Software Engineering
Beyond the Editing Canvas: Evidence Divergence in OOXML-to-LLM Ingestion
Side Liu, Jiangpeng Liu, Jinwen Xin et al. (+2 more)
LLM pipelines increasingly ingest Office Open XML (OOXML) documents (Word, Excel, and PowerPoint files) as first-class evidence in financial, compliance, and
retrieval-augmented workflows, implicitly assuming semantic integrity: that the evidence consumed by the model matches the content shown in the Microsoft Office suite editing canvas. We show that this assumption can fail in OOXML-to-LLM pipelines. The same specification-valid OOXML file can yield one evidentiary view in Microsoft Office and another when extracted for an LLM. Each view is treated as authoritative by its consumer, a condition we call plural ground truth. The ingestion contract rarely states which view and semantic roles become model evidence or preserves how that evidence was derived. We call the specification-grounded OOXML constructions that induce such divergence evidence forks. We systematically traverse and mine the OOXML specification and confirm 21 evidence forks across Excel, Word, and PowerPoint, spanning six dimensions of view construction. All 13 tools in our extraction panel emit evidence from at least one fork. We test four native-ingestion LLM APIs and seven web chatbots. Each test document carries a trap: a task-relevant fact exposed by extraction but not shown in Office. Across this 21-mechanism evaluation, the four APIs return the trap in 48–76% of trials. For 20 of 21 mechanisms, at least one of the eleven interfaces returns the trap. Our measurements further show that exposure is shaped upstream of the model by the ingestion path and extractor configuration. A source-level survey of sixteen popular open-source LLM projects further shows that default OOXML ingestion paths concentrate on affected extractor families.
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All authors Side Liu, Jiangpeng Liu, Jinwen Xin, Guojun Peng, Jiang Ming
Cryptography and Security
A Hybrid Security Framework for Mini-Programs: Visual UI Compliance and Network Risk Assessment
Panpan Shen, Lei Xie and Xiaoqi Li
With the continuous development of the WeChat ecosystem, WeChat Mini Programs, due to their advantages of not requiring installation, using little
memory, and being ready to use instantly, have seen a surge in user numbers and have now become an indispensable service carrier in mobile internet. However, as Mini Programs rapidly became popular, issues regarding the compliance of their interface interaction design and the safety of operational behavior have become increasingly apparent. Many Mini Programs have problems such as clickable buttons and icons not being standard in size, or ad pop-ups and payment entrances being placed in a way that is easy to misclick. The close or cancel buttons are often too small or hidden, making it easy to accidentally click on ads or payment content, and difficult to accurately click the cancel button. This can result in involuntary payments or being redirected to illegal pages, causing unnecessary financial losses and seriously harming users' property security and legal rights. To address the above issues, this article develops a detection program to check the position and size of various icons and buttons in Mini Programs, and analyze whether redirected links fall within a safe range. YOLOv8 is used to identify various buttons in images, displaying the corresponding icon and its data based on the mouse click position. Violations are flagged and recorded. At the same time, mitmproxy is used to capture relevant data requests generated during clicks, analyzing the safety of redirections, and presenting key information for user observation.
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Human-Computer Interaction
Do Vision-Language Models Agree on the Affective Qualities of Shape? A Cross-Model Audit for Generative Design Interfaces
Luca Bux, Thiago Rios, Ingo Scholtes et al. (+2 more)
Generative design interfaces increasingly expose semantic controls that let users steer output with concepts such as "more elegant" or "more minimalist," typically encoded by a
vision-
language model (VLM). A practical question is whether state-of-the-art VLMs represent objects consistently in terms of the same concept. We audit 6 VLMs by ranking untextured 3D objects along Kansei adjective pairs, where Kansei describes affective impressions of product form, with each axis defined as the difference between the text representations of its two poles.
Geometric pairs serve as positive controls, and pairs of unrelated adjectives establish an empirical null. Across 10 categories of ShapeNet database, affective axes converge above the null (mean pairwise rank correlation 0.36 vs. 0.14) but below the
geometric ceiling (0.44). The agreement between models is partial and highly uneven: on the three axes shared by all categories, mean convergence ranges from 0.21 for bookshelves to 0.51 for jars. Convergence depends primarily on whether a category's representational variation aligns with the semantic direction being evaluated, rather than simply on how much the objects vary in shape overall. Cross-model convergence does not imply agreement with human judgments. Based on our findings, we implement a UI prototype that shows how the audit can inform which Kansei descriptors to expose as controls for a given object class and which to withhold.
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All authors Luca Bux, Thiago Rios, Ingo Scholtes, Stefan Menzel, Bernhard Sendhoff
Robotics
Low-Resolution Perception for Robotic Packing
Giuseppe Fabio Preziosa, Federico Vignoni, Chiara Castellano et al. (+3 more)
This work tackles the problem of scalable perception for robotic packing with low-cost, low-resolution depth sensing. We propose a framework where reconstruction cues drive next-view selection and grasp evidence updates a per-object stability estimate, jointly deciding what to acquire next and when to grasp. During the reconstruction, a low-resolution Next Best View (NBV) strategy explicitly avoids redundant views while preserving task-relevant geometry. We validate the approach in two steps: (i) an ablation study of the utility function under very low resolution, and (ii) a full end-to-end evaluation across policies, showing how low-resolution perception is a practical, scalable option for robotic packing.
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All authors Giuseppe Fabio Preziosa, Federico Vignoni, Chiara Castellano, Marco Faroni, Andrea Maria Zanchettin, Paolo Rocco
Machine Learning
How Edge of Stability Hinders SCAFFOLD in Federated Optimization
Anant Khandelwal, Michael Crawshaw and Mingrui Liu
In
federated learning, it is well known that heterogeneous data can (in theory) slow down optimization, and much effort has been directed at designing optimization
algorithms that are unaffected by data heterogeneity, such as the SCAFFOLD
algorithm. Yet, despite strong theoretical guarantees, SCAFFOLD does not usually outperform the much simpler FedAvg in practice. In this work, we propose that this gap is due to the presence of Edge of Stability (EoS) and progressive sharpening in
federated optimization, supported by extensive empirical probing. First, we find that EoS-like dynamics occur with both FedAvg and SCAFFOLD under a variety of architectures and hyperparameters. We observe that the
equilibrium value of the sharpness is inversely proportional to the learning rate (as in GD), and interestingly, the degree of data heterogeneity (but not the number of local steps) also affects the
equilibrium value. Most importantly, we observe that SCAFFOLD's ability to estimate the gradient of the global objective is severely degraded at the EoS, as measured by the correlation between sharpness and SCAFFOLD's error in estimating the global gradient along the optimization trajectory. This suggests a mechanism for SCAFFOLD's lackluster performance in deep learning: with high sharpness at the EoS, SCAFFOLD cannot reliably estimate the global gradient.
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Robotics
VISTA: Visually Inferred Spatial ConTact Attention for Contact-Rich Manipulation
Jiayi Chen, Wenlong Dong, Yan Huang et al. (+5 more)
Contact-rich manipulation requires precise interaction feedback. While
vision-centric imitation learning is prevalent, external visual observations provide indirect and ambiguous cues about contact states, particularly under occlusion or subtle object–gripper interactions; dedicated tactile or force sensors can provide rich contact information but introduce additional hardware complexity, calibration requirements, and deployment costs. To bridge this gap, we propose VISTA-Policy, an imitation learning paradigm that utilizes the Visual Deformation Field (VDF), a 3D displacement representation of a compliant gripper, as high-dimensional visuo-physical feedback. The framework integrates: 1) a Physics-Aware Encoding Engine for real-time VDF decoding; 2) an Energy Aggregation Denoising Mechanism to isolate true interaction signals; and 3) a Deformation-Augmented Policy Network with incremental gripper actions for precise closed-loop correction. Extensive evaluations on Cross-Scale Object Grasping, Cap Unscrewing, and Calligraphy Writing demonstrate that VISTA-Policy outperforms the strong pure-
vision baseline 3D
Diffusion Policy and the tactile baseline. VISTA-Policy further demonstrates substantial out-of-distribution generalization to unseen object scales and robustness against dynamic disturbances, offering a durable and cost-effective route toward general-purpose fine-grained manipulation in unstructured environments. Project
videos and supplementary materials are available at: https://sites.google.com/view/vista-policy.
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All authors Jiayi Chen, Wenlong Dong, Yan Huang, Xianglin Chen, Zijian Lin, Jiaqi Yin, Yushan Liu, Wenbo Ding
Machine Learning
CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition
Junjie Meng, Ranxu Zhang, Zi-an Zhang et al. (+6 more)
Forecasting in large-scale e-commerce marketplaces is increasingly required to support planning: merchants need to evaluate sales outcomes under future action sequences such as budget schedules, rather than passively predicting what happens next. However, most existing time series
forecasting (TSF) approaches remain inherently passive. Even when incorporating operational decisions as auxiliary
covariates, they typically optimize for correlation-based extrapolation under historical policies. This design suffers from autoregressive inertia and conflates endogenous
market evolution with decision-induced transitions, leading to policy-insensitive rollouts and unreliable counterfactual analysis. To bridge this gap, we propose CEDAR (Controlled and Event-Driven Demand
forecasting via Action-aware Residual decomposition), a two-stage framework for robust decision-conditioned simulation. In Stage I, an Action-Interleaved
Transformer learns controllable action-conditioned state transitions for rollout under planned interventions. In Stage II, a Residual Correction Module leverages external event signals and LLM-assisted text representations to align noisy event descriptions with product context and correct event-driven deviations. Our study is enabled by a large-scale real-world
dataset from Alibaba 1688, comprising approximately 32 million product trajectories with paired state-action sequences and aligned event signals. Extensive offline experiments and online controlled experiments in production demonstrate that CEDAR consistently improves simulation accuracy over strong TSF baselines and delivers practical gains for real-world budget planning.
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All authors Junjie Meng, Ranxu Zhang, Zi-an Zhang, Shujun Liu, Xiaoning Qi, Xiaozhou Xu, Yanyong Zhang, Hui Xiong, Chao Wang
Computation and Language
Anchoring Bias in LLM-as-a-Judge Systems: Prior Scores Compromise Evaluation Independence
Ante Kapetanovic, Kemal Altwlkany, Andro Mercep et al. (+2 more)
Large
language models (LLMs) increasingly assess generated content, giving rise to the LLM-as-a-Judge paradigm. These systems now score outputs, filter content, and gate iterative refinement in production pipelines, where each judgment is often assumed to be independent of earlier evaluations. We test this assumption using three
prompt conditions: no metadata, revision framing, and anchored metadata containing revision, attempt, and prior-score fields. We show that prior scores, even when included only as context metadata, anchor judgments and systematically shift ratings toward their values. Across 192,000 attempted evaluations (185,271 successful), seven out of the eight evaluated models have 95% task-stratified bootstrap intervals below zero for the total anchored-metadata effect on 20 fixed texts. Cohen's $d$, a standardized measure of the difference between score distributions, reaches an absolute value of 0.71.
Token-level analysis of selected model-task probes suggests a threshold-like response pattern: introducing anchored metadata produces a marked redistribution of output-score probabilities, while changing the anchor value within the tested below-threshold range produces comparatively little additional variation. On categorical industry data with human-labeled ground truth, anchored metadata blocks 48% of error corrections and flips 10.18% of correct judgments toward an assigned wrong label, demonstrating the bias extends beyond numerical scoring to categorical decisions. Neither Chain-of-Thought nor a metadata-disregard warning reduces the total effect, although the warning improves the paired accuracy effect relative to baseline in the industry experiment. Reliable LLM evaluation demands careful context engineering rather than an assumption of impartiality. Effective mitigation must be validated for the intended model and task or domain.
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All authors Ante Kapetanovic, Kemal Altwlkany, Andro Mercep, Tomislav Duricic, Emanuel Lacic
Computer Vision
LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation
Karen Sanchez, Carlos Hinojosa, Albert A. Ávila et al. (+5 more)
Quantifying wound tissue composition is essential for monitoring chronic ulcer progression and guiding treatment decisions. However, pixel-level annotations are costly, and multi-tissue wound
datasets remain scarce, particularly for neglected diseases such as leprosy. We introduce LUTSeg, a longitudinal chronic ulcer
dataset comprising 141 images from 39 patients with wound masks and five tissue categories annotated by five expert clinicians, including a multi-expert gold-standard subset for inter-rater agreement analysis. To establish an initial
benchmark for LUTSeg, we further propose TiSage, a semi-supervised tissue
segmentation framework that integrates multi-scale semantic priors from a frozen medical
vision-
language model within a teacher-student architecture. We evaluate TiSage on LUTSeg and DFUTissue, showing improvements over supervised and semi-supervised baselines in most low-label settings. Code & data: https://github.com/carlosh93/TiSage
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All authors Karen Sanchez, Carlos Hinojosa, Albert A. Ávila, Andrea C. Riano-Rojas, Diego H. Romero, Jenny C. Páez, Martina Llinás, Bernard Ghanem
Robotics
MA-VLA: Multi-Arm Vision-Language-Action Model for Collaboration and Compositional Generalization
Zaibin Zhang, Junlan Xiao, Zhongbo Zhang et al. (+11 more)
Multi-arm collaboration is becoming a core capability in
embodied manipulation. Recent
vision-
language-action (VLA) models integrate perception,
language, and control, but most represent
language as a single global instruction and do not provide an explicit mechanism for assigning and composing arm-specific behaviors. This design limits transfer to collaboration patterns that differ from those observed during training. We present MA-VLA, a unified framework for multi-arm collaboration via atomic action assignment. MA-VLA decomposes cooperative behavior into mid-level atomic
prompts and allocates them to individual arms, enabling explicit subgoal specification and compositional reuse across tasks. To reduce reliance on fixed execution roles, we introduce Arm Shuffle, a training-time permutation of the observation, state, and assigned atomic
prompts for each arm. This permutation enforces role-agnostic instruction following and supports recomposition into unseen coordination patterns, which we term multi-arm compositional generalization. We also construct a
benchmark in which test-time collaboration patterns are absent in training set. Across simulation and real-world evaluations, prior state-of-the-art VLAs largely fail under these unseen collaborations, while MA-VLA consistently succeeds. These results indicate that structured, per-arm atomic action assignment offers a practical route to scalable generalization in multi-arm
embodied systems. Code, models, and data are available at https://github.com/zhangzaibin/future-
robots
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All authors Zaibin Zhang, Junlan Xiao, Zhongbo Zhang, Yifan Wang, Li Kang, Yiran Qin, Changxing Xia, Heng Zhou, Talas Fu, Enshen Zhou, Ruimao Zhang, Zhenfei Yin, Huchuan Lu, Lijun Wang
Computer Vision
Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation
Subir Kumar Parida, Rajbabu Velmurugan, Ketan Kotwal et al. (+2 more)
Demographic imbalance in synthetic face generation can propagate to downstream face recognition systems, making
fairness an important consideration when
diffusion models are used for data generation. Existing
fairness-aware generation approaches often require model retraining, architectural modifications, or repeated guidance throughout the reverse
diffusion process. In this work, we introduce Semantic Boundary Predictor (SBP), an inference-time framework that performs demographic guidance through a one-shot intervention during reverse denoising. Our approach is motivated by the observation that latent representations at different
diffusion timesteps play distinct semantic roles: late-stage latents provide stronger demographic separability, whereas early-stage latents offer greater flexibility for semantic intervention. SBP leverages this timestep decoupling by learning linear semantic boundaries from late-stage latent representations while applying them only once at the initial noisy latent, allowing the remainder of the reverse denoising process to proceed unchanged. The method requires neither retraining nor fine-tuning of the underlying Latent
Diffusion Model and operates without external balanced
datasets. Experiments on CelebA-HQ demonstrate substantial improvements in demographic
fairness, reducing
fairness disparity by 98% for gender, 95% for binary race, and 15% for four-class race, while maintaining perceptual image quality across demographic groups. Owing to its one-shot inference strategy and model-agnostic design, SBP introduces only a small computational overhead and can be readily integrated with existing pre-trained latent
diffusion models.
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All authors Subir Kumar Parida, Rajbabu Velmurugan, Ketan Kotwal, R.S. Sengar, Swati Hiremath
Computer Vision
Precipitation Downscaling Using Foundation Model-Conditioned Diffusion
Victor Nascimento Ribeiro, Jorge Guevara, Jorge Sebastian Moraga et al. (+7 more)
High-resolution precipitation fields are essential for hydrological impact assessment, yet global
climate model outputs are too coarse and biased for direct use. AI-based statistical downscaling with
diffusion models offers a promising approach, but the mechanism by which large-scale atmospheric predictors condition generation remains largely unexplored. We investigate three conditioning strategies for a denoising
diffusion probabilistic model applied to daily precipitation downscaling: channel concatenation of upsampled coarse predictors, cross-
attention conditioning with a learned convolutional encoder, and cross-
attention conditioning with the frozen encoder of the pretrained Prithvi WxC weather foundation model. All strategies are evaluated against an unconditioned baseline under identical conditions using probabilistic, distributional, spectral, and extreme-event metrics for the Colorado River Basin. Concatenation conditioning achieves the lowest point-wise CRPS and MSE, but tends to produce over-smoothed fields that suppress high-intensity events. In contrast, cross-
attention conditioning provides substantially better distributional realism and modest improvements in spectral fidelity. Improvements are greatest for extremes: the Prithvi-WxC conditioned model retains over half of >100mm/day events, although estimates are uncertain due to limited samples. When trained on the full
dataset, the learned convolutional model performs similarly to the foundation model-conditioned approach while requiring lower computational resources. However, the Prithvi-WxC-conditioned model achieves comparable performance with only five years of training data. These results indicate that cross-
attention conditioning offers advantages over simple concatenation for probabilistic precipitation downscaling, and that pre-trained foundation model representations may offer benefits in data-limited settings.
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All authors Victor Nascimento Ribeiro, Jorge Guevara, Jorge Sebastian Moraga, Chris Lucas, Natalie Lord, Andrew Taylor, Edward Lockhart, Will Trojak, Johannes Schmude, Anne Jones
Computational Engineering
Yi Zhou, Qipeng Wang, Yunqing Liu et al. (+3 more)
Multimodal protein language models (pLMs) learn joint
protein sequence-structure distributions, and their generation performance should also depend critically on inference-time sampling strategies. Yet prior work has focused more on model training than on how inference-time strategies behave. In this paper, we establish a three-stage investigation framework to empirically study the inference design space of
multimodal pLMs across three representative pLMs and four fundamental tasks. We evaluate vanilla sampling, task-specific classifier-free guidance, and reward-guided beam search on
multimodal pLMs, corresponding to controls over sampling distributions, per-step logits, and parallel trajectories. Throughout the complementary advancements centered on exploration-exploitation trade-off, we (1) reveal the suboptimality of default inference protocols and identify task-oriented sampling preferences; (2) observe substantial quantitative gains across tasks, consistently boosting the upper bound performance of
multimodal pLMs without updating model parameters; (3) derive conclusions about base models that differ from prior consensus.
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All authors Yi Zhou, Qipeng Wang, Yunqing Liu, Jun Xia, Qing Li, Wenqi Fan
Computation and Language
Key Point Analysis Needs Structure Recovery: Task Definition, Dataset Diagnosis, and a Structure-Aware Benchmark
Zhiqiang Shi and Oana Cocarascu
Key Point Analysis (KPA) aims to identify a concise set of key points that summarize a collection of arguments together with their prevalence. We argue that KPA is fundamentally a structured prediction problem that requires recovering semantic groupings, generating representative key points, ensuring coverage, and estimating prevalence. Under this formulation, we show that existing KPA
benchmarks suffer from limitations in grouping quality, redundancy, coverage, and argument-key point mappings, causing ceiling violation and selection failure in reference-based evaluation. To support future research on true KPA, we introduce a structure-aware, distribution-sensitive
benchmark built via a human-in-the-loop re-annotation. Human and LLM evaluations consistently show that the resulting structures yield more coherent groupings, higher-quality key points, better coverage, and more reliable prevalence estimates than existing annotations. We further release several annotation resources to support research on KPA evaluation, argument-key point matching, explainable KPA, and LLM-as-a-judge methodologies, and outline a research agenda for true KPA.
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Computer Vision
DEFUSE: Generalizable Backdoor Defense for Self-Supervised Encoders with Generative Priors
Tuo Chen, Jie Gui, Minjing Dong et al. (+4 more)
Self-supervised learning (SSL) encoders are vulnerable to
backdoor attacks, posing threats to both visual SSL encoders and
vision-
language encoders. Existing defenses are typically designed for only one of these paradigms and rely on restrictive assumptions such as access to uninfected in-distribution data or precomputed pseudo-labels, which are difficult to satisfy in practice. To address these limitations, we propose DEFUSE, a generalizable
backdoor detection framework for SSL encoders. Inspired by
Bayesian posterior inference, we reformulate
backdoor detection as a representation-conditioned image likelihood estimation problem parameterized by a conditional
diffusion generative model. Uninfected representations tend to yield semantically consistent reconstructions, whereas backdoored ones are more likely to be mapped to the attacker's target class or semantically meaningless images, deviating from the original semantics and thereby exposing the
backdoor. However, we find that the exact likelihood is intractable, because highly abstracted representations discard the low-level information necessary for pixel-faithful reconstruction. We therefore relax the objective to semantic reconstruction and evaluate it in a well-separated representation space provided by a reference encoder. Rather than training from scratch, we fine-tune a pretrained
diffusion model, leveraging its generative prior to map data onto the natural image
manifold while preserving semantic content. Extensive experiments demonstrate that DEFUSE substantially outperforms existing detectors across diverse attack settings, generalizing to both visual SSL and
vision-
language encoders. Notably, our method greatly reduces the reliance on prior knowledge about the victim encoder or the attack strategy. The source code is available at https://github.com/jsrdcht/DEFUSE .
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All authors Tuo Chen, Jie Gui, Minjing Dong, Lanting Fang, Ju Jia, Benlei Cui, Jian Liu
Numerical Analysis
A Neural-network-based multiscale Hybridizable Discontinuous Galerkin method for solving PDEs in porous media
Tony Haines and Ke Shi
We develop a neural-network-accelerated multiscale hybridizable discontinuous Galerkin method for
elliptic problems with heterogeneous coefficients. The method preserves the standard MsHDG local-to-global structure: fine-scale HDG problems on coarse blocks define discrete Dirichlet-to-Neumann operators, which are assembled through the standard MsHDG global skeleton equations. To reduce the cost of constructing these local operators, we train a neural network on coefficient fields defined on a reference block and use the predicted operators in place of repeated fine-scale local solves. The numerical experiments assess both the accuracy and online efficiency of the resulting NN-MsHDG method. For two-dimensional binary permeability fields with moderate contrast, the neural method reproduces the standard MsHDG solution with relative modeling errors of a few percent while reducing the total online computational cost by factors of approximately 5 to 16, depending on the coarse trace dimension. In the high-contrast regime, the chosen
polynomial coarse trace spaces already yield substantial discretization errors in the standard MsHDG method, indicating the need for more effective coarse spaces, such as coefficient-adapted spectral trace spaces. In addition, the learned local operators introduce modeling errors that become severe as the trace space is enriched. These results demonstrate the potential of neural surrogates for accelerating multiscale HDG computations while also highlighting the need for improved operator representations and a better understanding of error amplification in high-contrast problems.
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Artificial Intelligence · Cross-listed from eess.AS
Why ML-based cough models do not generalize: a systematic cross-dataset evaluation for tuberculosis screening
Wensi Zhang, Tomas Teijeiro, Jérôme Thevenot et al. (+1 more)
Cough acoustics are promising for non-invasive tuberculosis (TB) screening, yet whether machine learning (ML) models capture disease-related acoustics or artifacts of data collection remains unresolved. We evaluated the cross-
dataset generalizability of classical ML and deep learning (DL) cough-based TB classifiers across three independent
datasets. Despite moderate within-
dataset performance (ROC-AUC up to $0.755 \pm 0.056$), both pipelines fail to generalize, with external performance frequently below 0.6, indicating a possible limitation of the data. We further observed audio representations are organized by recording device and
dataset rather than TB status, predicted TB probability tracks country-level prevalence in CODA, and device mismatch degrades transfer while device-diverse training improves it. Additionally, a clinical-variable baseline generalizes more consistently (ROC-AUC $0.655 - 0.711$), indicating acquisition-specific variability is a stronger driver of poor generalizability than population shift. High within-
dataset performance is not enough. External validation is essential before cough-based TB models are clinically ready.
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All authors Wensi Zhang, Tomas Teijeiro, Jérôme Thevenot, David Atienza
Computer Vision
THA-Flow Generative Model: Prosthesis Geometry Prediction from Preoperative CT
Yiping Wang, Jie Li, Jingyu Shen et al. (+1 more)
Preoperative planning for total hip arthroplasty (THA) is commonly framed as selecting a single prosthesis configuration and placement for a patient's osseous anatomy. In practice, however, the same anatomy may admit several clinically reasonable solutions, making planning inherently a one-to-many problem that is better represented by a conditional probability distribution. We present THA-Flow, a conditional flow-matching model that generates three-dimensional prosthesis geometry directly from preoperative CT. Separate AutoencoderKL models compress preoperative bone anatomy and prosthesis geometry, while a three-dimensional UNet learns a rectified flow from Gaussian noise to the prosthesis latent space under spatial bone conditioning and optional structured prosthesis parameters. The retrospective cohort comprised 1,355 hips from 1,149 patients undergoing primary THA. Following rigid registration of postoperative CT to preoperative CT, the actual postoperative prostheses were transformed independently according to the pelvic and femoral registrations and represented as a dual-channel truncated signed distance field. The prosthesis autoencoder achieved a peak signal-to-noise ratio of 47.11 dB and a structural similarity index of 0.9964 on the validation set. Complete acetabular and femoral geometries were generated across seven major stem models representing 93.4% of the cohort. Repeated bone-conditioned sampling preserved component position,
alignment, and the principal bone-prosthesis interfaces while allowing limited local
geometric variation. To our knowledge, THA-Flow represents the first application of generative AI to three-dimensional
surgical planning for THA.
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All authors Yiping Wang, Jie Li, Jingyu Shen, Liao Wang
Machine Learning
VINCENT: Validated Interaction Network for Cross-drug Explanation of Therapeutics
Fan-Sheng Chuang, Xuchen Li, Yujing Bian et al. (+1 more)
Drug synergy prediction estimates whether two drugs produce a stronger joint effect than expected from their individual activities. For drug combination discovery, a single synergy score is often not enough: researchers also need to know which molecular regions jointly drive the prediction. We study motif-pair synergy explanation, which identifies pairs of chemically coherent regions, one from each drug, that jointly contribute to predicted synergy. Existing interpretable synergy models expose atom- or substructure-level signals, but their explanations are built into the predictor architecture, and none validates cross-drug region scores under repeated perturbations or feeds that evidence back to refine the explanation. A reliable motif-pair explanation should instead be chemically coherent, perturbation-stable, and aligned with predictor behavior. We introduce VINCENT (Validated Interaction Network for Cross-drug Explanation of Therapeutics), a post-training framework for a fixed interaction-aware synergy predictor. VINCENT extracts atom-pair evidence from
attention and gradient signals, groups atoms into chemically coherent motifs, and validates candidate motif pairs through repeated local perturbations. The validated evidence is fed back to refine motif assignments, yielding explanations that satisfy these three criteria. On a 25-pair literature-annotated subset, VINCENT achieves a mean motif recall of 0.826 (95% CI: 0.78-0.87), compared with 0.49-0.66 for baselines. Across all 71 test pairs, its validated interaction scores yield a TP/TN separation of 3.36. These results show that closed-loop perturbation validation recovers literature-supported molecular regions more accurately than existing alternatives while producing cross-drug interaction scores that better reflect predictor behavior.
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All authors Fan-Sheng Chuang, Xuchen Li, Yujing Bian, Kaixiong Zhou
Computers and Society
Non-Great-Power Conflict and AI Risk
Kristina Kempkey, Seán Boddy and Catherine Ge-Wang
Research on advanced AI and the risk of war has focused almost exclusively on great power conflict, on the grounds that confrontation between
nuclear-armed adversaries poses the greatest risk of catastrophic or existential harm. Considerably less
attention has been paid to non-great-power conflict (NGPC): wars between non-great powers, between non-great powers and great powers, civil wars, proxy wars, and conflicts involving nonstate actors. This paper evaluates the null hypothesis that NGPC is much less important than great power conflict (GPC) as a source of catastrophic risk in an era of increasingly capable AI, against the alternative that it is within an order of magnitude of GPC in importance. We assess three sub-hypotheses: that NGPC increases the likelihood of great power conflict; that it increases the expected harm from catastrophic terrorism; and that it increases the expected harm from loss of control over advanced AI systems. We find the null poorly supported for H1 and H2, and identify H3 as a priority for further work rather than a settled finding. We also identify five intermediate variables that recur across the pathways - information environment quality, decision-making timeline compression, great power threat perception, capability
diffusion, and norm erosion - and argue that these shared nodes are the highest-priority targets for further investigation and intervention.
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Systems and Control
Hard-Constrained Sampling on Embedded Riemannian Manifolds via Adjoint Schrödinger Bridges
Mattia Mosso, Jaemoo Choi and Heng Yang
A variety of tasks require sampling from unnormalized Boltzmann distributions supported on
manifolds. Building upon the foundations of adjoint matching and adjoint Schrödinger bridge sampling, this paper provides a theoretically justified method, through the lens of
stochastic optimal control, to address this problem on smooth, compact, path-connected embedded Riemannian
manifolds. As an element of novelty compared to existing literature, feasibility is enforced at the level of the state space, meaning the controlled
diffusion is defined intrinsically on the curved space. Empirical validations are provided for several physics applications.
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Numerical Analysis
Adaptive Nesterov Momentum Method for Electrical Impedance Tomography with the Complete Electrode Model
Kai Zhu, Jijun Liu and Min Zhong
We apply the adaptive Nesterov momentum (ANM) method [30] to electrical impedance
tomography under the complete electrode model. The forward problem is formulated in the variational CEM setting, accounting for finite electrode size, contact impedance, insulating gaps, and the mean-free voltage gauge. The resulting nonlinear inverse problem is treated within a unified dual-to-primal framework using three classes of strongly convex structural penalties: an L2-type penalty, an L1-type penalty promoting sparse deviations from a calibrated homogeneous background, and a TV-type penalty favoring approximately piecewise-constant conductivities with sharp interfaces. The TV class is implemented using both smoothed TV and Huber-TV formulations. The data- misfit gradient is computed through CEM adjoint equations and stabilized by Sobolev smoothing. The method is evaluated on the publicly available KIT4 tank measurement data after calibration of the background conductivity and contact impedance from no-object measurements. The experiments include single, multiple, mixed-conductivity, and geometrically challenging phantom configurations. The L2-type penalty generally produces smooth but diffuse reconstructions, whereas the L1-type penalty yields cleaner backgrounds with occasional
geometric distortion. The smoothed TV and Huber-TV penalties provide more spatially coherent localization and exhibit similar reconstruction behavior across most tested configurations. These results demonstrate the practical applicability of the adaptive Nesterov framework to measured CEM-EIT data.
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Computer Vision
Socialized Detector Learning: Trajectory-Guided and Reciprocal Distillation for Heterogeneous Object Detectors
Weihao Li, Yunqi Zhu, Zhihe Fan et al. (+5 more)
Object detection knowledge is fragmented across independently trained, heterogeneous detectors with complementary category supports. In socialized learning, this knowledge resides in a society, and learning aims to evolve the society collectively through exchange. However, aggregation-based socialization does not explicitly plan transfer order, whereas progressive multi-teacher
distillation considers order but remains a one-way student enhancement in a shared category space. Building on Socialized Learning, we formulate Socialized Detector Learning (SDL) for heterogeneous, category-specialized object detectors and propose Trajectory-Guided and Reciprocal
Distillation (TGRD).TGRD estimates directed operational Inter-Detector Transfer Difficulty (IDTD) from held-out feature-
alignment residuals, precomputes a fixed score table, and greedily constructs a carrier trajectory. Along the trajectory, knowledge is progressively consolidated into a union-category carrier and then returned to experts through reciprocal transfer. A conditional proxy-certificate analysis shows that, under stated assumptions, the progressive certificate is no larger than an aggregated-target counterpart. On MS COCO with four heterogeneous experts and two carrier initializations, final carriers outperform epoch-matched simultaneous aggregation controls by 2.6 AP in both settings. Reciprocal detectors attain 20.8–28.4 AP on previously unsupported categories while remaining within 1.3 AP of original expert-specific performance. These results support order-aware progressive consolidation followed by reciprocal transfer as a viable mechanism for detector-society
evolution.
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All authors Weihao Li, Yunqi Zhu, Zhihe Fan, Ruipu Zhao, Boan Tao, Xinjie Yao, Yan Fan, Pengfei Zhu
Artificial Intelligence · Cross-listed from physics.ao-ph
Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?
Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun et al. (+4 more)
AI weather prediction (AIWP) models rival physics-based models, yet the sources of their unexpected forecast accuracy and the degree of their physical fidelity remain unclear. Here, across a hierarchy spanning observation-based reanalysis, a general circulation model, and the multi-scale Lorenz system, we show that AI models can be trained to skillfully predict the past (backcast), though backcasts are systematically less accurate than forecasts. However, skillful backcasting appears to violate the second law of thermodynamics, and all these
forecasting and backcasting models miss the butterfly effect. We trace the surprising forecast accuracy, missing butterfly, and skillful backcasting to a single cause: inevitable coarse-graining of training data, which removes fast, small scales and/or some variables. From the Lorenz system to official Pangu-Weather models, reducing coarse-graining makes AI predictions more physics-like (arrow of time and butterfly-like effects emerge), but forecast accuracy declines. Results offer an explanation for AIWP models' forecast skill: unlike physics-based models, they implicitly learn how fast, small scales affect large scales without inheriting their rapid error growth. Broader implications are that AI models' proliferation calls for revisiting predictability theories and long-term
climate emulation strategies, and backcasting offers a useful, new lens for such analyses.
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All authors Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun, Jiangdi Wang, Alexander Wikner, Justin Finkel, Jonathan Q. Weare
Networking
SlimTCP: It's fast, but not because it's slim
Mihai Drosi Caju and Costin Raiciu
In this paper, the authors explore the possibility of improving the performance of TCP/IP stacks in the context of data-center networks. This paper will focus particularly on the claim that simplifying the code-base of the stack increases its performance.
Computation and Language
Skill Issue: Are Skills Language-Invariant in LLMs?
Bobby Cheng, Adam Gaber, Zhengyuan Liu et al. (+4 more)
Large
language models access knowledge inconsistently across
languages, but to what extent do they differ in their skill sets when interacting with different
languages? This work quantifies cross-lingual skill inconsistency orthogonally from knowledge and general
benchmark performance. We do this via
multilingual self-play: two instances of the same model compete in a text-based game, each interacting through a different
language interface. Since the model, opponent, rules, state space, and available actions remain fixed, this setting isolates the effect of
language on the model's realized behavior. We build a
multilingual extension to TextArena and evaluate three open-weight models across eight
languages and six games covering spatial
reasoning, imperfect information, resource allocation, and repeated interaction. We find that the same model can exhibit markedly different playing strength across
languages, with systematic variation in win–loss margins, invalid actions, and strategic tendencies. Detailed analyses reveal
language-specific failures in spatial
reasoning, card-conditioned decisions, and optimal move selection. In some settings, changing only the intermediate
reasoning language recovers much of the lost performance, suggesting that
language can affect different stages of the decision process. These results show that skill discrepancies are a measurable major roadblock in the development of truly
multilingual models. Better understanding these discrepancies can help us design models that perform more equitably across
languages.
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All authors Bobby Cheng, Adam Gaber, Zhengyuan Liu, Catherine Arnett, Omer Goldman, Cheston Tan, Leshem Choshen
Robotics
Anytime Global Tensor Motion Planning
Sai Coumar, An T. Le and Zachary Kingston
Global Tensor Motion Planning (GTMP) solves motion planning with batched tensor operations over a layered multipartite graph. We generalize GTMP so that adjacent-layer edges are realized by any black-box local planner (e.g., linear interpolation, splines, sampling-based planning, trajectory optimization, or generative sampling). We provide two anytime policies on top of this generalization: Anytime GTMP with random restarts at a fixed budget, which covers every homotopy class almost surely, and AO-GTMP with informed expansion with growing budgets, which converges to the optimal cost. We prove that a single sampled graph covers every endpoint-fixed homotopy class admitting a \(\delta\)-clear representative of bounded length. We also prove that additional samples per layer reduce the per-layer miss probability exponentially, whereas stronger local planners reduce the required layer count only sublinearly. On manipulation
benchmarks the method matches state-of-the-art performance, and on 2D
navigation it returns batches of topologically diverse solutions, while the informed baselines concentrate on one or two classes.
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Computer Vision
FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction
Tristan Gottwald, Michelle Bruch, Mubashir-Ul Hassan et al. (+6 more)
We present FlowMoDL, an unrolled neural network for highly accelerated 4D flow MRI reconstruction that directly optimizes for both anatomical magnitude and phase-derived velocity accuracy. Building on the MoDL framework, FlowMoDL alternates a learned (3+1)D spatiotemporal denoiser with conjugate-gradient data-consistency updates based on the SENSE forward model. A novel dual-pathway conditioning scheme adapts the denoiser features and data-consistency weighting, enabling a single model to handle varying acceleration factors ($10\times$ to $50\times$). To ensure physiological accuracy, the network is trained using a deep-supervision composite loss that explicitly penalizes velocity magnitude and angular errors, stabilized by a curriculum schedule. We evaluate FlowMoDL on the multi-center CMRx4DFlow
dataset against classical and deep-learning baselines (CG-SENSE, MoDL, FlowVN, and FlowMRI-Net). A key advantage of FlowMoDL is its superior gradient step efficiency. When evaluated under an equivalent, limited budget of gradient steps, competing flow-specific networks degrade significantly. In contrast, FlowMoDL robustly converges and strictly outperforms all competitors across all acceleration factors in magnitude SSIM, nRMSE, relative velocity error, and angular error, successfully recovering sharp structural details and temporally coherent velocity fields.
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All authors Tristan Gottwald, Michelle Bruch, Mubashir-Ul Hassan, Fatma Alickovic, Milan Kloiber, Daniel Tenbrinck, Torsten Panholzer, Melanie Schaller, Jana Hutter
Computation and Language
Unfolding Scientific Papers into Multi-Turn Generation Trajectories for Continued Pre-Training
Qiankai Xu, Qiguang Chen, Zixin Su et al. (+4 more)
A recent line of synthetic-data work reconstructs the thinking behind existing text rather than rewriting the text itself, but it operates on short web passages, recovers only local thoughts, and leaves the structure of whole documents untouched. Scientific papers are written to a clear and largely uniform structure and make a natural substrate for lifting this paradigm to the document level. We present a pipeline that unfolds each paper into a multi-turn generation trajectory in which a teacher model reconstructs the writing process of the whole paper: a writing request, a global plan, and pre-writing deliberation for each section. All section texts and the abstract are kept verbatim from the source paper. We apply the pipeline to 1.8M quality-filtered arXiv papers and obtain a 60B-
token corpus for continued pre-training (CPT) that is roughly twice the source text. The same reverse construction extends to instruction data and evaluation. We build an SFT
dataset of 200K samples using answers derived from paper text. We also use held-out papers to construct PAW-Bench, a
benchmark of 2,940 academic writing tasks with per-task rubrics and checklists. In controlled experiments, CPT on our corpus followed by SFT on public
datasets improves writing performance while preserving general
reasoning and improving long document reading. Replacing part of the writing SFT data with our synthetic instruction data further improves performance on PAW-Bench.
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All authors Qiankai Xu, Qiguang Chen, Zixin Su, Wenhao Huang, Yue Gao, Jiaheng Liu, Ge Zhang
Computation and Language
Localize-Then-Decide Guarantees for LLM Judgments
Xinyu Li, Yi Zhou, Guanqun Cao et al. (+3 more)
Large
language models (LLMs) are increasingly used as evaluators to assess output quality and preference
alignment, yet providing reliable guarantees of agreement with human judgments remains challenging. Recent work introduces confidence-thresholding methods that provide such guarantees for pairwise comparisons, relying on the assumption that higher estimated confidence implies lower disagreement risk with humans. However, this assumption can break down when the number of candidate responses increases, since distributing probability mass across many alternatives can distort confidence estimates. To address this issue, we propose a Localize-Then-Decide framework. First, conformal prediction localizes a small shortlist that contains the human-preferred response with high probability. Then, a calibrated confidence-based rule selectively chooses a single response from this shortlist or abstains. This design restores the monotonic relationship between confidence and disagreement risk and enables high-probability agreement guarantees. Experiments with multiple candidate sizes across several
datasets and judge LLMs demonstrate that our framework consistently achieves higher guarantee success rates and substantially higher coverage than single-stage baselines.
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All authors Xinyu Li, Yi Zhou, Guanqun Cao, Zeyu Fu, Tianjin Huang, Gaojie Jin