Artificial Intelligence · Cross-listed from cs.SE
AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair
Michael Fu, Qiyue Mei, Patanamon Thongtanunam et al. (+1 more)
Automated vulnerability repair aims to reduce the time and effort required to patch security flaws from a vulnerability triage report. Recent agentic AI approaches have shown promising results in automated program repair. However, vulnerability repair demands richer program context than general bug repair - context that security engineers routinely assemble in practice but that existing agentic approaches do not engineer. We identify three critical gaps: code-structure context capturing cross-file data flows and
memory operation patterns, runtime-execution context revealing crash semantics and
memory origins, and commit-history context recovering how fragile code patterns were introduced. We present AgenticRepair, an agentic vulnerability repair framework that addresses the gaps through multi-faceted program context engineering. AgenticRepair orchestrates three specialized LLM subagents to engineer the contexts, which are then embedded into the
memory of a dedicated repair subagent for context-conditioned patch synthesis. Evaluated on SEC-Bench comprising 300 real-world instances with sanitizer-based patch verification, AgenticRepair achieves a 73% success rate, substantially outperforming the strongest baseline by 29%. Our ablation study confirms that the three context facets are mutually complementary, and that multi-
agent scaffolding and base-model capacity each play an essential role. Collectively, these findings establish multi-faceted program context engineering as a promising design direction for agentic vulnerability repair.
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All authors Michael Fu, Qiyue Mei, Patanamon Thongtanunam, Kla Tantithamthavorn
Artificial Intelligence · Cross-listed from cs.LG
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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Artificial Intelligence
Beyond Component Testing: Validating Agentic AI Systems
Fabio Orazio Mirto, Luca D'Agati, Giuseppe Tricomi et al. (+4 more)
Agentic AI systems act through multi-step trajectories that combine planning, tool use,
memory, interaction, and adaptation. This behavior stretches validation practice beyond component testing and one-shot input-output evaluation, because acceptable system behavior now depends on how decisions unfold over time and under changing environmental conditions. This systematic mapping study synthesizes 262 papers spanning
agent evaluation, software assurance, cyber-physical systems, runtime monitoring, and regulatory guidance in order to characterize the validation problem for agentic systems. The review is organized around a five-dimension taxonomy covering behavioral, safety, temporal, regulatory, and multi-
agent concerns, and uses that taxonomy to map current approaches and identify dense and sparse pairings of approaches and dimensions. The resulting map shows that the literature concentrates on behavioral evaluation (105 of 262 papers) and is thinnest on temporal validity (14 papers); regulatory work rests mainly on assurance cases and regulatory analysis, largely from IEEE-indexed venues, and multi-
agent work mainly on
benchmarks. Three cross-domain case studies (medical care, industrial operations, smart-mobility systems) provide operational illustrations of how the five taxonomy dimensions recur in safety-critical settings, motivated by the failure patterns documented in the reviewed literature. The paper concludes with a lifecycle-oriented research agenda centered on bounded-autonomy specifications,
adversarial trajectory generation, runtime monitoring, and audit-ready evidence structures. The central claim is that trustworthy deployment of agentic AI depends on validating trajectories in context rather than assessing isolated components alone.
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All authors Fabio Orazio Mirto, Luca D'Agati, Giuseppe Tricomi, Stefano Silvestri, Francesco Longo, Antonio Puliafito, Giovanni Merlino
Artificial Intelligence · Cross-listed from cs.CV
Dense Temporal Contrast Synthesis via Conditioned Latent Transport
Smriti Joshi, Apostolia Tsirikoglou, Daniel M. Lang et al. (+15 more)
Dynamic contrast-enhanced
magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast
agents (GBCAs) restricts use in contraindicated populations, prolongs scan protocols, and presents environmental toxicity concerns. Contrast synthesis offers a non-invasive alternative; however, existing approaches struggle to balance spatial realism with temporal continuity, suffer from slow iterative sampling, underutilize structural priors, and lack clinical validation. We propose a novel conditioned latent transport framework that predicts contrast enhancement in a single forward pass. By anchoring the latent trajectory to the pre-contrast anatomy and applying continuous time conditioning, the model synthesizes patient-specific contrast
evolution at any acquisition time. The proposed approach outperforms baseline and the state-of-the-art models across spatial, perceptual, temporal, and distributional metrics. Evaluated on an independent external cohort, the method demonstrates robustness to domain shifts induced by scanner noise as well as differing acquisition protocol. Furthermore, our synthetic contrast enhancement significantly improved downstream tumor
segmentation performance, yielding a 22.4% relative increase in Dice coefficient (0.60 vs. 0.49 baseline pre-contrast, p < 0.01), reducing boundary
segmentation error by over 39%, while outperforming all other generative model baselines. Finally, a reader study involving four breast radiologists evaluated the image quality, kinetic fidelity, and diagnostic viability of our synthesized sequences across 40 randomly selected cases. The results demonstrated that in 70% of cases, synthesized images provided sufficient clinical information to support the same management decisions as real DCE-MRI, suggesting a path toward safer and faster contrast-free or contrast-reduced imaging workflows.
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All authors Smriti Joshi, Apostolia Tsirikoglou, Daniel M. Lang, Richard Osuala, Noah Márquez Varaa, Alejandro Guzman, Grzegorz Skorupko, Sebastian Ibarra Arregui, Lidia Garrucho, Akane Ohashi, Dimitra Ntoula, Eugen Divjak, Oğuz Lafc{\i}, Jan C. Peeken, Julia A. Schnabel, Fredrik Strand, Oliver Diaz, Karim Lekadir
Artificial Intelligence · 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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Artificial Intelligence · Cross-listed from cs.CL
Cross-Lingual Transfer for Machine Translation in Turkic Languages
Omer Burak Cinar, Mehmet Mert Dalkilic and Cagri Toraman
Cross-lingual transfer is central to low-resource machine translation, but its behavior within closely related
language families remains insufficiently characterized. We study transfer among five Turkic
languages; Turkish, Azerbaijani, Uzbek, Kazakh, and Kyrgyz; using pairwise transfer
matrices. In this setting, each model is fine-tuned with one transfer source and evaluated on a different transfer target while the translation target remains the same. Across mT5 experiments, we find that transfer is strongest between closely related Turkic pairs, especially Turkish-Azerbaijani and Kazakh-Kyrgyz. We also show that transfer direction matters, and that the same transfer source-transfer target pair can behave differently when the translation target changes. Latinization improves BLEU and chrF in several script-mismatched settings, but its effect is not uniform across metrics. Additional analyses show that transfer sources are mostly stable across different
datasets and model settings.
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Artificial Intelligence · Cross-listed from cs.LG
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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Artificial Intelligence · Cross-listed from cs.MA
SeekBrain: An Autonomous Multi-Agent System for Accelerating Neuroscience Discovery
Jiamin Wu, Peishan Xiang, Jingyang Chen et al. (+26 more)
Modern neuroscience relies on integrating multi-scale,
multimodal datasets to uncover the neural principles underlying intelligence. However, analytical challenges posed by highly heterogeneous data and fragmented workflows increasingly constrain discoveries. Here we introduce SeekBrain, an autonomous multi-
agent framework designed to accelerate neuroscience discovery through domain-grounded hierarchical planning and cross-modal data analysis. SeekBrain dynamically constructs a repertoire of analysis recipes extracted from code-paper pairs. By coupling this codified expertise with agentic planning and execution engines, the framework scalably generates hypotheses and analytical pipelines on demand. Systematic evaluation on the expert-annotated BrainArena
benchmark demonstrates that SeekBrain substantially outperforms state-of-the-art
agent baselines across various analysis tasks. Crucially, when deployed in real-world research, SeekBrain integrated behavioral, neural, and anatomical data to reveal structured, distributed neural representations of larval zebrafish behavior and a shared axis of regional decoding strength across the
brain in a mouse decision-making task. These results establish SeekBrain as a scalable and practical tool for accelerating data-driven discoveries in neuroscience.
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All authors Jiamin Wu, Peishan Xiang, Jingyang Chen, Yuqing Zhu, Yuxi Li, Ling Luo, Qihao Zheng, Jialiang Zu, Yongchao Wu, Mindong Liu, Haitao Wu, Chaofan Hu, Yijie Sun, Yuqi Hang, Yu Zhu, Shuo Li, Yue Fan, Shiyang Feng, Wanghan Xu, Tianlei Zhang, Jie Zhang, Wenlong Zhang, Bo Zhang, Kai Wang, Lei Bai, Mianxin Liu, Wanli Ouyang, Jiulin Du, Chunfeng Song
Artificial Intelligence · Cross-listed from cs.CV
DualDiT: A Conditional Dual-Output Diffusion Transformer for Joint OCT Image and Segmentation Mask Generation
Fernando García-Torres, Rocío del Amor, Sandra Morales et al. (+4 more)
Background and Objective: Generating realistic medical images with anatomically accurate
segmentation masks helps address the shortage of annotated data in medical imaging, particularly in optical coherence
tomography (OCT) of mouse eyes, where manual retinal layer delineation is labour-intensive due to tiny structures and required expertise, resulting in scarce
datasets. While
diffusion models perform well in medical image synthesis, joint image-mask generation has relied mainly on U-Net-based denoisers, leaving
diffusion transformers largely unexplored. Methods: We propose a conditional dual-output
Diffusion Transformer (DualDiT) for joint synthesis of OCT B-scans and
segmentation masks of the upper retinal
cell layers in ex vivo mouse retina. DualDiT encodes both modalities into a shared latent space via a pretrained VAE, concatenates their latent representations, and performs conditional
diffusion over the joint tensor. We compared DualDiT against two adapted
diffusion baselines: DDPM and LDM. Generative quality was assessed via Fréchet Inception Distance (FID) and spatial FID (sFID); practical utility via synthetic data augmentation for downstream U-Net
segmentation; and perceptual realism via evaluation by three domain experts. Results: DualDiT achieved the best generative quality (FID 56.14, sFID 114.35), outperforming DDPM and LDM. Expert panels misclassified 46% of synthetic samples as real and 42% of real samples as synthetic. Adding DualDiT-generated images and masks improved Dice and IoU scores on a held-out
segmentation test set. Conclusions: DualDiT shows that
transformer-based
diffusion models can effectively learn the joint distribution of OCT images and
segmentation masks, surpassing DDPM- and LDM-based baselines in generative fidelity, downstream utility, and perceptual realism, highlighting its potential for data augmentation in annotation-scarce medical imaging.
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All authors Fernando García-Torres, Rocío del Amor, Sandra Morales, Álvaro Barroso, Peter Heiduschka, Björn Kemper, Valery Naranjo
Artificial Intelligence · Cross-listed from cs.HC
The persuasive power of large language models does not depend on their perceived national origin
Ningzhi Liu, Yannic Hinrichs and Jonas R. Kunst
Conversational AI developed by geopolitical rivals reaches citizens worldwide, raising concerns that it could sway public opinion or be rejected as foreign propaganda, with consequences for democratic discourse and information sovereignty. Yet, whether an AI's perceived national origin shapes its persuasive power is unknown. In a preregistered randomized experiment, 403 adults from a nationally representative United States sample held a three-round debate with a chatbot introduced as either American ("DiscoveryAI") or Chinese ("ZhengheAI"), discussing a political or non-political topic. In all conditions, participants actually conversed with the same model (GPT-4o), instructed to argue against their initial position. We combined pre- and post-conversation self-reports of attitudes, trust, and collective narcissism with computational analyses of 1,209 participant turns, including LLM-coded stance and argumentative conduct, stance-sensitive
embeddings, and keyword-masked
emotion and toxicity classifiers. The conversations produced substantial attitude changes in every condition. Critically, the nationality label affected neither self-reported attitude change nor expressed stance, concessions, counterarguing, or affect, and equivalence tests and Bayes factors largely supported these null effects. The label's only reliable footprint was lower pre-conversation human-like trust in the Chinese model, whereas functionality trust was unaffected. Political topics slowed stance movement toward the AI's position, and collective narcissism predicted less attitude change regardless of origin, acting as a general barrier rather than an out-group filter. Users thus initially withhold social trust from a rival's AI yet still assimilate its arguments; origin labeling and transparency requirements alone may offer weak protection against foreign influence operations conducted through conversational AI.
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Artificial Intelligence
MAGA: Multi-Platform Self-Fusion of GUI Agents via Structured Action Distillation
Hang Yan, Zhangxuan GU, Beitong Zhou et al. (+5 more)
Graphical user interface (GUI)
agents based on large
language models are increasingly deployed across mobile, web, and desktop environments. However, existing
agents are typically domain-specific, limiting the deployment and user experience. This motivates the consolidation of specialized models into a single cross-environment policy. Weight merging directly merges domain-specific experts but can corrupt executable actions under expert disagreement, while on-policy
distillation (OPD) avoids conflicting teacher supervision yet still treats all response
tokens equally during
distillation, ignoring that action
tokens are the only interface between the environment and the
agent. To address this, We introduce MAGA that re-allocates training signal according to the structured action. Based on the correctness of the generated action, it suppresses unnecessary or invalid
distillation signals and focuses learning on erroneous actions. Besides, a training-only hint optimizes the supervision signal provided by domain-specific teachers without changing the student input. Across two model scales, MAGA achieves the highest mean success rate, outperforming the strongest baseline by 2.0% at 8B and achieves almost the same average performance with teachers.
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All authors Hang Yan, Zhangxuan GU, Beitong Zhou, Jiaxuan Chen, Runze Li, Yusong Hu, Shuheng Shen, Changhua Meng
Artificial Intelligence · Cross-listed from cs.CL
Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation
Yongshi Ye, Biao Fu, Chongxuan Huang et al. (+2 more)
Multi-domain machine translation (MDMT) poses a unique challenge due to varying levels of linguistic complexity across domains. Inspired by human translators' ability to adapt
reasoning effort based on difficulty, we propose TwT (Translation with Thought), a resource-rational framework that learns to modulate inference between intuitive and deliberate
reasoning. TwT is trained in two stages: (1) supervised fine-tuning on difficulty-aware long chain-of-thought traces distilled from DeepSeek-R1 and rewritten by GPT-4o to reflect human-like
reasoning economy, and (2) reinforcement learning with a hybrid reward to optimize translation quality and
reasoning efficiency. Evaluated on 15
benchmarks spanning in-domain and out-of-domain settings, as well as 3 seen and 59 unseen
languages, with ablations across three backbone models, TwT-7B and TwT-14B outperform much larger SOTA
reasoning models in translation quality, while reducing
token usage by 32–60%. These results confirm that aligning translation behavior with cognitive principles enables robust generalization, high translation quality, and efficient
reasoning in MDMT.
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All authors Yongshi Ye, Biao Fu, Chongxuan Huang, Yidong Chen, Xiaodong Shi
Artificial Intelligence · Cross-listed from cs.CV
OsteoCAD: A Human-in-the-Loop Cloud-Edge Framework for Bone Tumor Segmentation
Maximo Rodriguez-Herrero, Dante D. Sanchez-Gallegos, Heriberto Aguirre-Meneses et al. (+3 more)
Artificial Intelligence (AI) and Deep Learning (DL) have notably advanced medical image analysis, yet many health- care organizations struggle to adopt them due to limited com- putational resources and specialized expertise. To address these barriers, we introduce OsteoCAD, a modular eHealth framework that democratizes access to DL tools in clinical practice. Osteo- CAD delivers end-to-end DL capabilities-from
dataset creation and preprocessing to model training and inference-through an integrated and user-friendly interface. To mitigate local hardware constraints, the framework securely connects to remote GPU infrastructures. We validate OsteoCAD's feasibility through a real-world case study in Mexico focused on large bone tumor
segmentation. The results demonstrate the framework's ability to enable DL-powered eHealth solutions without demanding ad- vanced technical expertise or complex local configurations.
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All authors Maximo Rodriguez-Herrero, Dante D. Sanchez-Gallegos, Heriberto Aguirre-Meneses, Marco Antonio Núñez-Gaona, J. L. Gonzalez-Compean, Jesus Carretero
Artificial Intelligence
Tool Specifications Matter: Uncovering and Mitigating Safety Risks in AI Agents
Minghui Pan, Jiayuxuan Yang, Yuanyuan Yuan et al. (+2 more)
AI
agents extend large
language models (LLMs) with external tools, enabling them to perform complex tasks and translate model outputs into consequential real-world actions. Yet LLMs often become substantially less safe when deployed as
agents, and the source of this degradation remains poorly understood. In this paper, we identify schema-formatted tool specifications as a primary source of
agent safety degradation and show, through white-box representation analysis, that they weaken the model's internal refusal signals and contribute to unsafe tool execution. Building on this finding, we propose SafeKeep, an inference-time safeguard that decouples safety judgment from tool execution: it assesses requests using flattened textual tool specifications while retaining the original schema-formatted specifications for execution. Across two representative
benchmarks and four LLMs, including both white-box and black-box models, SafeKeep increases the average refusal rate for harmful requests from 23.8% to 70.6% and reduces the average attack success rate under observation-level
prompt injection from 25.6% to 2.5%. It also outperforms existing safeguards and preserves task-handling capability. We release the code and data at https://github.com/snowcatsmoking/SafeKeep .
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All authors Minghui Pan, Jiayuxuan Yang, Yuanyuan Yuan, Yu Jiang, Zhenpeng Chen
Artificial Intelligence · 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
Artificial Intelligence
Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL
Ruiming Liang, Yi Zhong, Yizhen Yuan et al. (+6 more)
Modern large
language models (LLMs) are expected not just to answer correctly, but to adapt their behavior to different human values and use cases. As a result, multi-reward reinforcement learning (RL) has become an increasingly important problem for LLMs, where each reward captures a different aspect of desired behavior. However, optimizing with multiple rewards suffers from a more severe
alignment tax issue, where different optimization objectives can trade off or even conflict with each other, leading to unstable and inefficient post-training. In this work, we propose PRISM, a new multi-reward RL framework built upon the idea of policy-space decomposition and composition. Instead of compositing different rewards, PRISM optimizes a set of standalone positive policies and a global negative policy. This alleviates the potential conflict during multi-reward policy optimization, while enabling controllability during inference by flexible policy composition. Experiments on scientific
reasoning, tool-use
reasoning, and helpfulness-safety
alignment show that PRISM consistently outperforms existing multi-reward RL baselines, with extra controllability for inference-time preference control.
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All authors Ruiming Liang, Yi Zhong, Yizhen Yuan, Yinan Zheng, Tianyi Tan, Tianyue Wang, Haiyun Guo, Jinqiao Wang, Xianyuan Zhan
Artificial Intelligence · 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
Artificial Intelligence · Cross-listed from cs.IR
RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems
Haoran Ling, Yuecheng Li, Zeyu Song et al. (+5 more)
Optimizing modern recommender models still depends heavily on engineers manually iterating over architectural, objective, and training-strategy changes. While LLM-based
agents can automate this trial-and-error process, allowing the LLM to both select modification directions and generate concrete hypotheses often leads to unstable search under limited experiment budgets. Inspired by the above challenge, we propose RecHarness, a Bandit-Routed Agentic Harness for automated recommender model optimization. RecHarness separates the optimization process into two steps: a bandit router selects the next modification direction according to historical validation feedback, while the LLM generates a concrete optimization hypothesis and executable code edit within the selected direction. To sustain long-horizon exploration, RecHarness uses a jump-basin mechanism to activate a structural-jump arm when local edits stagnate. Across multiple recommendation tasks,
datasets, and model backbones, RecHarness achieves more stable performance improvements and uses limited trial budgets more effectively than LLM-
reasoning search. During a 7-day online A/B test on a large-scale short-
video advertising platform, the selected candidate improves ADVV by 2.084%, Revenue by 0.534%, and Exposure by 0.559%. Code is available at https://github.com/6lyc/RecHarness.
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All authors Haoran Ling, Yuecheng Li, Zeyu Song, Jing Yao, Shuwen Kang, Chi Lu, Wenjin Wu, Peng Jiang
Artificial Intelligence · Cross-listed from cs.CV
When Model Priors Conflict with Visual Evidence: Mitigating Commonsense-Driven Hallucinations by Selective Prior Calibration
Kesheng Chen, Yamin Hu and Wenjian Luo
In
vision–
language models, commonsense-driven
hallucination (CDH) occurs when a model's commonsense prior overrides clear visual evidence of an atypical state. For example, a model may report that a visibly six-fingered hand has five fingers. We show that these errors are systematically directed: when a model answers a question about a counterfactual (CF) image incorrectly, its answer often coincides with the candidate it prefers without access to the image. Suppressing this prior indiscriminately can repair CF errors, but may also disrupt correct answers on matched commonsense (CS) images, where the same prior is helpful. We therefore propose Selective Prior Calibration (SPC), which subtracts candidate-level prior-preference estimates from image-conditioned scores with an instance-dependent strength and revises the original prediction only when the resulting score pattern strongly supports an alternative. Extensive experiments demonstrate that SPC substantially improves accuracy on CF images while largely preserving accuracy on matched CS images. Furthermore, these gains generalize across CDH categories, candidate-answer permutations, and other conflict
benchmarks, while SPC rarely alters predictions on
benchmarks without such conflicts.
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Artificial Intelligence · Cross-listed from cs.CL
Small Is Enough: Per-User Style Rewriting of AI-Edited Text via LoRA Adapters
Antorweep Chakravorty
InMyStyle is a
privacy-first, single-user system that adapts small
language models to rewrite AI-edited text towards an individual user's writing style without an instruction
prompt at inference. Given a user's documents, it uses multiple local helper LLMs to construct paired training examples and fine-tunes LoRA adapters on Qwen2.5 models ranging from 0.5B to 7B parameters. Length-aware generation budgets and automatic chunking support inputs of different lengths. We report a single-user case study: 219 evaluation pairs derived from 73 paragraphs of one author's scientific writing, with all adapters trained using the same rank-8, three-epoch recipe. The automatic composite score (0-1 scale) plateaus across model sizes under both greedy and sampled decoding ($Q=0.689$-$0.695$, with overlapping confidence intervals). In this setting, small models are sufficient for the measured rewriting task, and model size mainly determines efficiency trade-offs rather than a stable quality ranking. The gains favor content-preserving naturalization more than recovery of personal style, with authorship probabilities staying near the classifier's decision boundary (0.51–0.53) and stylometric improvement being near zero. As a secondary evaluation, 400 ratings from five LLM judges give InMyStyle outputs a mean perceived AI-ness score over 20% lower than their helper-generated inputs, with scores decreasing with model size in this sample. The study does not establish generalization across users.
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Artificial Intelligence · Cross-listed from cs.RO
FBFM: A Training-Free Asynchronous Feedback Mechanism for Flow-Matching in World-Action Models Execution
Peize Li, Ruimeng Zhang, Ru Zhang et al. (+3 more)
Although world-action models (WAMs) enhance long-horizon
robot control by predicting visual
evolution before acting, long-horizon reliability demands repeated re-grounding in real observations–not recursive rollout. Existing WAMs address this by refreshing history or KV cache with ground-truth data between chunks. However, such chunk-wise feedback operates at a coarse temporal granularity and thus fails to correct prediction errors at the individual time-step level. To address this, we propose Feedback Flow Matching (FBFM), a training-free inference mechanism that pushes re-grounding inside the actively generated chunk. During flow matching, FBFM applies a masked pseudoinverse correction to the conditional velocity field: it leverages the preceding action chunk to guide generation of the next action chunk, and uses the image observed after executing that preceding chunk to guide the next frame prediction. This cross-chunk pairing–where feedback from one chunk arrives in time to shape the next–creates an asynchronous loop that corrects errors without waiting for chunk boundaries. Being training-free, the mechanism improves responsiveness to unexpected events and suppresses drift in long-horizon tasks. We evaluate FBFM on both a joint-generation WAM (DreamZero) and a stage-wise WAM (LingBot-VA). On selected LIBERO and RoboTwin2.0 tasks, it improves success rates by over 5% in favorable settings, and real-world
robot observation-prediction diagnostics show notably better tracking. We argue that FBFM offers a new paradigm for fine-grained online correction, bridging open-loop flow generation with closed-loop real-world dynamics.
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All authors Peize Li, Ruimeng Zhang, Ru Zhang, Cong Huang, Kai Chen, Shanghang Zhang
Artificial Intelligence · Cross-listed from cs.NE
Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution
Vojtěch Novák and Ivan Zelinka
Swarm and evolutionary
algorithms are usually analyzed as complete procedural systems in which nonlinear selection, replacement, and adaptation obscure simpler structure within candidate generation. This paper introduces an operator–selection factorization that separates objective-independent variation from boundary repair and fitness-dependent selection, and uses it to study the proposal geometry of the Self-Organizing Migrating
Algorithm (SOMA) and Differential
Evolution (DE). The canonical SOMA proposal is shown to be affine in the search space and exactly linear in an augmented migrant–leader state. In leader-relative coordinates, the resulting operator provides a direct interpretation of interpolation, projection, overshooting, and coordinate masking. Under Bernoulli perturbation masks, we derive closed-form expressions for the proposal mean, covariance, expected squared step length, expected squared distance from the leader, active dimensionality, and coordinate coverage. For canonical DE/rand/1/bin, we derive the finite-population moments of differential mutation and characterize the additional covariance and coordinate dependence induced by forced-coordinate binomial crossover. Exact enumeration and Monte Carlo experiments verify the analytical identities and quantify the effects of mask conditioning, boundary repair, and fitness-based selection. The analysis further motivates geometry-controlled and rotation-aware SOMA variants, together with an adaptive population-reducing extension of iSOMA. Experiments on the complete noiseless BBOB
benchmark show that these operator-guided variants substantially improve upon canonical SOMA and are competitive with established DE methods in several dimension–budget regimes. The results demonstrate how proposal-level operator analysis can support both the interpretation and design of population-based optimizers.
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Artificial Intelligence · Cross-listed from cs.CR
MOSAIC: Masked Outsourcing of Secure AI Computations
James Hsin-yu Chiang, Sheila Zingg, Kari Kostiainen et al. (+1 more)
We address the challenge of securely and efficiently outsourcing AI computations from a trusted but computationally weak client to an untrusted but powerful server, in the setting where the client holds both the input and the model, and the server must learn neither. We present MOSAIC, whose core is a novel
matrix-multiplication masking protocol that scales to far larger
matrices than prior work, enabling the safe outsourcing of modern workloads such as large
transformer inference. By introducing small amounts of noise to the multiplication result and thereby relaxing correctness, MOSAIC achieves optimal asymptotic client overhead and concrete runtimes orders of magnitude faster than prior work. Its security reduces to the decisional LWE and LPN assumptions. Because this noise accumulates across the many layers of a
transformer, a key technical challenge is bounding error growth; MOSAIC addresses this with an error-scaling mechanism based on random Hadamard rotations. On large 70B
transformer models, MOSAIC's perplexity is comparable to popular quantization approaches and even matches full-precision BF16 inference on HumanEval. Finally, we present an end-to-end implementation showing how ideas like MOSAIC can promise a path towards large-scale confidential AI in modern data centers. Non-confidential inference is already distributed across phase (prefill/decode), layer, and time to maximize utilization of heterogeneous hardware, using RDMA-like networking to move activations, cached KV values, and weights across nodes. MOSAIC enables scaling of confidential compute by keeping the trusted computing base (TCB) small and outsourcing the bulk of the AI computation to untrusted accelerators.
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All authors James Hsin-yu Chiang, Sheila Zingg, Kari Kostiainen, Srdjan Capkun
Artificial Intelligence
MirrorCraft: Paired Evaluation under Hidden Rule Changes in Minecraft
Jianxin Gao, Beini Hu, Runze Li et al. (+2 more)
With the rapid progress of large
language models (LLMs), an interesting question has emerged: how do LLM-based
agents perform in Minecraft? To date, most Minecraft
benchmarks evaluate them under fixed game mechanics, which are extensively documented online. High performance in these settings does not show whether an
agent can continue making progress when familiar recipes, drops, and other rules change. In this paper, we introduce MirrorCraft, a paired
benchmark for evaluating
agents under hidden rule changes in Minecraft. Each Mirror world is a copy of its paired Vanilla world, with selected server-side rules silently modified by the datapack of one rule suite. Terrain, spawn, resource placement, task, interface, and action budget remain matched within every Vanilla-Mirror pair. MirrorCraft includes 10 Vanilla worlds in five controlled biomes, six rule suites, and three progression tasks, and we evaluate two LLMs and six
agent configurations under a shared Mineflayer interface. We measure progress with server-verified advancement milestones and completion with success rate, and define the Rule Intervention Effect (RIE) as the performance change between paired Vanilla and Mirror worlds. Experiments with 8,640 episodes show that hidden rule changes have sharply different effects across suites. Among the configurations evaluated without rule descriptions, ReAct, Reflexion, and Voyager achieve the highest pooled Mirror Scores, but ReAct loses the most under compressed resource drops. Providing ReAct with the exact rules yields modest gains in average progress and completion across all three tasks, yet most of the difference between suites remains once the rules are known. MirrorCraft extends Minecraft evaluation beyond fixed mechanics and provides a controlled setting for studying how
agents use gameplay outcomes when the rules of the current world differ from familiar ones.
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All authors Jianxin Gao, Beini Hu, Runze Li, Hongjian Fang, Wanli Peng
Artificial Intelligence · Cross-listed from cs.LG
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
Artificial Intelligence
CAGE: Certified Authorization under Typed-Return Uncertainty for Tool-Using Agents
Blaise Delattre, Cong Wang and Yang Cao
Tool-using LLM
agents act on typed tool returns, records pairing provenance and categorical fields with numerical values. Runtime permission gates generally authorize the observed return and action, leaving the decision unprotected against small errors in how the return was bound to its source. We ask whether a candidate action stays authorized over a declared neighborhood of plausible correctly bound returns: one admissible binding fault plus bounded numerical drift. We prove that certifying the categorical and numerical channels separately does not compose: perturbations that are safe on each channel alone can jointly turn the same action unsafe. CAGE certifies this joint neighborhood directly, enumerating the discrete branches exactly and certifying the continuous perturbation within each branch. Across synthetic, policy-as-code, regulatory, and real-transaction settings, CAGE removes the in-budget false allows that accurate pointwise gates admit, while keeping a useful fraction of decisions autonomous. When the policy is executable, CAGE-Exact certifies the policy itself; otherwise CAGE-Lip and CAGE-RS certify a learned gate under an explicit, measured fidelity assumption.
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Artificial Intelligence · Cross-listed from cs.LG
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
Artificial Intelligence · Cross-listed from cs.CV
MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation
Yifei Zhu, Mingyi Shi, Yangyang Cai et al. (+3 more)
Text-to-motion generation must produce motions that are semantically correct, temporally coherent, and physically plausible. A natural approach is to first project motion data into a structured semantic space and then train a generative model within that space. Such a paradigm has been highly successful in image generation through Representation Autoencoders (RAEs), where a frozen self-supervised encoder provides semantic features for
diffusion or flow models to learn from. However, direct transfer of such a paradigm to motion space using Motion-JEPA as the frozen encoder fails dramatically. We diagnose this failure geometrically and identify two motion-specific bottlenecks: (1) the JEPA feature space is spectrally ill-conditioned, making the Gaussian-to-data transport unstable; and (2) even with a well-conditioned spectrum, flow residuals tend to align with decoder-sensitive directions, where small latent errors are amplified into large motion artifacts after decoding. Based on these insights, we propose MoRAE. MoRAE addresses the two bottlenecks separately. A compact bottleneck distills the structured JEPA representation while removing weak and redundant directions, bringing the latent spectrum into a transport-stable regime. Motion-coupled training then aligns the retained latent geometry with the decoder, making characteristic flow errors less costly after decoding. With this flow-friendly latent, a standard non-autoregressive Flow-Matching DiT achieves state-of-the-art performance.
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All authors Yifei Zhu, Mingyi Shi, Yangyang Cai, Miao Cheng, Yoshifumi Kitamura, Taku Komura
Artificial Intelligence · Cross-listed from cs.LG
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
Artificial Intelligence · Cross-listed from cs.RO
CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning
Yuxin Chen, Hari Srikanth, Nathan Jew et al. (+7 more)
While
robot foundation models are growing increasingly capable, the strongest models are typically trained on proprietary data and remain closed-source, limiting downstream users' ability to adapt them to new tasks, embodiments, and deployment settings. Following the LLM community, an emerging access paradigm for closed-weight
robot foundation models is the managed supervised fine-tuning (SFT) API, where users submit training data and receive a tuned policy without access to model weights, gradients, or training internals. While such APIs let downstream users leverage powerful proprietary foundation models, they restrict policy improvement to pure imitation, ruling out reinforcement learning and other closed-loop methods that rely on internal training signals. This limitation is particularly acute for agile, contact-rich
humanoid manipulation, where the gap between policy outputs and deployed behavior is large due to novel states, action tracking dynamics, latency, and controller-specific failure modes. We study how effective this managed-API regime is for
humanoid adaptation, and how closed-loop improvement can be realized within it to push policies toward task mastery. We conduct one of the first empirical studies of managed-API adaptation on a real
humanoid, instantiated on Gemini Robotics On-Device (GROD). We find that direct SFT through the API substantially outperforms a leading open-weight VLA trained on the same demonstrations, yet still falls short of deployment-level mastery on agile, contact-rich tasks. To close this gap, we introduce CLIFT: Closed-Loop Iterative Fine-Tuning, which turns deployment-time reward feedback into API-compatible supervised data and enables closed-loop policy improvement without accessing weights, gradients, likelihoods, or losses-pushing GROD to near-perfect success after two flywheel cycles, all without "opening the model box."
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All authors Yuxin Chen, Hari Srikanth, Nathan Jew, Menglin Wu, Pengcheng Wang, Junli Ren, Masayoshi Tomizuka, Peng Xu, Jinyu Xie, Thomas Tian