks (SNNs) offer a promising alternative to traditional Von Neumann architectures, providing energy-efficient performance, massively parallel computation, and on-chip learning capabilities. Autonomous machines represent a critical application domain where these advantages are particularly valuable. We present the first comprehensive evaluation of SNNs for real-world automotive multi-object detection and tracking. Using transfer learning with the SpikeYOLO architecture, we achieve mean Average Precision of 0.937 on the KITTI
dataset and 0.771 on BDD100K MOT2020
dataset for object detection and a Higher Order Tracking Accuracy score of 0.701 (KITTI) and 0.445 (BDD100K MOT2020) for object tracking–results competitive with conventional deep learning methods. Our results demonstrate that SNNs can deliver high-performance object detection and tracking in an energy efficient manner, establishing their viability for perception in real-world autonomous systems.
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Artificial Intelligence · Cross-listed from cs.CV
Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
Johannes Kiechle, Richard Osuala, Daniel M. Lang et al. (+5 more)
In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated with neoadjuvant chemotherapy (NACT), effective treatment decision-making remains challenging, as therapeutic response can vary substantially across patients, calling for predictive models capable of accurately estimating individualized treatment response. To address this, we propose an imaging-based 3D spatio-temporal framework for treatment response prediction that integrates a state-of-the-art graph neural network with relational modeling of temporal interactions across timepoints alongside three novel complementary self-supervised treatment trajectory representation learning objectives. Experiments across a cohort of 585 patients from the public ISPY-2
dataset demonstrate that our method substantially outperforms both
vision and self-supervised learning baselines across several classification metrics. Alongside establishing a breast cancer pCR prediction
benchmark, we include a principled ablation of our method and further introduce and empirically assess the impact of the available number of DCE-MRI timepoints per patient trajectory and the inclusion of inter-scan time-differences. Overall, our study substantiates the utility of clinically meaningful longitudinal medical imagaging modeling for predicting NACT-induced pCR. We will publicly share our code repository and a user-friendly PyPI library for
dataset curation upon publication, effectively promoting reproducible open-source research.
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All authors Johannes Kiechle, Richard Osuala, Daniel M. Lang, Stefan M. Fischer, Ivana Janíčková, Karim Lekadir, Julia A. Schnabel, Jan C. Peeken
Artificial Intelligence
Medi-Gemma: A Hybrid Clinical Decision Support System Integrating Deterministic EMR Analytics and Retrieval-Augmented Generation
Mohammed Saim Ahmed Quadri, Yunzhe Xue, Justin W. Ady et al. (+1 more)
Deploying Large
Language Models (LLMs) in high-stakes clinical settings remains limited by structural
hallucinations, weak deterministic
reasoning over tabular patient data, and omissions in vector
retrieval. This paper presents the architecture and validation of Medi-Gemma, a Clinical Decision Support System (CDSS) for wound pathology triage and workflow automation. The platform introduces a decoupled framework that separates clinical perception from data orchestration while preserving traceable
reasoning. Medi-Gemma uses a multi-stage pipeline coordinated by a centralized ClinicalOrchestrator. Data requests are handled without generative inference by a DataManager that cleans unstructured Electronic Medical Record (EMR) files through type coercion. Natural
language queries are processed by a hierarchical IntentRouter, which routes requests to deterministic analytics paths executed by a PandasQueryEngine or to patient-specific
reasoning managed by a ClinicalRAGEngine using a CPU-optimized vector store. A key contribution is the Ground Truth Injection Module, which intercepts patient-specific queries, extracts numeric identification
tokens, queries the structured dataframe via Pandas, retrieves the latest validated clinical state, and embeds this snapshot as an overriding context block in the LLM
prompt before generation. Safety compliance is enforced by a deterministic ProtocolManager that maps clinical terminology to fixed evidence-based risk pathways, while a SafetyVerifier phrase filter prevents output rule violations. Validation shows that this architecture eliminates semantic context drift, prevents database compilation crashes, and improves factual adherence to backend clinical repositories. These results support Medi-Gemma as a safer pattern for LLM-based clinical decision support where structured data fidelity,
retrieval grounding, and deterministic safeguards are essential.
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All authors Mohammed Saim Ahmed Quadri, Yunzhe Xue, Justin W. Ady, Usman Roshan
Artificial Intelligence · Cross-listed from cs.CV
EventCoT: Event-centric Video Chain-of-thought for Reasoning Temporal Localization
Youngkil Song, Yoonjae Baek, Dongwon Kim et al. (+3 more)
Reasoning temporal localization (RTL) requires a model to generate an answer that itself contains the time interval supporting it, coupling high-level
reasoning with temporal grounding in a single response. To tackle this challenge, we propose the first event-centric
video chain-of-thought framework, dubbed EventCoT. EventCoT first performs event-centric tokenization, converting the
video into compact event
tokens that enable efficient identification of question-relevant events. It then reasons within these events to generate the answer, grounding the time interval via
embedding matching that aligns placeholder
tokens with visual
embeddings. EventCoT achieves state-of-the-art results on ActivityNet-RTL while using substantially fewer visual
tokens than previous work, and attains strong zero-shot results on the grounded
video question answering
benchmark ReXTime. Our code will be released for research purposes.
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All authors Youngkil Song, Yoonjae Baek, Dongwon Kim, Inho Kim, Dongkeun Kim, Suha Kwak
Artificial Intelligence
CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs
Qiuyi Qi, Jinjian Zhang, Mutian Bao et al. (+9 more)
Despite their strong
reasoning capabilities and extensive world knowledge, Large
Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications. This deficiency arises from a lack of systematic mechanisms to incorporate constraint information during the generation process. While existing approaches attempt to mitigate this by relying on external tools or task decomposition, they fail to enhance the model's intrinsic constraint awareness. To address this, we propose Constraint-Aware Reinforcement Learning (CARL), a novel RL framework designed to strengthen LLMs' intrinsic focus on constraints. CARL introduces a constraint-aware reward by comparing the model's output distributions under constrained and unconstrained inputs, encouraging constraint focus and penalizing neglect. Compatible with various RL frameworks and requiring no external solvers or top models, CARL enables scalable, end-to-end constraint-aware planning. Extensive experiments on BlocksWorld, TravelPlanner, and T-Eval demonstrate that CARL significantly outperforms standard Reinforcement Fine-Tuning (RFT) baselines and state-of-the-art
reasoning models, exhibiting a markedly increased focus on constraints.
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All authors Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu
Artificial Intelligence · Cross-listed from cs.SD
SynSFX: Multi-Model Sound Effects Synthesis Dataset for Deepfake Detection and Evaluation
Linxi Li, Yuncong Yu, Qianwei Guo et al. (+3 more)
While audio deepfake detection has advanced significantly, representative detectors show limited generalization to synthetic sound effects. Existing environmental audio
datasets such as EnvSDD provide important initial resources, but remain limited in scale and generation provenance for studying isolated sound-effect deepfakes. To support this direction, we present SynSFX, a large-scale corpus of 43374 clips (26452 synthetic, 16922 real) spanning 7 popular text-to-audio models.
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All authors Linxi Li, Yuncong Yu, Qianwei Guo, Liwei Jin, Yechen Wang, Carsten Maple
Artificial Intelligence · Cross-listed from cs.LG
Pretraining Curricula Enable Selective Fine-tuning
Sebastian A. Bruijns, Jirko Rubruck, Mia H. Whitefield et al. (+3 more)
Transformers follow implicit curricula whereby some tasks are learned before others. However, how explicit pretraining curricula influence learning, generalization, and the selectivity of fine-tuning is unclear. This is important for AI safety, where fine-tuning is used to selectively suppress misaligned behaviors. Here, we compare curricula that pretrain tasks in a balanced (sampled uniformly) or an imbalanced (one task early, the other late) fashion. We show that imbalanced learning of two conflicting copy tasks promotes in-context learning and improves the selectivity of refusal fine-tuning. Ablations and activation patching show that this occurs because imbalanced pretraining encourages tasks to be disentangled in separable neural
circuits, whereas balanced training routes both tasks through a common pathway. We extend these findings to a synthetic
language learning task involving rule-consistent and rule-violating data, where imbalanced curricula similarly lead to more localized, less entangled rule representations, resulting in more robust rule-following behavior. Together, these results suggest that imbalanced pretraining curricula may be an important tool for promoting disentangled representations, with direct consequences for the precision and reliability of safety fine-tuning.
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All authors Sebastian A. Bruijns, Jirko Rubruck, Mia H. Whitefield, Kai J. Sandbrink, Fazl Barez, Christopher Summerfield
Artificial Intelligence · Cross-listed from quant-ph
Energy Accuracy Is Not Enough: A Structure-Aware Benchmark and Evaluation Protocol for Quantum Architecture Search
Jiayang Niu, Akib Karim, Yan Wang et al. (+5 more)
Quantum architecture search for molecular ground-state estimation is commonly evaluated through energy accuracy, which does not describe
circuit cost or the physical properties of the prepared state. We introduce HamQASBench, a structure-aware
benchmark comprising eleven molecular
Hamiltonians of up to fourteen
qubits, selected using
Hamiltonian and target-state properties and supplied with exact references. Its evaluation protocol combines energy accuracy and success rates with reference-relative
circuit cost, local
entropy profiles for non-degenerate targets, and state identification within degenerate ground subspaces. Experiments with five methods spanning four search paradigms reveal differences hidden by energy-only comparisons. On a near-product instance under the shallow search budget, the best outputs of all five methods reach chemical accuracy while using between two and sixty-two gates. Equal-energy outputs on a degenerate instance occupy distinct
spin components. Local
entropy profiles distinguish inaccurate outputs and show that entangling-gate counts need not reflect realized
entanglement. Across the molecular instance ladder, product-state outputs can meet or miss chemical accuracy, motivating interpretation of success alongside target-state properties. These results support retaining energy as the task-success criterion while using
circuit-cost and state diagnostics for more informative comparisons. The
benchmark instances, references, evaluation implementation, and per-run data are released for reuse.
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All authors Jiayang Niu, Akib Karim, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Usman, Yongli Ren
Artificial Intelligence · Cross-listed from cs.CL
Evaluating the Effect of Linguistic Relatedness on Cross-Lingual Transfer in Large Multilingual Automatic Speech Recognition
Andrei Florian, Cynthia Jayne Amol, Hope Kerubo Ombaba et al. (+6 more)
Extending automatic
speech recognition (ASR) to low-resource African
languages is constrained by the prohibitive demands of data collection at scale. A promising direction is to leverage the linguistic relatedness between a low-resource target
language and
languages previously seen by a model to reduce the volume of target-
language data needed for effective adaptation. Although this approach has proven reliable for text-based models, its effectiveness in the
speech domain remains contested. We employ a systematic controlled experimental design spanning six factors, two Africa-centric corpora, and four large ASR models, sequentially adapting on a related auxiliary
language followed by the target to isolate whether linguistic relatedness reliably predicts cross-lingual transfer gains across these conditions. In every setting, pre-adaptation on related auxiliary
languages yields no practically meaningful improvements once as little as one hour of target-
language data is available, suggesting that relatedness alone may not reliably predict transfer gains in large
multilingual ASR, or constitute an effective strategy for extending such models to low-resource
languages.
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All authors Andrei Florian, Cynthia Jayne Amol, Hope Kerubo Ombaba, Xiaoyu Cui, Boniface Mwau, Biatus Maina Kamau, Lilian Diana Awuor Wanzare, Christiane Fellbaum, Happy Buzaaba
Artificial Intelligence · Cross-listed from cs.SE
An Exploration of Agentic Information Fusion for Test Maintenance Prediction
Jingxiong Liu, Nasser Mohammadiha and Gregory Gay
Test maintenance is a critical, yet costly, activity - particularly as codebases rapidly evolve. To assist, we present MAST, a multi-
agent framework that predicts which test cases require maintenance following changes to the production code. This identification task is necessary as a precondition to any subsequent maintenance activities, but remains challenging due to the complex relationships between production and test code. MAST advances the state-of-the-art by integrating multiple analyses – including static, lexical, and semantic analyses - through an intelligent fusion and post-check procedure and by focusing on a realistic use and evaluation setting - i.e., standardized input formats, repository-level analyses, and the ability to infer relations between test and production artifacts rather than assuming a pre-existing mapping. We evaluated MAST on 21 industrial Java repositories from Ericsson AB, considering situations where test maintenance both was and was not required in the ground truth. MAST yielded superior precision to a state-of-the-art baseline - resulting in a higher accuracy, F1, and F2 score - with only some loss in recall. Our ablation study demonstrates the value of each analysis in producing the final recommendations. MAST illustrates the potential of multi-
agent systems that can fuse multiple information sources when performing software testing tasks.
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Artificial Intelligence · Cross-listed from cs.SE
A Temporal Reasoning Benchmarking Framework for LRMs via Difficulty-controlled and Dynamic Test Generation
Shide Zhou, Kailong Wang, Ling Shi et al. (+1 more)
Defining the
reasoning boundaries and ensuring the reliability of Large
Reasoning Models (LRMs) remains a critical challenge. Current
benchmarks primarily rely on static
datasets susceptible to data contamination or synthetic tasks lacking fine-grained difficulty control. Furthermore, standard outcome-based evaluations often conceal
reasoning flaws by neglecting the
reasoning process. To address these limitations, we introduce TRACE, a testing framework that models temporal
reasoning as constraint satisfaction problems via Allen's Interval
Algebra. This approach enables precise regulation of logical complexity and incorporates a Trace-Based Verification Oracle to validate
reasoning faithfulness. Using this framework, we construct TRACEBench, an extensive
benchmark comprising 1,200 synthesized test instances across graded difficulty levels. We employ TRACE to evaluate eight widely used LRMs on TRACEBench. The results confirm a strong negative correlation between model performance and our difficulty metric (Pearson's r approximately -0.96), validating the effectiveness of our difficulty control mechanism. Moreover, our trace-based analysis exposes significant discrepancies between
reasoning validity and final answers, revealing a high spurious guessing rate of approximately 28% in mid-sized models. In addition, we diagnose scale-dependent failure modes, ranging from Degenerative Loops in small models to
Reasoning Explosion in advanced architectures. TRACE thus provides a robust, automated platform for benchmarking the true temporal
reasoning capabilities of LRMs.
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All authors Shide Zhou, Kailong Wang, Ling Shi, Haoyu Wang
Artificial Intelligence · Cross-listed from cs.LG
Predicting Drafted Deck Strength for "Magic: the Gathering"
Tomas Rigaux and Hisashi Kashima
Many real-world games do not admit a fixed, compact rule set: instead, their dynamics are defined by interactions among a large and often evolving collection of game pieces, making general-purpose policy learning impractical. Magic: the Gathering (MTG) exemplifies this setting, where the cards themselves define and alter gameplay rules, strategic constraints, and long-term outcomes, while the pool of available cards is ever-changing. We study Draft, a constrained deck-building format of MTG in which eight players make 39-45 sequential selections from semi-random packs to construct a 40-card deck under partial information. By isolating the card selection process from gameplay, Draft provides a tractable yet non-trivial setting for studying decision-making driven by combinatorial card synergies. We propose an encoder-based model that produces set-contextualized card
embeddings to encode the draft decision sequence, with a consistent improvement over linear baselines on large-scale real-world data, establishing a first learned
benchmark for outcome prediction in MTG Draft. Our code is available at github.com/akulen/MtGDraftEncoder.
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Artificial Intelligence · Cross-listed from cs.LG
Multi-Turn On-Policy Distillation with Prefix Replay
Baohao Liao, Hanze Dong, Christof Monz et al. (+3 more)
We study on-policy
distillation (OPD) for agentic tasks, where an LLM
agent interacts with an environment over multiple turns and a student imitates a teacher over these multi-turn interaction histories. Fully online OPD is costly because each update requires fresh student rollouts through the environment and teacher queries at visited histories. We propose Replayed-Prefix On-Policy
Distillation (ReOPD), an off-environment alternative that reuses pre-collected teacher trajectories as replayed prefixes: the student acts at selected steps, while the teacher provides dense per-step supervision without executing new environment interactions. We show that multi-turn OPD introduces a prefix trap: making histories more student-on-policy improves relevance to the student, but can query the teacher on histories where its target is unreliable. This creates a two-sided distribution shift between student occupancy and teacher reliability. ReOPD addresses this by treating multi-turn OPD as a reliability-aware prefix distribution design and implements it with a simple step-decaying sampling schedule that emphasizes early, lower-shift prefixes. Across mathematical
reasoning with Python and search environments over multiple teacher and student model scales, ReOPD preserves or improves OPD-level accuracy, uses zero tool calls during student training, and is at least 4$\times$ faster per rollout than OPD. ReOPD therefore turns expensive
agent-environment interaction into a reusable offline resource, enabling scalable
distillation across tools, tasks, and environments.
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All authors Baohao Liao, Hanze Dong, Christof Monz, Xinxing Xu, Li Dong, Furu Wei
Artificial Intelligence
AgenticPD: A Stage-Aware Agentic Framework for Closed-Loop Physical Design Optimization
Shuo Ren, Zijin Cheng, Yaohui Han et al. (+7 more)
Physical design quality-of-results (QoR) optimization is hard and expensive. Choices made at one stage can help or hurt later stages. Each evaluation requires a costly EDA run through the full flow. While existing methods still treat optimization as flat parameter tuning or a LLM-based script generation task, we present AgenticPD, a stage-aware agentic framework for physical design QoR optimization. Instead of re-running the full flow after every trial, AgenticPD is organized around the stage boundaries of the physical design flow, where a Judge
Agent navigates the search and stage-specialized
agents make local decisions within their own stage using stage-local tools. Additionally, the
agent harness in AgenticPD provides structured observations, execution history, and
agent context management. Experimental results show that AgenticPD achieves the best timing performance among the evaluated PD tuners while maintaining competitive power and area.
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All authors Shuo Ren, Zijin Cheng, Yaohui Han, Libo Shen, Leilei Jin, Wanting Tian, Rongliang Fu, Chao Wang, Bei Yu, Tsung-Yi Ho
Artificial Intelligence · Cross-listed from cs.LG
Zhengpeng Xie, Li Lyna Zhang, Zeke Xie et al. (+1 more)
Big goals are hard to achieve all at once; breaking them into small steps is wiser. We present Trust Region Policy
Distillation (TOP-D), which transforms the notoriously unstable, high-variance On-Policy
Distillation (OPD) into a stable training paradigm by dynamically constructing a proximal teacher. Theoretically, we establish a rigorous framework demonstrating that TOP-D inherently controls gradient variance. By providing a formal global convergence analysis alongside a monotonic improvement bound, we mathematically formalize the reliability and stability of the overall training dynamics. Empirically, TOP-D dramatically enhances training stability, sample efficiency, and final performance on mathematical
reasoning tasks. More importantly, TOP-D introduces zero additional computational overhead, positioning itself as a promising alternative to the well-established OPD paradigm.
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All authors Zhengpeng Xie, Li Lyna Zhang, Zeke Xie, Mao Yang
Artificial Intelligence
FM-ChangeNet: Learning Change through Pathwise Feature Transport
Roie Kazoom, George Leifman and Genady Beryozkin
We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal
reasoning as continuous transport in feature space rather than static endpoint comparison. Given encoded pre and post-temporal representations, we construct intermediate latent states and learn a time-conditioned velocity field $\hat{v}_\theta(z_t,t)$ along the transformation trajectory. This pathwise formulation constrains the predictor over a continuum of intermediate states, providing a denser and less ambiguous supervision signal than conventional endpoint-only
segmentation and enabling the model to capture temporal
evolution explicitly. The learned velocity field is not only a transport mechanism but also an interpretable representation of change: its magnitude serves as a spatially localized change cue that helps distinguish true structural variation from nuisance effects such as illumination shifts and spatial misalignment. We develop a hierarchical multi-scale architecture with cross-temporal
alignment, time-conditioned coarse-to-fine flow decoding, and a unified objective that couples flow supervision, trajectory consistency, spatial regularization, and
segmentation loss. Experiments on remote sensing
benchmarks show that the proposed framework produces more structured and robust change representations while achieving state-of-the-art performance.
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Artificial Intelligence · Cross-listed from stat.ML
Wasserstein Residuals: Learning Gradient Flows from Population Dynamics
Markus Heinonen, Yair Shenfeld, Ricardo Baptista et al. (+4 more)
Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient flow (WGF): a curve of distributions driven by an energy functional. Though there are multiple mathematical characterizations of a WGF, the dominant algorithmic approach relies on the Jordan–Kinderlehrer–Otto (JKO) scheme. JKO-based methods are inflexible to time discretisation and require solving costly optimal transport problems. We take a residual approach, enforcing the continuity equations via a non-negative loss function whose minimum is the WGF. Combined with a data-fitting divergence, this gives a single global objective. This perspective unifies several existing methods and leads to a new particle-based method, stitching, that is simulation-free and robust to large gaps between observations. We demonstrate that the stitching method achieves state-of-the-art performance across trajectory inference
benchmarks. For code see github.com/BasisResearch/wasserstein-residuals.
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All authors Markus Heinonen, Yair Shenfeld, Ricardo Baptista, Daniel Waxman, Dmitry Batenkov, Tim Cooijmans, Eli Bingham