Computational Complexity
Rational Reductions and Regular Languages of Constant Circuit Complexity
Stefan Göller and Amaldev Manuel
We study the
circuit complexity of regular
languages in terms of unbounded fan-in Boolean
circuit families. We characterize the regular
languages of constant
circuit complexity in terms of the one-variable fragment of first-order logic with regular predicates, in terms of the pseudovariety of stamps $\mathbf{QEJ}_\mathbf{1}$, suitable word congruences and regular expressions. We analogously characterize the neutral letter regular
languages of constant
circuit complexity. Our lower bound result implies that the class of regular
languages of sublogarithmic
circuit complexity coincides with the one of constant
circuit complexity. In addition we show that deciding whether a regular
language, given as a nondeterministic finite automaton, has constant
circuit complexity is $\mathbf{PSPACE}$-complete. We introduce a strong notion of reduction, called rational truth-table reduction, that is tailored towards algebraically defined classes of
languages. We show that, for a class of functions we call mild, rational truth-table reductions preserve both upper and lower bounds on
circuit complexity. We show that the class of regular
languages, whose
circuit complexity is bounded by a mild function, is in fact a length-multiplying variety of
languages. Slightly extending the class of regular
languages of constant
circuit complexity, we analogously characterize the class of regular
languages that are in the pseudovariety $\mathbf{QEACom}$. For these we derive logarithmic
circuit complexity upper bounds.
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Human-Computer Interaction
EasyFashion: A Human-AI Co-Creation System for Personalized Fashion Design and Sewing Pattern Generation
Hong Qu, Zhaoxiang Xu, Jinbo Luo et al. (+3 more)
People often want garments that reflect their aesthetic preferences, fit their bodies, and meet their sizing needs, yet turning these requirements into physical garments remains difficult. Ready-to-wear options provide limited personalization, while custom tailoring is costly and time-consuming. Recent generative artificial intelligence (AI) systems can visualize garment ideas but often stop short of supporting downstream production. To address this gap, we present EasyFashion, a human-AI co-creation system that enables users to iteratively refine design intent for personalized garment style and size, evaluate designs through virtual try-on on reconstructed personal avatars, and generate sewing patterns for garment production. Using reference images, text descriptions, and body photos as input, EasyFashion translates user intent into structured garment specifications and try-on results. Technical experiments, user studies, and a real-world production case demonstrate the value of EasyFashion for
multimodal design expression, body-specific evaluation, and production-oriented outputs in personalized garment design. Our project page is available at https://zoequ.github.io/easyfashion.github.io/.
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All authors Hong Qu, Zhaoxiang Xu, Jinbo Luo, Yujie Zhao, Jie Zhang, Yadie Yang
Robotics
Real-Time Bounded Catenary Solver for UAV Tether Modeling
Max Beffert and Andreas Zell
For non-stationary tethered multirotor
UAVs in real-world conditions, simulating the forces imposed on the drone by the aerodynamic drag of the tether becomes crucial, with online use cases placing a hard bound on the maximum solve time. In previous work, a quasi-analytical catenary tether model reached a mean solve time of 0.51 ms using a general-purpose root finder, but without any worst-case guarantees or proven convergence. In this work, we reformulate the inner solver by reducing the catenary boundary-value problem to a single transcendental equation in one well-conditioned unknown. We derive a closed-form bracket and prove monotonicity and convexity as well as existence and uniqueness of the root, which together guarantee convergence of the solver. We further propose a two-regime initial guess which approximates the true root within 3.4 % and reduces the mean iteration count by 69.2 % to 2.24 compared to the textbook initialization. Building on the hybrid root-finding method rtsafe (Newton-Raphson with bisection fallback giving bounded iteration counts), we implement a specialized variant that exploits the problem structure to omit unnecessary checks while retaining correctness, which gives up to 1.37 times speedup. With the proposed solver the full tether model achieves a nearly constant solve time of 4.0 us on average and 4.6 us at worst, a 68 times speedup over an optimized re-implementation of the previous method, while agreeing with it to a relative deviation of 8.7e-9. Because the reformulation leaves the underlying physical model untouched, the experimental validation of the previous work carries over unchanged. We further demonstrate its suitability for embedded, resource-constrained platforms with a Lua implementation running directly in ArduPilot on a drone's flight controller, where it stays well inside the scheduling budget with a mean solve time of 0.74 ms.
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Artificial Intelligence
Hyperbolic Graph Representation Learning for Differential Diagnosis on Biomedical Knowledge Graphs
Pietro Miotto, Lucia Mellini, Tommaso Marzi et al. (+5 more)
Biomedical knowledge graphs combine ontology-derived hierarchies with transversal associations among heterogeneous entities such as phenotypes, diseases, genes,
proteins, and patients. This hybrid structure raises the question of whether
hyperbolic embeddings, which naturally capture tree-like organization, remain useful beyond purely hierarchical graphs. We present a preliminary study of
hyperbolic graph representation learning for Mendelian-disease differential diagnosis on a patient-integrated biomedical graph. Experiments on isolated ontology subgraphs show that
hyperbolic models achieve strong performance in substantially lower dimensions than Euclidean baselines. We then evaluate the models on a link-prediction task that ranks candidate diseases for each patient. Results suggest that
hyperbolic embeddings can exploit biomedical hierarchical structure while supporting diagnostic
reasoning over heterogeneous patient-level graphs.
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All authors Pietro Miotto, Lucia Mellini, Tommaso Marzi, Cesare Alippi, Elena Casiraghi, Alberto Paccanaro, Giorgio Valentini, Mauricio Soto-Gomez
Human-Computer Interaction
Verify, Offload, Extend & Recommend: Selective Complementarity in AI Support for Physical Activity Planning with Longitudinal Patient Data
Pavithren V S Pakianathan, Rania Islambouli, Diogo Branco et al. (+5 more)
Self-tracking technologies create longitudinal patient-generated health data, yet integrating these data into clinical decision-making can increase information-processing demands. Generative AI may support sensemaking, but its value depends on clinical context and expertise. We investigate AI augmentation of a clinical decision support system for physical-activity planning in cardiovascular disease. In a counterbalanced within-subjects study, 26 exercise physiologists developed plans for four real cardiovascular cases with and without AI support, followed by evaluation of an AI exercise-plan generator. AI did not significantly improve workload, usability, confidence, or plan quality overall; however, its effect on plan quality increased as visualization literacy decreased and its effect on workload increased as visualisation literacy increased. Interviews and 152 chatbot queries revealed three recurring uses: verifying, offloading, extend and generate. Our findings position AI support as a selective complement to professional expertise while highlighting validation challenges when clinicians seek support precisely where their own knowledge is limited.
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All authors Pavithren V S Pakianathan, Rania Islambouli, Diogo Branco, Gil Batista Rosa, Rita Pinto, Albrecht Schmidt, Tiago Guerreiro, Jan David Smeddinck
Cryptography and Security
A Global Readiness and Sovereignty Capability Model for Post-Quantum Cryptography Migration
Mohamed Aly Bouke
Cryptographic dependence predates the
quantum era, but the migration to post-
quantum cryptography (PQC) opens a rare window to reshape it, because the
algorithms, implementations, hardware, and standards adopted now can lock in dependence or sovereignty for decades. This paper introduces the Readiness-Sovereignty Capability Model (RSCM), a national measurement model that operationalizes PQC readiness together with cryptographic sovereignty, which current maturity models score only as readiness and the sovereignty literature defines without measuring. RSCM decomposes sovereignty into three distinct constructs, indigenous cryptographic capacity, indigenous post-
quantum control, and external dependency, and certifies a post-
quantum maker only through a gate requiring demonstrated, institutionally sustained creation in at least one core layer, whether design, implementation, or validation. Applying it to fifty-seven documented cryptographic actors coded from cited public evidence, and testing that coding with an independent second coder, a plausible-state bootstrap, and convergent-validity checks, we find that twenty countries clear the gate, fifteen as full-stack makers and five as research makers, eleven hold strong general capacity without post-
quantum control, one is a ready adopter, and twenty-five are dependent. The gate
cells show substantial weighted agreement, a quadratic-weighted kappa of 0.71, and the maker classification is stable in its core though uncertain at the threshold. Readiness tracks independent cyber indices at rank correlations up to 0.70, while post-
quantum creation shows no significant correlation with the commitment index, a rank correlation of only 0.22 that separates control from readiness. The paper contributes the framework, the evidence-graded assessment, and policy directions for building indigenous
quantum-safe capacity.
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Computer Vision
CADSplat: Sparse-View 3D Gaussian Splatting Aided by CAD Models for Robust, Photorealistic Digital-Twin Reconstruction
Kristof Overdulve, Lode Jorissen and Nick Michiels
We present CADSplat, a framework that reconstructs photorealistic, geometrically accurate digital twins from sparse ($<15$ views), wide-baseline posed images of an object by regularizing 3D Gaussian Splatting (3DGS) with an explicit CAD shape prior. Using such a prior requires finding a CAD model whose shape resembles the object depicted in the images and determining the pose of each camera relative to the object. We obtain both by matching segmented object silhouettes against silhouettes rendered from a CAD library and keeping the camera-to-object poses of the best-matching model. We then anchor 3D Gaussian primitives to the surface of the retrieved model and jointly optimize the 3DGS parameters, the camera-to-object registration, and a non-rigid deformation field to account for shape differences between the physical object and the CAD model. Across two real-world
datasets, CADSplat outperforms unconstrained, few-shot, and mesh-texturing baselines and degrades gracefully to as few as 3 views. Our experiments show that most of the gain in rendering quality comes from how the splats are constrained—a fixed set of splats tied to a surface and moved by a single smooth deformation field—rather than from the CAD shape itself. The CAD model adds shape knowledge where views are scarcest, in the sparsest captures and on strongly self-occluded objects, and it places every camera in the object's own frame. This enables applications beyond novel-view synthesis, such as markerless augmented reality registration, per-image object pose estimation, physical simulations, and the transfer of part labels from the design to the reconstruction.
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Artificial Intelligence
First Token Matters: Understanding Safety Collapse in Large Reasoning Models
Yizheng Yang, Haining Yu, Yuechen Wang et al. (+4 more)
Large
Reasoning Models (LRMs) exhibit strong problem-solving abilities, yet their safety
alignment often degrades when handling harmful queries. Existing approaches to improving safety largely rely on additional training or preference optimization, while offering limited understanding of the internal mechanisms behind safety failures. In this work, we investigate this failure through a
token-level positional analysis of refusal dynamics and identify a localized vulnerability at the onset of
reasoning, which we term Onset Refusal Collapse (ORC). We find that the refusal-related signal of LRMs drops sharply at the first generated
token under harmful queries, which is associated with unsafe response generation. Motivated by this finding, we propose SafeToken, a lightweight inference-time intervention that injects a learned continuous safety anchor precisely at
reasoning onset. Despite updating only a single
token embedding, SafeToken effectively mitigates ORC, improves safety on harmful-query
benchmarks, and largely preserves
reasoning utility. These results suggest that safety failures in LRMs can arise from a transient breakdown at the critical transition from understanding to generation.
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All authors Yizheng Yang, Haining Yu, Yuechen Wang, Yikai Hou, Xing Fu, Jinbo Yang, Tianqing Zhu
Multimedia
Divide and Conquer: Mixture-of-Bottleneck Experts in Informative Ordinal Space for Video-based Multimodal Sentiment Analysis
Ronghao Lin, Qiaolin He, Zefeng Lu et al. (+5 more)
Video-based
Multimodal sentiment analysis (MSA) must handle information from text, audio, and image sequence in human speaking
videos, yet current methods often fail to integrate modalities with task awareness. Most models treat
video sentiment prediction as a single task, overlooking its ordinal nature, and their fusion strategies struggle to capture diverse unique and synergic cues across modalities. To address these limitations, we adopt a divide-and-conquer perspective by reformulating MSA as an ordinal regression problem and decoupling it into polarity recognition and intensity prediction. Driven by information theory, we introduce a Mixture-of-Bottleneck (MoB) framework that assigns different latents to polarity- and intensity-specific experts for different modalities. With the learning of information bottleneck, each expert learns compact and task-relevant representations while filtering out redundancy and noise. A
multimodal bottleneck routing fusion module then fuses these expert latents with hard mining strategy, guiding the prediction in the ordinal sentiment space. Extensive experiments on 4 MSA
datasets and 4
language models show that MoB effectively leverages informative latents from diverse modalities and captures general sentiment structure. Beyond stronger performance, MoB comprehensively captures fine-grained intra- and inter-modal dynamics, enabling more trustworthy localization of nuanced
video sentiment signals.
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All authors Ronghao Lin, Qiaolin He, Zefeng Lu, Yichu Liu, Li Huang, Sijie Mai, Haifeng Hu, Yap-peng Tan
Machine Learning
Spatially Adaptive Noise Injection
Frantzeska Lavda, Maciej Falkiewicz, Van Khoa Nguyen et al. (+1 more)
Diffusion samplers reverse a learned noising process using either
stochastic (DDPM) or deterministic (DDIM) updates, which represent endpoints of a single family controlled by a scalar noise-injection variance that is applied identically at every spatial location. This uniform approach neglects the geometry of natural images: high-
curvature regions such as edges and textures, where the denoiser is uncertain, benefit from
stochastic correction, whereas smooth regions, where the score is precise, are degraded by injected noise. This work investigates whether each pixel requires
stochastic correction at a given timestep and introduces Spatially Adaptive Noise Injection (SANI), a novel sampling framework that dynamically adjusts noise application on a per-pixel basis. SANI integrates a probabilistic gating mechanism with a derived spatially adaptive variance, ensuring that noise is injected precisely where needed to refine complex features while preserving well-formed structures. Experimental results and decoupling ablations demonstrate that SANI consistently improves Fréchet Inception Distance (FID) over the vanilla DDPM and DDIM endpoint samplers across diverse sampling timesteps, while remaining competitive with variance-learning baselines, highlighting the importance of spatial adaptivity in
diffusion sampling.
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All authors Frantzeska Lavda, Maciej Falkiewicz, Van Khoa Nguyen, Alexandros Kalousis
Computer Vision
GeoCond: A Conditioning-Aware Reliability Adapter for Feed-Forward 3D Reconstruction
David Ahmedt-Aristizabal, Mohammad Ali Armin, Russell Tsuchida et al. (+1 more)
Feed-forward 3D foundation models such as VGGT predict cameras, depth, and point maps in a single pass, but can fail silently under low overlap, low parallax, and extreme relative rotation. Stratified analyses over these factors show that these failures are governed by
geometric conditioning and are poorly captured by native aleatoric confidence. We introduce GeoCond, a lightweight reliability adapter for frozen feed-forward 3D backbones. GeoCond reads
geometric conditioning cues and the backbone's own stability signals and outputs pose-level uncertainty and a refinement gate. During training, it can be supervised by frame-permutation orbit variance, ground-truth pose error when labels are available, or cycle residuals from unlabelled independent pose graphs. At inference, the default head requires only one backbone pass and a small MLP. On VGGT, GeoCond improves out-of-distribution (OOD) AUSE (area under the sparsification-error curve; lower is better) from $0.32$ to $0.20$ over native confidence on held-out outdoor scenes (and to $0.21$ when trained on indoor data only) and avoids the collapse caused by applying bundle adjustment uniformly, without improving on feed-forward poses there. For three of six backbones, cycle-distilled variants provide a ground-truth-free adaptation route, including cases where permutation variance vanishes on equivariant models. The same reliability signal supports gated refinement, pose-graph weighting, calibration, curation, and capture decisions. Reliable feed-forward 3D reconstruction requires not only predicting geometry, but also knowing when that geometry should be trusted.
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All authors David Ahmedt-Aristizabal, Mohammad Ali Armin, Russell Tsuchida, Lars Petersson
Computer Vision
CSWAM: Better Causal Semantic Representations for Out-of-Distribution Generalization in World Action Models
Tianbin Liu, Jian Zhu, Taiyi Su et al. (+5 more)
FastWAM-style world action models enable efficient action-only inference, but generalize poorly under visual distribution shifts. Their reconstruction-oriented representations emphasize appearance-specific details, limiting generalization to unseen scenes and objects. Without observation history, the model also lacks temporal evidence for robustly identifying task-relevant state changes and motion in unfamiliar visual conditions. To address these limitations, we present the Causal Semantic World Action Model (CSWAM), which augments FastWAM with a causal semantic expert built on V-JEPA 2.1. V-JEPA provides temporally grounded representations of semantic state changes and motion with less dependence on appearance-specific details. The expert learns their future
evolution from a sparse history of current and past observations and shares the history-derived context with both the
video and action streams through causal
attention. At inference, CSWAM conditions action denoising on the current
video state and observed semantic history, retaining efficient action-only inference. We conduct simulation and real-
robot experiments to evaluate generalization under distribution shifts. With
embodied pretraining, CSWAM raises Randomized success on RoboTwin 2.0 Clean-to-Randomized transfer from 10.16% to 45.18%, a gain of 35.02 percentage points over FastWAM. Across two real-
robot tasks and three OOD difficulty levels, CSWAM improves average success over FastWAM by 42.5 percentage points, from 27.5% to 70.0%.
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All authors Tianbin Liu, Jian Zhu, Taiyi Su, Jianjun Zhang, Chong Ma, Zitai Huang, Weiyi Lu, Yi Xu
Artificial Intelligence
Disentangling Long-Term Memory via Latent Neuro-Symbolic Reasoning
Cai Ke, Xinghao Chen, Xiaoyu Shen et al. (+6 more)
Personalized
agents are required to reason over long-term history interactions to infer both explicit preferences and implicit behavioral evidence. While early flat
retrieval methods score
memory fragments independently and neglect the distributed information, current structured
memory frameworks rely on query-agnostic static graphs that fail to capture the context-dependent relations. Crucially, raw textual
memories are inherently entangled and noisy, making fine-grained personalization and cross-session
reasoning computationally prohibitive. To this end, we present LGM, a novel neuro-symbolic framework that shifts long-term
memory disentanglement into a continuous latent space. Specifically, (i) instead of persisting fixed graphs, we design a tailored latent graph construction with a sparse autoencoder. Subject to each query, it maps historical interactions into latent
memory nodes and disentangles the
memory traces into sparse concept activations, dynamically synthesizing query-aware relational edge weights. (ii) A graph encoder then treats the query
embedding as a conditioning preference to direct non-linear message passing across the task-specific latent subgraph. This yields a highly expressive
memory representation for effective activations. Extensive experiments on long-term personalization
benchmarks demonstrate that LGM significantly outperforms state-of-the-art baselines in capturing both explicit and implicit preferences while enabling personalized responses.
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All authors Cai Ke, Xinghao Chen, Xiaoyu Shen, Keyu Chen, Siyu An, Junnan Dong, Ruifeng Xu, Ruizhi Qiao, Xing Sun
Artificial Intelligence
Collective Loss of Control in LLM Agent Systems: An Epidemic Account of Mutation, Contagion, and Recovery
Xiangfan Wu, Zonghao Ying, Huiyu Wu et al. (+4 more)
How does a multi-
agent system evolve from a local deviation into collective loss of control? We propose an epidemic explanation organized around accidental mutation, contagion, and recovery. A spontaneous deviation creates a seed; communication enables other
agents to adopt and retransmit its unsafe strategy; collective failure can emerge when propagation outpaces correction and containment. Thus, rare individual deviations can coexist with substantial collective risk. Motivated by reported OpenAI
agent coordination incidents, we examine two ingredients of this mechanism. A deployment audit identifies implicit communication paths between nominally independent evaluation runs and verifies transport through a default Docker backend. RogueHandoff-20, a
benchmark of 20 executable scenarios, tests recipient susceptibility by injecting unsafe trajectories generated by a modified Qwen-27B route. Across four native-pending routes, executed harm is 0-5% on normal tasks and 40-95% after injection, exceeding paired direct malicious requests by 5-45 percentage points. These results support low observed baseline harm alongside high conditional susceptibility; they do not establish natural rare-event rates or demonstrate an autonomous cascade. The account motivates complementary defenses: strengthen resistance and recovery alongside prevention of spontaneous deviations, and audit and restrict unintended communication paths that can turn local failures into collective loss of control.
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All authors Xiangfan Wu, Zonghao Ying, Huiyu Wu, Xing Zheng, Huangsheng Cheng, Xiaorong Shi, Jing Guo
Cryptography and Security
SEEK: Secure and Efficient Encrypted Keyword Search For Privacy-Preserving Messaging Protocols
Soumyadyuti Ghosh and Michail Maniatakos
Encrypted communication protects sensitive user data but can facilitate harmful or unlawful exchanges, creating a trade-off between detecting dangerous messages and preserving end-user
privacy. To address this, we propose SEEK, a practical and efficient encrypted keyword-search protocol for
privacy-preserving messaging that combines homomorphic encryption with secure two-party computation (2PC). SEEK first partitions messages into ciphertext fragments with the minimum sufficient overlap, then homomorphically correlates them using encrypted keyword trapdoors. For long messages, this design can reduce sender-side encryption and upload overhead by up to two orders of magnitude over state-of-the-art baselines. It supports ASCII case-insensitive matching with one fixed-size encrypted trapdoor and one homomorphic multiplication per fragment, yielding up to 5.47x faster correlation computation than the strongest fragmentation-based baselines. SEEK then invokes 2PC-based selected decoding, blinded zero testing, and secure aggregation, revealing only the keyword presence-or-absence bit while hiding the keyword, its length, message contents, match counts, and locations. SEEK achieves 100% accuracy under case variations that result in exact-matching failures, without requiring additional trapdoors or online communication. We further realize SEEK as an end-to-end web and cross-platform mobile application. Prototype evaluation on a weekly messaging history yields an online computation time of 1.92 s per search, demonstrating the practical feasibility and efficiency of SEEK.
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Cryptography and Security
AIJon: Automated Generation of Annotations for Fuzzing
Jayakrishna Menon Vadayath, Hulin Wang, Moritz Schloegel et al. (+6 more)
Modern fuzzers use code coverage as feedback to guide their exploration which has proven to be an effective strategy for driving exploration. However, this strategy overlooks inputs that may be interesting to the target program even without uncovering new code paths. Fortunately, prior research has shown that annotations generated by human domain experts can provide additional feedback, guiding the fuzzer towards interesting parts of the program. In this paper, we replicate experiments presented in IJON and extend them to real-world vulnerability detection at scale. To mitigate the scalability challenge, imposed by the need for human domain expertise, we propose utilizing LLMs to automatically generate annotations. We demonstrate the applicability of LLMs for this purpose and observe that LLMs can generate annotations that perform comparably to human-generated annotations. Motivated by this finding, we design AIJON, a system that leverages LLMs to automatically generate IJON-style annotations. We evaluate AIJON on the Magma
benchmark and surprisingly observe that annotation-based fuzzing does not perform strictly better than AFL++. We conduct several experiments to identify the cause of our results and identify key insights regarding the impact of annotations on fuzzing campaigns, including their effect on the energy distribution of the fuzzer. Notably, we observe that LLMs can generate annotations that achieve comparable results to human generated ones, thus opening the door for future research to perform further studies on the impact of annotations at scale.
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All authors Jayakrishna Menon Vadayath, Hulin Wang, Moritz Schloegel, Jie Hu, Wil Gibbs, Tiffany Bao, Adam Doupé, Ruoyu "Fish" Wang, Yan Shoshitaishvili
Systems and Control
Optimization Design and Simulation Validation of a Variable Stiffness Actuator Based on a Crossed Four-Bar Mechanism
Yuanlong Ji, Ruizhe Jiang, Xiangyu Xie et al. (+3 more)
This paper presents a bio-inspired antagonistic variable stiffness actuator (VSA) based on two crossed four-bar compliant transmission elastic units (CFB-CTEs). The design addresses the difficulty of combining nonlinear elastic shaping with low structural inertia in antagonistic VSA mechanisms. Inspired by the crossed constraint behavior of the anterior and posterior cruciate ligaments during knee flexion, the proposed actuator uses
geometric transmission, elastic energy storage, and bilateral antagonistic arrangement to shape the output torque and equivalent stiffness. A multi-objective optimization model is established to balance torque tracking accuracy, equivalent inertia, and mass. The selected compromise design achieved a torque root-mean-square error (RMSE) of $0.883~\mathrm{N\,mm}$ and a total mass of $50.2~\mathrm{g}$. Its average equivalent inertia was reduced by about 78% compared with an independent torque-only optimized design. An ADAMS multibody model was further built to verify the structural response under single-input, opposite-input, and same-input conditions. The results support the feasibility of the proposed crossed four-bar elastic unit as a lightweight nonlinear elastic branch for antagonistic VSAs.
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All authors Yuanlong Ji, Ruizhe Jiang, Xiangyu Xie, Junheng Lin, Dongrun Jin, Xingbang Yang
Robotics
ForwardDLO: Model-Based Bimanual Shape Matching of Unconstrained Deformable Linear Objects
Tim Missal, Berk Guler, Lucas Domingues et al. (+3 more)
Ropes, cables, and other deformable linear objects appear in tasks from untangling to cable routing and suturing, yet controlling their shape remains a challenge in
robot manipulation. We study model-based shape control in a general setting: the object lies unfixated on a support surface and two arms may grasp and move it anywhere along its length. Because each arm chooses a grasp point, direction, and magnitude, the joint action space is combinatorially large, and the dynamics model's per-prediction cost bounds how much of it a planner can search. We present ForwardDLO, a recurrent latent dynamics model for this unfixated bimanual setting that predicts per-segment displacements grounded in the observed rope state at every step. Our model reaches accuracy comparable to more expensive baselines while containing no explicit segment-to-segment operations, which makes batched evaluation of candidate actions cheap. On open-loop prediction of real rope motion it reaches the lowest error of the learned models we evaluate, 13% below the strongest baseline. Within a fixed time budget it scores 8 to 22 times more candidate actions than models of comparable accuracy while matching them in real-world shape matching; and on a simulated routing task at a 30Hz control rate, this throughput converts into 98% task success versus at most 30% for the baselines at their own budgets. We release the model, code, and a
dataset of 2.42 million simulated and 14,107 real rope transitions at https://anonymous.4open.science/r/ForwardDLO/
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All authors Tim Missal, Berk Guler, Lucas Domingues, Simon Manschitz, Jan Peters, Paula Dornhofer Paro Costa
Artificial Intelligence
The Mirage of Calibrated Confidence: Trajectory-Independence of Verbalized Confidence in Vision-Language Models
Jisoo Yang, Jaeho Han, Trung X. Pham et al. (+1 more)
A calibrated
Vision-
Language Model (VLM) can repeatedly self-correct, say "Wait, I should recheck," arrive at the wrong answer, and still report high confidence. We find that this occurs because verbalized confidence is largely trajectory-independent in the VLMs and calibration methods we evaluate. We examine this through three complementary lenses: content variation,
token masking, and the model's own hesitation markers. We show that confidence is insufficiently sensitive to what the
reasoning trajectory actually contains, and that calibration training can paradoxically worsen this disconnect. Since existing metrics like ECE and AUROC cannot detect this problem, we propose the Trajectory-Grounding Score (TGS) in two complementary forms: TGS-self, which compares confidence with and without access to the model's own trajectory, and TGS-pair, which tests whether the model assigns higher confidence to correct trajectories than to flawed ones along the
vision,
reasoning, and answer axes. We propose TGS-Bench, a model-agnostic suite spanning 10
benchmarks with controlled good/bad trajectory pairs, and show that conventional calibration rankings diverge from trajectory-grounding rankings, exposing a blind spot in current evaluation practice.
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All authors Jisoo Yang, Jaeho Han, Trung X. Pham, Junyeong Kim
Robotics
VLM-MPPI: Grounding Natural Language in Behaviorally Diverse Trajectories for Aerial Navigation
Hanbing Zhang, Fangguo Zhao, Zerui Li et al. (+3 more)
We present a hierarchical
UAV navigation framework that aligns natural-
language intent with dynamically feasible flight behaviors in cluttered indoor environments. To bridge the gap between abstract semantics and low-level control, we employ a parallelized ensemble of six behavior-conditioned Model Predictive Path Integral (MPPI) planners. Crucially, by designing mode-specific guiding costs and sampling biases, we induce distinct trajectory modes that converge to unique behavioral means, yielding a compact set of intentionally diverse candidates rather than mere
stochastic variations. We project these 3D candidates onto the onboard first-person-view RGB stream, turning
language grounding into a visual action selection problem. A pretrained
vision–
language model (VLM) asynchronously selects the candidate index given the overlaid FPV image and a natural-
language prompt, while MPPI replans at 20Hz and a PID-based low-level controller tracks the selected trajectory. We implement the full pipeline in NVIDIA Isaac Sim and on a real-world quadrotor platform equipped with
LiDAR and RGB sensing. Experiments in both simulation and real-world flights show semantically meaningful behavior diversity, robust
language alignment despite VLM latency, and safe, repeatable flight across all modes, achieving 100% task success in our evaluated scenarios.
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All authors Hanbing Zhang, Fangguo Zhao, Zerui Li, Xin Guan, Peng Cheng, Shuo Li
Distributed Computing
From Pixels to Semantics: Edge AI for UAV-Based Critical Infrastructure Inspection
Reza Farahani, Naser Hossein Motlagh, Zoha Azimi et al. (+4 more)
Critical infrastructure assets such as bridges, tunnels, dams, and power line networks require timely and scalable inspection. While conventional manual inspection remains costly and hazardous, unmanned aerial vehicle (
UAV)-based inspection has emerged as an efficient alternative for monitoring difficult-to-access structures. Existing
UAV inspection pipelines have evolved from cloud-centric offline processing toward edge-based perception using lightweight object detectors such as YOLO for real- time defect localization. This article explores the transition toward fully edge-native semantic inspection powered by lightweight
vision language models (VLMs), where
UAVs move beyond object detection toward contextual structural understanding. It categorizes existing
UAV inspection architectures, identifies their key system challenges and architectural requirements, and experimentally assesses the feasibility of semantic edge intelligence on NVIDIA Jetson
UAV-class hardware using the COCO-Bridge
dataset. The evaluation integrates a fine-tuned YOLO-26M for object localization and a lightweight SmolVLM-256 for semantic
reasoning. Finally, it outlines future directions toward agentic, autonomous, trustworthy, and collaborative semantic
UAV inspection across the edge-cloud continuum.
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All authors Reza Farahani, Naser Hossein Motlagh, Zoha Azimi, Christian Timmerer, Lorenzo Carnevale, Sasu Tarkoma, Schahram Dustdar
Computation and Language
M-SQE: Multilingual Skill Quality Estimation for Enhancing Language Equality in Agentic Skill Use
Yilun Liu, Shimin Tao, Minggui He et al. (+9 more)
Agent skills, reusable procedural documents that extend LLM
agents beyond their parametric
memory, have become an important interface for deploying
agents on real-world tasks. Community-maintained skill libraries built around this interface are growing rapidly. However, this ecosystem remains deeply English-centric: our audit finds that low-resource
languages such as Swahili and Hindi have no in-
language skill content, so
retrieval often returns a skill written in a different
language than the query, degrading accuracy and recall. A practical solution is to synthesize in-
language skills for
retrieval but the quality can be unreliable, so relevance in this setting alone often surfaces a related but unusable candidate. To address this, we propose M-SQE, a post-
retrieval Multilingual Skill Quality Estimation framework that scores candidates via a Theory view for intrinsic quality and an Action view for task-grounded utility, unified into a domain-conditioned final score. We evaluate M-SQE across three skill-use domains: general, tool-use, and cultural tasks. Empirically, we build three-layer candidate skill pools mirroring today's ecosystem, where M-SQE's task success exceeds existing baseline's average by at least +3.5 points across three different retrievers. Particularly, M-SQE lifts the lowest-resource
languages most (+12.9pp on Hindi and +5.6pp on Swahili) and achieves strong performance across all six culture regions, thereby moving agentic skill use toward linguistic and cultural equality.
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All authors Yilun Liu, Shimin Tao, Minggui He, Chenxin Liu, Li Zhang, Chen Liu, Miao Zhang, Jiaxin Guo, Min Zhang, Liqun Deng, Xiaojun Meng, Daimeng Wei
Computer Vision
DR.WILSS: Diffusion-Based Replay for Weakly Supervised Continual Semantic Segmentation
Leon Arthur Marx, Francesco Barbato, Matteo Caligiuri et al. (+1 more)
Weakly supervised class-incremental semantic
segmentation (WILSS) aims to train a
segmentation model over multiple steps, each introducing new concepts to be learned with only image-level supervision. We introduce DR$.$WILSS, an innovative approach to address catastrophic forgetting in continual learning using
diffusion-based generative replay. Our framework leverages
language clues to guide the
diffusion process, employing self-inpainting and regularization techniques to efficiently produce replay data, aiding the learning process. By generating high-quality replay data, the information from previously learned classes can be preserved during continual updates, a critical challenge in incremental learning scenarios. To further align the statistics of replay data with those of training samples, we apply LoRAs to the generative model. Experimental results demonstrate state-of-the-art performance across multiple
benchmarks and generative architectures, while avoiding storage of training data and the use of additional resource-demanding tools during training. The proposed technique enables an optimal tradeoff between training complexity and inference-time accuracy, making DR$.$WILSS a promising solution for real-world applications.
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All authors Leon Arthur Marx, Francesco Barbato, Matteo Caligiuri, Pietro Zanuttigh
Artificial Intelligence
Risk-Aware World Modeling with Flow-Guided Occupancy Evolution for Selective Trajectory Planning in Automated Driving
Rongxiang Zeng, Linsen Cai, Jiafu Zhang et al. (+7 more)
Safe motion planning in automated driving requires anticipating evolving traffic risks and deciding when to revise the current planned trajectory. We introduce RiskWorld, a risk-aware world modeling framework for shared occupancy
forecasting and selective trajectory replacement. Spatial risk fields and temporal actor context are fused with visual bird's-eye-view features. Flow-guided
evolution transports occupancy and scene features, while signed residuals correct occupancy after transport. One forecast is generated per planning step and reused across candidates. Each candidate is compared with a current-state persistence reference, yielding a nonnegative collision-score correction. The trajectory selected by current-world evaluation serves as the planning anchor and is replaced only when additional predicted risk triggers intervention and an alternative satisfies component-wise constraints on predicted risk and trajectory error. Candidate geometries remain unchanged. We evaluate RiskWorld for open-loop planning on nuScenes using camera features, annotation-derived current and historical actor states, and
dataset-provided map context. RiskWorld achieves the lowest collision rate at a long evaluation horizon of 3 s, and the second-best average L2 error among various state-of-the-art baselines, while running at 11.5 FPS on a single NVIDIA RTX 4090 with 90.81 M parameters. Within-setting ablations show that RiskWorld achieves lower collision rates than the current-state rescoring baseline, while forecast reuse enables additional candidates to be evaluated at low marginal computational cost.
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All authors Rongxiang Zeng, Linsen Cai, Jiafu Zhang, Yijie Zhong, Yide Tao, Shuai Wang, Nan Zheng, Hai L. Vu, Alvaro Garcia Hernandez, Yongqi Dong
Artificial Intelligence · Cross-listed from econ.GN
Multitask Reinforcement Learning for Assisting Choice Model Specification
Gabriel Nova, Stephane Hess and Sander Van Cranenburgh
Discrete choice model specification is a time-consuming task in which modellers often specify and estimate multiple models while balancing goodness-of-fit, parsimony, and behavioural plausibility. We present Delphos, a multitask reinforcement learning framework that learns transferable specification strategies across transport choice
datasets. Delphos frames model specification as a sequential decision-making problem in which it applies a sequence of modelling actions and receives feedback from an estimation environment based on model performance and convergence. To transfer modelling decisions across
datasets with different sets of variables, Delphos represents utility specifications as sets of modelling terms using a DeepSet-Q architecture, allowing a shared specification policy to learn across multiple
datasets. Trained on nine transport choice
datasets, Delphos consistently outperforms independently trained single-task
agents, indicating that sharing modelling experience improves learning efficiency and helps identify promising sequences of modelling decisions with fewer unsuccessful estimation attempts. When applied without further training to the unseen Swissmetro and Decisions
datasets, the same
agent identifies competitive specifications in less than 20 minutes on a standard CPU. It achieves a higher log-likelihood per observation than the VNS metaheuristic on Swissmetro and performance comparable to a published MNL specification developed by expert modellers on Decisions. These findings show that accumulating and reusing modelling experience enables Delphos to function as an intelligent assistant for discrete choice model specification. It reduces manual trial-and-error while allowing modellers to retain control over model diagnosis, refinement, and final selection.
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Computation and Language
Planning or Improvisation? Stress-Testing the Poetry Planning Site on Open Models and Open Cross-Layer Transcoders
Éric Jacopin
Lindsey et al. (2025) report that Claude 3.5 Haiku plans rhymes: features for candidate rhyme words are active on the newline before a line is written, and a suppress-and-inject intervention redirects the line only when applied there (their Figure 13). We test how far this generalizes on seven
cells crossing four open models (0.6B to 2.6B parameters) with six open cross-layer transcoders (CLTs), on one consumer GPU, decomposing the claim into position specificity (C1), newline site identity (C2), and a newline-resident plan (C3). This is a stress test rather than a faithful reproduction: attribution graphs are unavailable for these CLTs, so features are found bottom-up from decoder vectors. C1 generalizes, in every
cell and in all 247 of 444
prompt-by-inject pairs with a detectable effect, but the effective position is the final
prompt token, adjacent to emission, and only two
cells reach behaviorally meaningful probabilities. C2 and C3 are not recovered by any probe: a census of every active feature finds no rhyme-anticipating enrichment at the newline, and steering the newline while the model composes the whole line, over 36 runs and 8,640 sampled lines, shows why. That intervention is strong but one
token long, making the injected word the first word of the composed line in 703 of 720 samples and leaving the rhyme six words later untouched. A final test drops the transcoder entirely: patching the newline's whole residual, at every layer, from a minimal-pair poem whose third line ends on a different rhyme moves the rhyme in 11 of 1,260 composed lines against 4 at baseline, with a design resolving 1.4%. We read this as a boundary condition rather than a refutation: at this scale and with these transcoders, the causal site is emission-adjacent. We reproduce Figure 13's shape, not its mechanism. Code and data are public (code: github.com/PCfVW/poetry-planning-site).
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Information Retrieval
When RAG Doesn't Help: LLMs for Plausible Material Prediction of Vehicle Components
Frederik Wagner, Sabine Schulte im Walde and Annerose Eichel
In this work, we explore whether LLMs can predict and explain plausible materials for vehicle components such as brake discs or fuel injectors without extensive fine-tuning. We compare a standard generative LLM baseline, single-pass
Retrieval-Augmented Generation (RAG), and an iterative Chain-of-Verification (CoVe) variant across two LLMs, using a domain-filtered Wikipedia corpus that proved helpful in prior work. As no gold standard exists, we develop and release a reusable web-based annotation tool supporting crucial functions for structured domain-expert judgments, such as highlighting, ratings and ranking. Expert agreement is reasonable for plausibility, lower for ratings. LLM-based generation compares favorably to prior work. RAG and CoVe do not improve on it and are partly rated worse by experts. Contrary to prior work, Wikipedia-based
retrieval does not help in our setup.
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Artificial Intelligence
WetRobo: A Reproducible Robot Kit for Coding Agents in Biological Laboratories
Yuna Oikawa, Kei Endo, Takanori Uzawa et al. (+5 more)
Automating biological research requires general-purpose, reproducible
robot systems that allow individual wet-lab researchers to delegate
robot tasks without performing teleoperation or neural-network training.
Vision-
language-action policies have been proposed for general-purpose arms, but can lose performance when their operating environment changes. We therefore built WetRobo, a
robot kit that can readily transfer between laboratories. It consists of one
robot arm, laboratory equipment (an incubator, a reagent bottle with a cap, and a Petri dish), the existing code that moves the arm, teleoperation demonstrations of each task that we recorded, and a general
AGENTS.md skill file. A biological experimentalist provides natural-
language tasks without collecting local teleoperation training data or training a neural network. The coding
agent observes the local laboratory and writes and executes programs, using external tools as needed for adaptation. We demonstrate use of WetRobo with OpenAI Codex (gpt-5.6-sol) on three successful tasks: lifting a Petri dish lid, removing a bottle cap, and opening the incubator door, all in real-world laboratories. The coding
agent achieved the cap task in both laboratories, Lab X and Lab Y, whereas a VLA fine-tuned on Lab X demonstrations succeeded there but failed to transfer to Lab Y. These results point to a practical route for laboratory robotics: instead of training a policy for each laboratory, distribute a kit and let a coding
agent adapt it in each laboratory. Code, demonstrations, and the evolved programs are available at https://github.com/tsudalab/WetRobo.
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All authors Yuna Oikawa, Kei Endo, Takanori Uzawa, Yunzhe Zhang, Manan Anjaria, Lerrel Pinto, Sherry Yang, Koji Tsuda
Robotics
Hardware-Free Robotics Laboratories in Mixed Reality
Santiago Berrezueta-Guzman, Habiba-Loai Khalil, Andrei Koshelev et al. (+2 more)
Teaching robotics relies on screen-based simulation, showing
robot motion in an abstract coordinate frame rather than at real scale in the learner's own space, while access to physical hardware is limited by cost, safety, and scheduling constraints. We present MR-Robotics LAB, a mixed-reality (MR) platform that replays MATLAB-generated
robot trajectories at real scale within the learner's physical environment. A browser-based service validates a MATLAB workspace file (.mat), normalizes units, and publishes a versioned JSON trajectory; a Unity application on a Meta Quest 3 then reproduces the authored joint configurations under position control and replays them at the declared frame rate within a physics-enabled scene that supports collision detection and end-effector grasping. A formative single-group evaluation with engineering students found that participants reported low setup effort (M = 4.67 on a 5-point scale) and perceived support for workspace understanding from multi-viewpoint inspection (M = 4.56), and 83% of participants affirmed their willingness to use the platform in an introductory robotics course. The evaluation instrument records only perceived outcomes, without counterbalancing or a learning measure, so no comparative advantage over desktop simulation is claimed. The contribution is a reusable simulation-to-MR trajectory pathway and design guidance for hardware-free
robot visualization in engineering education.
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All authors Santiago Berrezueta-Guzman, Habiba-Loai Khalil, Andrei Koshelev, Vanesa Metaj, Stefan Wagner
Computer Vision
Occluded Gait Recognition with Mixture of Experts: An Action Detection Perspective
Panjian Huang, Yunjie Peng, Saihui Hou et al. (+4 more)
Extensive occlusions in real-world scenarios pose challenges to gait recognition due to missing and noisy information, as well as body misalignment in position and scale. We argue that rich dynamic contextual information within a gait sequence inherently possesses occlusion-solving traits: 1) Adjacent frames with gait continuity allow holistic body regions to infer occluded body regions; 2) Gait cycles allow information integration between holistic actions and occluded actions. Therefore, we introduce an action detection perspective where a gait sequence is regarded as a composition of actions. To detect accurate actions under complex occlusion scenarios, we propose an Action Detection Based Mixture of Experts (GaitMoE), consisting of Mixture of Temporal Experts (MTE) and Mixture of Action Experts (MAE). MTE adaptively constructs action anchors by temporal experts and MAE adaptively constructs action proposals from action anchors by action experts. Especially, action detection as a proxy task with gait recognition is an end-to-end joint training only with ID labels. In addition, due to the lack of a unified occluded
benchmark, we construct a pioneering Occluded Gait database (OccGait), containing rich occlusion scenarios and annotations of occlusion types. Extensive experiments on OccGait, OccCASIA-B,Gait3D and GREW demonstrate the superior performance of GaitMoE.OccGait is available at https://github.com/BNU-IVC/OccGait.
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All authors Panjian Huang, Yunjie Peng, Saihui Hou, Chunshui Cao, Xu Liu, Zhiqiang He, Yongzhen Huang