Artificial Intelligence · Cross-listed from cs.LG
Memory Is No Longer a Bottleneck: Memory-Efficient Graph Filtering for Scalable Collaborative Filtering
Jin-Duk Park and Won-Yong Shin
Graph convolutional networks (GCNs) have demonstrated significant success in capturing complex user-item relationships for collaborative filtering (CF). However, due to their reliance on extensive model training, training-free graph filtering (GF)-based CF methods have emerged as a promising alternative, offering computational efficiency by smoothing graph signals via
matrix operations. In particular,
polynomial GF-based approaches demonstrate improved accuracy through their ability to design more expressive and flexible filtering functions. Despite these advantages, existing GF methods suffer from a critical
memory bottleneck: they necessitate storing the full item similarity graph, incurring prohibitive
memory costs for large-scale
datasets, which limits their practical applicability. To tackle this challenge, we propose Mem-GF (
Memory-efficient GF), a new GF-based CF method that departs from conventional designs by principally leveraging the structure of Krylov subspaces as a core mechanism for approximating
polynomial graph filters without explicitly storing the item similarity graph. We theoretically analyze the minimum Krylov subspace size that guarantees lossless approximation. Through extensive experiments, we demonstrate that Mem-GF achieves up to 5.74$\times$ lower
memory usage and 4.38$\times$ speedup in runtime, while consistently exceeding the recommendation accuracy of state-of-the-art GF and GCN-based methods. Mem-GF robustly scales to
datasets with tens of millions of interactions, establishing itself as a practically viable and theoretically grounded solution for efficient CF.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Backpropagating Through Simulation: Analytic Policy Gradients for Sample and Learning Efficient Differentiable Continuous Control
Yueci Deng
Model-free reinforcement learning
algorithms such as Proximal Policy Optimization (PPO) treat the environment as a black box, estimating policy gradients from sampled rewards; this process demands millions of interactions and relies on high-variance advantage estimates. When environment dynamics are differentiable, the return is an end-to-end differentiable function of the policy parameters, enabling exact gradient computation via backpropagation through simulation. We term this approach Analytic Policy Gradients (APG) and evaluate it against PPO on four continuous control tasks of increasing dynamical complexity: a one-dimensional point-mass target-reaching task, a 2D point-mass
navigation task with obstacle avoidance, a 2D rigid-body T-block pushing task, and a 7-DOF Franka FR3 end-effector reaching task. Both
algorithms share identical model architectures, observation normalization, and optimizer settings. To decouple sample efficiency from compute efficiency, we design a multi-axis evaluation protocol that records performance against environment steps and gradient steps. We report a segmented backpropagation scheme with MC and critic-based bootstrap modes that mitigates gradient degradation on long-horizon tasks, and present ablations over segment length and bootstrap strategy.
Read the full abstract ▾
Artificial Intelligence
Balancing Performance and Diversity in GRPO Autoregressive Text-to-Image Post-Training
Yuanhao Chiang, Hongbo Duan, Chunru Yang et al. (+3 more)
Autoregressive text-to-image (T2I) generation has recently advanced rapidly, yet aligning generated images with human preferences remains challenging. GRPO-style online reinforcement learning provides an effective framework; however, existing methods typically treat reference-policy divergence as fixed, despite its direct impact on policy optimization. We study this overlooked factor within a unified f-divergence framework, encompassing forward KL, reverse KL, and JS divergence, for GRPO-style autoregressive T2I
alignment. Our systematic theoretical analysis reveals that different divergences reshape
token-level updates in distinct ways. In particular, under the sampled-
token shaping form used, JS regularization achieves a favorable trade-off by mitigating uniform bias relative to the reference policy while still discouraging large deviations. Extensive experiments on LlamaGen and Janus-7B show that JS divergence achieves the strongest or highly competitive optimization performance on most evaluation metrics while maintaining favorable generation diversity. The code is available at https://github.com/tuoyou-hao/BPD-GRPO.
Read the full abstract ▾
All authors Yuanhao Chiang, Hongbo Duan, Chunru Yang, Jiahua Pei, Yi Liu, Xueqian Wang
Artificial Intelligence · Cross-listed from cs.LG
Breaking Chains with Trees: Model-Parallel Deep Learning with $\mathcal{O}(\log N)$ Time Complexity
Neeraj Mohan Sushma, Aditya Nagarsekar, Cabrel Teguemne Fokam et al. (+4 more)
Modern deep neural networks are trained using error backpropagation, which requires sequential forward and backward computations across network layers. As these networks become deeper, this introduces limitations, since layer-wise updates are strictly interdependent and cannot proceed in parallel. These constraints restrict training procedures to data-parallel schemes, thereby prohibiting model-parallel training. We propose TreeProp, an architecture-agnostic variational learning framework that organizes network layers into a tree-structured hierarchy. During training, TreeProp replaces sequential forward computations and backward gradient propagation with hierarchical computations. This allows intermediate representations and learning signals to be constructed in time complexity of $\mathcal{O}(\log N)$ for a network of $N$ layers. To the best of our knowledge, TreeProp is the first learning
algorithm for deep neural networks with logarithmic parallel time complexity for both forward computation and backward gradient propagation during training. Furthermore, we show that multiple valid paths through the hierarchy exist, such that TreeProp implicitly learns subnetworks with different effective depths, but without additional training effort. We evaluate TreeProp on
vision classification and autoregressive
language modeling, matching the performance of conventional end-to-end training for a variety of tasks and outperforming previous contrastive training approaches. We further demonstrate the applicability of TreeProp to recurrent neural networks that otherwise rely on backpropagation through time.
Read the full abstract ▾
All authors Neeraj Mohan Sushma, Aditya Nagarsekar, Cabrel Teguemne Fokam, Robin Schiewer, Amit Kumar Pal, Anand Subramoney, David Kappel
Artificial Intelligence · Cross-listed from cs.RO
Decoupling the Declarative from the Procedural in Vision-Language-Action Models
Nikolaos Tsagkas, Andreas Sochopoulos, Chris Xiaoxuan Lu et al. (+2 more)
Deploying generalist robotic
agents in the real world requires transferable skills. Specifically, a policy trained to clone a behavior from object-specific demonstrations must generalize beyond that object, otherwise data collection requirements become intractable. Recently, fine-tuning of pre-trained billion-parameter
Vision-
Language Models (VLMs), initially on large-scale
robot datasets and then on fewer scenario-specific demonstrations, has emerged as the predominant paradigm for designing
Vision-
Language-Action (VLA) models. While these policies achieve state-of-the-art manipulation performance in-distribution, they remain brittle to minor spatial, semantic, and task variations. In this work, we address the inability of current models to decouple the declarative (i.e., concepts and entity semantics) from the procedural knowledge (i.e., how to do something) encoded in their parameters, which is a fundamental bottleneck for zero-shot skill transfer to novel objects. To address this, we propose w$^{2}$VLA, a new VLA model with restructured information flow. Rather than feeding all
multimodal tokens from the VLM encoder into a large, opaque
transformer-based action expert, our approach modulates the
robot state sequence with visual, spatial, and skill information in a compositional and interpretable manner. Unlike popular, state-of-the-art VLAs, we show that our modular approach successfully decouples knowledge representations, enabling robust behavior cloning and unprecedented zero-shot skill transfer capabilities across dissimilar, unseen objects.
Read the full abstract ▾
All authors Nikolaos Tsagkas, Andreas Sochopoulos, Chris Xiaoxuan Lu, Oisin Mac Aodha, Alexandros Kouris
Artificial Intelligence · Cross-listed from cs.LG
Predicting High-Risk Colorectal Polyps in African Americans Using Pre-Colonoscopy Clinical Features: Machine Learning Model Development and Temporal Validation
Basheer Qolomany, Mrinalini Deverapall, Adeyinka Laiyemo et al. (+5 more)
Risk stratification for advanced colorectal polyps typically relies on colonoscopy and/or pathology findings. However, there is growing interest in whether non-invasive features available prior to colonoscopy can help identify patients at higher risk. Such approaches may enhance clinical decision-making by prioritizing surveillance for individuals most likely to harbor high-risk polyps, when colonoscopy resources are limited while potentially reducing unnecessary procedures in lower-risk patients. Importantly, the use of non-invasive, pre-procedural information may also help promote more equitable access to risk stratification, particularly in settings where colonoscopy resources are limited or unevenly distributed. We aimed to develop and externally validate machine learning models to predict high-risk colorectal polyps using only non-invasive, pre-colonoscopy demographic, clinical, and behavioral features in a diverse, predominantly African American, urban cohort. We conducted a retrospective cohort study using demographic, lifestyle, and comorbidity data from patients who underwent colonoscopy at Howard University Hospital to develop and validate several machine learning models, including neural networks, random forest, support vector machines (SVM), Naive Bayes, logistic regression, decision trees, k-nearest neighbors (KNN), and XGBoost, for predicting high-risk colorectal polyps. High-risk polyps (HRP) were defined as villous or tubullovillous adenomas, high-grade dysplasia, polyps >= 10 mm in size, and/or the presence of >= 3 polyps per procedure; all other cases were classified as low-risk polyps (LRP). The
dataset included 4,681 patients from 2015-2022 used for internal validation and 1,562 patients from 2023-2024 used for external validation.
Read the full abstract ▾
All authors Basheer Qolomany, Mrinalini Deverapall, Adeyinka Laiyemo, Zaki Sherif, Mori Yuichi, Omer Ahmed, Hassan Brim, Hassan Ashktorab
Artificial Intelligence
Towards Transparent Mental Health Insights: An Explainable AI Model for Career-Related Depression and Anxiety Among University Students Using Structured Data
Arsham Azam, Rasikh Ali, Tayyaba Farhat et al. (+1 more)
Career anxiety and depression among university students present a growing challenge to mental health and academic achievement. This study proposes an Explainable AI (XAI) framework using
multimodal data and
Federated Learning (FL) to identify early indicators of career-related mental health problems in a
privacy-preserving and culturally responsive manner. The framework combines structured behavioral data and facial
emotion features from interview
videos via an intermediate fusion neural network with
attention mechanisms. Label smoothing was applied to improve model generalizability. FL was used across institutions to enable collaborative training without raw data sharing. Evaluation was conducted using the Student Mental Health Survey
dataset from university students across Pakistan. Our model attained an F1-score of 89.12%, recall of 86.54%, accuracy of 92.08%, and precision of 91.88%. Using Integrated Gradients and SHAP, the model identified key behavioral markers of depression including avoidance of direct gaze, lower facial expressiveness, and social withdrawal, consistent with psychological theory. This research presents an interpretable, scalable, and context-sensitive AI system for mental health pre-diagnosis with potential integration into student support services globally.
Read the full abstract ▾
All authors Arsham Azam, Rasikh Ali, Tayyaba Farhat, Sheeraz Akram
Artificial Intelligence · Cross-listed from cs.CL
Evaluation of Small Language Models for Arabic Language Processing
Jumana Alsubhi, Ahmed Alhusayni, Abdulrahman Gharawi et al. (+5 more)
This paper evaluates the performance of twelve Small
Language Models (SLMs) on Arabic natural
language processing tasks. The study introduces a
benchmark of 240 Arabic test items distributed across eight domains and ten
language skills, covering both comprehension-oriented and generation-oriented tasks. All models were evaluated under a controlled zero-shot setting using a standardized Arabic-only
prompt template. Model responses were assessed through a multi-model LLM-as-a-judge framework involving GPT-4.1 Mini, Claude Haiku 4.5, and DeepSeek-Chat, with scores aggregated across judges and analyzed by task, skill, and model family. The results show that Gemma 3 (12B) achieved the highest overall score (4.548/5), followed by Aya and C4AI Command Arabic. The observed results suggest that model size alone does not explain Arabic SLM performance. Models with stronger Arabic
alignment and more reliable instruction-following behavior tended to perform better across tasks. Common failure patterns among lower-performing models include
prompt leakage,
hallucination,
language drift, incomplete generation, and weak task adherence. Overall, the
benchmark provides a structured reference for evaluating compact Arabic
language models and supports future work on efficient, reliable, and culturally appropriate Arabic AI systems.
Read the full abstract ▾
All authors Jumana Alsubhi, Ahmed Alhusayni, Abdulrahman Gharawi, Israa Hamdine, Alshaymaa Allahim, Lamees Alhumaid, Ahmad Shabana, Rafik Madani
Artificial Intelligence · Cross-listed from cs.HC
CORTIS: Text-Only Adaptation of Spoken Language Models for Task-Oriented Voice Agents
Youngwon Choi, Hyeonyu Kim, Taeyoun Kwon et al. (+2 more)
Task-oriented voice
agents need to map spoken user requests to structured outputs such as semantic frames, executable actions, and function calls. A common approach is to cascade ASR with a text-based LLM, but transcription errors can propagate to downstream structured output generation, especially under noisy conditions. Spoken
language models (SLMs) offer a direct
speech-based alternative, yet adapting them to new tasks typically requires paired
speech-target annotations. Motivated by this gap, we present CORTIS, a text-only adaptation framework for task-oriented voice
agents. CORTIS fine-tunes SLMs using text-form task supervision, enabling
speech-based structured output generation at inference time without task-specific
speech-target annotations during adaptation. We evaluate CORTIS on two Qwen2.5-Omni backbones and three task-oriented
speech datasets, including an in-house product
dataset, and compare it with matched ASR-LLM cascades trained with the same text-form task supervision. Results show that CORTIS performs competitively with matched cascades and offers clearer advantages under acoustic degradation, particularly in preserving high-level task semantics. These findings suggest that text-only fine-tuning of SLMs can serve as a practical adaptation strategy for voice
agents when paired
speech-target data are costly to collect.
Read the full abstract ▾
All authors Youngwon Choi, Hyeonyu Kim, Taeyoun Kwon, Donghyuk Jung, Myeongkyun Cho
Artificial Intelligence
AutoRAS: Learning Robust Agentic Systems with Primitive Representations
Yang Yue, Xuancheng Zhu, Yuyang Ma et al. (+7 more)
The automated design of agentic systems offers a promising pathway for scaling large
language models (LLMs) beyond single-
agent reasoning. While prior work has advanced task performance through handcrafted or automatically generated multi-
agent workflows, robustness is often treated as an afterthought, leaving systems vulnerable to external adversaries and internal failures. We propose AutoRAS, a framework for the Automated design of Robust Agentic Systems. AutoRAS formulates system design as generating a sequence of symbolic primitives that jointly encode structural connectivity and behavioral actions, and learns to optimize this sequence using execution-derived safety signals and flow-based sequence-level objectives. Extensive experiments show that AutoRAS achieves the best performance in both vanilla and
adversarial settings, with the smallest performance degradation under attacks. Further analyses demonstrate strong transferability, stable optimization behavior, stability across primitive sets, and favorable cost trade-offs. Our code is available at $\href{https://github.com/guohezuy/AutoRAS}{\text{this https URL}}$.
Read the full abstract ▾
All authors Yang Yue, Xuancheng Zhu, Yuyang Ma, Guoshun Nan, Zihan Dou, Jingru Shan, Congyu Guo, Ji Zhang, Hua Wang, Jingfeng Zhang
Artificial Intelligence · Cross-listed from cs.PF
Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study
Alfarizy Alfarizy, Hung Truong Thanh Nguyen, René Richard et al. (+2 more)
Mixture-of-Experts (MoE)
language models are often described as ideal for resource-constrained inference. Each
token activates only a small subset of experts, so the per-
token compute cost, in floating-point operations (FLOPs), resembles that of a much smaller dense model. Whether that FLOP advantage survives in practice is far less clear. We ask whether MoE models actually run faster and cheaper than comparable dense models on consumer-grade and edge hardware. We
benchmark OLMoE-1B-7B (1.3 B active of 6.9 B total) against three dense baselines on an Apple M2 Pro and an NVIDIA Jetson Orin Nano 8 GB through llama$.$cpp, measuring throughput,
memory, and on-device energy. The answer is device-dependent: OLMoE's active-parameter advantage is only partly realised on the laptop (~10% behind the same-active Llama-3.2-1B) and erodes on the edge device (~31% behind, at 2.1$\times$ the energy per
token, with peak
memory at the 8 GB ceiling). Patching llama$.$cpp to time the decode graph node-by-node shows routing accounts for under 9% of MoE-block compute on the cleaner edge backend, so the gap reflects total-parameter
memory footprint, expert dispatch, and KV-cache pressure rather than routing. The implication is that on bandwidth-bound edge hardware, inference cost tracks total parameters, not active ones, and sparse activation does not buy back what the device is constrained on. These findings are bounded to one MoE model at this parameter scale and two devices, and we release the full measurement harness and per-run data.
Read the full abstract ▾
All authors Alfarizy Alfarizy, Hung Truong Thanh Nguyen, René Richard, Roozbeh Razavi-Far, Hung Cao
Artificial Intelligence · Cross-listed from cs.CV
MIRCaps: A Large-Scale Mixed-Domain Dataset with Image-Level and Region-Level Captions for Fine-Grained Vision-Language Learning
Arlindo Luciano Tulumba Roberto and Hyungjoon Kim
Despite recent progress in
Vision-
Language Models (VLMs), mixed-domain image-caption
datasets for both general-purpose and CCTV-based
video surveillance systems remain limited. To address this gap, we introduce a large-scale
multimodal dataset comprising 141,364 images, 981,947 image-level captions, 1,742,264 region-level captions, and 1,391,779 bounding box annotations. Each image is associated with an average of seven image-level captions describing different aspects of the overall scene, as well as seven region-level captions for each annotated bounding box. These complementary caption types are designed to help VLMs learn fine-grained visual attributes, including object categories, estimated sizes, colors, actions, states, and surrounding environmental context. We demonstrate the effectiveness of the
dataset on two important downstream tasks: image captioning and object detection. Experimental results show that lightweight VLMs, including SmolVLM-256M-Instruct, BLIP, BLIP2, and Qwen2.5-VL 3B-Instruct, can be effectively fine-tuned using our
dataset. Our
dataset and code are publicly available at https://zenodo.org/records/20418601.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from eess.IV
2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models
Sampath Rapuri, Jeremy Ko, Benjamin D. Killeen et al. (+2 more)
The ability to synthesize realistic X-ray images has catalyzed the development of AI models for X-ray image-guided procedures, which otherwise suffer from a lack of available annotated data. Prior work has demonstrated the effectiveness of mechanistic simulation of digitally reconstructed radiographs (DRRs) as a training data source for a myriad of tasks, including
segmentation and anatomical landmark detection, with comparable or superior performance to real data training. However, mechanistic DRR synthesis still relies on the availability of annotated high-resolution anatomical models. Deriving these from CT images of real patients or specimens imposes an undesirable bottleneck on data quantity and variability. In this work, we explore two methods for synthesizing training data: (1) a 3D conditional latent
diffusion model that generates CT volumes to use as inputs for mechanistic DRR generation without real, 3D anatomical models, and (2) a view-conditioned 2D
diffusion model that produces synthetic X-rays. In controlled experiments, we demonstrate that synthetic 2D
diffusion-based X-rays can be used to train an anatomical landmark detection model that generalized to real X-ray images with performance rivaling that of a model trained on real X-ray images. Thus, we provide preliminary evidence that synthetic, 2D
diffusion-based training data can substitute for real X-ray data, identifying a promising avenue towards generating large, diverse
datasets for training robust AI models in interventional X-ray imaging.
Read the full abstract ▾
All authors Sampath Rapuri, Jeremy Ko, Benjamin D. Killeen, Russell H. Taylor, Mathias Unberath
Artificial Intelligence
Don't Blindly Trust It: How Unreliable Feedback Breaks Tool-Using LLM Agents
Chubin Zhang, Zhenglin Wan, Xingrui Yu et al. (+5 more)
Tool-augmented
agents are typically evaluated by their gains under reliable external feedback. Yet these gains leave open a key counterfactual: when feedback is unreliable, would the
agent be better off receiving no task evidence? We study this question with a controlled matched-loop comparison that fixes the
agent loop,
prompt, action space, and decoding, while varying only the returned observation: faithful, misleading, or absent. Across question answering and fact verification, persistent misleading feedback produces a value inversion:
agents that benefit from clean tools can perform worse than the matched no-feedback fallback. On HotpotQA, Qwen2.5-7B reaches 44.8 F1 with clean
retrieval and 22.3 F1 with no feedback, but drops to 4.7 F1 under shuffled
retrieval. The inversion persists under stronger clean
retrieval and locally plausible distractors, but weakens when later clean evidence can repair the trajectory. Early trajectory signals predict many failures, yet simple repairs remain fallback-limited: rejecting bad evidence helps only when the exposed fallback is reliable. These results show that clean-tool gains can overstate tool value, and that matched no-feedback fallback controls are necessary for evaluating tool-augmented
agents.
Read the full abstract ▾
All authors Chubin Zhang, Zhenglin Wan, Xingrui Yu, Pengfei Zhou, Wangbo Zhao, Jingxuan Wu, Yaxin Zhou, Ivor Tsang
Artificial Intelligence · Cross-listed from cs.DC
SwarmX: Agentic Scheduling for Low-Latency Agentic Systems
Yeqi Huang, Yanwei Ye, Guomin Chen et al. (+8 more)
Agentic AI applications compose multiple model calls and tool executions, creating new scheduling challenges for GPU-CPU clusters. Their inference time and model-call structure often depend on
prompt semantics, making conventional scheduling approaches ineffective for low-latency serving. This paper presents SwarmX, a system that implements agentic scheduling for low-latency agentic applications. SwarmX uses scheduling-specific neural predictors to capture
prompt, device, runtime, and target-model features; exposes distributional predictions to routers and scalers for tail-aware decisions; and provides mechanisms for predictor training and online adaptation. These predictors and mechanisms are integrated into a scheduler-
agent framework that provides a common substrate for integration with existing scheduling and model-serving infrastructure. We evaluate SwarmX using production deployment (nearly one thousand GPUs and one million CPU cores) and controlled experiments on a 128-GPU testbed. Across multi-
agent code generation, deep research, and
multimodal agentic workflows, SwarmX reduces tail latency by up to 61.5% compared to state-of-the-art schedulers and sustains up to 2x the throughput of production schedulers under the same SLO.
Read the full abstract ▾
All authors Yeqi Huang, Yanwei Ye, Guomin Chen, Wenhao Su, Bin Gong, Jialian Li, Zhan Lu, Yangshen Deng, Xuan Sun, Le Xu, Luo Mai
Artificial Intelligence
Calibration Is Not Control: Intervention Value for LLM-Agent Oversight
Chubin Zhang, Zhenglin Wan, Xingrui Yu et al. (+5 more)
Runtime oversight often intervenes when an LLM
agent's calibrated failure score crosses a threshold. Yet states with the same failure risk can differ in whether intervention helps. Strictly increasing recalibration preserves the threshold policy class and cannot recover this distinction. We formalize when a summary is sufficient for intervention decisions and the utility lost when it is not. We evaluate the consequences by replaying
agent prefixes and executing alternative actions from the same state. On ALFWorld, holding features,
estimator, and router fixed while changing the supervision target from failure to intervention utility lowers regret from 0.51 to 0.09; the gain replicates on a second suite of mid-episode prefixes. A deployable intervention-trained scalar also beats the failure-score threshold rule selected on test outcomes. Online, on 300 unseen tasks with a fixed stronger-model handoff, a frozen prefix-feature controller improves utility over failure-triggered routing, handing off less often (35% vs 48%) and succeeding more often (45% vs 37%). Gains depend on intervention value and are small on two
reasoning benchmarks. Oversight signals should be evaluated by the decisions they support alongside their predictive quality. Code is available at https://github.com/bennidict23/calibration-is-not-control.
Read the full abstract ▾
All authors Chubin Zhang, Zhenglin Wan, Xingrui Yu, Jingxuan Wu, Qi Wen, Pengfei Zhou, Wangbo Zhao, Ivor Tsang
Artificial Intelligence · Cross-listed from cs.CR
Evaluating LLMs for Real-World Web Vulnerability Detection
Sebastian Neef, Luca Jungnickel, Antonio Benjamin Buchholz et al. (+2 more)
Large
Language Models (LLMs) have emerged as a promising tool for automated vulnerability detection, yet their effectiveness on web-specific vulnerabilities remains to be explored. This work
benchmarks six frontier (Claude Opus 4.6, Codex GPT-5.4, Gemini 3.1-pro-preview) and open-weight models (Qwen 3.5, Qwen 3 Coder Next, MiniMax M2.5) on their ability to detect real-world web vulnerabilities using static analysis in WordPress plugins, including SQL injection, stored cross-site scripting, path traversal, and remote code execution. Using five
prompt designs of varying structure, scope, and complexity across three experiment iterations, we aim to answer how model and
prompt choice affects vulnerability detection. Our results show that all models are capable of detecting valid security issues, but the detection rate varies depending on the model and
prompt. For example, Claude Opus 4.6 achieved the highest web vulnerability detection rate (63%), while open-weight MiniMax M2.5 performs on par with other frontier models (48%), and self-hosted Qwen 3.5 only achieved 35%. We show that scoped
prompts that narrow the vulnerability scope outperform open-ended ones, whereas the
prompt complexity has little impact. Surprisingly, no model achieved full reporting consistency across three experiment iterations, with some as low as 50%. Our experiments demonstrate the opportunities and limits of LLM-based vulnerability detection, as no model correctly identified one baseline vulnerability in one of the plugins. Additionally, we derive practical lessons learned for security practitioners and publish all code and data to support future research.
Read the full abstract ▾
All authors Sebastian Neef, Luca Jungnickel, Antonio Benjamin Buchholz, Valene Spence, Vicente Birke Gonzalez
Artificial Intelligence · Cross-listed from cs.LG
Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability
Mathieu Cyrille Simon, Pascal Frossard and Christophe De Vleeschouwer
This paper explores unsupervised disentangled representation learning from a functional perspective. We define latent concepts as factors that influence observations through locally orthogonal directions, formalized as an orthogonality constraint on the Jacobian of the generative mapping. We prove that this condition yields identifiability of general nonlinear generative models, without requiring statistical independence or causal assumptions, provided the latent domain admits all combinations of factor values. Experiments with orthogonality-regularized normalizing flows empirically confirm the theory, demonstrate reliable recovery of ground-truth factors, and shed light on the success of VAEs. These findings challenge the prevailing impossibility claims for unsupervised disentanglement and provide a principled alternative foundation.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.CV
EnTrust: Modeling Inter-Modal Conflict for Trustworthy Multimodal Medical Image Analysis
Dwarikanath Mahapatra, Abhijit Das, Behzad Bozorgtabar et al. (+5 more)
Multimodal medical imaging fuses complementary anatomical and functional information, yet modalities frequently disagree in pathologically heterogeneous regions. Current
segmentation models handle this in one of two inadequate ways: deterministic fusion that averages away disagreement, or post-hoc uncertainty estimation decoupled from the fusion process that produces it. Both obscure the clinically critical question: why is this prediction unreliable? We present EnTrust, a framework that treats inter-modal conflict as the primary source of predictive uncertainty. Our EnFuse module decomposes
multimodal features into three disentangled components: shared anatomical consensus (F_c), modality-specific cues (F_{u,m}), and spatially localized conflict signals (F_{cf}), with independence enforced via a cross-covariance objective. This structured decomposition conditions SegDiff, a
diffusion-based generative
segmentation model whose sampled hypotheses diverge specifically in regions of modal disagreement. TrustMap then translates this hypothesis divergence into calibrated, pixel-wise uncertainty using ensemble
entropy, conflict-guided perturbation probing, and a learned calibration head, enabling clinicians to understand not only where predictions are uncertain, but why. Across four
benchmarks spanning
brain, cardiac, lesion, and oncology domains, EnTrust achieves state-of-the-art
segmentation accuracy while reducing calibration error by 40% compared to the strongest baseline. Notably, it outperforms 5x deep ensembles using a single model at roughly half the
memory footprint. Code and checkpoints are available at https://github.com/GenMI-Lab/EnTrust.git.
Read the full abstract ▾
All authors Dwarikanath Mahapatra, Abhijit Das, Behzad Bozorgtabar, Zongyuan Ge, Sudipta Roy, Deepak Nayak, Mauricio Reyes, Imran Razzak
Artificial Intelligence · Cross-listed from cs.CV
Graph-of-Differences: Anatomy-Structured Difference Alignment for Medical Image Re-Identification
Nichula Wasalathilaka, Abhijit Das, Imran Razzak et al. (+1 more)
Medical image re-identification (MedReID) enables longitudinal patient linkage but remains vulnerable to shortcut learning and often produces decisions that clinicians cannot audit against named anatomy. We propose Graph-of-Differences (GoD), which grounds identity comparisons in explicit anatomical structure. Each image is represented as an anatomy graph whose nodes correspond to named anatomical regions; given an image pair, soft node correspondence is established, and differences are computed over matched anatomy. A graph-level difference
alignment objective ties these anatomy-matched differences to the global backbone difference, ensuring the
retrieval signal is anchored in homologous structures rather than arbitrary spatial
tokens. Explanations are defined over named graph nodes and quantitatively audited via node insertion/deletion tests, replacing unstable pixel heatmaps with verifiable structure-level evidence. On internal
benchmarks, GoD improves Rank-1 by +7.1 pp on fundus and +3.1 pp on CXR over a strong frozen-backbone baseline, with further gains on zero-shot external transfers confirming that anatomy grounding improves both accuracy and generalization. Code is available at https://github.com/GenMI-Lab/GoD.git.
Read the full abstract ▾
All authors Nichula Wasalathilaka, Abhijit Das, Imran Razzak, Dwarikanath Mahapatra
Artificial Intelligence · Cross-listed from eess.AS
Sexualised synthetic personas encode and amplify gendered power asymmetries through voice
Alice Ross, Ariadna Sanchez, Elin Kanhov et al. (+2 more)
This work examines sexualised AI-generated English-speaking voices offered by a popular commercial platform. New technologies may enable sexual empowerment and greater diversity in gender expression, yet toxic masculinity, heteronormativity, and the abuse of women and LGBTQ+ people remain pervasive online. Drawing on a Feminist HCI perspective, we examine how commercial voice AI systems reproduce and circulate particular performances of gender. We conducted a listening experiment with a diverse group of listeners, combining quantitative adjective selection, qualitative free-text responses, and acoustic analysis. Participants evaluated male- and female-coded voices presented with either sexualised scripts or neutral text. Results reveal a narrow range of gender expression, largely binary and heteronormative. Female-coded voices are more frequently described using sexualised and submissive terms, while male-coded voices are more often associated with dominance and positive traits.
Read the full abstract ▾
All authors Alice Ross, Ariadna Sanchez, Elin Kanhov, Catherine Lai, Éva Székely
Artificial Intelligence · Cross-listed from cs.SD
LambdaMark: Semantic Audio Watermarking for Robustness and Radioactivity
Kexin Li, Xiao Hu, Ilya Grishchenko et al. (+1 more)
Recent advances in generative audio have made voice cloning increasingly effortless, enabling voice fraud, impersonation, and other forms of unauthorized use. A common attack finetunes a
speech generation model on recordings of a target speaker, allowing the model to synthesize
speech in that speaker's voice. Audio watermarking offers a promising defense by
embedding detectable signals into audio. A practical watermark must satisfy two key properties: robustness and radioactivity. Existing audio watermarking methods typically embed signals into low-level representations, such as waveforms or spectrograms, which makes them vulnerable to signal-level manipulations and limits their transfer to downstream models. We introduce LambdaMark – the first generic radioactive watermarking scheme. Unlike all previous approaches, LambdaMark achieves generic radioactivity by
embedding multi-bit watermark information into semantic audio latent representations. Our watermarks have semantic interpretation and are thus more likely to be learned by a downstream model through finetuning. LambdaMark includes a lightweight watermark encoder to inject multi-bit message-dependent perturbations into semantic audio representations and a decoder to detect watermark presence and recover the embedded bit information. Encoder and decoder are trained using a custom multi-component loss that preserves fidelity of the watermarked audio, increases bit-level recovery rate, and improves robustness against common distortions and
adversarial removal attempts. Experiments show that LambdaMark achieves near-perfect robustness under common distortions. LambdaMark is also the only watermark that is robust against all evaluated removal attacks. Furthermore, LambdaMark exhibits general and robust radioactivity and remains robust to distortions and
adversarial removal attacks even on the generated outputs of those finetuned models.
Read the full abstract ▾
All authors Kexin Li, Xiao Hu, Ilya Grishchenko, David Lie
Artificial Intelligence · Cross-listed from cs.LG
SOHET: Sequence Of Heterogeneous Events Transformer with Self-Supervised Pre-Training
Kees Jan de Vries, Mustafa Radha and Mathijs de Jong
Many machine learning applications rely on heterogeneous event streams to make predictions, either causally as events arrive or bidirectionally over complete sequences. We propose SOHET (Sequence Of Heterogeneous Events
Transformer), a hierarchical architecture combining event-type-specific tabular encoders with temporal and type
embeddings, processed by a causal or bidirectional
transformer. We introduce three self-supervised pre-training objectives for the causal setting. On a proprietary large-scale real-world Booking.com fraud detection task with 17 event types, SOHET outperforms FlexTPP, NAPPT, and CIPPT by 5.8%. Pre-training yields an additional 2.6% gain and 2.4% faster convergence. On the EBES
benchmark, bidirectional SOHET matches or exceeds the published best on 6 out of 8 tasks.
Read the full abstract ▾
Artificial Intelligence
Mind the Noise: Sensitivity of Transformer-based Interaction-Aware Trajectory Prediction Models to Noisy Data
Shahab Salehi, Luca Lusvarghi, Miguel Sepulcre et al. (+1 more)
Trajectory prediction allows autonomous vehicles to anticipate the future behavior of surrounding objects (or
agents) and, accordingly, maximize the safety and efficiency of their driving. State-of-the-art Transformed-based interaction-aware trajectory prediction models, which rely on
attention mechanisms to capture multi-
agent interactions and maximize prediction accuracy, are commonly trained and evaluated on long-range high-quality
datasets. These
datasets are typically obtained by aggregating data from multiple vehicles or drones and removing any object detection or tracking noise offline. Yet, information about a surrounding object's state (its position, speed, heading) is far from being noiseless in real-world deployments. Object state estimation is affected by perception uncertainties and localization errors that can be particularly large for objects received via Vehicle-to-Everything (V2X) communications. In this paper, we analyze the impact of noisy object state information on the trajectory prediction accuracy of a state-of-the-art
Transformer-based interaction-aware trajectory prediction model. Our study demonstrates that trajectory prediction accuracy can rapidly deteriorate as the noise intensity increases. Numerical results show that the prediction accuracy can reduce by a 1.3x factor under small noise levels and by as much as a 3.9x factor under the highest (yet realistic) noise conditions. These findings reveal the strong sensitivity of trajectory prediction models to noisy data, underscoring the need for more realistic training and evaluation
datasets as well as noise mitigation strategies.
Read the full abstract ▾
All authors Shahab Salehi, Luca Lusvarghi, Miguel Sepulcre, Javier Gozalvez
Artificial Intelligence · Cross-listed from cs.LG
DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams
Cong Wan, Zeyu Guo, Zijian Cai et al. (+6 more)
Raw
multimodal streams are abundant but noisy, redundant, and unaligned with any particular training objective. Turning them into supervision today means either brittle heuristics or repeatedly querying a proprietary
vision-
language model, a cost that recurs with every new sample. We ask whether this conversion can instead be learned once and reused, and formalise intent-conditioned Data Tailoring: given a raw stream and a high-level intent, a model must return schema-aligned, evidence-grounded training instances. Training DataClaw0 at 4B, 9B and 27B, we find that whether five heterogeneous domains should share one model depends on capacity. A jointly trained model is worse than per-domain experts at the two smaller scales and better at the largest, placing the crossover near 18B parameters. Matched-data comparisons, in which the joint model sees exactly the same data per domain as that domain's expert, attribute the reversal to cross-domain transfer rather than to data volume: it gains most where a domain is data-poor, and recovers 59% of in-domain performance on domains withheld from training entirely. Downstream post-training reproduces this ordering on GUI
navigation, action
video generation and spatio-temporal VQA, and the joint configuration is also the cheaper to deploy, serving one model instead of five. Github: https://github.com/vancyland/DataClaw0
Read the full abstract ▾
All authors Cong Wan, Zeyu Guo, Zijian Cai, Jiangyang Li, SongLin Dong, Lin Peng, Xiangyang Luo, Zhiheng Ma, Yihong Gong
Artificial Intelligence · Cross-listed from cs.LG
Ramanujan Graph Rewiring with Non Negative Resistance Curvature
Hugo Attali and Rachid El Jouhri
Graph Neural Networks (GNNs) have emerged as a powerful paradigm for learning on graph-structured data by iteratively propagating and aggregating information across edges. However, conventional message passing schemes often suffer from over-squashing, whereby exponentially large neighborhoods are compressed into fixed-dimensional
embeddings, impeding effective long-range dependency learning. In this work, we introduce Ramanujan Propagation, a graph rewiring strategy that leverages Ramanujan graphs to alleviate topological bottlenecks in GNNs. We first establish that suitably chosen Ramanujan graphs guarantee non-negative resistance
curvature, which mitigates over-squashing and facilitates efficient information flow. We then propose an algorithmic framework to construct a Ramanujan rewired graph that preserves the local connectivity of the original graph. Our experiments demonstrate that our method outperforms nine state-of-the-art rewiring techniques. These results establish Ramanujan graphs as a rigorous structural prior for scalable,
topology-aware message passing in GNNs.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.HC
Warning labels shift perceptions of sycophantic AI, but not its influence
Lujain Ibrahim, Myra Cheng, Cinoo Lee et al. (+4 more)
Recent work has raised concerns about the influence of sycophantic AI on user judgment and relationships. One proposed mitigation, which has received regulatory
attention, is to warn users about potentially harmful AI behaviors such as sycophancy. In a preregistered experiment in which participants (N = 2,610) discussed real interpersonal conflicts with an AI system, we test whether warning labels mitigate sycophancy's influence. We find that a basic AI disclosure (“This chatbot is AI”) has no detectable effect. Labeling the system as sycophantic (“...may agree with you and validate you even when you are wrong...”) does shift users' perceptions, reducing perceived objectivity and trust, but it does not reliably reduce sycophancy's influence on users' self-perceived rightness or their willingness to repair the conflict. Our results reveal a gap between AI perception and AI influence: by shifting perception without reducing influence, warning-based interventions may offer a false sense of protection. Addressing the harms of sycophancy will therefore require understanding the specific mechanisms through which it shapes judgment, and improving model behavior itself.
Read the full abstract ▾
All authors Lujain Ibrahim, Myra Cheng, Cinoo Lee, Pranav Khadpe, Desmond Ong, Dan Jurafsky, Diyi Yang
Artificial Intelligence
Social World Model for Lifelong Social Intelligence
Yu Luo
Social intelligence is a core competency for
language agents, yet current research primarily focuses on static capability evaluation rather than how these skills are continuously shaped and accumulated. This gap calls for a shift toward sustainable learning paradigms. Currently, two methodological pain points exist: social interaction trajectories lack unified structured representations to form iterable learning signals, and capability improvement and retention are typically studied in isolation, hindering the assessment of continuous
evolution. To bridge this gap, we propose the Social World Model. We decompose social interaction into five dimensions (scene setting, observation, mental state, action, and dialogue) to build a closed-loop learning framework. In this setup,
agents collect interaction experiences, convert them into preference signals for model updating, and redeploy the updated policy for continued learning. Additionally, we provide a reusable data synthesis mechanism and a lifelong learning
benchmark, transforming social capabilities from an "object of evaluation" into an "object of sustainable training". Validating our framework on the ASCENT-Bench, the interactively trained Qwen2.5-7B model outperforms its baseline across all five core metrics. Notably, it matches the closed-source Gemini 3 Flash in completion rate, exceeds it in pass rate, and achieves zero forgetting across three difficulty levels. Unlike prior works that merely report static comparisons or capability decay, this end-to-end approach provides a trainable, verifiable, and retainable pathway, demonstrating that small open-source models can sustainably acquire competitive social coordination capabilities.
Read the full abstract ▾
Artificial Intelligence · Cross-listed from cs.LG
Task-Differentiated Atomic Skill Expansion and Routing for Continual Learning Across Highly Heterogeneous Tasks
Jiacheng Wang, Xinjia He, Qi Ding et al. (+5 more)
Continual learning (CL) is commonly studied under the assumption that sequential tasks are semantically related or structurally similar. However, in highly heterogeneous settings, where tasks differ substantially in
reasoning patterns and input-output formats, existing methods often suffer from catastrophic forgetting and inefficient capacity allocation. To address this challenge, we propose Task-differentiated Atomic Skill Expansion and Routing (TASER), a CL framework that jointly determines how many new atomic skills to introduce for each task and which skills to activate. The framework first uses atomic skill incremental learning to dynamically expand capacity based on task divergence and model uncertainty. It then applies orthogonality-enhanced skill detection to ensure these skills remain semantically distinct and independently reusable. Finally, a skill dynamic routing mechanism composes task-relevant skills through lightweight task-conditioned gating. We further introduce HeteroCLBench, a highly heterogeneous
benchmark for CL, comprising 19 diverse tasks across 9 cognitive dimensions under a standardized sequential protocol. Experiments on HeteroCLBench show that TASER consistently outperforms strong baselines by improving plasticity and reducing catastrophic forgetting.
Read the full abstract ▾
All authors Jiacheng Wang, Xinjia He, Qi Ding, Yutao Yang, Jie Zhou, Liyang Yu, Liang Dou, Qin Chen
Artificial Intelligence
Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment
Xiaoliang Wu, Qiyang Sun, Yupei Li et al. (+3 more)
Dysarthria severity assessment is essential for therapy planning and longitudinal monitoring, yet manual perceptual rating is time-consuming and variable across clinicians. Although deep learning models achieve strong performance, their black-box nature limits clinical adoption. Existing
speech explainability methods typically provide acoustic feature importance scores that are difficult for end-users to interpret. We propose an influence-based, instance-level explainability framework that explains each decision through supportive and competing training samples. Using gradient-based influence approximations, we compute per-utterance influence scores to identify supportive and competing training samples for each prediction. Controlled deletion experiments from 5 to 20 percent validate the explanations, showing that removing highly influential samples systematically shifts predictions. This approach provides auditable explanations by linking decisions to perceptible reference cases.
Read the full abstract ▾
All authors Xiaoliang Wu, Qiyang Sun, Yupei Li, Erfan Loweimi, Jennifer Williams, Zhengjun Yue