Artificial Intelligence · Cross-listed from cs.CV
P2Voxel: Pyramid Pivot Voxelization for 3D Mesh Tokenization
Zhenhong Sun, Haozhe Liu, Yifu Wang et al. (+6 more)
Triangle meshes provide explicit and accurate surface geometry, yet their irregular
topology connectivity makes 3D mesh tokenization a
geometric sampling problem: how to sample and organize
geometric evidence into compact, structured and learnable
tokens. Beyond field-centric volumetric sampling and edge-intersection surface sampling, we retarget mesh tokenization as local surface evidence sampling: identifying the minimal
geometric evidence inside each active voxel that is sufficient for deterministic surface recovery. To this end, we introduce P2Voxel, a pyramid pivot voxelization framework for compact and reconstruction-aware mesh tokenization. P2Voxel is built on three key innovations. Under the Local Planarity assumption, Pivot Voxelization represents each active voxel with a surface pivot and an orientation sign, providing minimal local evidence that can induce the corner values required for deterministic reconstruction. Under the Spatial Complexity assumption, Pyramid Pivot Voxelization exploits the spatial non-uniformity of real surfaces by allocating finer pivot
tokens to geometrically complex regions while keeping smooth regions coarse and compact. Under the Block Reconstructability assumption, a Pyramid VAE learns compact multi-resolution latent codes over locally reconstructable pivot blocks, avoiding the need to model the entire high-resolution voxelized shape as a dense global field. Together, these designs convert meshes into compact, structured, and learnable pyramid pivot
tokens, enabling efficient mesh reconstruction for downstream 3D tasks.
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All authors Zhenhong Sun, Haozhe Liu, Yifu Wang, Xibin Song, Senbo Wang, Huadong Mo, Daoyi Dong, Hongdong Li, Pan Ji
Artificial Intelligence · Cross-listed from cs.GR
NuclearDiffusion: Text-to-Image Foundation Models for Learning Nuclear Energy Concepts
Mohammed I. Radaideh, Jeremy Moon, Andre Gala-Garza et al. (+3 more)
Generative artificial intelligence (AI) has transformed text-to-image synthesis, yet its ability to represent specialized engineering domains remains largely unexplored. As an exmaple in
nuclear engineering, general-purpose foundation models frequently generate physically incorrect or conceptually inconsistent images because they lack domain-specific knowledge. This work presents one of the first systematic studies of domain adaptation for
nuclear text-to-image generation through fine-tuning of open-source
diffusion models. We curate a
dataset of 1,000 captioned
nuclear energy images spanning reactors, fuel cycles, radiation, and related concepts, and use it to fine-tune three state-of-the-art open-source models: Stable
Diffusion XL (SDXL), SD-v3.5-Medium, and the flow-matching Flux.1 model. Their performance is evaluated using both quantitative image-similarity metrics and qualitative expert assessment against the corresponding zero-shot models. Fine-tuning substantially improves the fidelity of SDXL, provides only limited gains for SD-v3.5-Medium, and yields no measurable improvement for Flux.1, demonstrating that adaptation effectiveness depends strongly on the underlying generative architecture rather than model scale alone. We further compare the fine-tuned models against three leading commercial systems–GPT-Image-2, Gemini-3.1-Flash-Image, and Midjourney. Although GPT-Image-2 and Gemini generate convincing images for broad
nuclear concepts, they frequently fail on specialized engineering
prompts, where the fine-tuned open-source models produce more accurate and technically consistent outputs. These results establish domain-specific fine-tuning as a practical pathway for developing trustworthy generative AI tools for domain-specific applications.
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All authors Mohammed I. Radaideh, Jeremy Moon, Andre Gala-Garza, Emma Son, Yug Shah, Majdi I. Radaideh
Artificial Intelligence · Cross-listed from cs.CR
Single Canonical Prompts Underestimate LLM Safety's Surface-Form Sensitivity
Yongxi Zhou, Junwei Yao, Yuanzhe Liu et al. (+4 more)
A
benchmark score is a measurement instrument, yet most
benchmarks read each item at a single canonical surface form. We ask whether that reading is faithful: when an item's intent is held fixed and only its meaning-preserving surface form varies, does the canonical-form score estimate model behavior well, and how much of any variation is decoding/judge noise rather than signal? We instantiate this in safety, a high-stakes setting with no gold label to average toward. To avoid prior confounds, we pre-author the reformulations (refusal-free, mostly non-LLM: machine back-translation and a
Matrix-
Language-Frame code-switch generator) so an identical surface form reaches every model, score all responses with one human-anchored, vendor-neutral judge (Claude, kappa = 0.86 vs. human on unsafe compliance, stable across
languages, cross-checked by GPT-4o), and verify intent preservation. On 370 seeds x 5 surface forms x 5 models, no single transformation is uniformly most dangerous (6 of 20 per-transformation McNemar tests survive correction, most protective). Yet evaluating only the canonical
prompt underestimates unsafe compliance: the union of unsafe outcomes across forms exceeds even the worst single form by 3.3-12.9 pp, with bootstrap 95% CIs excluding zero for all five models, and 5-13% of seeds safe on canonical are unsafe under some reformulation – above a zero stochasticity floor (canonical resampled five times at temperature 0 gives 0/370 new exposures). The size of this gap is model-dependent (largest on Gemini 2.5 Pro). One form recovers only ~53% of a model's observed unsafe surface and about three reach 85% – a redundancy characterization of this form set, not of a defined population. A benign control (XSTest) suggests the instability is bidirectional, though the benign and harmful pools are not item-matched. We release the
dataset, code, and per-response labels.
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All authors Yongxi Zhou, Junwei Yao, Yuanzhe Liu, Zihan Dong, Wenbo Ye, Jiaxi Wen, Lai Yun Choi
Artificial Intelligence · Cross-listed from cs.CR
ZK-SR117: A Chunked Zero-Knowledge Attestation Design for Aggregated Fair-Lending Metrics, with a Control Mapping toward Full SR 11-7 Coverage
Mohammad Nasir Uddin, Rahnuma Tabassum Orpita, Eklachur Rahman Bhuiyan et al. (+1 more)
Deploying ML models in regulated decision-making (credit underwriting, fraud detection, loan approval) requires demonstrating
fairness and robustness to auditors without exposing model weights or customer data. We address this attestation problem for U.S. bank supervision under SR 11-7 and OCC 2011-12 guidance. We present a chunked zero-knowledge
circuit design that attests an aggregated
fairness statistic - the demographic-parity gap - on committed, nonce-sampled batches of real 2022 HMDA mortgage data, and demonstrate it end-to-end: 32,768 rows, 32 independently verified zkSNARK proofs, aggregated attested gap within 0.0029 of the true held-out value, per-chunk proving under 4 seconds. We also demonstrate extensibility by attesting a second control on the identical architecture - expected calibration error at 10 bins - with all 32 chunks verified, per-chunk proving at about 14.7 seconds, and attested ECE within 0.00037 of the plaintext value on the same committed rows. We compare this design against two alternatives - a flat summation
circuit, which overflows past a few thousand rows, and a tree-reduction
circuit, numerically exact but intractable to compile - and find the chunked design is the only one that reached this scale. We discovered and root-caused a genuine data-quality failure (a sentinel-code outlier distorting
circuit proving and the
fairness statistic itself) and resolved it with a published preprocessing specification. We also propose a fuller mapping from SR 11-7 and OCC 2011-12 control
language to zero-knowledge statements (nine control elements spanning soundness, calibration, robustness, and drift), a nonce-based sampling protocol resisting bank-side cherry-picking, and a threat model for deployment, proposed as design work, not implemented results. Two controls, one model class, one task are demonstrated end-to-end; the rest is scoped and left as future work.
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All authors Mohammad Nasir Uddin, Rahnuma Tabassum Orpita, Eklachur Rahman Bhuiyan, Asaduzzaman Anik
Artificial Intelligence · Cross-listed from cs.LG
CT-HEG: A Bidirectional, Timestamp-Attributed Event Graph for ICU In-Hospital Mortality Prediction - An Architectural Ablation Study
Mohammad Nasir Uddin, Rahnuma Tabassum Orpita, Asaduzzaman Anik et al. (+4 more)
Accurate ICU mortality prediction requires modeling irregular clinical observations across heterogeneous entity types. Existing sequence models handle irregular sampling but ignore typed relational structure; existing graph models assume fixed-interval inputs. We introduce the Continuous-Time Heterogeneous EHR Graph (CT-HEG) schema and evaluate which architectural choices drive predictive performance. CT-HEG encodes each ICU stay as a typed, timestamped graph with three node types (visit, vital, lab_event) and 2D edge attributes (t_hours/48, value_norm) encoding timing and value without imputation. We instantiate CT-HEG as CHIRP-Net, a four-layer heterogeneous GATv2Conv network, evaluated on MIMIC-IV v3.1 (31,142 ICU stays, LOS>=48h, 13.4% mortality) with five seeds and bootstrapped confidence intervals, against logistic regression, mTAND, a
Transformer, and GRU-D, plus an ablation study. CHIRP-Net achieved 5-seed mean AUROC 0.8449+/-0.0071 (AUPRC 0.4958+/-0.0209); the ensemble achieved AUROC 0.8618 (95% CI: 0.8485-0.8745). Removing reverse edges disconnected observation nodes from the visit readout, cutting AUROC by 0.1968+/-0.0073. Time-attentive edge features contributed 0.0247+/-0.0093 AUROC. Collapsing heterogeneous edge types into one relation (7x fewer parameters) outperformed the full model on all seeds. Post-calibration ECE was 0.0307. Temporal and demographic subgroup analyses were explored but not reported here, pending follow-up work. Bidirectional connectivity was necessary for the model to use its inputs at all, and CT-HEG was reasonably well calibrated after validation-fitted temperature scaling. These results support CT-HEG for irregular EHR data, while external validation, a pre-specified temporal evaluation, and a demographic
fairness audit remain necessary before any claim of robustness. Code: https://github.com/nasiruddinstudents-ctrl/chirp-net-mimic-iv.
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All authors Mohammad Nasir Uddin, Rahnuma Tabassum Orpita, Asaduzzaman Anik, Eklachur Rahman Bhuiyan, Marjahan Risalat, SM Wali Ullah, Asif Ahamed
Artificial Intelligence · Cross-listed from cs.LG
Verifier-Guided Model Discovery for Physical Dynamical Systems with Pretrained Symbolic Transformers
Farbod Faraji and Francesco Belardinelli
Reliable
forecasting of nonlinear physical systems underpins scientific discovery and engineering decision-making. Yet high-fidelity simulations are prohibitively costly, and machine-learning surrogates can be opaque and encode assumptions about system dynamics, limiting generalizability. Pretrained
transformers mapping synthetic ODE trajectories to equations offer interpretable alternatives, promising transfer without system-specific equation knowledge. Transferring them reliably to high-dimensional physical data, however, remains an open challenge. We develop a verifier-guided (VG) workflow around ODEFormer as a symbolic backbone, using dynamical and physical-admissibility criteria to select from a multi-trajectory candidate equation pool, enabling transfer. On canonical Van der Pol oscillators, VG outperforms the original ODEFormer workflow across held-out initial conditions. We then address
vortex shedding, a phenomenon occurring in atmospheric and
plasma systems of societal relevance, through coordinate reduction and symbolic discovery at fixed and varying Reynolds numbers. VG discovers fixed-parameter reduced-order equations that recover the fundamental shedding oscillator and higher harmonics without a wake-specific candidate library or prescribed Navier-Stokes structure, while the cross-parameter model generalizes to withheld regimes. Reconstruction fidelity alone did not determine symbolic discoverability, highlighting the importance of compatibility between latent dynamics and the backbone's pretraining distribution. This work establishes a verifier-guided neural-to-symbolic methodology for interpretable and physically auditable
forecasting in the natural sciences.
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Artificial Intelligence · Cross-listed from cs.CY
AI Alignment and Fiduciary Obligation
Benjamin Lange
Advanced AI assistants engage users in extended interactions across a widening range of roles, including advice, decision support, collaboration, learning, emotional support, and companionship among others. Current
alignment efforts consider what
alignment criteria should govern these relationships, drawing on moral traditions developed for human relationships such as bioethics, virtue ethics, care ethics, and relationship science. This paper considers AI
alignment criteria in the user-AI-developer triad, since every user-AI interaction is mediated by a developer who exercises discretionary control over a system's behaviour,
memory, and engagement parameters. Drawing on business ethics and legal scholarship, I argue that fiduciary theory applies to extended AI assistant deployment. On this basis, the four canonical fiduciary duties of loyalty, care, good faith, and candour can generate
alignment criteria for the developer-user relationship. I map four user-side risks of extended AI assistant deployment to the four duties and specify institutional measures that follow from discharging each duty. The discussion complements existing approaches by grounding
alignment criteria in obligations the developer owes the user, rather than in values the user-AI interaction should promote, and by showing that those obligations hold independently of any de facto harm to users.
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Artificial Intelligence · Cross-listed from cs.CR
Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States
Jianshuo Dong, Yiming Liu, Maosen Zhang et al. (+6 more)
Agentic LLMs are vulnerable to indirect
prompt injection (IPI) attacks, e.g., malicious side-tasks hidden in external tool results. While many efforts have sought to address this threat, little is known about the internals of agentic LLMs when they are exposed to IPI attacks. For simplicity, we refer to this condition as IPI exposure. In this paper, we study IPI exposure from three perspectives. (1) Probing: Across eight models, including the 753B-parameter GLM-5.2 and the 2.8T-parameter Kimi-K3, simple linear probes trained on pre-generation hidden states can predict LLMs' IPI exposure. These probes achieve 0.90+ AUROC on unseen attacks,
agent instructions, and task suites; they remain robustly predictive under adaptive attacks and in cross-lingual settings. (2) Defense: We reveal and diagnose a knowledge-action gap: post-trained LLMs encode signals predictive of IPI exposure, yet do not reliably bind these signals to safe agentic actions. We therefore introduce a probe-gated
reasoning-based defense to bridge this gap at test time. On difficult AgentDojo settings, it substantially reduces attack success rate, e.g., from 34.6% to 0% on Qwen3.5-27B, and better preserves clean-task utility than the baselines. (3) Explanation: We introduce an analysis framework that identifies natural-
language explanations strongly correlated with probe-captured signals. The resulting profiles differ across models: latent signals can align with either direct IPI-exposure sensing or indirect operational cues. Code is available at https://github.com/jianshuod/IPI-exposure-signal.
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All authors Jianshuo Dong, Yiming Liu, Maosen Zhang, Nan Deng, Peng Xu, Xiaoping Zhang, Tianwei Zhang, Jie Zhang, Han Qiu
Artificial Intelligence · Cross-listed from cs.CR
Secure AI Watermarking Framework for IP Protection in Multi-Tenant Cloud Platforms
M Anjan Kumar, Kishor Kumar Gajula and Ch Prathima
The Secured data safe guard transaction with multi-tenant environments run on private-protected authenticate platforms runs by secured handed environments that emerges with the expansion of cloud-based AI services. To enhanced this secured leakage address challenges solution to protect a secure AI Watermarking system incorporating key distributed between trusted parties based on key authentication as we proposed solution to guided safe guarded way to reactive, and proactive security alert systems using
algorithms. This proposed system before attacks can be prevented through the active measures. domain run base restrictions with limited access. Conversely, Proposed system reactive methods to captured on watermarking and biometric identification owner device specific IP leakage that occur during the exchange of data and models in
federated and remote learning
algorithms.
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Artificial Intelligence
CADIR: A Cross-Backend Editable Intermediate Representation for Agentic CAD Generation
Yu Liu, Jingzhe Ni, Yiming Chen et al. (+4 more)
Large
language models have made it possible to generate executable computer-aided design (CAD) programs from natural-
language descriptions or images. However, existing methods represent modeling processes as backend-specific sequential scripts with implicit dependencies or as static geometry, making it difficult to simultaneously preserve construction history, stable topological references, and feature-level editability across different CAD systems. We present CADIR, an
agent-friendly executable intermediate representation for CAD generation and cross-backend editing. Built on the OCCT geometry kernel via OCP, CADIR provides explicit, compositional modeling operations and fine-grained execution diagnostics. During program execution, CADIR records modeling operations, parameter dependencies, constraints, and
topology selections in a construction graph. To enable reliable cross-backend reconstruction, we introduce
Geometric Signature Matching, which identifies corresponding edges and faces despite parameter changes and backend differences, allowing adapters to reconstruct native editable feature histories in FreeCAD, SolidWorks, and Fusion 360. Building on this representation, we further propose a construction-graph
retrieval method for text and image queries that supports both full-graph and subgraph
retrieval, enabling
agents to leverage complete models and modeling substructures. Extensive experiments demonstrate that CADIR achieves higher
geometric fidelity and execution reliability than existing CAD representations, that construction-graph
retrieval further improves model generation quality, and that cross-backend editing enables reliable model reconstruction and post-reconstruction editing across multiple CAD environments.
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All authors Yu Liu, Jingzhe Ni, Yiming Chen, Junqi Huang, Ruofeng Tong, Min Tang, Peng Du
Artificial Intelligence
Neuro-Evolved Heuristics for Variable Gapped Common Subsequence Identification
Marko Djukanović, Christian Blum, Aleksandar Kartelj et al. (+2 more)
This study addresses the Variable Gapped Longest Common Subsequence Problem (VGLCSP), a variant of the classical longest common subsequence problem with additional gap constraints and applications in sequence
alignment and time-series analysis. While the two-sequence version has been widely studied using dynamic programming, the generalized multi-sequence form is usually solved with beam search-based heuristics, whose hand-crafted designs often lack robustness. To overcome this limitation, we propose a learning-based approach for automatically designing more effective data-driven heuristics. The heuristics are represented by a neural network with predefined architecture, whose weights are optimized by a genetic
algorithm within a neuro-evolutionary framework. The learning process alternates between weight optimization and evaluation within an iterative multi-source beam search procedure, a state-of-the-art method for the problem. Rather than constructing solutions directly, the neural network learns to guide the search process, producing a neuro-evolved heuristic. We further introduce an ensemble heuristic that combines the scores of learned and the best-performing hand-crafted heuristic. Integrated into the iterative multi-source beam search framework, the resulting hybrid approach outperforms existing methods on both synthetic
benchmark instances and newly introduced real-world instances with data-driven gap constraints.
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All authors Marko Djukanović, Christian Blum, Aleksandar Kartelj, Saso Dzeroski, Ziga Zebec
Artificial Intelligence
Assuming You Knew: Fixing an Epistemic Semantics for Flow Policies Using Agentic AI
David A. Naumann
Many high-level security requirements are about the allowed flow of information in programs and are difficult to make precise because they involve selective downgrading. Notions from epistemic logic have emerged as a good approach to policy semantics but a robust general framework remains elusive. A paper appearing in CSF 2018, entitled “Assuming You Know: Epistemic Semantics of Relational Annotations for Expressive Flow Policies”, attempted to provide a unifying framework—but the formalization was sketchy and a correction was announced during the conference presentation. With aid from an agentic AI coding assistant, a corrected formalization has been machine checked in the Rocq proof assistant. The simplicity and generality of the framework may help compare different policy specification styles and enforce them by leveraging existing techniques.
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Artificial Intelligence
Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation
Muhammad Irfan Khan, Eero Lehtonen, Joni Obradovic et al. (+4 more)
Federated Learning (FL) enables collaborative training of machine learning models across multiple institutions without sharing sensitive data, making it particularly suitable for medical imaging applications. However, heterogeneous data distributions across institutions and potential information leakage through model updates remain important challenges. In this work, we propose DP-SimAgg, a
privacy-preserving
federated learning framework that integrates similarity-weighted aggregation with a server-side differential
privacy mechanism. The proposed method applies L2 clipping to bound collaborator updates, computes similarity-based aggregation weights to mitigate the effects of non-IID data distributions, and injects calibrated Gaussian noise at the central server, providing per-round
privacy guarantees under the assumed sensitivity bound. The framework is implemented using Intel's OpenFL platform and evaluated on the FeTS 2022
dataset consisting of 1251 multi-modal MRI scans for
brain tumor
segmentation. Experimental results demonstrate that DP-SimAgg maintains competitive
segmentation performance while providing
privacy protection. Under a strict per-round
privacy budget (epsilon = 1, cumulative epsilon_total = 20 over 20 rounds), the method achieves Dice scores of 0.6357, 0.5305, and 0.5274 for the enhancing tumor (ET), tumor core (TC), and whole tumor (WT) regions, respectively. With a more relaxed per-round budget (epsilon = 10, cumulative epsilon_total = 200), performance approaches that of the non-private baseline while incorporating a central Gaussian mechanism with per-round (epsilon, delta)-DP accounting under the assumed sensitivity bound. These results highlight the potential of DP-SimAgg for enabling
privacy-preserving collaborative learning in medical imaging applications.
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All authors Muhammad Irfan Khan, Eero Lehtonen, Joni Obradovic, Elina Kontio, Esa Alhoniemi, Suleiman A. Khan, Mojtaba Jafaritadi
Artificial Intelligence · Cross-listed from eess.AS
REIMU: Efficient Heterogeneous Hierarchical Reasoning for SSL-Based Speech Deepfake Detection
Kwok-Ho Ng, Tingting Song, Bingwen Feng et al. (+1 more)
The increasing realism of
speech generated by text-to-
speech and voice conversion systems poses growing challenges to media integrity and voice authentication. Self-supervised learning (SSL) has substantially advanced
speech deepfake detection, where downstream backbones conventionally process SSL representations through a single forward pass. This work investigates the practical effectiveness of recurrent hierarchical
reasoning for this task. We term this controlled study REIMU and systematically compare conventional single-pass backbones, weight-shared recurrence, homogeneous HRM, and heterogeneous HRM across four Base-scale SSL frontends. We further examine heterogeneous high- and low-level modules that combine self-
attention with linear
attention. Experiments on the ASVspoof 2019 and 2021 evaluation sets show that recurrence and hierarchical decomposition do not inherently improve detection, whereas heterogeneous operator assignment provides a more competitive configuration. Notably, the heterogeneous design remains competitive while using 10.8% fewer downstream parameters than the matched baseline, demonstrating its potential for parameter-efficient
speech deepfake detection.
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All authors Kwok-Ho Ng, Tingting Song, Bingwen Feng, Peiya Li
Artificial Intelligence · Cross-listed from cs.LG
Deep Learning CNN and Recurrence Analysis for Alpha Gamma EEG Biomarkers in Fragile X Syndrome
Zag ElSayed, Payton Siekierski, Jack Yanchen Liu et al. (+1 more)
Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by reduced expression of fragile X mental retardation
protein (FMRP), leading to disrupted synaptic plasticity, cortical hyperexcitability, and impaired network synchronization. Electroencephalography (
EEG) provides a noninvasive window into these mechanisms and consistently reveals abnormalities in alpha (8 to 12 Hz) and gamma (30 to 100 Hz) oscillations that relate to inhibitory control, sensory processing, and cognition. This paper proposes a multi representation deep learning framework for automated characterization of FXS
EEG phenotypes by integrating convolutional neural networks (CNNs), long short-term
memory (LSTM) networks, and recurrence plot (RP) analysis. Band limited
EEG signals are decomposed into alpha and gamma components and transformed into complementary representations, including temporal feature sequences, time frequency maps, and RP images encoding the nonlinear recurrence structure. CNN modules learn discriminative spatial-spectral and dynamical textures from image based representations, while LSTM modules model temporal modulation of oscillatory activity; a hybrid CNN LSTM architecture jointly captures spatial, temporal, and nonlinear dependencies. Subject-independent evaluation demonstrates that the hybrid model outperforms single modality baselines, with gamma features providing strong discriminative power and alpha gamma integration yielding the best overall performance. These findings support deep learning with nonlinear representations as a scalable approach for
EEG biomarker development in FXS, with potential utility for diagnosis, stratification, and treatment monitoring in translational settings.
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All authors Zag ElSayed, Payton Siekierski, Jack Yanchen Liu, Ernest Pedapati
Artificial Intelligence · Cross-listed from physics.med-ph
Anticipatory Digital Twins for Online Head-and-Neck Adaptive Proton Therapy via Foundation-Model Registration
Yizhou Wu, Yuheng Li, Xiaofeng Yang et al. (+1 more)
Head-and-neck (HN)
proton therapy is highly sensitive to anatomical change over a 4-to-6-week course, as tumor shrinkage, weight loss, and setup variation can misposition the Bragg peak near critical organs such as the parotids, oral cavity, brainstem, and spinal cord, leading to target underdosing or organ-at-risk overdosing. Online adaptive
proton therapy replans on the anatomy of the day, yet standard workflows rely on offline replanning that requires repeated CT acquisition and roughly a week of preparation, adding burden, cost, and delay. We investigate whether a patient's treatment-day anatomy can be predicted before image acquisition by transferring longitudinal change from a population database. We propose a digital-twin framework built on a pretrained foundation-model deformable registration network used without patient-specific training. A first registration aligns a prior patient's planning CT to the target and carries the prior's during-treatment quality assurance CT (QACT) into the target frame; a second registration estimates the prior's planning-to-QACT change, which is then applied to the target's own planning CT to synthesize predicted CTs (pdCTs) with propagated contours. Using 88 HN patients, each with a planning CT and three QACTs, we show that pdCTs better match treatment-day anatomy than the static planning CT. Compared with the planning CT alone, normalized cross-correlation improves by 22.8%, Dice for organs-at-risk by 20.2%, and CT-number error decreases by 23.4%. Gains are largest for patients with major anatomical change and negligible when anatomy is stable. This cross-patient motion transfer leverages the digital-twin concept to anticipate treatment-day anatomy, enabling personalized online adaptive
proton therapy without repeated imaging.
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All authors Yizhou Wu, Yuheng Li, Xiaofeng Yang, Chih-Wei Chang
Artificial Intelligence
Isotropy Cliffs: The Geometric Signature of Decision-Making in Large Language Models
Okan S. Coskun, Florian Rottach, Carsten Eickhoff et al. (+1 more)
We investigate the geometry of decision-making in Multiple Choice Question Answering (MCQA) through the lens of isotropy. Analyzing five open-weight models across diverse
datasets, we identify decision-critical transition layers characterized by a shift in isotropy, coinciding with a major representational change and the emergence of task-relevant clusters. We demonstrate that this synchronized
geometric behavior is strongly correlated with downstream accuracy ($r\approx0.84$), displaying its relevance for successful decision-making. Furthermore, we show that this transition is robust to
prompt variations, suggesting that it reflects a general mechanism of model behavior.
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All authors Okan S. Coskun, Florian Rottach, Carsten Eickhoff, William Rudman
Artificial Intelligence
The Scaling Paradox in Human-AI Collaboration
Anyan Qi and Mengxin Wang
The discovery of scaling laws has highlighted the extraordinary potential of AI systems with a striking empirical pattern: as AI systems scale, their capabilities tend to improve predictably. Yet, in real-world applications, AI rarely operates in isolation; instead, it often works alongside humans, raising the question of whether these gains persist in human-AI collaboration. In this work, we develop an analytical model to examine when the empirical scaling benefits of AI translate into improved human-AI joint system performance. We demonstrate that the performance of a human-AI system can scale positively as the AI scales up-provided that humans have an accurate perception of the AI's capabilities. Human misperception, however, can fundamentally alter this relationship: i) when humans over-perceive the AI's capabilities, a scaling paradox may arise, in which greater AI scale reduces overall system performance and amplifies firm-level profit losses, and (ii) when humans under-perceive the AI's capabilities, performance still improves with scale but at a substantially slower rate. We further show that firms can actively manage these distortions through operational policies such as cost internalization and perception
alignment, whose effectiveness depends on the economics of AI deployment and the direction of human misperception. These findings suggest that organizations may benefit more from managing the human-AI interface than from simply investing in larger, more expensive AI systems. More broadly, our results suggest that AI scaling should be viewed not only as a technological challenge, but also as a behavioral and operational one, and caution against the view that larger AI systems will automatically lead to better operational outcomes. Whether AI scaling creates value ultimately depends on how increased AI capabilities shape human beliefs and collaborative efforts.
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Artificial Intelligence
Large language models improve physician accuracy but lead to false reliance
Tirtha Chanda, Christoph Wies, Franziska Schramm et al. (+10 more)
Retrieval-augmented large
language models (LLMs) promise source-linked clinical support, but their value depends on whether displayed evidence guides rather than distorts physician reliance. We developed CORA, an agentic
retrieval-augmented LLM, to investigate how source-linked assistance affects physician decision-making. CORA maintained
benchmark performance and achieved larger gains on cases published after the models' training-data cutoffs. In a study of 46 physicians, accuracy increased from 70.8% unaided to 82.6% with CORA. Supporting citations predicted correct answers (87.7% vs 65.5%), but citations created an important asymmetry: perceived support increased adoption of correct advice from 34% to 76.9% but when an incorrect LLM answer appeared citation-supported, physician resistance to it fell from 92% to 34.8%. These findings show that source-linked LLM assistance can improve physician accuracy while introducing a grounding-dependent safety risk.
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All authors Tirtha Chanda, Christoph Wies, Franziska Schramm, Carina Nogueira Garcia, Nicolas B. Merl, Martin J. Hetz, Jochen S. Utikal, Phillip Tschandl, Cristian Navarrete-Dechent, Alexander Thiem, Jakob N. Kather, Consortium, Titus J. Brinker
Artificial Intelligence
AgentSLABench: Evaluating and Benchmarking Agentic Systems Under Resource Constraints
Meher Bhaskar Madiraju and Meher Sai Preetam Madiraju
We present AgentSLABench, a resource-aware evaluation framework for autonomous AI
agents that measures correctness alongside latency, cost, compute,
memory, and network usage under declared resource budgets. Unlike standard
benchmarks that report only accuracy, AgentSLABench produces a multi-dimensional profile per
agent per task - the same way systems profilers (perf, pprof, cProfile) measure resource consumption of code, but extended with task correctness as a first-class dimension. AgentSLABench provides 16 task environments across 6 categories (5 core: multi-hop QA, retail substitution, code generation, web shopping, travel planning; 11 extended) with isolated Docker containers, declared CPU/
memory/time/network budgets, sealed test sets with SHA256 hashes, and a standardized profiling protocol. We profile 5 general-purpose baseline
agents (ReAct, PlanAndSolve, Reflexion, CoT, Random) plus 4 task-specialized
agents, finding that specialized
agents achieve 100% success on 3/5 core tasks (fact_qa, web_shopping, travel_planning) and 66.7-83.3% on retail and code_gen, while general baselines fail entirely on 4/5 domain tasks. Crucially, we report the Efficiency-Adjusted Success Rate (EASR) - success weighted by resource consumption relative to declared budgets - revealing that high accuracy at unbounded cost is not production-viable. We release the full infrastructure, sealed test sets, and profiling results to enable reproducible, resource-aware
agent evaluation.
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Artificial Intelligence
Measurement Without Validity: The Compounding Reliability Problem in Agentic AI Evaluation
William Caban
Agentic AI evaluation pipelines produce
benchmark scores that justify deployment decisions, safety certifications, and regulatory compliance claims. No formal framework has characterized how validity degrades across these stages. We present a knowledge-based three-layer compounding validity model, $V_{\rm total} \leq V_1 \times V_2 \times V_3$, that captures multiplicative degradation across task generation ($V_1$), human-simulator calibration ($V_2$), and automated judgment ($V_3$). Applying it to five widely-used agentic
benchmark configurations, we find $V_1$ and $V_2$ independently measurable and both degraded, but $V_3$ not currently quantifiable for any: three of the four
benchmarks score with a single deterministic rule, so no second rater exists and reliability is undefined, and the fourth's partial LLM grader has no published grader-vs-human measurement. A structured survey of 55 published agentic evaluation papers finds that 26 of them report no chance-corrected inter-rater statistic of any kind, a determination four independent raters recover at Krippendorff's $\alpha = 0.834$. On the broader judgment of whether a reported metric fits its rating design, approximately 80% of this purposive sample use structurally mismatched, incomplete, or absent inter-rater reliability (IRR) metrics (four-rater range 60.7%-84.4%). Both are consistent with systematic $V_3$ collapse. We further document $V_1$ failures (task validity flaws in 7 of 10
benchmarks) and $V_2$ miscalibration (up to 9 percentage points inter-simulator variance, with systematic demographic disparities for non-Standard American English speakers). We derive eight prescriptions grounded in psychometric science and domain-stratified reliability thresholds (intraclass correlation $\geq 0.70$; $\alpha \geq 0.67/0.70/0.80$ by consequence level) that practitioners can apply immediately.
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Artificial Intelligence
FinDeepIndicator: Benchmarking Deep Research Agents in End-to-End Financial Indicator Construction
Chaoqun Yang, Fengbin Zhu, Xinyu Lin et al. (+5 more)
Financial indicators are essential tools for transforming raw financial data into interpretable measures for various downstream tasks, such as valuation, risk assessment, and economic analysis. However, existing financial
benchmarks largely focus on answer-level accuracy and often assume that relevant data are already provided, leaving the assessment of the intermediate process of indicator construction underexplored. In this work, we propose FinDeepIndicator, the first
benchmark dedicated to evaluating Deep Research (DR)
agents in end-to-end financial indicator construction. Specifically, FinDeepIndicator evaluates DR
agents across four stages in indicator construction: formula specification, data collection, indicator calculation, and answer generation, and covers fundamental, technical, and macroeconomic indicators organized into 21 fine-grained sub-categories. It contains 3,350 curated question-answer (QA) pairs derived from both U.S. and Chinese
markets, 10 years of historical financial data, and 800 listed companies. Extensive experiments on search-equipped Large
Language Models (LLMs) and DR
agents show that, while LLMs generally perform well in formula specification, their accuracy drops substantially during data
retrieval and numerical execution. DR
agents consistently outperform search-equipped LLMs, yet remain unreliable in realistic financial analysis settings. These findings provide insights for developing more capable and trustworthy DR
agents in finance.
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All authors Chaoqun Yang, Fengbin Zhu, Xinyu Lin, Long Bai, Xiaoluan Liu, Ke-Wei Huang, Roger Zimmermann, Tat-Seng Chua
Artificial Intelligence · Cross-listed from cs.SE
Less Is More: Tuning Configurable Systems with Imperfect Fidelity
Yulong Ye, Miqing Li and Tao Chen
Configuration tuning is essential for optimizing the performance of highly configurable systems, e.g., throughput or runtime, under a given environment. Yet, this is a challenging process as there can be many options to tune, and configuration measurement is often highly expensive. In this paper, we demonstrate the phenomenon of “less can be more”: system configuration tuning can be greatly improved with much superior budget utilization by partially tuning under the imperfect-fidelity—an environment that is similar, but cheaper to measure, compared with the concerned perfect-fidelity of environment under which the system should be tuned. We codify a conceptual framework of fidelity for configurable systems, drawing on which allows us to propose MFTune, a tuner that proactively explores in the space of $>10^4$ possible imperfect-fidelity settings to approximate a useful one, which strikes for the wideness of tuning. This creates high-quality seeds for the perfect-fidelity, which in turn ensures the tuning depth. Experiment results against $10$ state-of-the-art tuners, obtained from running diverse real-world systems for $19$ months $24 \times 7$, show that MFTune performs considerably better on $83.33$% cases with up to $19.34\%$ improvement while achieving hours of budget saving in general.
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Artificial Intelligence · Cross-listed from cs.ET
CN101 - A Digital Thermodynamic Computer for Generative AI
Lars Holdijk, Denis Melanson, Zier Mensch et al. (+14 more)
Thermodynamic computing is an emerging hardware paradigm, in which
stochastic physical dynamics serve as the direct computational primitive. The recent explosion of generative AI has only sharpened the search for alternative approaches to compute, and, as we show in this work, thermodynamic computing turns out to be well suited to this space. An important class of methods realises a function as the stationary expectation of an ergodic
stochastic process: the answer is encoded in the time-averaged statistics of an equilibrating trajectory. To date, this equilibration-style class has been formulated exclusively through Langevin dynamics, restricting its implementations to analogue substrates and the engineering challenges those bring. In this work, we propose a substrate-independent formalisation of the equilibration-style formulation, in which the only object of design is the dynamical generator L* of an arbitrary ergodic process. The formalisation makes three hardware-level properties of the formulation explicit: the precision of a result is a knob set by how long the dynamics are run, sample averages decompose across independent trajectories, and dependent stages of a computation operate concurrently rather than serially, a property we call sequential parallelism. We instantiate the formalisation by fabricating a prototype digital thermodynamic computing chip, named CN101, that implements the formulation through discrete accumulator dynamics on standard CMOS using
stochastic computing principles. We characterise CN101's success across conventional generative AI workloads in the form of VAEs and flow matching, applied to both image generation and scientific problems. Together, the formalisation and its digital instantiation show that the equilibration-style formulation is substrate-independent, and that its computational properties can be exploited on standard digital hardware.
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All authors Lars Holdijk, Denis Melanson, Zier Mensch, Brandon Birchall, Vincent Cheung, Nicholas Lehrter, Maxwell Aifer, Samuel Duffield, Jan Ole Ernst, Rajath Salegame, Antonio J. Martinez, Gavin Crooks, Miranda Cheng, Zach Belateche, Marc Bright, Patrick J. Coles, Faris Sbahi
Artificial Intelligence · Cross-listed from cs.HC
Me and My Bot: What Users Talk About in AI Companion Communities on Reddit
Richard A. Fabes
AI companion communities on platforms such as Reddit are widely characterized as spaces where users discuss their relationships with AI bots. This study examines whether and how that characterization holds, guided by the Synthetic Resonance framework's claim that human-AI relationships can carry genuine relational meaning for the user. Multiple LLMs were employed to code 5,504 Reddit posts from eight AI companion communities for relationship focus, primary topic, and users' emotional valence. Although search terms were weighted toward relational and attachment
language, only 45% of posts concerned the user's own relationship with their bot. Posts about users' own bots differed markedly from posts about bots in general in both topic and emotional expression, with 85% of general-bot posts containing no user
emotion language compared to 33% of own-bot posts. Among the 970 posts that were relationally focused, companionship and romance each accounted for roughly 46% of discussion, sexual content for 8%, and emotional valence varied across these subtopics. The findings suggest that users engage with these relationships with AI bots as meaningful, and that the discourse about them is broad and emotionally complex.
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Artificial Intelligence · Cross-listed from cs.RO
When Prompts Control Robots: Prompt Injection Attacks in Multi-Agent Robotic Systems
Neha Nagaraja, Amisha Bagari and Hayretdin Bahsi
Large
language models are increasingly integrated into autonomous robotic systems for task planning and control, but this integration exposes them to
prompt injection attacks that can lead to unsafe decisions and physical harm. Multi-
agent settings increase the risks through cross-
agent contamination and broader attack surfaces. In this paper, we evaluate
prompt injection attacks against an LLM-based multi-
agent robotic system, considering both direct injections into task instructions and indirect injections through perception modules. In our experiments across varying attack-goal complexities and injection strategies in both single-
agent and multi-
agent settings, we show that
prompt injection can induce
adversarial actions while reducing task completion. We find that attacks can propagate from one
agent to others through shared
prompt structures, with impacts varying depending on
prompt composition and the targeted
agent. We further analyze how architectural changes affect LLM queries and, consequently, the attack success. To the best of our knowledge, this is the first study that systematically investigates
prompt injection attacks in a multi-
agent LLM-based robotic system.
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Artificial Intelligence
Whetstones: Measuring Coevolution Between Adaptive Malware and Behavioral Defense
Zihan Luo
The literature on self-adapting malware is open-loop: adaptation is evaluated against static detectors in simulators, with fitness computed by experimenters. This report closes the loop. We built both sides of the adaptive-malware confrontation and the selection loop between them, measuring four generations of engagement under pre-registered protocols. On the red side, a trained adaptive adversary completes reconnaissance-persistence-exfiltration task chains with zero detections under four concurrent commercial stacks (Microsoft Defender RTP, Falco, Wazuh 4.9.2, Suricata 8.0.3, 52,151 ET Open rules). On the blue side, a behavioral-grammar detector – a 0.88M
Transformer prior over an 8-
token event grammar fused with four further detection faces – catches that adversary at 93% with a 3.84% onboarding false-positive rate. The payoff is a law chain: blacklisting creates no selection pressure (R1: 12
cells, 0 alerts); grammar-level hunting pushes selection onto the adversary's body (R2: 9 morphs, 0% survival); body constants bound the rhythm gene's reachable space (G0: the clamp backfires); and the fourth generation produced the loop's first fit morph – H7-full, 100% survival across three runs, task chains complete, zero detections in six patrol rounds – whose genome realizes the two escape axes theory predicted (cap cession; non-hidden landing). A power-latency frontier prices each escape axis (N* ~ 240 events); a network-side sibling firewall shows the method transfers (85/85 attacks, 0.10% FPR); and three structural asymmetries explain why
equilibrium favors the defender pressing the propagation plane. The report contains the full engineering detail: laboratory, both organs, frontier, four generations of rounds, complete evaluation (baselines, OOV hard-subset, an honest ADFA-LD negative result, E1-E4 studies), coevolution economics, and registered protocols E-A through E-K.
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Artificial Intelligence · Cross-listed from cs.DC
Multi-tenant Kubernetes Use Cases for AI, Secure Computing and Data Services, and More
Jake Watson, Sadaf R Alam, Christopher Woods et al. (+6 more)
Kubernetes, as a container orchestration engine, has been widely used in cloud-native ecosystems for several years. In supercomputing ecosystems, especially where bare-metal performance for compute and network devices are considered, the adoption is somewhat limited. However, with the increasing diversity of use cases such as AI, secure and confidential computing for sensitive data, and mixed workload orchestration, a traditional, single-tenant batch computing system does not offer the flexibility and reproducibility to which public cloud users are accustomed. Note that Kubernetes is not considered a replacement for batch scheduling systems, which have powerful features for large-scale MPI jobs with thousands of network end points. Rather, it is a complementary service provided as part of a national AI Research Resource. We evaluate Kubernetes deployment on a Hewlett Packard Enterprise (HPE) Cray EX supercomputerwith HPE Slingshot interconnect, called Isambard-AI, with co-design use cases. One is a Trusted Research Environment used for medical and health sciences. The other combines KubeRay, Ray, and vLLM to provide a distributed, sandboxed, persistent AI model hosting service targeting multi-tenant confidential computing. We discuss challenges and lessons learned, and where further development is needed to offer a production Kubernetes-as-a-Service on HPE Cray EX (and later) platforms.
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All authors Jake Watson, Sadaf R Alam, Christopher Woods, Abdelwahab Kawafi, Thomas Green, Ian Johnson, Ellis Pires, Jessica R. Jones, Utz-Uwe Haus
Artificial Intelligence · Cross-listed from cs.SE
EduPluginBench: Executable Assurance for AI-Generated Educational Plugins
Nizam Kadir
Code-generation models can produce executable components, but compilation and functional tests do not establish compliance with least privilege, telemetry consent, provenance, privileged-write authority, lifecycle constraints, or bounded failure. We introduce EduPluginBench, an executable
benchmark and staged admission method for generated plugins in governed software ecosystems. Across 1,440 activation-checked first-order mutants from 30 specifications, P0-P4 increased release-blocking-defect recall by 74.7 percentage points (specification-clustered 95% CI 73.4-75.8) over P0-P2, with no observed rejection among 120 clean references (95% Wilson upper bound 3.1%). A frozen transfer study of 600 unmodified generations from two current coding models found that 300/600 parsed, but none passed P0 or achieved P0-P4 conformance (95% upper bound 0.64%); downstream assurance estimands were undefined. An independently labelled Moodle study retained 16 vulnerable/fixed pairs; the frozen generic PHP detector found no vulnerable revisions. These negative transfer results prevent controlled contract consistency from being read as independent real-defect effectiveness. An earlier 540-generation diagnostic found that post-hoc bounded repair yielded 112 P0 passes, all nonconforming, with recall increasing from 13.4% to 100%. The artifact retains protocols, public-source provenance, raw generations, row-level decisions, audits, analysis code, and reproduction instructions.
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Artificial Intelligence · Cross-listed from cs.LG
An Embedded RISC-V Evaluation of Kolmogorov–Arnold Networks in Hard-Constrained Recurrent Physics-Informed Models
Enzo Nicolas Spotorno and Josafat Leal Filho
Hard-constrained recurrent physics-informed networks (HRPINNs) embed known dynamics inside a recurrent numerical integrator and restrict a neural branch to learning only the residual dynamics that the first-principles model does not capture. Kolmogorov–Arnold Networks (KANs) have been proposed as parameter-efficient replacements for multilayer perceptrons (MLPs) in such residual branches, but their learnable B-spline activations follow a markedly different execution profile. Building on prior work that characterized when a vanilla B-spline KAN matches or underperforms an MLP as an HRPINN residual branch in discovery accuracy, this paper asks whether that parameter efficiency survives deployment. Using identical trained weights, we measured execution latency, energy per integration step, and dependability under post-training quantization in the closed recurrent loop on a RISC-V RV64GC platform without vector extensions (StarFive VisionFive~2, SiFive U74). For the two accuracy-comparable pairs, the KAN residual branch executed $13.5\times$ and $8.0\times$ slower and consumed $11.3\times$ and $5.6\times$ more energy per integration step (3.7\,$\mu$J against 0.33\,$\mu$J for the smallest pair); across all four parameter-matched size tiers the ranges are $4.7\times$–$14.5\times$ and $4.7\times$–$18.7\times$. Under INT8 quantization, KAN trajectories diverged up to $43\times$ earlier than matched MLPs; the damage traces to weight quantization, not to input-side
knot-interval misassignment. These results indicate that the parameter efficiency reported for KANs does not transfer to deployment cost on scalar embedded cores, and that an MLP residual branch is the more dependable default for embedded HRPINN deployment unless specific quantization co-design is used.
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