Artificial Intelligence · Cross-listed from physics.med-ph
A digital-twin framework for forecasting treatment-day imaging with contour uncertainty in adaptive proton radiotherapy
Yizhou Wu, Jie Ding, Justin Roper et al. (+6 more)
Head-and-neck anatomy changes over a six-to-seven-week
proton course, and the anatomy of a later week cannot be imaged when the plan is made. We present a digital-twin framework that forecasts a patient's treatment-day anatomy as an ensemble of predicted CTs with propagated contours and quantifies the uncertainty of the forecast contours. The twin is a library of previously treated patients with planning and weekly quality-assurance CTs (QACTs), made patient-specific by a two-step foundation-model deformable registration: a cross-patient field carries each library patient onto the current patient, and a longitudinal field, estimated in the current patient's frame, carries that patient's planning-to-QACT change onto the current patient's own planning CT. A library of 302 observations from 88 patients yields about 300 replicates per patient, each a deformation that occurred in a treated patient. The dispersion of the propagated contours, resolved by outward normal, is six-direction contour uncertainty in millimeters. This is uncertainty in the input to the forecast, which library patient the current patient follows, rather than in model parameters, and it is unchanged when the registration engine is exchanged. On ten patients with clinician contours on two QACTs, the library alone fixes the anisotropic shape of the uncertainty (4.5 to 6.2 mm); the first QACT narrows it by a factor of 3.2 to 3.6 without a contour being drawn; an approved contour improves the center but not the width. The estimate orders directions correctly but is not Gaussian-calibrated. A clinical target volume expansion is worked out as one application.
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All authors Yizhou Wu, Jie Ding, Justin Roper, Minglei Kang, Yuheng Li, Sibo Tian, David S. Yu, Xiaofeng Yang, Chih-Wei Chang
Artificial Intelligence · Cross-listed from cs.CR
Reasoning Topology Matters: A Controlled Study of LLM-Based Cybersecurity Analysis
Jiling Zhou, Aisvarya Adeseye, Antti Hakkala et al. (+2 more)
Large
Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent
reasoning over complex and heterogeneous data. However, existing prompting approaches typically focus on eliciting
reasoning without explicitly considering how intermediate
reasoning steps are structurally organized. We introduce Security
Reasoning Topology, which models
reasoning through three representative structures: Linear, Branching, and Graph. To evaluate their effects, we conduct controlled experiments on three cybersecurity
datasets covering MITRE ATT&CK network traffic, cyber threat intelligence (CTI), and CVE vulnerability analysis. We evaluate multiple LLMs, including Llama 2 (7B, 13B, 70B), GPT-5.1, and Mistral Large 3, while keeping task inputs consistent and controlling
reasoning structure through system-level prompting. Results show that
reasoning topology substantially affects performance: Graph
reasoning achieves the highest overall accuracy, improving over few-shot prompting by 9.8-12.2 percentage points across
datasets, while Branching provides a strong intermediate solution. The results further show that the effect of
reasoning topology remains consistent across model families and scales, highlighting
reasoning topology as an important design factor for LLM-based cybersecurity analysis.
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All authors Jiling Zhou, Aisvarya Adeseye, Antti Hakkala, Seppo Virtanen, Jouni Isoaho
Artificial Intelligence · Cross-listed from cs.HC
"MeBo Leaves a Piece of You Behind": Designing a Relational Voice-Based Memory Companion for Older Adults
Hasibur Rahman, Mahsa Nasri, Manasi Vaidya et al. (+3 more)
Autobiographical remembering supports identity, well-being, and social connection in later life, yet voice-based
memory technologies largely rely on isolated
prompts. We designed and built MeBo, a fully functional relational voice-based
memory companion, through participatory design with 11 older adults. Their accounts shaped four Design Strategies that guided MeBo's interaction design and multi-
agent implementation. In a mixed-methods evaluation with 20 older adults, participants found MeBo exceptionally usable (SUS = 87.75), enjoyable, sociable, emotionally responsive, and trustworthy. Participants reported higher positive affect and momentary social connection and lower negative affect after the session than before. Participants described how MeBo followed their stories, returned to earlier
memories, adapted to their preferences, and made its growing
memory visible and controllable. MeBo's relational framing surfaces tensions around what it should remember, who may access
memories produced through interaction, and what becomes of them when the user or MeBo is no longer present.
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All authors Hasibur Rahman, Mahsa Nasri, Manasi Vaidya, Melika Vafafar, Jessie Chin, Smit Desai
Artificial Intelligence · Cross-listed from cs.CL
Adapting Tree-Structured Speculative Decoding to DeepSeek-V4 for Efficient Inference
Changxu Liu and Zhaogeng Li
Repeated execution of the target model during autoregressive decoding is a major source of LLM inference latency. Unlike linear speculation, which follows a single candidate chain, tree-structured speculation retains multiple branches from shared prefixes; under the same budget, this broader coverage can improve acceptance and efficiency. Adapting it to DeepSeek-V4 is nontrivial: its CSA/HCA online compressed
attention concentrates the difficulty on the target-verify side, where branches diverging from a shared prefix compress into different states, breaking cross-branch state consistency. We integrate tree-structured speculative decoding into the DeepSeek-V4-Flash pipeline via branch-aware causal verification, temporary state isolation, and accepted-path state refresh, keeping verification and compressed-state updates consistent across branches. Across budgets D=5 to D=8, batch sizes 1 to 64, and three
datasets (GSM8K, MBPP, ShareGPT), tree speculation achieves a higher accepted length than the matched linear configurations in all settings (e.g., at D=8 about 2.83–3.41 versus 2.39–2.84) and improves throughput in nearly all configurations—marginal only at the smallest budget—by up to about 18.5%. More importantly, the gains follow stable, transferable regularities: the relative gain grows with the budget and is most pronounced for less predictable workloads at small-to-medium batch sizes, while beyond a certain budget throughput plateaus and decouples from the still-rising accepted length. These results show that retaining multiple candidate paths under the same budget can effectively improve DeepSeek-V4 decoding efficiency, and offer experience for adapting speculative decoding to future models with compressed, sparse, or structured context representations.
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Artificial Intelligence · Cross-listed from cs.CV
What Makes a Good Medical Image Tokenizer? Rethinking Reconstruction and Generation in Medical Image Tokenization
Niklas Bubeck, Yundi Zhang, Vasiliki Sideri-Lampretsa et al. (+4 more)
Latent
diffusion models now dominate medical image generation, and every such pipeline rests on a tokenizer that compresses images into the latent codes for image generation to operate on. Thereby, the tokenizer choice bounds every downstream task from reconstruction fidelity and generation quality to the representations available for downstream analysis. Yet, medical imaging pipelines routinely utilize tokenizers from natural imaging on the hypothesis that their behavior carries over. However, this is an assumption never tested in the medical imaging regime, where
datasets are orders of magnitude smaller and images exhibit far lower inter-sample variance. We present a systematic evaluation of medical image tokenizers evaluating thirty configurations across ten model families on twelve
datasets at three compression factors, spanning reconstruction, generation, latent geometry, downstream classification, and memorization. We find that (1) performance on image reconstruction and generation strongly correlate, unlike prior reports on natural images; (2) modern tokenizers use nearly all of their codebook entries, but still leave most of the latent space unused; (3) training-set memorization is mild and is further suppressed by stronger latent space compression; and (4) discrete quantization can largely preserve downstream classification, with lookup-free schemes being the main exception.
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All authors Niklas Bubeck, Yundi Zhang, Vasiliki Sideri-Lampretsa, Julian McGinnis, Jiancheng Yang, Daniel Rueckert, Jiazhen Pan
Artificial Intelligence · Cross-listed from cs.SD
Understanding Hyperspherical Geometry of ECAPA-TDNN Embedding and Its Impact on Zero-Shot Voice Conversion
Mathilde Abrassart, Nicolas Obin and Axel Roebel
Angular-margin speaker encoders are widely used in voice conversion, yet the geometry of their classifier prototypes remains poorly understood. We analyze ECAPA-TDNN classifier prototypes as points on the unit hypersphere and characterize their organization using rotation-invariant angular statistics together with global and local effective dimensionality measures. Our analysis shows that standard training can induce angular concentration and a substantial reduction in effective dimensionality. To address this, we investigate two
geometric regularization strategies (hinged Riesz log-energy and effective-dimension maximization) applied to classifier prototypes to encourage more uniform hyperspherical coverage. The resulting prototype sets exhibit higher effective dimensionality and improved isotropy, with configuration-dependent effects on speaker-recognition performance. When the corresponding ECAPA-TDNN models are used as speaker encoders for Fast-VGAN, the regularized systems also exhibit improved robustness in zero-shot voice conversion, particularly for previously unseen speakers.
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Artificial Intelligence
TimeLitmus: A Diagnostic Benchmark for Cross-Modal Understanding and Explanation Faithfulness in Event-Conditioned Time-Series Prediction
Jie Gong, Maowei Jiang, Zhiwei Liu et al. (+6 more)
Large
language models (LLMs) are increasingly used to make predictions from numerical time-series histories and textual events. Yet accuracy alone cannot reveal whether correct answers reflect effective integration of the two inputs or instead arise from event polarity, unimodal priors, or superficial cues. Likewise, plausible explanations may rationalize predictions without faithfully reflecting the evidence that drives model behavior. We introduce TimeLitmus, a diagnostic
benchmark for cross-modal understanding and explanation faithfulness in event-conditioned time-series prediction. TimeLitmus contains 4,856 evaluation records across Finance and Traffic, combining natural prediction with controlled counterfactual and contrastive interventions, explanation-targeted faithfulness tests, and systematic shortcut controls. Across ten representative LLMs, standard prediction accuracy substantially overstates reliable cross-modal understanding: Hard Paired Contrast (HPC) pair correctness peaks at only 19.2% in Finance and 11.7% in Traffic, and all ten models show lower-than-expected consistency on Finance series-side controls. Models often recognize scenario relations explicitly yet fail to apply them during independent prediction. Explanation faithfulness shows a similar gap: in Traffic, most models cite the manipulated temporal factor in over 90% of cases, while behavioral support remains below 22%. Human annotators outperform LLMs on matched controlled and hard-pair diagnostics, confirming that these distinctions are recoverable from the inputs. Natural-only adaptation yields selective gains in evidence selection and input sensitivity, but not consistent gains in controlled or hard-pair behavior. The
benchmark, evaluation suite, and supervised adaptation data will be released publicly.
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All authors Jie Gong, Maowei Jiang, Zhiwei Liu, Yankai Chen, Guojun Xiong, Xue Liu, Min Peng, Qianqian Xie, Sophia Ananiadou
Artificial Intelligence · Cross-listed from cs.CR
Trust in Edge-Enabled IoT Security: Features, Challenges and Research Directions
Esin Ece Ayd{\i}n, Şerif Bahtiyar and Gürkan Gür
Providing autonomous intelligence, pervasive connectivity and usability to human life and industry has led to the emergence of the Internet of Things (IoT). To support time-sensitive and resource-constrained applications, IoT systems nowadays increasingly rely on edge computing. This brings computation and decision-making closer to end devices. In edge-enabled IoT architecture, latency and communication overhead are reduced, but interactions among a larger and more diverse set of devices, edge nodes, services, and data sources are introduced as well. In such environments, security and
privacy mechanisms provide the foundation for protection, while trust management can assess the reliability of interacting entities and adapting secure decisions. In this paper, we systematically review the current state of trust management in edge-enabled IoT. To this end, we propose a comprehensive taxonomy that maps physical, network, and application architectural IoT layers against the consumer, commercial, industrial, and infrastructure IoT domains. We further investigate state-of-art research based on their trust design, how trust integrated into secure IoT operations, the attacks that effect trust management process. Based on these findings, we identify key gaps in current research and outline future directions for context-aware and adaptive trust management in edge-enabled IoT.
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Artificial Intelligence
Beyond Endpoint Performance: Process-Level Evaluation of Self-Evolving Agents
Hongqiang Lin, Chao Liu, Xiaofan Bai et al. (+4 more)
Self-evolving
agents convert interaction feedback into persistent artifacts, such as
memories or skills, which in turn guide subsequent decisions. As these artifacts are iteratively updated throughout an experience stream, the capabilities they support may evolve. Consequently, endpoint performance alone offers an incomplete view of self-
evolution. Process-level evaluation is therefore essential to identify when a target capability emerges and whether later updates strengthen, preserve, or weaken it. Motivated by this, we propose EvoPathBench, a
benchmark that tracks individual capabilities during artifact-level self-
evolution. EvoPathBench fixes the base model, tools, freezes evolving artifacts at successive checkpoints, and evaluates the target capability on held-out episodes. This
benchmark evaluates
agent self-
evolution using public trading data and calibrated trajectories. It tests three capabilities: generalization to unseen tasks, retention after unrelated learning, and rule adaptation to new evidence. Experimental results show that gains on similar unseen tasks often weaken under distribution shift, retention losses are concentrated in a minority of
evolution paths, and no method achieves reliable rule adaptation. Moreover, while self-
evolution enables
agents to generate candidate artifacts with substantial held-out gains, the selected updates consistently fall short of realizing this potential. Together, these findings establish capability-level process evaluation as a foundation for analyzing self-
evolution, identifying candidate evaluation and selection as key targets for improvement.
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All authors Hongqiang Lin, Chao Liu, Xiaofan Bai, Xuan Jin, Yuhong Li, Nenggan Zheng, Xipeng Cao
Artificial Intelligence
DUMA-Bench: A Dual-Control Multi-Agent Benchmark for Evaluating LLM Agent Security
Ivan Aleksandrov, German Kochnev, Sabrina Sadiekh et al. (+1 more)
LLM-based
agents increasingly operate in environments where they interact with users, tools, and external systems. Yet most security evaluations assume passive users and static control, ignoring the interactive dynamics that shape real
agent behavior. We introduce DUMA-Bench, a
benchmark and evaluation protocol for measuring
agent security under dual-control interaction, where both the
agent and the user can influence the shared environment state. DUMA-Bench extends $\tau^2$-bench ~\cite{barres2025tau} with
adversarial environments covering eight vulnerability classes, including RAG poisoning, cross-
agent manipulation, and unsafe output handling. We evaluate 14 models from five model families (OpenAI, Anthropic, DeepSeek, Qwen, and Z.ai) across eight domains and multiple user-behavior regimes. Across our experiments, introducing dual-control interaction increases the attack success rate from 26.9% to 41.1%. These results show that
agent security is not solely a property of the model but emerges from the interaction between the model, the user, and the environment. DUMA-Bench provides a missing evaluation layer for studying security in realistic
agent deployments.
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All authors Ivan Aleksandrov, German Kochnev, Sabrina Sadiekh, Yaroslav Rogoza
Artificial Intelligence · Cross-listed from cs.RO
Touch2Robot: Robot Touch in the Human Demonstration Loop
Shengcheng Luo, Xiaoyang Cheng, Hong Ying et al. (+6 more)
Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a
robot hand. Collecting demonstrations directly on the target
robot avoids this mismatch but substantially increases the cost of data collection. To address this trade-off, we present Touch2
Robot, a framework that lets humans collect demonstrations while seeing how the target
robot hand would contact the object. We capture human hand motion, tactile-glove measurements, and object motion during human manipulation. These recordings guide object-specific RL policies to reproduce the demonstrated object motion while favoring contacts consistent with the recorded human touch. We distill the learned behaviors into a unified real-time retargeter that maps incoming human observations and object geometry to
robot hand configurations. During collection, the predicted
robot configuration is synchronized with the tracked object pose in simulation to reconstruct
robot-object contacts, which are visualized to help the demonstrator adapt subsequent interactions to the target hand. Across four real-world tasks, Touch2
Robot improves average real-
robot replay completion from 37.9% to 72.1% over visual-only feedback, while reducing the collection time per replay-successful demonstration from 58.6s to 18.2s. Reconstructed target-hand contacts achieve 44.2% F1 against real-
robot tactile measurements, and policies trained on Touch2
Robot demonstrations improve downstream
Diffusion Policy performance by 29.1 percentage points over visual-only feedback. These results show that bringing
robot touch into the human demonstration loop improves both the quality and efficiency of scalable dexterous data collection. Project webpage: https://Touch2
Robot.github.io/.
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All authors Shengcheng Luo, Xiaoyang Cheng, Hong Ying, Xiaoying Zhou, Jiaming Jiang, Haoran Guo, Wanlin Li, Ziyuan Jiao, Chenxi Xiao
Artificial Intelligence · Cross-listed from cs.LG
Corrective Forcing: Unified Post-Training for Diffusions and Flows in Generative Speech Enhancement
Qing Yao, Lijian Gao and Qirong Mao
Diffusion and flow models, as promising generative paradigms for
speech enhancement, face a training–inference mismatch: training uses analytical path states, whereas inference recursively evaluates models on self-generated rollout states along discretized sampling trajectories. This mismatch causes prediction and discretization errors to accumulate. To address it, we introduce Corrective Forcing (CoF), a post-training paradigm that forces
diffusion and flow models to learn from self-generated rollouts and correct their predictions. CoF corrects clean-
speech predictions on rollout states toward the ground truth under dynamic sampling schedules, exposing the model to varying inference conditions. It further regularizes local
evolution using locally corrected counterfactual transitions as references for factual transitions. By expressing model outputs through a shared clean-
speech prediction parameterization, CoF applies the same post-training objective across
diffusion and flow formulations. Experiments with SB-VE and OT-CFM demonstrate improvements in perceptual quality and reconstruction fidelity, together with robust performance across different numbers of sampling steps.
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Artificial Intelligence · Cross-listed from cs.LG
iSDFT: Information-Proximal Self-Distillation for Continual Learning in LLMs
Ahmed Khaled Khamis, Xiaotong Ji, Hassan Jaber et al. (+4 more)
On-policy self-
distillation fine-tuning (SDFT) learns new skills from demonstrations while reducing forgetting, but it always distils toward the full demonstration-conditioned teacher. This fixes teacher influence at the full-teacher endpoint, providing no control over how much demonstration information should be transferred at each prediction state. We introduce Information-Proximal SDFT (iSDFT), which instead treats the teacher as a budgeted source of information. At each
token, iSDFT selects the distribution closest to the current student that satisfies a prescribed teacher-information constraint, yielding a closed-form exponential target with a locally determined tilt. To control cumulative drift, we further anchor the student to its frozen base policy. Across four heterogeneous LLM backbones and two specialisation tasks, iSDFT improves vanilla SDFT in 7 of 8 model-task settings and matches it in the remaining one. It also provides tighter retention on the original SDFT
benchmark suite, with 73% of evaluations remaining within 0.5 points of the base model versus 52% for the strongest baseline, while achieving the largest mean improvement on all ten additional mathematics, coding, and competition-mathematics
benchmarks. These results show that controlling how much and when teacher information is introduced improves specialisation while preserving broader capability.
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All authors Ahmed Khaled Khamis, Xiaotong Ji, Hassan Jaber, Rasul Tutunov, Matthieu Zimmer, Jun Wang, Haitham Bou-Ammar
Artificial Intelligence · Cross-listed from cs.HC
Annie, Are You Okay? How Style- and Context-Based Personalization Shape AI-Assisted Decision-Making
Hasibur Rahman, Benjamin R. Cowan and Smit Desai
As people turn to generative AI for financial advice, these systems can personalize how they communicate and what they say. Whether these forms of personalization shape decisions differently remains unclear. We conducted a preregistered 2 x 2 between-subjects factorial experiment (N=240): participants ranked three comparably viable stocks, discussed them with an AI, and reranked them. Participants perceived both forms of personalization, but only context-based personalization reliably changed ranking behavior: it increased reconsideration and moved rankings toward the AI's assigned recommendation. Participants felt more influenced without judging the AI as more correct, trustworthy, intelligent, likeable, or high-quality. Those initially farther from its recommendation moved more toward it while judging its advice less correct; exploratory analyses suggest greater susceptibility among lower-expertise participants. These findings show how personalized AI can steer decisions among defensible options with only a minimal evaluative trace, raising concerns for the design and governance of personalized decision support.
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Artificial Intelligence · Cross-listed from cs.RO
From Semantic Decisions to Feasible Trajectories: Self-Evolving LLM-Guided Optimal Control for Narrow-Space Parking
Zhengbao Yao, Yuanfu Luo and Kehan Xue
Autonomous parking in nonconvex and narrow environments remains challenging. Although optimal-control methods can explicitly enforce vehicle dynamics and collision constraints, nonconvexity compromises solver robustness and can cause failures. Large
language models (LLMs) exhibit strong semantic
reasoning capabilities, but directly generating dense trajectories makes it difficult to guarantee physical feasibility. We introduce SE-LLM-OCP, a unified framework in which LLMs make high-level discrete maneuver decisions, while an optimal-control module enforces low-level vehicle dynamics and collision constraints. Online, the LLM proposes sparse maneuver plans, decomposing the parking task into a sequence of short-horizon trajectory-optimization problems. A low-level solver then sequentially solves optimal-control problems. If the solver fails, the LLM aggregates failure evidence from the solver and validation stages to guide replanning. Offline, SE-LLM-OCP automatically evolves a structured decision-making knowledge base from scratch, driven by accumulated online failures. We validate our proposed framework in simulation on a car-like vehicle model and on a differential-drive
robot. Our experimental results show that SE-LLM-OCP enables safer autonomous parking in narrow scenarios and demonstrates transfer of the same maneuver representation to a different kinematic platform.
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Artificial Intelligence · Cross-listed from cs.LG
Augmented Hypothesis Testing with Persona-Based LLM Simulations
Ziyad Benomar, Aymen Al Marjani, Paul Missault et al. (+1 more)
A/B testing requires large sample sizes, long timelines, and significant costs. When auxiliary predictions of experimental outcomes are available from machine learning models, uncertain prediction quality precludes replacing human experiments entirely, yet these predictions may still contain useful signal. We propose a principled framework for learning-augmented hypothesis testing that leverages predictions of unknown quality to reduce sample sizes while maintaining statistical validity. Predictions naturally vary in granularity, from coarse aggregate signals to fine-grained individual-level estimates, and our framework addresses both ends of this spectrum: (1) for population-level directional predictions, where only a binary signal on the treatment effect sign is available, we use an asymmetric test and prove consistency and robustness bounds within the learning-augmented
algorithms paradigm; (2) for individual-level predictions, we introduce Generalized PPI++ (GPPI), extending Prediction-Powered Inference to handle nonlinear prediction errors through higher-dimensional transformations. Both methods benefit from accurate predictions while remaining robust to inaccurate or
adversarial ones. We validate our framework using persona-based LLM simulations, where AI
agents equipped with user personas predict individual behavior, as a natural prediction source spanning both granularity levels. Experiments on four real-world
datasets demonstrate that our methods, combined with persona-based predictions, substantially reduce experimental costs while preserving rigorous statistical validity.
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All authors Ziyad Benomar, Aymen Al Marjani, Paul Missault, Saab Mansour
Artificial Intelligence · Cross-listed from cs.CV
FedMust: Semi-supervised Multi-task Student-Teacher Federated Learning for Multi-organ CT Segmentation
Ashkan Moradi, Bendik Skarre Abrahamsen and Mattijs Elschot
Multi-organ
segmentation using deep learning requires large amounts of annotated patient data; however, institutions often lack sufficiently large and diverse annotated
datasets.
Privacy constraints further prevent institutions from sharing patient data to overcome this limitation. Moreover, due to the labor-intensive nature of annotation and the scarcity of diverse expertise, institutions typically have labels for only a small portion of their local data, leaving the larger unlabeled portion unused. In this work, we propose a flexible semi-supervised
federated multi-task student-teacher framework that leverages
federated learning (FL) to improve multi-organ
segmentation using both labeled and unlabeled data across participating sites. At each communication round, the proposed framework initiates local training, where clients with labels for the same task form a federation to produce an aggregated teacher model. The resulting teachers generate task-specific features for all data at each client. Subsequently, all clients form a second federation to train a multi-task student model with a shared encoder and task-specific decoders that replicate the teacher-generated features across all
segmentation tasks. The aggregated student model is then used to update the local teachers and initiate the next training round. Extensive experiments demonstrated the effectiveness of the proposed method compared with local and
federated single-organ models, yielding an average performance gain of 13 percent across clients. The experiments also demonstrated the impact of multi-task learning and unlabeled data and the applicability of the framework in relaxing labeled-data requirements for client participation. The code is available at https://github.com/AshknMrd/FedMust.
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Artificial Intelligence
Custom Named Entity Recognition and Topic Classification for Global Health Publications
Genis Skura, Antoine Geissbühler and Jean-Luc Falcone
How should natural
language processing models be selected and adapted for global health literature in environments where annotated data and computational resources are limited? This thesis investigates these challenges through experiments on semantic tag discovery, named entity recognition (NER), and multi-label topic classification. First, skip-gram word2vec models trained on progressively larger specialized corpora are compared with BioWordVec to assess how corpus size and domain context influence tag discovery. Vocabulary coverage and qualitative evaluation indicate that broader coverage does not necessarily yield more useful domain-specific associations. The analysis then turns to entity extraction, comparing convolutional spaCy models with a RoBERTa-based
transformer on 1,000 annotated sentences. Under a lenient scoring protocol, the
transformer achieves 0.80 micro-F1 versus 0.65-0.69 for convolutional models, but takes 82 seconds rather than 5-6 seconds. This trade-off motivates fine-tuning convolutional models and integrating a disease recognizer that achieves 81.33% test F1 on the NCBI Disease Corpus. Combined with PDF preprocessing, entity filtering, and MeSH enrichment, the resulting pipeline supports document-level indexing. To complement entity extraction with thematic annotation, MiniLM-based few-shot classification is compared with BART-MNLI zero-shot inference across 50 topics and 1,000 handcrafted test sentences. BART-MNLI achieves 95.2% single-label accuracy versus 59%; reported multi-label accuracies are 88% and 32% under partly manual assessment. However, its higher inference cost limits practical integration. The results show where domain specialization and lightweight adaptation offer practical value, and where
transformer accuracy justifies higher inference costs, providing an empirical basis for building knowledge systems under resource constraints.
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Artificial Intelligence
Ascent: An Agentic System over the Model Context Protocol for Real-World Clinical Data Analysis
Angelo Ziletti, Leonardo D'Ambrosi, Melanie Tuchardt et al. (+1 more)
Answering epidemiological questions from real-world clinical data requires medical coding, schema-aware SQL, and validation of implicit choices about populations, denominators, and time. We present Ascent, an agentic system that exposes medical coding, question answering, and cohort analysis through a shared Model Context Protocol tool surface for standardized and native schemas. We introduce EpiTrap, a
dataset testing whether systems avoid recognized pharmacoepidemiological errors, and compare a fixed pipeline with
agents across models and orchestrators. With capable models,
agents improve accuracy over the fixed pipeline by an average of 27 and 20 percentage points on native and standardized schemas, respectively. These gains require more tool calls and longer runtimes. Experience from real projects highlights the system's value for feasibility assessment, diagnostic iteration, and expert-guided analysis.
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All authors Angelo Ziletti, Leonardo D'Ambrosi, Melanie Tuchardt, Tim Kondziella
Artificial Intelligence · Cross-listed from cs.LG
GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude et al. (+1 more)
Electricity
forecasting often involves spatially related signals observed over regions, substations, and feeders, and Graph Neural Networks (GNNs) provide a natural way to represent these relations. Building a complete GNN
forecasting experiment is nonetheless laborious, because graph construction, model selection, training, aggregation, and interpretation sit in incompatible tools. We present GraphToolbox, an open-source Python framework that unifies these stages in one configurationdriven pipeline built on PyTorch
Geometric. It offers data-driven graph construction, an adapter that instantiates and trains 51 of the 65 PyTorch
Geometric convolutions together with the recurrent
cells of PyTorch
Geometric Temporal, online expert aggregation,
forecasting interpretability, and significance testing on cached forecasts. We evaluate the pipeline in two case studies. On French regional load, the 48 convolutions included in the complete
forecasting sweep fall in a band from 1.14% to 1.60% error, online aggregation lowers this to 0.98%, and the graph models improve on classical additive and boosting baselines. On net-load, direct graph models are less accurate than a classical additive model, while
forecasting each physical component separately improves them without closing that gap. Both comparisons use the same experimental interface, illustrating the role of GraphToolbox in systematic architectural evaluation.
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All authors Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude, Argyris Kalogeratos
Artificial Intelligence · Cross-listed from cs.LG
Overlay\_dx - Automating forecasting evaluation
Long Ngo, Mohammed Amine Chamli, Jonathan Rivalan et al. (+1 more)
Traditional evaluation metrics provides numerical values but often lack comprehensibility, hindering effective differentiation of model performances. Our work addresses this challenge by introducing overlay\_dx, a novel evaluation metric measuring the performance of time series prediction models. Overlay\_dx is a visual metric that represents the percentage of predictions falling within a confidence interval around actual values. Additionally, once evaluation results are plotted, overlay\_dx computes the area under the overlay curve, providing a quantitative measure of
alignment between predicted and actual values across different thresholds and predictions. Through extensive experiments, we demonstrate that our approach offers a unified evaluation framework that combines both visual and numerical assessments, enabling improved model comparison and providing valuable insights for further research and optimization efforts in time series prediction.
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All authors Long Ngo, Mohammed Amine Chamli, Jonathan Rivalan, Thomas Jaillon
Artificial Intelligence · Cross-listed from cs.LG
$t_0$: A Time-Series Foundation Model for Forecasting with Context
Lucas Meyer, Claudio Sole, Huikan Xiang et al. (+6 more)
We present $t_0$, a family of open-weights foundation models for
forecasting with multivariate context. We release its first two members: $\texttt{t0-alpha}$ and $\texttt{t0-beta}$, respectively 102M and 256M parameters. Both condition their forecasts on target history, past
covariates, and known-future
covariates, without task-specific retraining. Their
transformer layers alternate
attention along time and across variates. They produce probabilistic forecasts through
quantile predictions. Pretraining combines curated public data with synthetic generator families constructed to contain
covariate-to-target dependencies. On GIFT-Eval, $\texttt{t0-alpha}$ reaches an aggregate CRPS of 0.4941, and $\texttt{t0-beta}$ a CRPS of 0.4738 and a MASE of 0.6865, third on both and within 4.0% of the best zero-shot TSFM. On fev-bench they score 42.2 and 46.7 in skill, the latter third again and 2.0 points behind the leader. We analyze $\texttt{t0-alpha}$ in depth. Known-future
covariates raise its skill by 6.3 percentage points across 30 tasks. The report also examines its calibration, its rollout strategy on long horizons, and its robustness to missing data. On the Victoria electricity-demand
benchmark, $\texttt{t0-beta}$ is among the most accurate models with a context of nearly a year. In an independent Macrocosm evaluation of hourly ERCOT prices over 29 months, both cut the MAE of the lagged-price baseline by 38%.
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All authors Lucas Meyer, Claudio Sole, Huikan Xiang, Nicolas Li, Lucas Franceschino, Arnau Quera-Bofarull, Maarten P. Scholl, Joachim Fainberg, Geoffrey Négiar
Artificial Intelligence
The Endless Exam: Mathematical Constructions from Today's Models toward Superintelligence
Muhan Zhang
We introduce the Endless Exam, a
benchmark spanning fourteen parameterised families of mathematical construction problems, with verifiable scores that distinguish progress before and beyond published mathematical frontiers. Each submitted object is checked automatically for validity and assigned a relative quality score against a published frontier or construction baseline, without capping improvements at 1. The
benchmark draws long-term challenges from open mathematical problems and generates larger instances by varying their parameters. Compact certificates allow large constructions to be verified without listing every element. Across nine models evaluated on 69 distinct instances, continuous quality scores distinguish performance even though none of the 30 published-frontier references is surpassed. Size-quality curves show how construction quality changes as problem size increases. We release the generators, verifiers, references, model responses and analysis to support continued measurement before and beyond human frontiers.
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Artificial Intelligence · Cross-listed from cs.CL
QLoRA Fine-Tuning of Ministral LLM for Sequence-to-Function Protein Annotation
Demian Pavlyshenko and Bohdan Pavlyshenko
Functional annotation of newly sequenced
proteins remains a bottleneck in molecular biology: the number of sequences in public repositories grows far faster than the capacity for manual curation. Most computational approaches consider annotation as multi-label classification over a fixed ontology, which constrains predictions to a predefined label set. In this work we study the the
protein annotation as a sequence-to-text generation problem. We fine-tune the 3B-parameter Ministral 3 base model with QLoRA (4-bit NF4 quantization with low-rank adapters) on sequence annotation pairs. We assess predictions with an LLM-as-expert protocol: a GPT model prompted as a senior molecular-biology curator scores organism identification as binary and function annotation quality. We conclude that QLoRA-fine-tuned compact LLMs can generate curator-style annotations with genuine biological value for a substantial subset of
proteins. We also discuss future directions in data quality, model scaling, and evidence grounding that are needed to make the approach sufficiently reliable for practical use.
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Artificial Intelligence
Not All Task Vectors Need Equal Rank: Energy-Proportional Allocation for Model Merging
Hyunjoong Cho and Jinhyeok Jang
Model merging aims to combine multiple fine-tuned models derived from a common pretrained model into a single multi-task model without additional joint training. Recent spectral merging methods improve over simple weight averaging by exploiting low-rank structures of task-specific updates, but they commonly assign the same rank capacity to every task. This uniform allocation ignores that task vectors can have heterogeneous spectral complexity, causing the shared merging space to be used suboptimally. In this paper, we propose Spectral Energy-proportional Rank Allocation (SERA), a simple task-adaptive strategy that allocates ranks according to the singular-value energy structure of each task vector. By assigning richer spectral capacity to complex or isolated tasks and fewer directions to compact tasks, SERA extends SVD-based model merging from uniform-capacity merging to task-dependent capacity allocation. Experiments under standard
vision model merging protocols show that SERA improves multi-task merging performance while preserving the same total rank budget as existing spectral merging methods. Further analysis demonstrates that task-level spectral concentration is closely related to the per-task effect of adaptive rank allocation, providing insight into when and why SERA is effective.
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Artificial Intelligence · Cross-listed from cs.CR
Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents
Anastasia Pustozerova, Eugene Bagdasarian, Luca Beurer-Kellner et al. (+22 more)
AI
agents are being deployed rapidly, accompanied by a growing number of AI-specific attacks and corresponding incidents. As incident reporting becomes increasingly important for legal compliance, governance, accountability, and security; current frameworks must be adapted to the unique characteristics of AI
agents. In this paper, two editorial authors compare AI systems and AI
agents and, drawing on input from 23 experts in academia and industry, identify the information required for reporting incidents where the security of AI
agents is harmed. %involving AI
agents. Potential reporting elements include, for example,
agent memory and
memory accesses, actual and potential levels of autonomy, and tool usage. Based on these findings, we identify several open research questions, including how to efficiently record incidents and how to determine whether vulnerabilities and incidents generalize. Expert feedback also highlighted potential reporting weaknesses, such as risks of data leakage and attacks targeting the reporting infrastructure itself, creating additional research needs. Lastly, we summarize
privacy requirements and outline research directions for the secure and trustworthy deployment of AI
agents.
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All authors Anastasia Pustozerova, Eugene Bagdasarian, Luca Beurer-Kellner, Battista Biggio, Nico Ebert, David Filip, Marc Fischer, Heather Frase, David Hofer, Juliane Hoffmann, Daphne Ippolito, Somesh Jha, Sean McGregor, Esfandiar Mohammadi, Luca Nannini, Cristina Nita-Rotaru, Alina Oprea, Kevin Paeth, Andrew Paverd, Jonathan Petit, Andreas Rauber, Christian Riess, John Sotiropoulos, Andreas Wespi, Kathrin Grosse
Artificial Intelligence · Cross-listed from cs.LG
On Emergent Capabilities and Model Merging
Luca Zhou and Emanuele Rodolà
Fine-tuned checkpoints and adapters now fill public repositories, and the most common operation applied to these artifacts is model merging: arithmetic on their weights that assembles capabilities cheaply. We ask what this operation does to emergent capabilities: behaviors an artifact carries that were never an explicit training target. Studying two independent testbeds (activation oracles and emergent-misaligned models) across three model families, we find that the answer is threefold. First, merging preserves an emergent capability that both parents carry: merging two misaligned checkpoints retains most of their broad misalignment across the whole mixing range. Second, merging cannot create an emergent capability that is superadditive in its parents: no weighted merge of two single-task oracles reaches the jointly-trained oracle's
auditing ability. Third, when only one parent carries the capability, merging dilutes it faster than the trained capability that accompanies it: the gap is significant in most settings. In short, emergent behaviors of an artifact do not compose the way its trained capability does.
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Artificial Intelligence · Cross-listed from cs.LG
Lifted Bellman Linear Programming for Offline Reinforcement Learning
Hyukjun Yang, Jongchan Park, Narim Jeong et al. (+1 more)
Offline reinforcement learning (RL) typically trains a critic by minimizing a regression loss against bootstrapped value targets stabilized by target networks with exponential moving average (EMA) updates. Multi-step targets incorporate behavior-policy actions and therefore require off-policy correction. We instead impose in-sample Bellman optimality on the critic through inequality constraints. We formulate the Lifted Bellman Linear Program (LBLP), which lifts the linear programming characterization of Bellman optimality to the joint $(Q,V)$ space so that every constraint involves only state-action pairs in the
dataset. Its unique minimizer is the in-sample optimal pair, and constraints along $K$-step segments of
dataset trajectories leave this minimizer unchanged for any rollout policy and horizon. Under deterministic dynamics, this minimizer lies between the best
dataset return and the optimal value. Relaxing the constraints into hinge penalties recovers the same solution above a finite penalty coefficient in the tabular case. Approximate Lifted Bellman Unconstrained Minimization (ALBUM) implements this relaxation with neural networks and detaches the $K$-step rollout targets by stop gradient. Its objective contains no squared regression onto bootstrapped targets, so it can be trained without target networks or EMA updates. Under deterministic dynamics, the LBLP solution is a stationary point of the detached update under a coefficient condition independent of $\gamma$ and $K$, and the inequality constraints allow discounted returns along
dataset trajectories to serve as lower bounds without off-policy correction or action chunking. On OGBench, ALBUM uses a single critic with a Gaussian policy, matches the average performance of FQL, and is comparable to recent action-chunking methods, while using the fewest parameters and the least peak GPU
memory among all compared methods.
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All authors Hyukjun Yang, Jongchan Park, Narim Jeong, Donghwan Lee
Artificial Intelligence · Cross-listed from cs.CV
AgentSTAR: Agentic Shape Tracking and Reconstruction from Monocular Videos
Kirill Mazur, Nikita Karaev, Matthew Chang et al. (+2 more)
In this work, we present a method for shape reconstruction and tracking from
video via agentic analysis-by-synthesis. Unlike prior methods which first estimate dense pixel correspondences and then recover object motion from them, our method infers a structured 3D object model, including its geometry and kinematic structure, and uses this model to optimise object track estimates over time. In our optimisation loop, a
Vision-
Language Model (VLM)
agent iteratively refines shape or generalised pose through a render-and-compare loop, combining coarse visual
reasoning with numerical pose optimisation for precise state estimation. This structured formulation enables our method to track through large motion, articulation, and severe occlusion without relying on pixel-matching objectives. Quantitatively, on ARCTIC, our method substantially outperforms state-of-the-art 3D point-tracking baselines for articulated objects, and on HOT3D it outperforms all evaluated rigid-object tracking baselines.
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All authors Kirill Mazur, Nikita Karaev, Matthew Chang, Jitendra Malik, Nur Muhammad "Mahi” Shafiullah
Artificial Intelligence · Cross-listed from cs.CV
VPRune: Efficient Training-free Pre-LLM Visual Token Pruning
Guangchuan Lv, Dianxing Shi and Dingjie Fu
Visual
token pruning is a promising approach to reducing the inference cost of large
vision-
language models (LVLMs), yet aggressive
token reduction often causes substantial performance degradation. We identify three key factors behind this degradation: text-guided selection bias, information loss from discarded
tokens, and positional distortion caused by sequence compaction. Based on these observations, we propose VPRune, a training-free pre-LLM
pruning framework consisting of visual-only diversity selection, similarity-guided
token recycling, and position-preserving restoration. Experiments on FastVLM-1.5B across multiple
vision-
language benchmarks demonstrate that VPRune achieves a favorable accuracy–compression trade-off, with particularly pronounced advantages under aggressive compression. Furthermore, evaluations on edge-device show that VPRune effectively reduces end-to-end inference latency while maintaining superior task performance, demonstrating its practicality for resource-constrained LVLM deployment.
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