Graphics
Orientation in Extended Position-Based Dynamics: Application to Rigid Bodies and Cosserat Rods
Samuel Tobin and Caleb Rucker
Rotational degrees of freedom in Extended Position-Based Dynamics (XPBD) require computations on the nonlinear
manifold of 3D rotations. We show that Lie theory provides a clean, unified framework for expressing rotations, constraints, interpolation, and differentiation in XPBD, enabling both improved rigid-body constraints and higher-order finite-element Cosserat rods. We derive explicit Lie-theoretic constraint formulations and their gradients for rigid-body simulation, improving the dynamic consistency of constrained rigid-body simulations in XPBD by a factor of over $10^4$ compared to the state-of-the-art. Our framework naturally extends to finite-element Cosserat rods by enabling on-
manifold interpolation of nodal rotations. Linear finite elements outperform the conventional chain-of-rigid-bodies discretization, while higher-order basis functions provide even smoother solutions and faster convergence. Utility is demonstrated in a variety of examples with large deformations and contact.
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Machine Learning
Leveraging Remote Traffic Data for Local Air Pollutant Estimation: A Scenario-Based Machine Learning Study Across London Monitoring Sites
Valeria Legaria-Santiago, Amadeo Arguelles, Magdalena Saldana-Perez et al. (+2 more)
Vehicular traffic is a major source of air pollution; however, the contribution of remotely acquired traffic information to local machine-learning (ML) air-pollution models remains insufficiently characterised. This study evaluates four interpretable tree-based ML models (Random Forest, Extra Trees, LightGBM, and XGBoost) under six predictor scenarios combining progressively larger predictor sets, ranging from remotely acquired traffic, meteorological, and temporal variables alone to the inclusion of measurements from one and four neighbouring monitoring stations, to estimate NO$_2$, PM$_{10}$, PM$_{2.5}$, and O$_3$ concentrations across several sites in London. ML model performance was compared with a ridge linear regression model as a baseline, with spatial interpolation methods and with a cross-site validation experiment. When modelling without data from neighbouring stations, the RMSE for NO$_2$ ranged from 9.73 to 11.66 $\mu$g/m$^3$ without traffic information, compared with 8.72 to 11.52 $\mu$g/m$^3$ when traffic information was included. Additionally, for NO$_2$, SHAP analyses indicate that traffic-related variables can contribute at levels comparable to pollutant measurements from neighbouring monitoring stations in traffic-dominated~environments.
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All authors Valeria Legaria-Santiago, Amadeo Arguelles, Magdalena Saldana-Perez, Jocelyn Richardson, Marcella Bona
Artificial Intelligence
Context as an Environment: Programmatic Context Management for Long-Horizon Agents
Yin Lin, Elaine Ang, Erkang Zhu et al. (+2 more)
LLM
agents increasingly take on long-running tasks whose history grows far beyond a single model context window. Existing approaches compress earlier interactions or extract selected information into fixed
memory representations, committing to what to preserve before future needs are known. We present Scroll, a context manager that treats each
agent session as an executable Session Environment. The environment is backed by an append-only Event Log and a sandboxed, persistent Python kernel. The kernel maintains a typed namespace across model calls, allowing tool outputs, retrieved history, and derived state to be bound to variables rather than serialized into the
prompt at each call. Model-written code searches, materializes, and transforms session state through exec; only explicitly printed projections enter the model's working view for the next call. Context management thus becomes a programming task that inherits the improving coding abilities of LLMs, while the Event Log preserves lossless historical ground truth. As the working view approaches its budget, stale spans are evicted but remain recoverable: an eviction index keeps compact landmarks tied to exact Event Log addresses, so that the
agent navigates directly to evicted regions instead of searching the full log. With Qwen3.8-Max as the backbone, Scroll achieves 94.8% on LongMemEval_S; 73.1% on BEAM_10M, surpassing the best published
memory system by 5.1 points; and 86.7% on LOCA_256K, exceeding the best published long-horizon
agent by 37.4 points.
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All authors Yin Lin, Elaine Ang, Erkang Zhu, Bolin Ding, Jingren Zhou
Human-Computer Interaction
UrbanGazeVis: A Visualization System for Analyzing Eye-Tracking Data on Urban Safety Perception
Andres De La Puente, Luis Sante, Felipe Moreno-Vera et al. (+2 more)
Perceived safety in streetscapes depends on where people look, yet how gaze relates to visual cues of urban disorder remains poorly understood. Prior work treats safety as an image-level label, offering little insight into how
attention to specific elements (e.g, buildings, greenery, people, signs of decay) shapes these judgments. We present a head-mounted eye-tracking study in which 30 participants viewed and rated the safety of 150 street-view images from Rio de Janeiro using a HoloLens 2 headset. Gaze traces were mapped onto semantic segments and disorder cues (e.g., damaged walls, graffiti, overhead cables), yielding a
multimodal dataset linking gaze dynamics, scene semantics, and safety scores. To analyze it, we introduce UrbanGazeVis, an interactive visual analytics system with image- and participant-centric views that connects the spatial, temporal, and semantic dimensions of gaze to perceived safety, supporting comparisons between safe and unsafe scenes, inspection of divergent ratings for similar images, and region-of-interest analysis via glyph-based summaries. Statistical models show that sustained
attention to physical disorder is associated with lower perceived safety, while the visual analysis reveals context-specific effects often masked by global aggregation. Together, these analyses offer actionable insights for urban design and planning.
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All authors Andres De La Puente, Luis Sante, Felipe Moreno-Vera, Mauro Diaz, Jorge Poco
Robotics
In-Situ Reconstruction of the International Space Station Using 3D Gaussian Splatting and Astrobee
Hudson Kim, Ryan Soussan, Brian Coltin et al. (+1 more)
This article presents a novel 3D reconstruction and mapping of the interior of the International Space Station (ISS) using 3D Gaussian Splatting (3DGS). Using existing grayscale images from the Astrobee free-flying
robot dataset, we construct a full 3D splat of the ISS' Kibō or Japanese Experiment Module (JEM). 3DGS has in recent years shown promise in providing novel view synthesis of scenes captured from many images or
videos, this article applies this approach to human spaceflight systems. We compare our 3DGS architecture to existing methods such as Nerfacto and TensoRF and show that reconstruction improves the state-of-the-art in both scene quality and rendering speed. We show that with as little as 500 in-situ images, a high-fidelity map can be constructed using Astrobee's
Navigation Camera (NavCam) during free-flight in the JEM. These reconstructions could enable free-flyers to rapidly create and update interior maps for intra-vehicular habitats like the ISS.
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All authors Hudson Kim, Ryan Soussan, Brian Coltin, Jordan Kam
Sound
MusPyExpress: Extending MusPy with Enhanced Expression Text Support
Phillip Long, Hao-Wen Dong, Julian McAuley et al. (+1 more)
Current work in modeling symbolic music primarily relies on representations extracted from MIDI-like data. While such formats allow for modeling symbolic music as sequences of notes, they omit the large space of symbolic annotations common in western sheet music broadly known as expression text, such as tempo or dynamics, which specify time- and velocity-dependent controls on the musical composition and performance. To alleviate this gap, we present MusPyExpress, an extension to the popular symbolic music processing library MusPy that enables the extraction of expression text along with symbolic music for downstream modeling. Utilizing this extension, we parse the PDMX
dataset to illustrate the wealth of expression text available in MusicXML
datasets. Additionally, we introduce multiple generative tasks, including joint expression-note generation, expression-conditioned music generation, and expression tagging, that take advantage of this additional notational information.
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All authors Phillip Long, Hao-Wen Dong, Julian McAuley, Zachary Novack
Machine Learning
Read, Write, Relax: Why Neural PDE Surrogates Need Both Global and Local Processing
Anuj Kumar, Heiko Zimmermann, Josiah Bjorgaard et al. (+4 more)
Recent mesh-based simulation advances have, in no small part, relied on neural surrogates of two distinct families: global models that route information through a small set of latent
tokens, and local models that perform message passing across mesh edges. Consistent with both classes is the inability to perform beyond low-dimensional problems and small-scale or oversimplified meshes, the simulation regimes where industrial problems reside. Our work shows this explicitly and presents a unified formulation. In global approaches, latent-
token attention acts as a spatial low-pass filter, while local message passing lacks the global reach necessary to propagate information across large mesh spaces. Viewed through the error, the two operators are the halves of a multigrid cycle: one corrects errors at the lower end of the spectrum, the other at the higher end, and neither can do the other's job. We introduce Read-Write-Relax (RWR), which interleaves latent
attention with message-passing relaxation under a unified formulation. The interleaved processor lowers error across the entire spectrum, making RWR the most accurate model in nearly every comparison across our industrial and public
benchmarks. It is also markedly data-efficient in the scarce-data regimes, accurate on the engineering quantities of interest, and scales full-field predictions to challenging, large-scale problems.
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All authors Anuj Kumar, Heiko Zimmermann, Josiah Bjorgaard, Jacan Chaplais, Nikolaos Bouklas, Matteo Salvador, Alexander Lavin
Robotics
Lifelong Robot Recomposition via Persistent Categorical Modeling for Unified Task-Driven Co-Design, Verification, and Planning
Steven Swanbeck and Mitch Pryor
Robotic systems are traditionally designed and deployed in static configurations, with assumptions made at design-time becoming immutable constraints during runtime. This design-then-deploy paradigm produces performant systems under narrow operating conditions, but renders
robots brittle when qualities of themselves, their tasks, or their environments unexpectedly change. We address this challenge with a compositional framework that formalizes robotic systems as abstract
circuits within a strict symmetric monoidal category, in which design and runtime composition of hardware, software, and behavior are synthesized simultaneously via an SMT-based solver, with monoidal functors projecting the system into lifecycle-specific views and free symbolic variables simultaneously solving for parameters and entire component specifications within larger compositions. This persistent model also supports queries a long-lived system needs beyond plan existence across its entire lifecycle, including mapping Pareto fronts over candidate compositions, diagnosing why a composition has become infeasible, finding its minimal restoration, and reconfiguring with limited change to the deployed system. We evaluate against official implementations of optimal numeric, stream-based, and SMT-based planners all measured onboard a deployed
robot and demonstrate the approach end-to-end in a search-and-rescue scenario in which the
robot recognizes when it has become unfit and synthesizes and assumes new holistic configurations to restore operation. We release our solver and supporting software open-source.
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Machine Learning
SynEHR: Joint Modeling Inter-visit Temporal Evolution and Intra-visit Clinical Structure for Longitudinal EHR Synthesis
Ximiao Li, Lin Jiang, Rongchao Xu et al. (+3 more)
Longitudinal electronic health records (EHRs) document patients' sequences of clinical visits over time, preserving the temporal
evolution of disease progression and care delivery. However, real longitudinal EHRs are difficult to access because they contain large amounts of fine-grained, patient-specific information. Synthetic EHR generation therefore provides a valuable approach for preserving the statistical patterns and clinical structure of patient visit trajectories, enabling broader modeling and analysis when real records are limited. Although recent generative models have made progress in producing future visit sequences, they remain limited in explicitly integrating inter-visit irregular temporal
evolution and intra-visit clinical event structures in EHRs, leading to clinically inconsistent and temporally unrealistic visit sequences. In this work, we propose SynEHR, a lightweight adaptive LLM-based framework for longitudinal EHR synthesis. There are two novel designs in SynEHR, i.e., a Temporal State Conditioning Module captures irregular temporal states across visits and a Temporal-Relational Adaptation Module combines these states with patient history to dynamically construct patient-specific relational representations. SynEHR then builds on a parameter-efficient LoRA-adapted
language-model generator with next-visit generation capability to train the two modules for temporally and clinically informed generation. Extensive experiments on real-world EHR
datasets across fidelity,
privacy, and downstream utility evaluations demonstrate that SynEHR outperforms state-of-the-art models by generating more clinically coherent and temporally faithful longitudinal EHR data.
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All authors Ximiao Li, Lin Jiang, Rongchao Xu, Dahai Yu, Zhe He, Guang Wang
Human-Computer Interaction
Who Bears the Cost of Honesty? A FAccT Workshop Synthesis and Research Agenda for Equitable AI Disclosure
Runlong Ye, Jessica He, Finola Finn et al. (+4 more)
AI disclosure is increasingly promoted and sometimes required as a route to transparency, accountability, provenance, and trust. Yet disclosure can also expose AI users to suspicion, stigma (e.g., competence penalties), and surveillance, affecting minoritized groups in particular. This paper reports on Who Bears the Cost of Honesty?, a CRAFT workshop at the 2026 ACM Conference on
Fairness, Accountability, and Transparency that used scenario-anchored power mapping and design fiction to explore the benefits, harms, tensions, and power asymmetries that emerge under AI disclosure norms and mandates. We document the workshop design and analyze the disclosure approaches participants co-created, comprising four completed power maps, three context cards, and one interface prototype. These artifacts span education, workplace, politics/journalism, and interpersonal contexts. They depict disclosure as a multi-actor accountability process, surface concerns that the use of accessibility-related AI could be held against workers in performance evaluations, and explore how context-specific, bottom-up disclosures may support transparency while mitigating some risks of stigma and misinterpretation. We contribute (1) a documented two-stage workshop method; (2) an artifact-grounded thematic synthesis; and (3) a diagnostic framework, the Cost-of-Honesty Stack, with provisional design suggestions and research directions.
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All authors Runlong Ye, Jessica He, Finola Finn, Angel Hsing-Chi Hwang, Donal Khosrowi, Seyun Kim, Morgan Klaus Scheuerman
Artificial Intelligence
From Mastery Profile to Simulated Response: Stochastic Student Knowledge Graphs (SSKG) for Faithful LLM Student Simulation
Yuan An, Emily Wang, Benjamin Wang et al. (+1 more)
Large
language models (LLMs) are increasingly used to simulate students at different mastery levels to generate synthetic training data and stress-test tutoring systems. Existing approaches primarily leverage an LLM to answer questions or perform tests while instructing it to simulate various skill levels. However, LLMs tend to execute at their full built-in capability regardless of target skill levels. We demonstrate this limitation on SAT questions across five mastery levels, where three LLMs consistently achieve 96.8-100% accuracy. To faithfully simulate student behavior, we propose a
Stochastic Student Knowledge Graph (SSKG)-based approach that leverages the strengths of LLM in different ways. Using human-in-the-loop workflows, LLMs extract a Curriculum Knowledge Graph (CKG) from course materials, decompose solution paths into CKG triples, and map incorrect distractors. A student's mastery profile is represented as an SSKG, assigning mastery probabilities to CKG triples. When solving a problem, the SSKG is sampled to determine the outcome, after which an LLM generates a rationale consistent with the sampled result. Compared to seven baselines which fail to separate profiles and maintain 86.5-100% accuracy, our SSKG-based approach achieves 44.1-85.2% accuracy from low to high-mastery profiles, creating a clear monotonic gradient. An ablation study demonstrates that taxonomy awareness contributes most significantly to these fidelity gains, particularly for lower-mastery profiles.
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All authors Yuan An, Emily Wang, Benjamin Wang, Ruhma Hashmi
Cryptography and Security · Cross-listed from quant-ph
Tasdiqul Islam, Rasman Mubtasim Swargo, Engin Arslan et al. (+1 more)
Conventional
Quantum Key Distribution (QKD) requires the transmission of
qubits proportional to or exceeding the length of the key, as protocols such as BB84 transmit more
qubits than the final key size due to basis sifting and
privacy amplification. Since
quantum networks are still in their infancy and have limited capacity, this overhead puts significant pressure on network resources. To address this issue, we propose a Multi-
Qubit Greenberger–Horne–Zeilinger (GHZ) State-based QKD scheme that reduces the number of
qubits transmitted over the
quantum channel. The proposed method transmits one GHZ
qubit between endpoints and reuses the resulting
entanglement to convey multiple classical key bits with the help of
Quantum Non-Demolition (QND) measurements. Under the stated assumptions on authenticated classical communication, local reset verification, and bounded-error QND discrimination, one can transfer $L$ classical bits by generating an (L+1)-
qubit GHZ state and transferring one
qubit to the remote party. We verify correctness using the NetSquid
quantum network simulator: the protocol achieves 100% raw-key fidelity for keys of length up to 12 bits under both ideal conditions and depolarizing noise up to p = 0.005 per round. We further show that the proposed QKD
algorithm can be extended to multi-party QKD and server-client deployment. The proposed scheme offers a transmitted-
qubit-efficient, noise-tolerant alternative for bandwidth-limited
quantum networks.
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All authors Tasdiqul Islam, Rasman Mubtasim Swargo, Engin Arslan, Md Arifuzzaman
Artificial Intelligence
Measuring Activation Control in Large Language Models
Marek Mateusz Kowalski, Joshua Fonseca Rivera, Uzay Macar et al. (+1 more)
Safe deployment of increasingly capable models will likely come to rely on latent-space monitoring as a complement to behavioral evaluations, especially when evaluation-aware models exhibit scheming or deception. However, if models can also control their own activations, deception could extend into the latent space itself. With this in mind, we introduce the Activation Controllability
Benchmark to quantify the extent to which models can modulate their residual stream via natural-
language instruction. Across model families and capability levels, we find that most LLMs can control the direction and magnitude of their residual stream activations with some degree of temporal resolution, though performance varies considerably across models. In simple tasks, this level of control can evade activation-based monitoring methods (including linear probes, natural
language autoencoders, activation oracles, and the Jacobian lens), albeit imperfectly. These results suggest that control over the activation space itself could become a confound for monitoring as introspective capabilities increase; therefore, we recommend that frontier labs and evaluators track activation controllability in future models.
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All authors Marek Mateusz Kowalski, Joshua Fonseca Rivera, Uzay Macar, David Demitri Africa
Information Retrieval
Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals
Meysam Varasteh, Veronika Bogina, Noam Koenigstein et al. (+1 more)
The topic of explanation in recommender systems has seen steady research
attention since the earliest days of the field. With some exceptions, this work has focused on the explanation of single items in a recommendation list and, especially recently, has emphasized approaches that are decoupled from the logic of the recommendation
algorithm itself. Based on findings in the psychology of interpersonal communication, we propose a new task, pairwise interpretation of item rankings, asking the comparative question “Why is item A ranked higher than item B?”. An effective solution to this task, we argue, is inherently grounded in the operation of the recommendation
algorithm. We propose a class of techniques based on counterfactual learning to uncover the items in a user's profile that have contributed to the relative ranking of items. Using multiple
datasets, we show that it is possible to identify such items as potential basis for comparative explanation.
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All authors Meysam Varasteh, Veronika Bogina, Noam Koenigstein, Robin Burke
Machine Learning
Variational Structure at the Edge of Stability
Eric Regis
When discrete-time optimizers operate at the edge of stability, they exhibit near-two-periodic behavior. These oscillatory dynamics are reminiscent of conservative systems, such as the dynamics generated by
symplectic integrators. However, a precise formulation of the connection between discrete-time optimizers at the edge of stability and discrete mechanics remains underexplored. Recently, Litman introduced the "edge coupling": a functional on consecutive gradient descent iterates whose critical points encode the fixed points and two-point orbits of the gradient descent dynamics. Here we extend the edge coupling to heavy-ball and Nesterov momentum. We show that its critical points characterize the fixed points and two-point orbits, with its Hessian characterizing their stability. We also show that the edge coupling can be identified with the symmetric Verlet action, formalizing the connection between the edge of stability and discrete mechanics.
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Computer Vision
SketchFlow: Zero-Shot Vector Sketch Generation via GMM Prior Flow in CLIP Latent Space
Jin Zhou, Hongliang Yang, Pengfei Xu et al. (+1 more)
Vector sketches remain one of the most concise and immediate mediums for abstract human expression. However, generating high-quality vector strokes that exhibit human-like drawing styles remains an open challenge due to the severe scarcity of fine-grained, high-quality text-to-sketch paired data. Existing text-conditioned generation methods often rely on unstable, time-consuming optimization or struggle to generalize to unseen categories in a zero-shot manner. To address these limitations, we present SketchFlow, a novel generative framework rooted in Optimal Transport (OT) theory and flow matching. By leveraging pre-trained CLIP models to bypass labor-intensive image-level text annotations, we formulate cross-modal
alignment as a continuous mapping problem directly within the CLIP latent space. To bridge the inevitable modality gap between discrete text concepts and continuous sketch features, we first inject noise into discrete category
embeddings to construct a continuous Gaussian Mixture Model (GMM) prior. We then utilize an Optimal Transport Conditional Flow Matching (OT-CFM) model to learn a deterministic vector field mapping from this continuous GMM prior to the target sketch feature distribution. Finally, a Hybrid
Diffusion Decoder, fusing 1D U-Net and
Transformer architectures, is designed to decode these features into fast and high-fidelity stroke trajectories. Extensive experiments demonstrate that SketchFlow substantially outperforms existing baselines in visual quality and adherence to natural human drawing styles. Furthermore, our geometry-preserving framework demonstrates promising local zero-shot synthesis for
prompts beyond the QuickDraw training vocabulary, including unseen concept labels and semantic modifiers, while enabling smooth, continuous semantic interpolation between distinct concepts. Source code is available at: https://github.com/doudin404/SketchFlow.
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All authors Jin Zhou, Hongliang Yang, Pengfei Xu, Hui Huang
Hardware Architecture
Model Compression and Hardware-Aware Acceleration for Deep Learning on FPGAs: A Co-Design Taxonomy and Comparative Analysis
Peter Forcha, H. Kajekusumadhar, Mbua Peter et al. (+3 more)
Deploying deep neural networks on Field-Programmable Gate Arrays (FPGAs) requires joint
reasoning about model compression and hardware acceleration, however the most comprehensive existing cross-platform treatment of this space, Deng et al.~\cite{deng2020model}, compared compression techniques against CPU, GPU, FPGA, and ASIC targets at the level of broad, qualitative trade-offs, and not specific FPGA resource consequences. This survey instead restricted the scope to FPGAs alone and organized 25 compression-hardware co-design case studies (2015–2026) into a five-category taxonomy defined by which FPGA resources each strategy primarily reshapes: DSP-eliminating, DSP-repurposing/mixed-precision, sparsity-exploiting,
memory-hierarchy-driven, and toolchain/deployment-level. Normalizing these case studies along a common set of dimensions (compression ratio, accuracy change, throughput, energy efficiency, and DSP/LUT/BRAM utilization) surfaces a central, quantitative finding; of the 25 reviewed works, only one reported a compression ratio and accuracy change measured against a single common baseline, and only two reported energy efficiency normalized against a common GPU baseline, exposing a field-wide characterization gap that no individual toolchain (FINN, HLS4ML, Vitis AI, or DNNWeaver) resolves on its own. Building on this taxonomy and meta-analysis, we formalize six open challenges: toolchain fragmentation, accuracy–efficiency characterization, automated mixed-precision optimization, sparse computation reliability, persistent
memory bottlenecks, and FPGA-based training. Each is paired with a concrete next step grounded in extending an existing, cited technique, not a general call for future work.
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All authors Peter Forcha, H. Kajekusumadhar, Mbua Peter, Muhammed Kawser, Audrey Cyriell Mo, Christophe Bobda
Computation and Language
Mitigating Database Leakage in RAG Systems with Keyword-Grounded Fact Substitution
Ziliang Zhang, Yubo Zhu, Wei Tong et al. (+4 more)
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for combining large
language models (LLMs) with external knowledge sources. However, RAG systems remain vulnerable to
prompt injection attacks, which may mislead the retriever or generator to expose sensitive database contents. To address this issue, we propose KFS-RAG, a defense that mitigates information leakage by reformulating the retrieved context. Specifically, our method first identifies a small set of influential keywords from the retrieved context via an
attention rollout plus a causal perturbation mechanism. These keywords are then used to guide an auxiliary LLM to generate a compact set of keyword-grounded facts from the retrieved passages. Finally, the original context is substituted with these curated facts, ensuring that the generator operates on sanitized evidence rather than the raw retrieved text. Experimental evaluations demonstrate that KFS-RAG significantly reduces the risk of database leakage under injection attacks while maintaining response accuracy and relevance. This work highlights a practical pathway toward building secure and trustworthy RAG systems.
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All authors Ziliang Zhang, Yubo Zhu, Wei Tong, Jingyu Hua, Zijian Wang, Yuan Zhang, Sheng Zhong
Machine Learning
Bounded Precision-Geometry Scaling for Robust Multi-Task Learning under Loss Scale Mismatch
Krishna Subedi
Multi-task learning often combines losses that span several orders of magnitude, causing homoscedastic uncertainty weighting to degrade severely. We propose Bounded Precision-Geometry Scaling (BPGS), a method that maps each task's log-variance through a bounded sigmoid parameterisation anchored to detached batch loss statistics, and decouples network optimisation from uncertainty optimisation. Its normalised task weights are provably invariant to uniform rescaling under non-degenerate loss scales. We evaluate BPGS on synthetic stress tests and three real-world
benchmarks: NYUv2 dense prediction, Yeast multi-label classification, and RF1 multi-target regression. Under pure loss rescaling from $\times 1$ to $\times 1000$, its macro score changes from 0.777 to 0.778, whereas Kendall weighting drops from 0.780 to 0.637; $\ell_1$-normalising Kendall's weights does not close the gap. On NYUv2, BPGS records the lowest depth absolute relative error (0.223), depth RMSE (0.790), and total loss (1.891) among all compared methods, including Nash-MTL. Sensitivity studies on batch size and calibration show small variation across the tested ranges, and runtime overhead relative to Kendall is under 1%. BPGS posts the highest Yeast micro-F1 (0.616) and is competitive on RF1, though PCGrad leads RMSE and MAE there. These findings establish BPGS as a scale-robust alternative to homoscedastic uncertainty weighting, notably effective when loss-scale disparities dominate multi-task optimisation.
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Machine Learning
GeoQ: Geometry-Aware Conditional Quantile Error Estimation for Scientific Surrogate Models
Khoa Nguyen, Daniel Serino, Aviral Prakash et al. (+1 more)
Neural-network surrogate models are increasingly used to accelerate scientific simulations, but their deployment in extrapolative and autoregressive settings requires input-dependent estimates of prediction error. In this work, we introduce GeoQ (Geometry-Aware Conditional
Quantile Error Estimation), a non-intrusive calibration framework for estimating surrogate error at individual query points. GeoQ represents the error at a query point as an anchor-averaged calibration error plus a learned nonnegative correction. This correction is modeled as an upper conditional
quantile of the anchor-relative error increment, using geometry-based features that encode representation-space displacement and local support density. A cross-fitting procedure generates approximately out-of-sample calibration tuples, while a feature-space k-nearest-neighbor support score identifies regions \textcolor{black}{where the learned error model is supported by calibration data}. We evaluate GeoQ on scalar regression, chaotic dynamics, medium-range weather
forecasting, and Richtmyer-Meshkov instability prediction. The results demonstrate that geometry-aware conditional
quantile modeling provides a practical and non-intrusive approach for validity-aware error estimation in scientific surrogate models.
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All authors Khoa Nguyen, Daniel Serino, Aviral Prakash, Marc Klasky
Machine Learning
Power-Performance Characterization of TinyML Systems
Yujie Zhang, Dhananjaya Wijerathne, Zhaoying Li et al. (+1 more)
TinyML systems are enabling machine learning (ML) inference at the edge. However, there is little quantitative analysis of such systems. This paper presents a systematic performance and power characterization of diverse TinyML applications on microcontrollers (MCUs), spanning neural network models, software libraries, operating systems, and hardware architectures. We focus on the impact of the multiple layers of abstraction that provide higher programmability at the expense of performance and energy efficiency. We propose a model to estimate the costs of different abstraction layers and make recommendations for minimizing those costs. Our findings can help designers with Neural Architecture Search (NAS) and CNN inference optimization on edge devices.
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All authors Yujie Zhang, Dhananjaya Wijerathne, Zhaoying Li, Tulika Mitra
Machine Learning · Cross-listed from math.RT
Piecewise Linear Equivariant Maps for Compact Groups
Valeriano Aiello
Motivated by equivariant neural networks, we study piecewise linear equivariant maps between finite-dimensional real representations of compact groups. We show that all genuinely non-linear piecewise linear behaviour is confined to the subspaces on which the identity component of the group acts trivially, while equivariance forces linearity on the corresponding orthogonal complements. As a consequence, we obtain a compact-group analogue of the finite-group existence criterion of Gibson–Tubbenhauer–Williamson for non-zero equivariant piecewise linear maps between irreducible representations, with the identity component giving rise to a
rigidity phenomenon absent from the finite-group case.
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Cryptography and Security
Cross-Layer Roots of Trust: Integrating Biometrics, PUFs, and Hardware Obfuscation
Nima Karimian
Modern cyber–physical, Internet-of-Things (IoT), wearable, and edge systems increasingly require trust in three distinct entities: the human requesting access, the physical device executing the computation, and the hardware function that is permitted to operate. These requirements are usually studied in separate communities. Biometrics establish human identity but remain vulnerable to presentation attacks, intra-user variability, template leakage, and limited revocability. Physical unclonable functions (PUFs) provide device-specific physical identity and on-demand secret derivation, yet must address environmental instability, helper-data exposure, side channels, and modeling attacks. Hardware obfuscation and logic locking condition correct
circuit behavior on an activation secret, but face oracle-guided, approximate, structural, removal, and physical attacks. This survey develops a unified human–device–function view of trust. We first decompose each primitive into its complete processing chain and identify the corresponding security assumptions, implementation mechanisms, and evaluation metrics. We then formalize pairwise compositions—biometric–PUF, PUF–obfuscation, and biometric–obfuscation—and a three-way architecture in which correct functionality is bound jointly to an authorized user and a genuine device. Particular
attention is given to biometric key reconstruction, PUF stabilization and modeling resistance, logic-locking attack evaluation, cross-layer error propagation, enrollment trust, key lifecycle, and interface leakage. The survey concludes with a taxonomy and research agenda for revocable human–device credentials, compositional security, leakage-aware integration, reconfigurable activation, and standardized end-to-end evaluation.
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Information Retrieval
Why didn't more people see it? Recommendation: Transparency for providers
Meysam Varasteh and Robin Burke
Transparency in recommender systems has been widely studied from the perspective of those receiving recommendations, yet the needs of item providers, the creators whose content is distributed through these platforms, remain largely unexplored. Providers often lack insight into how their items do or do not receive exposure in users' recommendation lists. In this work, we address this gap by proposing a surrogate modeling approach to explain item exposure at a system level. Rather than explaining individual user-item pairs, we train a proxy model to approximate the exposure distribution produced by a recommender. By quantifying the contribution of each feature, we seek to explain the factors driving the recommendation model's decisions across the entire user base. We evaluate our approach on two
datasets and three recommendation models. Results show that the surrogate model captures the global behavior of all three recommenders with high fidelity and that the most influential factors vary meaningfully across models and domains.
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Machine Learning
Large-Scale Evaluation of Advanced Imputation Methods for Missing Values in Smart Meter Data
Daniela Stojcheska, Marija Markovska, Dimitar Taskovski et al. (+2 more)
Accurate and reliable collection of electricity consumption data through Advanced Metering Infrastructure (AMI) is of great importance for the operation of smart grids, especially for the detection of non-technical losses (NTL). However, real-world
datasets frequently suffer from missing values due to communication failures. This paper presents an empirical evaluation of three advanced
algorithms for large-scale data imputation: the Optimally Weighted Average (OWA) method, Low-Rank
Matrix Completion via SoftImpute, and a Shape-Modeling Autoencoder. Existing studies on missing value imputation in electricity consumption data often lack validation on larger
datasets. Therefore, the goal of this paper is to validate the selected
algorithms on a large-scale real-world electricity consumption
dataset from North Macedonia that includes 17,428 commercial smart meters over two years. The robustness of each
algorithm is evaluated by simulating continuous gaps in the data ranging from 1 to 168 hours. The results indicate that OWA provides the lowest overall reconstruction error across the evaluated gap sizes and strong stability in worst-case scenarios for gaps of up to one week. In contrast, the autoencoder exhibits higher variance, while SoftImpute has stable but inferior accuracy. These findings suggest that imputation methods should be selected based on the characteristics of load curve data and highlight the potential for hybrid algorithmic architectures in future grid management systems.
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All authors Daniela Stojcheska, Marija Markovska, Dimitar Taskovski, Branislav Gerazov, Boris Nikolov
Computer Vision
Semantic Slots for Video Object-Centric Learning
Khalil Sabri, Guillaume-Alexandre Bilodeau, Nicolas Saunier et al. (+1 more)
Video Object-Centric Learning (OCL) has traditionally focused on refining the encoder architecture to ensure temporal consistency. In this paper, we argue that the primary bottleneck lies in the decoder. We show that traditional decoders force slots to be spatially anchored, hindering their ability to adapt to motion. We propose SemanticSlots, which uses a
Transformer-based decoder that leverages image context, relieving slots from encoding boundary precision and spatial location. This allows slots to function as semantic queries that are inherently object position invariant, retrieving matching features rather than memorizing coordinates. More importantly, this property allows slots computed from a single frame to decompose subsequent
video frames, eliminating the need for complex temporal predictors or auxiliary temporal losses. Results on YouTube-VIS show that SemanticSlots improves upon VideoSAUR by 31 points in mBO and outperforms current state-of-the-art methods by 21 points, achieving 86.6% ARI and 62.8% mBO.
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All authors Khalil Sabri, Guillaume-Alexandre Bilodeau, Nicolas Saunier, Wassim Bouachir
Robotics
OpenSCvx: An Open-Source Modular and Extensible Nonlinear Trajectory Planning Package
Christopher R. Hayner, Griffin J. Norris, Fabio Spada et al. (+4 more)
Trajectory optimization computes dynamically feasible motions that enable autonomous systems to accomplish complex tasks while satisfying operational and environmental constraints. This tutorial presents OpenSCvx, an open-source Python framework that bridges the gap between high-level problem specification and efficient numerical optimization. Rather than requiring users to derive solver-specific mathematical formulations, OpenSCvx provides a symbolic modeling interface that automatically constructs and solves trajectory optimization problems from modular descriptions of objectives, dynamics, and constraints. Beyond simplifying problem formulation, OpenSCvx supports (i) continuous-time constraint modeling, (ii) temporal and logical specifications, (iii) automatic vectorization for scalable and batched optimization, and (iv) a modular architecture that enables new
algorithms, models, and solver backends to be incorporated with minimal effort. These capabilities allow researchers and practitioners to rapidly prototype, solve, and extend state-of-the-art trajectory optimization methods.
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All authors Christopher R. Hayner, Griffin J. Norris, Fabio Spada, Samet Uzun, Avi Mittal, Behcet Ac{\i}kmese, Karen Leung
Machine Learning
ChequeMark: An Ensemble Machine Learning Framework for After-Hours Business Deposit Fraud Detection
Ann Youduo Xu, Emily Yu, Justin Leski et al. (+1 more)
Cheque fraud is a material risk in after-hours business deposit operations because funds may be released within one business day, while cheque clearing takes several days. This timing gap creates a fraud exposure window for financial institutions. Prior mitigation relies on static, deposit-level checks and therefore miss historical client behavior and evolving patterns. To address this gap, we propose a multi-view ensemble ML framework that combines: Extreme Gradient Boosting (XGBoost) for known fraud patterns, Isolation Forest for label-free anomaly detection, and Graph Sample and Aggregate (GraphSAGE) for relational patterns associated with transaction activities. We then combine the three outputs into a single client-level risk score. Under stable conditions, performance is comparable to XGBoost; under a targeted distribution shift, our framework performs best (F1: 83.77%, FPR: 0.69%) versus XGBoost (F1: 82.77%, FPR: 0.72%). These results indicate improved robustness to distribution shift while preserving interpretability through plain-
language explanations grounded in behavioural, anomaly, and relational evidence.
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All authors Ann Youduo Xu, Emily Yu, Justin Leski, William Lam
Software Engineering
ExploreAI: Agentic Exploration Knowledge Bases for Reproducible Observable-Regression Testing of Black-Box VR and 3D Applications
Jiajie Wang, Kebin Peng, Wei Wang et al. (+3 more)
Black-box VR and 3D applications are difficult to regression test because observable failures depend on where a tester moves, what objects are visible, and which views are captured. Manual exploratory testing can find such failures, but its evidence is time-consuming to reproduce; systematic sweeps are reproducible, but they lack semantic guidance and spend exploration budget on low-value viewpoints. We observe that an LLM can make the high-level decisions a human tester makes during exploration: interpreting a task, choosing which objects to inspect, grouping related objects, recording what it saw, and deciding when missing evidence should trigger another attempt. Based on this observation, we present ExploreAI, an LLM-driven agentic framework that offloads repeated perception,
navigation, multi-view capture execution, and logging to specialized modules while using the LLM for planning, evidence recording, capture-policy decisions, and verification decisions. ExploreAI constructs an Exploration Knowledge Base (EKB): a structured, per-object record of one exploration run. For each object the
agent finds, the EKB stores the scan evidence that exposed it, the selected target, the
navigation path, the multi-view capture, and the self-verification result. The EKB is a reusable testing artifact that supports reproducible observable-regression checking across versions of a VR or 3D application. Across six indoor and outdoor scenes in Unity, AI2-THOR, and BeamNG, ExploreAI constructs high-completeness EKBs under both complete and target exploration, and an LLM-module ablation shows where semantic planning, capture policy, evidence recording, and self-verification contribute. Reproduction pilots further show that EKB-guided traces help both humans and LLM-based reproducers reproduce exact object-view evidence more effectively than conditions without EKB context.
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All authors Jiajie Wang, Kebin Peng, Wei Wang, Xiaoyin Wang, Sen He, Xue Qin
Machine Learning
Rethinking Communication Metrics: How Should We Measure Meaning?
Niloofar Tavakolian, Hakimeh Purmehdi and Jungyeon Baek
Semantic communication shifts the objective of communication systems from accurate symbol reconstruction toward meaning preservation, task accomplishment, and efficient information exchange. However, its evaluation remains fragmented across telecommunications, natural
language processing, computer
vision, and machine learning, and no single metric can characterize semantic quality across modalities, tasks, and channel conditions. This article surveys key performance indicators (KPIs) for text- and image-based semantic communication systems from a unified, evaluation-centered perspective. Unlike prior surveys primarily organized around architectures, applications, or transmission strategies, this work focuses on how semantic success should be defined and measured. Existing KPIs are classified according to communication goal, source modality, receiver output, reference availability, evaluation level, and channel or resource constraints. The survey reviews reconstruction-based, task-oriented, reference-free, representation-level, perceptual, and channel-aware metrics, and presents a cross-modality comparison of their roles, strengths, and limitations. It further analyzes how unresolved semantic-KPI challenges affect monitoring, quality assurance, resource optimization, fault diagnosis, and standardization. Key open problems include the absence of universal semantic success criteria and standardized semantic ground truth, semantic drift, limited reference-free evaluation, weak integration of machine-learning metrics with communication constraints, and the lack of relation-level and
multimodal KPIs. Finally, future research directions are outlined toward standardized, interpretable, adaptive, task-aware, and communication-aware evaluation frameworks.
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