arXiv Papers

Published: 2026-03-06 18:59:36

Authors: Vishal Thengane, Zhaochong An, Tianjin Huang, Son Lam Phung, Abdesselam Bouzerdoum, Lu Yin, Na Zhao, Xiatian Zhu

Categories: cs.CV, cs.LG

Abstract:
Incremental Few-Shot (IFS) segmentation aims to learn new categories over time from only a few annotations. Although widely studied in 2D, it remains underexplored for 3D point clouds. Existing methods suffer from catastrophic forgetting or fail to learn discriminative prototypes under sparse supervision, and often overlook a key cue: novel categories frequently appear as unlabelled background in base-training scenes. We introduce SCOPE (Scene-COntextualised Prototype Enrichment), a plug-and-play background-guided prototype enrichment framework that integrates with any prototype-based 3D segmentation method. After base training, a class-agnostic segmentation model extracts high-confidence pseudo-instances from background regions to build a prototype pool. When novel classes arrive with few labelled samples, relevant background prototypes are retrieved and fused with few-shot prototypes to form enriched representations without retraining the backbone or adding parameters. Experiments on ScanNet and S3DIS show that SCOPE achieves SOTA performance, improving novel-class IoU by up to 6.98% and 3.61%, and mean IoU by 2.25% and 1.70%, respectively, while maintaining low forgetting. Code is available https://github.com/Surrey-UP-Lab/SCOPE.

            {
  "tldr": "SCOPE introduces a novel framework for incremental few-shot 3D segmentation that utilizes background context to enhance prototype learning, achieving state-of-the-art performance on benchmarks like ScanNet and S3DIS.",
  "whats_new": [
    "• Proposes a background-guided prototype enrichment framework for 3D segmentation.",
    "• Achieves significant improvements in novel-class IoU by up to 6.98%.",
    "• Introduces a plug-and-play design that integrates with existing segmentation methods.",
    "• Demonstrates reduced forgetting while adapting to new classes."
  ],
  "method": "SCOPE employs a three-stage pipeline: Base Training, Scene Contextualization, and Incremental Class Registration. It uses a class-agnostic model to extract high-confidence pseudo-instances from background regions, forming an Instance Prototype Bank (IPB) that aids in enriching few-shot prototypes without retraining the backbone.",
  "results": [
    "• Achieves up to 6.98% improvement in novel-class IoU on ScanNet.",
    "• Improves mean IoU by 2.25% on ScanNet and 1.70% on S3DIS.",
    "• Maintains low forgetting rates, with FPP decreasing from 1.49 to 1.27.",
    "• Outperforms existing methods like GW and CAPL across multiple metrics."
  ],
  "limitations": [
    "• Performance relies on the quality of pseudo-instance masks from the class-agnostic model.",
    "• Limited availability of effective class-agnostic models may hinder broader adoption.",
    "• Potential degradation in performance if background proposals are inaccurate."
  ],
  "why_useful": "SCOPE addresses the challenges of learning new categories in 3D segmentation with minimal supervision, making it highly relevant for applications in robotics and autonomous systems. Its ability to leverage background context enhances adaptability and reduces forgetting, crucial for real-world deployments.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code is available at https://arxiv.org/abs/2603.06572v1."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 18:59:33

Authors: Ievgen Dubovyk, Ayres Freitas, Janusz Gluza, Johann Usovitsch

Categories: hep-ph

Abstract:
We present the calculation of the so far missing ${\cal O}(α^2α_\mathrm{s})$ corrections to the quantity $Δr$, which relates the Fermi constant to the W-boson mass, and enables precision predictions of the latter. While the ${\cal O}(α^2α_\mathrm{s})$ corrections from diagrams with two closed fermion loops are already known, we here focus on the subset with one closed fermion loop, which is a substantially more complex problem. The calculation has been carried out through a combination of analytical and numerical techniques for the three-loop integrals and the on-shell renormalization. The impact of the new corrections is numerically significant, raising the Standard Model prediction for the W-boson mass by more than 3 MeV.

            {
  "tldr": "This paper calculates the previously missing O(α²αₛ) corrections to the W-boson mass prediction from muon decay, increasing the prediction by over 3 MeV. This work is crucial for improving theoretical precision in electroweak measurements.",
  "whats_new": [
    "• Introduces O(α²αₛ) corrections to muon decay, enhancing W-boson mass predictions.",
    "• Focuses on complex diagrams with one closed fermion loop, previously uncalculated.",
    "• Utilizes advanced numerical and analytical techniques for three-loop integrals.",
    "• Provides significant insights for future high-precision experiments."
  ],
  "method": "The authors computed genuine O(Nf α²αₛ) three-loop corrections using a combination of analytical and numerical techniques. They employed the on-shell renormalization scheme, allowing results to be expressed in terms of experimentally determined parameters.",
  "results": [
    "• The new corrections raise the W-boson mass prediction by +3.14 MeV.",
    "• The theoretical uncertainty in W-boson mass predictions is reduced from 4 MeV to a new estimate.",
    "• The O(α²αₛ) correction is significantly larger than previous contributions.",
    "• The findings are crucial for upcoming high-energy lepton collider experiments."
  ],
  "limitations": [
    "• The study does not include purely electroweak contributions of O(α³).",
    "• Further detailed studies are needed to fully assess the impact on theoretical uncertainties.",
    "• The numerical evaluations may have limitations in precision due to complex integrals."
  ],
  "why_useful": "This research is vital for enhancing the precision of electroweak measurements, particularly in light of upcoming experiments at the HL-LHC and future lepton colliders. It addresses a significant gap in theoretical predictions, aiding in the understanding of fundamental particle interactions.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 18:58:04

Authors: Boqiang Zhang, Lei Ke, Ruihan Yang, Qi Gao, Tianyuan Qu, Rossell Chen, Dong Yu, Leoweiliang

Categories: cs.CV

Abstract:
Vision Language Model (VLM) development has largely relied on scaling model size, which hinders deployment on compute-constrained mobile and edge devices such as smartphones and robots. In this work, we explore the performance limits of compact (e.g., 2B and 8B) VLMs. We challenge the prevailing practice that state-of-the-art VLMs must rely on vision encoders initialized via massive contrastive pretraining (e.g., CLIP/SigLIP). We identify an objective mismatch: contrastive learning, optimized for discrimination, enforces coarse and category-level invariances that suppress fine-grained visual cues needed for dense captioning and complex VLM reasoning. To address this issue, we present Penguin-VL, whose vision encoder is initialized from a text-only LLM. Our experiments reveal that Penguin-Encoder serves as a superior alternative to traditional contrastive pretraining, unlocking a higher degree of visual fidelity and data efficiency for multimodal understanding. Across various image and video benchmarks, Penguin-VL achieves performance comparable to leading VLMs (e.g., Qwen3-VL) in mathematical reasoning and surpasses them in tasks such as document understanding, visual knowledge, and multi-perspective video understanding. Notably, these gains are achieved with a lightweight architecture, demonstrating that improved visual representation rather than model scaling is the primary driver of performance. Our ablations show that Penguin-Encoder consistently outperforms contrastive-pretrained encoders, preserving fine-grained spatial and temporal cues that are critical for dense perception and complex reasoning. This makes it a strong drop-in alternative for compute-efficient VLMs and enables high performance in resource-constrained settings. Code: https://github.com/tencent-ailab/Penguin-VL

            {
  "tldr": "Penguin-VL introduces a compact vision-language model that outperforms traditional models by using a vision encoder initialized from a text-only LLM, enhancing visual fidelity and efficiency for multimodal tasks.",
  "whats_new": [
    "• Proposes Penguin-Encoder, a vision encoder initialized from a text-only LLM.",
    "• Introduces a mixed supervision pretraining strategy for improved data efficiency.",
    "• Develops a unified training recipe that harmonizes image and video capabilities.",
    "• Achieves strong performance in compact model sizes (2B and 8B parameters)."
  ],
  "method": "Penguin-VL employs a vision encoder initialized from a text-only LLM, enhancing alignment with language tasks. It utilizes a comprehensive training pipeline that includes mixed supervision and a two-stage fine-tuning strategy to optimize performance across various multimodal benchmarks.",
  "results": [
    "• Achieves state-of-the-art performance on document understanding tasks with scores like 94.1 on DocVQA.",
    "• Outperforms leading models like Qwen3-VL in mathematical reasoning tasks, scoring 67.3 on MathVista.",
    "• Demonstrates superior video understanding with a score of 61.5 on ActivityNetQA.",
    "• Maintains competitive performance in OCR tasks, scoring 810 on OCRBench."
  ],
  "limitations": [
    "• Performance on some math-focused tasks is slightly lower compared to larger models.",
    "• The model's architecture may still benefit from further optimization for real-time applications.",
    "• Future work is needed to enhance agentic capabilities for interactive environments."
  ],
  "why_useful": "Penguin-VL addresses the need for efficient vision-language models suitable for deployment on resource-constrained devices. Its innovative approach to visual representation learning can enhance applications in document understanding, video analysis, and interactive AI systems.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code available at https://github.com/tencent-ailab/Penguin-VL"
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 18:49:04

Authors: Fangrui Zhu, Yunfeng Xi, Jianmo Ni, Mu Cai, Boqing Gong, Long Zhao, Chen Qu, Ian Miao, Yi Li, Cheng Zhong, Huaizu Jiang, Shwetak Patel

Categories: cs.CV

Abstract:
Egocentric video understanding is inherently complex due to the dynamic 4D nature of the environment, where camera motion and object displacements necessitate a continuous re-evaluation of spatial relations. In this work, we target a suite of under-explored egocentric 4D reasoning tasks, including fixture interaction counting, viewpoint-relative fixture location, object movement itinerary tracking, and stationary object localization, that require fundamentally different cognitive operations: spatial anchoring, temporal tracking, and duration reasoning. We observe that these structural differences make task-agnostic approaches insufficient: generic Chain-of-Thought methods lack task-appropriate reasoning primitives, and uniform reinforcement learning actively destabilizes performance on spatial tasks. To address this, we propose EgoReasoner, a two-stage framework that aligns both the reasoning scaffold and the reward signal to each task's cognitive structure. In the first stage, Task-Adaptive Thinking Templates guide the synthesis of structured CoT traces that teach the model to reason adaptively across task types via supervised fine-tuning. In the second stage, task-aware reward functions verify entity grounding, temporal alignment, and task-adaptive logical consistency, selectively strengthening each reasoning pathway via reinforcement fine-tuning with GRPO. Our 3B-parameter model, trained on only 16K samples, achieves 37.5% average accuracy on the challenging HD-EPIC benchmark, surpassing Qwen2.5-VL-7B (25.7%) by over 10 points.

            {
  "tldr": "EgoReasoner introduces a two-stage framework for egocentric 4D reasoning, improving task-specific performance through structured thinking templates and task-aware reinforcement learning. It achieves 37.5% accuracy on the HD-EPIC benchmark, outperforming existing models significantly.",
  "whats_new": [
    "• Introduces Task-Adaptive Thinking Templates for structured reasoning across diverse tasks.",
    "• Employs task-aware reinforcement learning to enhance reasoning pathways.",
    "• Achieves notable performance improvements with a smaller model compared to larger baselines.",
    "• Addresses unique challenges in egocentric video understanding."
  ],
  "method": "EgoReasoner employs a two-stage approach: first, it uses Task-Adaptive Thinking Templates for supervised fine-tuning to teach structured reasoning. Second, it applies task-aware reinforcement learning to verify and strengthen reasoning pathways against ground-truth metadata.",
  "results": [
    "• Achieves 37.5% average accuracy on the HD-EPIC benchmark.",
    "• Surpasses Qwen2.5-VL-7B by over 10 points (25.7%).",
    "• Reaches 59.5% accuracy on Object Movement Counting, improving by 26.5% over the best baseline.",
    "• Demonstrates significant performance gains across multiple reasoning tasks."
  ],
  "limitations": [
    "• Stationary Object Localization remains challenging due to long video durations.",
    "• Performance may degrade in high-density egocentric environments without task-aware rewards.",
    "• Requires extensive training data for optimal performance."
  ],
  "why_useful": "This paper is significant for advancing egocentric video understanding, crucial for applications in embodied AI and augmented reality. The proposed methods can enhance the performance of AI agents in dynamic environments, improving their interaction capabilities.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 18:47:00

Authors: Shicheng Zhang, Aonan Zhang, Ilse Maillette de Buy Wenniger, Paul M. Burdekin, Jerzy Szuniewicz, Steven Sagona-Stophel, Sarah E. Thomas, Ian A. Walmsley

Categories: quant-ph, physics.atom-ph, physics.optics

Abstract:
Temporal modes of photons are a promising encoding scheme for high-dimensional quantum networks due to their high channel capacity and fiber compatibility. However, realizing their full potential requires devices capable of synchronizing, processing and interfacing these modes across photonic and atomic bandwidths. In this work, we demonstrate a programmable high-dimensional temporal mode processor using a Raman quantum memory in warm cesium vapor. We exploit the single-mode nature of the Raman interaction kernel, dynamically shaping the control field to synthesize a tunable coherent filter that selectively addresses specific temporal waveforms. This mechanism enables on-demand storage, filtering, and conversion, providing a coherent interface between MHz- and GHz-bandwidth modes. We validate the platform's selectivity across a basis of 30 orthogonal Hermite-Gaussian modes and certify high-fidelity quantum operation via 5-dimensional process tomography. By combining deterministic mode conversion with bidirectional bandwidth interfacing, we establish the Raman memory as a critical active node for scalable quantum information processing.

            {
  "tldr": "This paper presents a programmable high-dimensional temporal mode processor using a Raman quantum memory in warm cesium vapor, enabling efficient synchronization and manipulation of temporal modes for quantum networks.",
  "whats_new": [
    "• Demonstrates a high-dimensional temporal mode processor using Raman quantum memory.",
    "• Achieves on-demand storage, filtering, and conversion of temporal modes.",
    "• Validates selectivity across 30 orthogonal Hermite-Gaussian modes.",
    "• Certifies high-fidelity quantum operation via 5-dimensional process tomography."
  ],
  "method": "The approach utilizes a Raman quantum memory to dynamically shape control fields for synthesizing tunable coherent filters. This allows for the selective addressing of specific temporal waveforms, enabling efficient storage and retrieval of quantum states.",
  "results": [
    "• Achieved an average storage efficiency of approximately 30% for matched modes.",
    "• Demonstrated a fidelity of 0.943±0.015 in quantum operation relative to an ideal single-mode filter.",
    "• Enabled bandwidth conversion with factors ranging from 1 to 10.",
    "• Successfully filtered components from superposition states, confirming coherent mode filtering."
  ],
  "limitations": [
    "• Current storage efficiencies are relatively low compared to theoretical limits.",
    "• Performance is constrained by the trade-off between coupling strength and mode selectivity.",
    "• The demonstrated dimension of 30 is limited by practical constraints in the experimental setup.",
    "• Further optimization is needed to enhance efficiency without sacrificing selectivity."
  ],
  "why_useful": "This work is significant for advancing quantum networking by providing a versatile interface for high-dimensional temporal modes. It has potential applications in secure quantum key distribution, distributed quantum computing, and quantum-enhanced metrology.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 18:44:37

Authors: Harshavardhan Kamarthi, Shangqing Xu, Xinjie Tong, Xingyu Zhou, James Peters, Joseph Czyzyk, B. Aditya Prakash

Categories: cs.LG

Abstract:
Hierarchical time-series forecasting is essential for demand prediction across various industries. While machine learning models have obtained significant accuracy and scalability on such forecasting tasks, the interpretability of their predictions, informed by application, is still largely unexplored. To bridge this gap, we introduce a novel interpretability method for large hierarchical probabilistic time-series forecasting, adapting generic interpretability techniques while addressing challenges associated with hierarchical structures and uncertainty. Our approach offers valuable interpretative insights in response to real-world industrial supply chain scenarios, including 1) the significance of various time-series within the hierarchy and external variables at specific time points, 2) the impact of different variables on forecast uncertainty, and 3) explanations for forecast changes in response to modifications in the training dataset. To evaluate the explainability method, we generate semi-synthetic datasets based on real-world scenarios of explaining hierarchical demands for over ten thousand products at a large chemical company. The experiments showed that our explainability method successfully explained state-of-the-art industrial forecasting methods with significantly higher explainability accuracy. Furthermore, we provide multiple real-world case studies that show the efficacy of our approach in identifying important patterns and explanations that help stakeholders better understand the forecasts. Additionally, our method facilitates the identification of key drivers behind forecasted demand, enabling more informed decision-making and strategic planning. Our approach helps build trust and confidence among users, ultimately leading to better adoption and utilization of hierarchical forecasting models in practice.

            {
  "tldr": "This paper presents a novel method for interpreting hierarchical time-series forecasting models, enhancing their explainability and applicability in industrial demand forecasting. It introduces subtree approximation and quantile-based adaptations to improve understanding of model predictions.",
  "whats_new": [
    "• Introduces subtree approximation for efficient interpretability in hierarchical forecasting.",
    "• Proposes quantile-based adaptations for explaining probabilistic forecasts.",
    "• Establishes a synthetic benchmark for evaluating interpretability methods.",
    "• Demonstrates real-world case studies showing practical applications and insights."
  ],
  "method": "The authors adapt existing interpretability methods for hierarchical time-series forecasting by introducing subtree approximation to manage hierarchical dependencies and using quantiles to explain probabilistic outputs. This approach allows for efficient computation and clearer insights into model predictions.",
  "results": [
    "• Achieved 62.0% improvement in importance accuracy score (IAS) for deterministic forecasts.",
    "• Observed 26.0% improvement in IAS for probabilistic forecasts.",
    "• Improved external variable detection accuracy (EVDA) by 40.4% on Dow’s dataset.",
    "• Successfully explained changes in forecasts due to input variable modifications."
  ],
  "limitations": [
    "• The method is primarily focused on numerical time-series data.",
    "• Further research is needed to extend the approach to multimodal inputs.",
    "• The synthetic benchmark may not fully capture all real-world complexities."
  ],
  "why_useful": "This paper addresses the critical need for interpretability in hierarchical time-series forecasting, particularly in industrial applications. By enhancing model transparency, it supports better decision-making and trust among stakeholders, facilitating the adoption of advanced forecasting models.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://github.com/AdityaLab/Hiereinterpret"
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 18:39:37

Authors: Archie Sage, Salvatore Greco

Categories: cs.CL

Abstract:
This paper describes the KCLarity team's participation in CLARITY, a shared task at SemEval 2026 on classifying ambiguity and evasion techniques in political discourse. We investigate two modelling formulations: (i) directly predicting the clarity label, and (ii) predicting the evasion label and deriving clarity through the task taxonomy hierarchy. We further explore several auxiliary training variants and evaluate decoder-only models in a zero-shot setting under the evasion-first formulation. Overall, the two formulations yield comparable performance. Among encoder-based models, RoBERTa-large achieves the strongest results on the public test set, while zero-shot GPT-5.2 generalises better on the hidden evaluation set.

            {
  "tldr": "The KCLarity team participated in SemEval-2026's CLARITY task, focusing on detecting political evasion and ambiguity. They compared encoder-based and zero-shot models, finding RoBERTa-large performed best on public data while GPT-5.2 excelled in hidden evaluations.",
  "whats_new": [
    "• Introduced two modeling approaches: direct clarity prediction and evasion-based clarity.",
    "• Evaluated zero-shot performance of various decoder models, notably GPT-5.2.",
    "• Explored auxiliary training strategies to address class imbalance.",
    "• Provided insights into the challenges of detecting political evasion."
  ],
  "method": "The study utilized fine-tuned encoder models like RoBERTa and DeBERTa-v3, and zero-shot decoder models. Two training targets were explored: predicting clarity directly and inferring clarity from evasion labels using a hierarchical taxonomy.",
  "results": [
    "• RoBERTa-large achieved a macro F1 of 0.661 for clarity on the public test set.",
    "• GPT-5.2 scored a macro F1 of 0.626 for clarity in a zero-shot setting.",
    "• Evasion-based clarity and direct clarity showed comparable performance.",
    "• The best zero-shot model, GPT-5.2, ranked 22nd in Task 1 and 13th in Task 2 on hidden evaluations."
  ],
  "limitations": [
    "• Class imbalance affected model performance, particularly for rare clarity labels.",
    "• Zero-shot models were not fine-tuned, potentially limiting their effectiveness.",
    "• Annotation discrepancies may lead to underestimating model performance.",
    "• Input representation analysis conflated multiple factors, complicating interpretation."
  ],
  "why_useful": "This paper contributes to the understanding of political communication by providing automated methods for detecting evasion and ambiguity. Its findings can enhance the analysis of political discourse and improve transparency in political responses.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://github.com/semeval-2026-kclarity/clarity"
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 18:35:48

Authors: Edward Morgan, Nenyi K Dadson, Corina Barbalata

Categories: cs.RO, eess.SY

Abstract:
Accurate and adaptive dynamic models are critical for underwater vehicle-manipulator systems where hydrodynamic effects induce time-varying parameters. This paper introduces a novel uncertainty-aware adaptive dynamics model framework that remains linear in lumped vehicle and manipulator parameters, and embeds convex physical consistency constraints during online estimation. Moving horizon estimation is used to stack horizon regressors, enforce realizable inertia, damping, friction, and hydrostatics, and quantify uncertainty from parameter evolution. Experiments on a BlueROV2 Heavy with a 4-DOF manipulator demonstrate rapid convergence and calibrated predictions. Manipulator fits achieve R2 = 0.88 to 0.98 with slopes near unity, while vehicle surge, heave, and roll are reproduced with good fidelity under stronger coupling and noise. Median solver time is approximately 0.023 s per update, confirming online feasibility. A comparison against a fixed parameter model shows consistent reductions in MAE and RMSE across degrees of freedom. Results indicate physically plausible parameters and confidence intervals with near 100% coverage, enabling reliable feedforward control and simulation in underwater environments.

            {
  "tldr": "This paper presents a new framework for adaptive dynamics in underwater vehicle-manipulator systems, addressing time-varying parameters and uncertainties. It demonstrates improved model fidelity and online feasibility through experimental validation.",
  "whats_new": [
    "• Introduces a unified regressor formulation for underwater vehicle-manipulator dynamics.",
    "• Employs moving horizon estimation with convex physical constraints for online parameter adaptation.",
    "• Provides uncertainty quantification for adaptive models, enhancing reliability.",
    "• Validated through experiments showing rapid convergence and high prediction accuracy."
  ],
  "method": "The framework uses a regressor-based approach to model dynamics, integrating moving horizon estimation and convex constraints to ensure physical plausibility. It adapts parameters online based on observed states and inputs, while quantifying uncertainty from parameter changes.",
  "results": [
    "• Achieved R² values between 0.88 and 0.98 for manipulator torque predictions.",
    "• Median solver time for updates was approximately 0.023 seconds.",
    "• Consistent reductions in MAE and RMSE compared to fixed parameter models.",
    "• Vehicle dynamics reproduced with good fidelity, especially in surge, heave, and roll."
  ],
  "limitations": [
    "• Lower fidelity in sway and pitch dynamics due to weaker excitation.",
    "• Sensitivity to sensor noise affecting parameter observability.",
    "• Initial parameters were set far from true values, requiring significant adaptation."
  ],
  "why_useful": "This work enhances the modeling and control of underwater vehicle-manipulator systems, making it applicable for real-time control and simulation in underwater environments. It contributes to the development of accurate digital twins and improved localization methods.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://github.com/RKinDLab/floating-KinDyn"
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 18:31:10

Authors: Yuhan Zhou, Mehri Sattari, Haihua Chen, Kewei Sha

Categories: cs.CV

Abstract:
Next-generation autonomous vehicles (AVs) rely on large volumes of multisource and multimodal ($M^2$) data to support real-time decision-making. In practice, data quality (DQ) varies across sources and modalities due to environmental conditions and sensor limitations, yet AV research has largely prioritized algorithm design over DQ analysis. This work focuses on redundancy as a fundamental but underexplored DQ issue in AV datasets. Using the nuScenes and Argoverse 2 (AV2) datasets, we model and measure redundancy in multisource camera data and multimodal image-LiDAR data, and evaluate how removing redundant labels affects the YOLOv8 object detection task. Experimental results show that selectively removing redundant multisource image object labels from cameras with shared fields of view improves detection. In nuScenes, mAP${50}$ gains from $0.66$ to $0.70$, $0.64$ to $0.67$, and from $0.53$ to $0.55$, on three representative overlap regions, while detection on other overlapping camera pairs remains at the baseline even under stronger pruning. In AV2, $4.1$-$8.6\%$ of labels are removed, and mAP${50}$ stays near the $0.64$ baseline. Multimodal analysis also reveals substantial redundancy between image and LiDAR data. These findings demonstrate that redundancy is a measurable and actionable DQ factor with direct implications for AV performance. This work highlights the role of redundancy as a data quality factor in AV perception and motivates a data-centric perspective for evaluating and improving AV datasets. Code, data, and implementation details are publicly available at: https://github.com/yhZHOU515/RedundancyAD

            {
  "tldr": "This paper investigates redundancy in multisource and multimodal data for autonomous driving, showing that selectively removing redundant labels can enhance object detection performance. Experiments on nuScenes and Argoverse 2 datasets demonstrate measurable improvements in detection accuracy.",
  "whats_new": [
    "• First systematic modeling and measurement of redundancy in multisource and multimodal data for object detection.",
    "• Introduction of a task-driven data selection method based on bounding box completeness.",
    "• Validation of redundancy reduction strategies on YOLOv8, showing improved detection performance.",
    "• Empirical analysis of cross-modal redundancy between camera images and LiDAR data."
  ],
  "method": "The study models redundancy by evaluating overlapping fields of view in multisource camera data and multimodal camera-LiDAR data. It employs a Bounding Box Completeness Score (BCS) to guide the removal of redundant observations, followed by training YOLOv8 on datasets with varying redundancy levels to assess performance impacts.",
  "results": [
    "• In nuScenes, mAP50 improved from 0.66 to 0.70 after removing redundant labels.",
    "• A V2 dataset saw a 4.1–8.6% reduction in labels with mAP50 remaining near 0.64.",
    "• Models trained on less redundant datasets achieved comparable or improved detection performance.",
    "• Cross-modal redundancy analysis revealed significant overlap between camera and LiDAR data."
  ],
  "limitations": [
    "• The study primarily focuses on specific datasets (nuScenes and A V2), limiting generalizability.",
    "• Future work is needed to explore redundancy across diverse driving environments and additional modalities.",
    "• The impact of redundancy on other autonomous driving tasks beyond object detection remains unexplored."
  ],
  "why_useful": "This research highlights the importance of redundancy as a data quality factor in autonomous driving, offering actionable insights for improving dataset efficiency and model performance. It encourages a data-centric approach to enhance the reliability of autonomous vehicle perception systems.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code, data, and implementation details are publicly available at: https://github.com/yhZHOU515/RedundancyAD."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 18:28:10

Authors: Sanket Goutam, Hunter Kippen, Mike Grace, Amir Rahmati

Categories: cs.CR

Abstract:
Device logs are essential for forensic investigations, enterprise monitoring, and fraud detection; however, they often leak personally identifiable information (PII) when exported for third-party analysis. Existing approaches either fail to minimize PII exposure across all stages of log collection and analysis or sacrifice data fidelity, resulting in less effective analysis. We present Proteus, a privacy-preserving device logging framework that enables forensic analysis without disclosing plaintext PII or compromising fidelity, even when facing adversaries with access to multiple snapshots of the log files. To achieve this, Proteus proposes a two-layer scheme that employs keyed-hash pseudonymization of PII fields and time-rotating encryption with ratcheted ephemeral keys to prevent multi-snapshot correlation. For controlled sharing, clients export ratchet states that grant time-bounded access, permitting decryption of pseudonymized tokens that enable linkage and timeline reconstruction without exposing the underlying PII. Subsequent ratchet rotations ensure forward secrecy, while DICE-based attestation authenticates device provenance. We implement Proteus as a transparent extension to Android's logcat and evaluate it across three generations of hardware. Our results demonstrate a median latency of 0.2 ms per message and an average per-PII-field size overhead of only 97.1 bytes.

            {
  "tldr": "Proteus is a privacy-preserving logging framework that prevents the exposure of personally identifiable information (PII) while maintaining forensic utility. It uses a two-layer cryptographic approach to ensure data confidentiality and integrity during log collection and analysis.",
  "whats_new": [
    "• First in-situ privacy-preserving framework for mobile logging.",
    "• Formal threat model for mobile endpoint forensics with multi-snapshot adversaries.",
    "• Practical implementation as an Android library with low latency and storage overhead.",
    "• Provable security guarantees through a hierarchical ratcheting mechanism."
  ],
  "method": "Proteus employs a two-layer scheme that pseudonymizes PII fields using keyed hashes and encrypts them with daily-rotated keys. This design prevents unauthorized correlation while allowing controlled access for forensic analysis. It integrates with existing logging frameworks like Android's logcat.",
  "results": [
    "• Median latency of 0.2 ms per log message.",
    "• Average storage overhead of 2.41% for PII fields.",
    "• Successfully processed 30.3 million log entries from the LogHub dataset.",
    "• Achieved robust protection against multi-snapshot adversaries."
  ],
  "limitations": [
    "• If the hash key is compromised, it may allow offline dictionary attacks.",
    "• Coarser key rotation granularity leads to longer exposure windows compared to some protocols.",
    "• Metadata leakage remains observable, which could reveal behavioral patterns."
  ],
  "why_useful": "Proteus addresses the critical need for privacy in mobile logging by ensuring that sensitive data is protected at the point of emission. Its practical implementation allows for forensic analysis without compromising user privacy, making it relevant for enterprises and developers concerned with data protection.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://arxiv.org/abs/2603.06540v1"
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 18:26:35

Authors: Shreyashi Sinha, Ayan Jana, Suchanda Mondal, Ravi Prakash Singh, Manoranjan Kumar, Sujit Manna

Categories: cond-mat.mes-hall, cond-mat.mtrl-sci

Abstract:
Understanding how local structural order governs electronic correlations is essential for revealing the microscopic mechanism underlying emergent behavior in two-dimensional magnets. In the layered van der Waals ferromagnet Fe\textsubscript{5-x}GeTe\textsubscript{2}, intrinsic Fe-site disorder provides a natural platform to probe this interplay. Here, we establish a direct atomic scale correlation between Fe-site ordering and local electronic structure by combining high-resolution scanning tunneling microscopy with density functional theory calculations. Scanning tunneling microscopy resolves two coexisting surface phases, a $\sqrt{3} \times \sqrt{3}$ superstructure associated with ordered Fe(1) configurations and an undistorted $1 \times 1$ hexagonal Te lattice in Fe(1)-deficient regions. Spatially resolved spectroscopy shows that the $\sqrt{3}$-ordered domains exhibit metallic behavior, whereas Fe(1) vacant areas display a suppressed density of states(DOS) near the Fermi level, indicative of pseudogapped electronic states. The nanoscale coexistence of these distinct electronic responses provides direct evidence of electronic phase separation driven by Fe-site ordering. First-principles calculations reveal that symmetry allowed hybridization between Fe 3d and Te 5p orbitals reconstructs the low-energy electronic structure, giving rise to the contrasting tunneling signatures of ordered and disordered phases. Bias-dependent local DOS simulations reproduce the experimentally observed contrast evolution and reveal that hybridization induced out of plane orbital character governs the spatial modulation of tunneling conductance. These results provide a microscopic framework linking atomic-scale structural order to nanoscale electronic inhomogeneity in van der Waals magnets.

arXiv Page | PDF

Published: 2026-03-06 18:15:24

Authors: Kübra Yeter-Aydeniz, Nora M. Bauer

Categories: quant-ph, cond-mat.supr-con

Abstract:
We present a method for calculating the ground state energy of the Fermi-Hubbard model leveraging Rydberg atom processors and sample-based quantum diagonalization (SQD). By exploiting the perturbative relationship between the Fermi-Hubbard and Heisenberg models, the procedure samples from the Heisenberg model as prepared on the Rydberg atom processor, and uses the samples to diagonalize the Fermi-Hubbard model for large U. We include anisotropy and next-nearest-neighbor interactions and discuss the relevant regime for quasi-superconductivity in the 1-dimensional Fermi- Hubbard model. Numerical and experimental results on the Aquila quantum processor are presented for ground state energy calculations as well as the chemical potential. We find that the Heisenberg model sampling in the studied regime is sufficient to converge near to the ground state for up to 56 qubits, and we see a clear advantage of Rydberg atom sampling as opposed to random sampling even with 10x more samples for diagonalization. We also present a gate-based implementation of the gate-based SQD algorithm on IBM Quantum hardware for 56-qubit Hubbard model as a benchmark. Finally, we provide a gap analysis for studying emergent superconductivity using this method.

arXiv Page | PDF

Published: 2026-03-06 18:11:46

Authors: Nina Holden, Pu Yu

Categories: math.PR, math.CV

Abstract:
We prove that embedded infinite plane triangulations in ergodic scale-free environments are close to their circle packing and Riemann uniformization embedding on a large scale, as long as suitable moment and connectivity conditions are satisfied. Ergodic scale-free environments were earlier considered by Gwynne, Miller and Sheffield (2018) in the context of the invariance principles for random walk, and they arise naturally in the study of random planar maps and Liouville quantum gravity.

arXiv Page | PDF

Published: 2026-03-06 16:43:39

Authors: Sergey Foss, Michael Scheutzow, Anton Tarasenko

Categories: math.PR

Abstract:
A random variable $ξ$ has a {\it light-tailed} distribution (for short: is light-tailed) if it possesses a finite exponential moment, $\E \exp (λξ) <\infty$ for some $λ>0$, and has a {\it heavy-tailed} distribution (is heavy-tailed) if $\E \exp (λξ) = \infty$, for all $λ>0$. In \cite{LSK1}, the authors presented a particular example of a light-tailed random variable that is the minimum of two independent heavy-tailed random variables. In \cite{FKT}, it was shown that any light-tailed random variable with right-unbounded support may be represented as the minimum of two independent heavy-tailed random variables, with further generalisations of the result in a number of directions. We analyse an ``inverse'' question. Namely, we obtain necessary and sufficient conditions on the distribution of a heavy-tailed random variable, say $ξ_1$, that allow to find another independent heavy-tailed random variable, say $ξ_2$, such that their minimum $\min (ξ_1,ξ_2)$ is light-tailed. We also provide a number of extensions of this result

arXiv Page | PDF

Published: 2026-03-06 16:08:10

Authors: Slavko Moconja, Predrag Tanović

Categories: math.LO

Abstract:
We confirm Martin's conjecture for a broad subclass of weakly quasi-o-minimal theories.

arXiv Page | PDF

Published: 2026-03-06 13:36:11

Authors: Yueyang Wang

Categories: math.DS, math.CV

Abstract:
We show that every polynomial of degree $d \geq 2$ in the connectedness locus with an attracting cycle which attracts at least two critical points and no indifferent cycles is not combinatorially rigid. In particular, we prove that a hyperbolic polynomial with connected Julia set is combinatorially rigid if and only if it is not of the ``disjoint type''.

            {
  "tldr": "This paper shows that polynomials with attracting cycles that attract at least two critical points are not combinatorially rigid. It establishes conditions under which hyperbolic polynomials are combinatorially rigid or not.",
  "whats_new": [
    "• Proves that polynomials with attracting cycles attracting at least two critical points are not combinatorially rigid.",
    "• Establishes a necessary and sufficient condition for hyperbolic polynomials to be combinatorially rigid.",
    "• Provides infinitely many counterexamples to the Combinatorial Rigidity Conjecture."
  ],
  "method": "The paper employs the theory of external rays and rational laminations to analyze the dynamics of polynomials. It constructs specific polynomials and demonstrates their properties through quasiconformal maps and deformation techniques.",
  "results": [
    "• Every polynomial of degree ≥3 with an attracting cycle attracting at least two critical points is not combinatorially rigid.",
    "• A hyperbolic polynomial is combinatorially rigid if and only if it is of disjoint type.",
    "• Provides examples showing that polynomials with unique critical points can still be combinatorially rigid."
  ],
  "limitations": [
    "• The results are limited to polynomials without indifferent cycles.",
    "• The implications for higher-degree polynomials remain less explored."
  ],
  "why_useful": "This paper contributes to the understanding of combinatorial rigidity in complex dynamics, particularly in polynomial maps. Its findings have implications for the study of dynamical systems and could influence future research in the field.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 13:32:12

Authors: Alexis Kafantaris

Categories: cs.NE, cs.AI, cs.LG, cs.SC

Abstract:
This essay is about a neural implementation of the fuzzy cognitive map, the FHM, and corresponding evaluations. Firstly, a neural net has been designed to behave the same way that an FCM does; as inputs it accepts many fuzzy cognitive maps and propagates them in order to learn causality patterns. Moreover, the network uses langevin differential Dynamics, which avoid overfit, to inverse solve the output node values according to some policy. Nevertheless, having obtained an inverse solution provides the user a modification criterion. Having the modification criterion suggests that information is now according to discretion as a different service or product is a better fit. Lastly, evaluation has been done on several data sets in order to examine the networks performance.

            {
  "tldr": "This paper presents a neural implementation of fuzzy cognitive maps (FHM) using a glass box architecture, which enhances transparency and minimizes overfitting. It evaluates the model's performance across various datasets.",
  "whats_new": [
    "• Introduces a glass box neural network architecture for fuzzy cognitive maps.",
    "• Utilizes Langevin differential dynamics to prevent overfitting.",
    "• Provides a modification criterion for output node values.",
    "• Evaluates performance on multiple datasets, including synthetic and real data."
  ],
  "method": "The methodology involves a mathematical model that maps to Python code, allowing for a transparent learning process. The neural network learns information structure while minimizing objective loss, resembling physics-informed neural networks.",
  "results": [
    "• Achieved 99.29% accuracy on the Base Urban Policy with 9 nodes.",
    "• 90.89% accuracy on the Extended Urban Policy with 14 nodes.",
    "• 76.83% accuracy on the Sachs Protein Network with 11 nodes.",
    "• Stable performance across synthetic and real datasets."
  ],
  "limitations": [
    "• The architecture's performance on larger datasets remains untested.",
    "• Potential challenges in generalizing results to different domains.",
    "• Limited exploration of alternative neural network designs."
  ],
  "why_useful": "This paper contributes to the field of neuro-symbolic computing by providing a transparent model for fuzzy cognitive maps, which can improve decision-making processes in service design. Its findings may influence future neural network architectures.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://arxiv.org/abs/2603.06272v1"
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 13:31:54

Authors: Mina Farajiamiri, Jeta Sopa, Saba Afza, Lisa Adams, Felix Barajas Ordonez, Tri-Thien Nguyen, Mahshad Lotfinia, Sebastian Wind, Keno Bressem, Sven Nebelung, Daniel Truhn, Soroosh Tayebi Arasteh

Categories: cs.LG, cs.AI

Abstract:
Agentic retrieval-augmented reasoning pipelines are increasingly used to structure how large language models (LLMs) incorporate external evidence in clinical decision support. These systems iteratively retrieve curated domain knowledge and synthesize it into structured reports before answer selection. Although such pipelines can improve performance, their impact on reliability under model variability remains unclear. In real-world deployment, heterogeneous models may align, diverge, or synchronize errors in ways not captured by accuracy. We evaluated 34 LLMs on 169 expert-curated publicly available radiology questions, comparing zero-shot inference with a radiology-specific multi-step agentic retrieval condition in which all models received identical structured evidence reports derived from curated radiology knowledge. Agentic inference reduced inter-model decision dispersion (median entropy 0.48 vs. 0.13) and increased robustness of correctness across models (mean 0.74 vs. 0.81). Majority consensus also increased overall (P<0.001). Consensus strength and robust correctness remained correlated under both strategies (\r{ho}=0.88 for zero-shot; \r{ho}=0.87 for agentic), although high agreement did not guarantee correctness. Response verbosity showed no meaningful association with correctness. Among 572 incorrect outputs, 72% were associated with moderate or high clinically assessed severity, although inter-rater agreement was low (\k{appa}=0.02). Agentic retrieval therefore was associated with more concentrated decision distributions, stronger consensus, and higher cross-model robustness of correctness. These findings suggest that evaluating agentic systems through accuracy or agreement alone may not always be sufficient, and that complementary analyses of stability, cross-model robustness, and potential clinical impact are needed to characterize reliability under model variability.

            {
  "tldr": "This study evaluates how agentic retrieval-augmented reasoning affects decision reliability among 34 large language models (LLMs) in radiology question answering, revealing improved consensus and correctness but also persistent error risks.",
  "whats_new": [
    "• Introduces a framework for assessing collective reliability under model variability.",
    "• Demonstrates that agentic reasoning reduces inter-model decision dispersion significantly.",
    "• Shows that while consensus strength increases, it does not always correlate with correctness.",
    "• Highlights the clinical severity of incorrect outputs, emphasizing the need for comprehensive evaluation metrics."
  ],
  "method": "The study evaluated 34 LLMs on 169 radiology questions using zero-shot and agentic retrieval-augmented inference. The agentic condition provided structured evidence reports to all models, allowing for controlled comparisons of decision stability, consensus strength, and correctness.",
  "results": [
    "• Median decision entropy decreased from 0.48 to 0.13 (P=5.6 × 10−9), indicating improved stability.",
    "• Mean robustness of correctness increased from 0.74 to 0.81 (P=5.6 × 10−9).",
    "• Majority consensus strength rose from 0.85 to 0.97 (P=2.9 × 10−5).",
    "• 72% of incorrect outputs were linked to moderate or high clinical severity."
  ],
  "limitations": [
    "• The study is based on a limited dataset of 169 questions, which may not capture all variability.",
    "• It does not incorporate multimodal data, limiting generalizability to real-world clinical scenarios.",
    "• Clinical severity assessments showed low inter-rater agreement, indicating subjective variability.",
    "• The fixed orchestration pipeline may amplify correlated errors among models."
  ],
  "why_useful": "This paper provides insights into the reliability of LLMs in clinical decision-making, crucial for deploying AI in healthcare. It emphasizes the importance of evaluating not just accuracy but also stability and clinical impact, guiding future research and applications in medical AI.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://github.com/minafarajiamiri/stability"
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 13:31:52

Authors: Zuo-Ru Zhang, Hongkuan Zhao

Categories: math.CO

Abstract:
Recently, Archer et al.\ studied cyclic permutations that avoid the decreasing pattern $δ_k=k(k-1)\cdots21$ in one-line notation and avoid another pattern $τ$ of length $4$ in all their cycle forms. There are three cases in total to consider, namely, $τ=1324, 1342$ and $1432$. They determined two of them, leaving the case $τ=1432$ as an open question. In this paper, we resolve this case by deriving explicit formulas based on an analysis of the structure of cycle forms and an application of Dilworth's theorem.

            {
  "tldr": "This paper resolves an open question regarding cyclic permutations that avoid the pattern 1432. It provides explicit formulas for counting such permutations based on their structure and applies Dilworth's theorem.",
  "whats_new": [
    "• Resolves the open case of pattern avoidance for 1432 in cyclic permutations.",
    "• Derives explicit counting formulas for cyclic permutations avoiding specific patterns.",
    "• Applies Dilworth's theorem to analyze the structure of cycle forms."
  ],
  "method": "The authors analyze the structure of cyclic permutations and derive formulas for counting those that avoid the pattern 1432. They utilize Dilworth's theorem to establish necessary conditions for pattern avoidance in one-line notation and cycle forms.",
  "results": [
    "• For n ≥ 5, the number of cyclic permutations avoiding δ3 and 1432 is a◦ n(δ3; 1432) = ⌊n2 + 7 / 2⌋ - 2n.",
    "• For n ≥ 5, a◦ n(δ4; 1432) = 2n - 1 - ⌊3n - 5 / 2⌋.",
    "• For n ≥ 5 and k ≥ 5, a◦ n(δk; 1432) = 2n + 1 - 2n - (n choose 3)."
  ],
  "limitations": [
    "• The results are only applicable for n ≥ 5.",
    "• Further exploration is needed for patterns beyond 1432."
  ],
  "why_useful": "This paper contributes to the field of combinatorial mathematics by providing a complete solution to a previously unresolved problem. The findings can be applied in areas such as algorithm design and theoretical computer science, where pattern avoidance plays a crucial role.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 13:31:39

Authors: Paola F. Antonietti, Omer M. O. Abdalla, Michelangelo G. Garroni, Ilario Mazzieri, Nicola Parolini

Categories: math.NA

Abstract:
Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) are essential for next-generation ultrasonic sensing and imaging due to their bidirectional electromechanical behavior, compact design, and compatibility with low-voltage electronics. As PMUT arrays grow in size and complexity, efficiently modeling their coupled electromechanical-acoustic behavior becomes increasingly challenging. This work presents a novel computational framework that combines model order reduction with a Discontinuous Galerkin Spectral Element Method (DGSEM) paradigm to simulate large PMUT arrays. Each PMUT's mechanical behavior is represented using a reduced set of vibration modes, which are coupled to an acoustic domain model to describe the full array. To further improve efficiency, a secondary acoustic domain is connected via DG interfaces, enabling non-conforming mesh refinement, with variable approximation order, and accurate wave propagation. The framework is implemented in the SPectral Elements in Elastodynamics with Discontinuous Galerkin (SPEED) software, an open-source, parallelized platform leveraging domain decomposition, high-order polynomials, METIS graph partitioning, and MPI for scalable performance. The proposed methodology addresses key challenges in meshing, supporting high-fidelity simulations for both PMUT transmission and reception phases. Numerical results demonstrate the framework's accuracy, scalability, and efficiency for large PMUT array simulations.

            {
  "tldr": "This paper presents a novel computational framework combining model order reduction with a Discontinuous Galerkin Spectral Element Method for simulating large Piezoelectric Micromachined Ultrasonic Transducer (PMUT) arrays, enhancing efficiency and accuracy.",
  "whats_new": [
    "• Introduces a hybrid framework for simulating large PMUT arrays.",
    "• Combines model order reduction with high-fidelity DGSEM.",
    "• Implements non-conforming mesh refinement for improved wave propagation.",
    "• Demonstrates scalability and efficiency in numerical results."
  ],
  "method": "The framework utilizes a reduced set of vibration modes for each PMUT, coupled with an acoustic domain model. It employs the Discontinuous Galerkin Spectral Element Method (DGSEM) for high-order accuracy and integrates domain decomposition for parallel computing.",
  "results": [
    "• Achieves accurate simulations for large PMUT arrays with reduced computational cost.",
    "• Demonstrates scalability with simulations involving up to 262.6 million degrees of freedom.",
    "• Reduces interface setup time by over two orders of magnitude with DG optimization.",
    "• Validates the framework against COMSOL simulations, showing close correspondence."
  ],
  "limitations": [
    "• The framework may not fully capture nonlinear behaviors at high excitation levels.",
    "• Future work needed to incorporate thermoacoustic effects.",
    "• Performance may vary based on mesh partitioning strategies."
  ],
  "why_useful": "This paper is significant for researchers and engineers working on ultrasonic sensing and imaging technologies. The proposed framework facilitates efficient design and optimization of PMUT arrays, making it applicable in various fields such as biomedical diagnostics and industrial sensing.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://arxiv.org/abs/2603.06267v1"
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 13:24:10

Authors: Songqun Gao, Zanxi Ruan, Patrick Floor, Marco Roveri, Luigi Palopoli, Daniele Fontanelli

Categories: cs.LG, cs.AI

Abstract:
The orienteering problem with time windows and variable profits (OPTWVP) is common in many real-world applications and involves continuous time variables. Current approaches fail to develop an efficient solver for this orienteering problem variant with discrete and continuous variables. In this paper, we propose a learning-based two-stage DEcoupled discrete-Continuous optimization with Service-time-guided Trajectory (DeCoST), which aims to effectively decouple the discrete and continuous decision variables in the OPTWVP problem, while enabling efficient and learnable coordination between them. In the first stage, a parallel decoding structure is employed to predict the path and the initial service time allocation. The second stage optimizes the service times through a linear programming (LP) formulation and provides a long-horizon learning of structure estimation. We rigorously prove the global optimality of the second-stage solution. Experiments on OPTWVP instances demonstrate that DeCoST outperforms both state-of-the-art constructive solvers and the latest meta-heuristic algorithms in terms of solution quality and computational efficiency, achieving up to 6.6x inference speedup on instances with fewer than 500 nodes. Moreover, the proposed framework is compatible with various constructive solvers and consistently enhances the solution quality for OPTWVP.

            {
  "tldr": "This paper presents a learning-based framework, DeCoST, for solving the Orienteering Problem with Time Windows and Variable Profits (OPTWVP), achieving significant improvements in solution quality and computational efficiency over existing methods.",
  "whats_new": [
    "• Introduces DeCoST, a two-stage optimization framework for OPTWVP.",
    "• Achieves up to 6.6x inference speedup on instances with fewer than 500 nodes.",
    "• Proves global optimality for the service time optimization stage.",
    "• Demonstrates compatibility with various constructive solvers."
  ],
  "method": "DeCoST decouples the discrete routing decisions and continuous service-time allocations into two stages. The first stage uses a parallel decoder for path prediction and initial service time allocation, while the second stage optimizes service times through a linear programming formulation.",
  "results": [
    "• DeCoST outperforms state-of-the-art methods in solution quality and computational efficiency.",
    "• Achieves a gap of 1.06% in the TW=100 setting with 15.1 score.",
    "• Reduces the gap from 13.4% to 3.38% when combined with the STO module.",
    "• Maintains high solution quality with only 1329ms runtime for n=500, TW=100."
  ],
  "limitations": [
    "• The integration with non-autoregressive approaches limits batch processing and overall efficiency.",
    "• Performance may degrade as problem size increases, a common trend in combinatorial optimization."
  ],
  "why_useful": "This paper addresses a complex variant of the orienteering problem that is relevant in various real-world applications, such as logistics and robotic planning. The proposed method enhances solution quality and efficiency, making it a valuable tool for practitioners in optimization fields.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code & data available at https://github.com/SwonGao/DeCoST."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 13:15:30

Authors: Kaleen Shrestha, Harish Dukkipati, Avni Hulyalkar, Kyla Penamante, Ankita Samanta, Maja Matarić

Categories: cs.HC

Abstract:
In peer mediation--an approach to conflict resolution used in many K-12 schools in the United States--students help other students to resolve conflicts. For schools without peer mediation programs, socially assistive robots (SARs) may be able to provide an accessible option to practice peer mediation. We investigate how elementary school students react to a peer mediator role-play activity through an exploratory study with SARs. We conducted a small single-session between-subjects study with 12 participants. The study had two conditions, one with two robots acting as disputants, and the other without the robots and just the tablet. We found that a majority of students had positive feedback on the activity, with many students saying the peer mediation practice helped them feel better about themselves. Some said that the activity taught them how to help friends during conflict, indicating that the use of SARs for peer mediation practice is promising. We observed that participants had varying reading levels that impacted their ability to read and dictate the turns in the role-play script, an important consideration for future study design. Additionally, we found that some participants were more expressive while reading the script and throughout the activity. Although we did not find statistical differences in pre-/post-session self-perception and quiz performance between the robot and tablet conditions, we found strong correlations (p<0.05) between certain trait-related measures and learning-related measures in the robot condition, which can inform future study design for SARs for this and related contexts.

            {
  "tldr": "This study explores how elementary students engage in peer mediation role-play with socially assistive robots (SARs) compared to a tablet. Results indicate positive feedback from students, suggesting SARs may enhance conflict resolution training.",
  "whats_new": [
    "• First use of SARs as mock disputants in peer mediation training.",
    "• Positive student feedback indicating improved self-esteem and conflict resolution skills.",
    "• Insights on the impact of reading levels on participation and engagement.",
    "• Correlations found between personality traits and learning outcomes in the robot condition."
  ],
  "method": "A between-subjects study was conducted with 12 elementary students, comparing interactions with SARs versus a tablet during a peer mediation role-play. Participants completed pre- and post-session surveys to assess self-perception and quiz performance.",
  "results": [
    "• 11 out of 12 participants reported feeling better about themselves after the activity.",
    "• No significant differences in self-perception scores between robot (M=-0.5) and tablet (M=0.11) conditions.",
    "• Both conditions showed a small increase in quiz performance (0.2 points on average).",
    "• Strong correlations (p < 0.05) between certain personality traits and learning measures in the robot condition."
  ],
  "limitations": [
    "• Small sample size limits generalizability of findings.",
    "• Reading skills affected participant engagement and performance.",
    "• Single-session design may not capture long-term changes in self-perception.",
    "• Potential bias in responses due to perceived expectations."
  ],
  "why_useful": "This research highlights the potential of SARs in educational settings, particularly for teaching conflict resolution. It opens avenues for further exploration of how technology can support social-emotional learning in schools.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 13:05:19

Authors: Zhao-Yi Zhou, Da-Jian Zhang

Categories: quant-ph

Abstract:
Precise estimation of physical parameters underpins both scientific discovery and technological development. A central goal of quantum metrology and sensing is to exploit quantum resources like entanglement to devise optimal strategies for estimating physical parameters as precisely as possible. While substantial progress has been made in single-parameter quantum metrology, the multiparameter scenario remains significantly more challenging due to the issue of parameter incompatibility. In this work, we present a unified and computable approach for the simultaneous estimation of multiple parameters that attains the ultimate precision permitted by quantum mechanics. The core of our approach is to integrate the quantum tester formalism into the recently proposed tight Cramér-Rao type bound. This formulation enables us to figure out the highest achievable precision via upper and lower bounds that are computable via semidefinite programs. More importantly, within this formulation, diverse quantum resources, including entanglement, coherence, quantum control, and indefinite causal order, are treated on equal footing and systematically optimized for the purpose of achieving the ultimate precision in multiparameter estimation. As a result, our approach is applicable to various metrological strategies both in the presence and absence of noise. To demonstrate its utility, we revisit three-dimensional magnetic-field estimation, uncovering the strengths and limitations of existing analytical results and further establishing a strict hierarchy among different types of strategies.

            {
  "tldr": "This paper presents a unified and computable method for optimal multiparameter estimation in quantum metrology, addressing challenges like parameter incompatibility and noise. It integrates quantum tester formalism with tight Cramér-Rao bounds to achieve ultimate precision.",
  "whats_new": [
    "• Introduces a unified framework for multiparameter estimation using quantum testers.",
    "• Combines quantum tester formalism with tight Cramér-Rao bounds for improved precision.",
    "• Establishes a strict hierarchy among different estimation strategies under noisy conditions.",
    "• Demonstrates application to three-dimensional magnetic field estimation."
  ],
  "method": "The approach formulates the estimation problem as a conic programming task, optimizing over quantum testers and various quantum resources. It derives upper and lower bounds for achievable precision using semidefinite programming.",
  "results": [
    "• Achieves ultimate precision limits in multiparameter estimation.",
    "• Identifies that sequential strategies outperform parallel strategies in noisy environments.",
    "• Confirms that existing heuristic probe states are not optimal for parallel strategies.",
    "• Establishes a strict hierarchy among parallel, sequential, causal-superposition, and indefinite-causal-order strategies."
  ],
  "limitations": [
    "• The implementation of general indefinite-causal-order strategies remains an open problem.",
    "• Further research is needed to rigorously prove the tightness of analytical bounds for small parameter sets.",
    "• The approach may require extensive computational resources for larger systems."
  ],
  "why_useful": "This work provides a comprehensive framework for understanding and optimizing multiparameter quantum metrology, which is crucial for advancements in quantum technologies. It serves as a benchmark for developing optimal strategies in various practical applications.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Code accompanying this work is available at https://github.com/zhaoyizhou98/highest-precision-multiparameter."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 12:55:41

Authors: Stavroula Makri

Categories: math.GT, math.DS

Abstract:
We study the interplay between braid group theory and topological dynamics in three dimensions. While classical braid theory has been extensively applied to surface homeomorphisms to analyze fixed and periodic points, an analogous framework for three-dimensional manifolds has been lacking. In this work, we introduce loop braid groups as a three-dimensional generalization of classical braid groups in order to investigate homeomorphisms of the 3-ball that leave invariant a finite collection of circles. In our main theorem, we associate the Burau matrix representations of loop braid elements with the generalized Lefschetz number. This result provides important information on the existence and interaction of fixed and periodic points. As an application of our theorem, we obtain an estimate for the number of periodic points. Our result extends a classical two-dimensional theorem to the three-dimensional setting, providing a framework in which the topological and algebraic aspects of loop braid groups can be used to study topological dynamical properties.

            {
  "tldr": "This paper introduces loop braid groups as a three-dimensional generalization of classical braid groups to study homeomorphisms of the 3-ball, linking them to the generalized Lefschetz number. It extends existing results from two-dimensional dynamics to three dimensions.",
  "whats_new": [
    "• Introduces loop braid groups for analyzing 3-dimensional homeomorphisms.",
    "• Establishes a connection between Burau matrix representations and the generalized Lefschetz number.",
    "• Extends classical results from surface dynamics to a 3-dimensional setting.",
    "• Provides a method to estimate the number of periodic points in 3-manifolds."
  ],
  "method": "The paper employs loop braid groups to analyze homeomorphisms of the 3-ball that preserve a finite set of circles. It associates Burau matrix representations of loop braid elements with the generalized Lefschetz number to derive results about fixed and periodic points.",
  "results": [
    "• The trace of specific matrices associated with loop braid elements equals the abelianized generalized Lefschetz number.",
    "• Provides a formula relating the trace of the matrix representation to fixed point indices.",
    "• Estimates the number of periodic points, showing |Perp,C(f)| ≥ p(Mp,C - np).",
    "• Extends a two-dimensional theorem to a three-dimensional context."
  ],
  "limitations": [
    "• The results may not provide insights into linking behavior for certain loop braids.",
    "• The applicability of the findings to more complex dynamical systems remains to be explored.",
    "• Further research is needed to establish a precise methodology for studying periodic orbits in 3-manifolds."
  ],
  "why_useful": "This work is significant as it bridges braid theory and topological dynamics in three dimensions, offering new tools for studying fixed and periodic points. Its findings could have implications in various fields, including dynamical systems and topology.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 12:34:27

Authors: Yunlong Chu, Minglai Shao, Yuhang Liu, Bing Hao, Yumeng Lin, Jialu Wang, Ruijie Wang

Categories: cs.CL

Abstract:
Explicit Chain-of-Thought improves the reasoning performance of large language models but often incurs high inference cost due to verbose token-level traces. While recent approaches reduce this overhead via concise prompting or step pruning, they largely truncate what the model says rather than internalize what the model thinks. Latent reasoning offers a promising alternative by performing computation in the hidden space, yet prior methods face two critical challenges. Many existing approaches rely on rigid point-to-point alignment, forcing a latent token to approximate the final representation of a reasoning step, which can be insufficient to capture the dense, variable-length semantics of an entire reasoning segment. Furthermore, these methods often suffer from a lack of interpretability: latent states are commonly produced by unconstrained optimization or embedding mixing, yielding vectors that are difficult to decode or audit under the pretrained language head. We propose SPOT, a flexible framework that compresses explicit CoT into compact latent pause tokens without enforcing a fixed response template. At the core of SPOT is Span-level Semantic Alignment, a Sinkhorn optimal-transport objective that softly matches each pause token to the semantics of an entire reasoning segment, overcoming the rigidity of step-end alignment. To further improve interpretability, SPOT introduces a Frozen-Head Decoding Constraint that keeps latent states directly decodable as token distributions under the frozen pretrained LM head, enabling readable keyword interpretations of latent thoughts. Experiments on reasoning benchmarks demonstrate that SPOT improves accuracy by 2.3 points on average while reducing generated tokens by 37.5% and provides faithful semantic interpretations of the latent reasoning process.

            {
  "tldr": "The SPOT framework compresses explicit reasoning in large language models into compact latent tokens, improving efficiency and interpretability. It achieves better accuracy while reducing output length significantly.",
  "whats_new": [
    "• Introduces SPOT, a novel framework for compressing explicit reasoning into latent tokens.",
    "• Employs Span-level Semantic Alignment using a Sinkhorn optimal transport objective for flexible reasoning.",
    "• Implements a Frozen-Head Decoding Constraint for interpretable latent thoughts.",
    "• Demonstrates significant improvements in accuracy and reduction in token generation across multiple benchmarks."
  ],
  "method": "SPOT compresses explicit Chain-of-Thought (CoT) into latent <pause> tokens, using span-level alignment to match these tokens to entire reasoning segments. It employs a two-stage training process that includes alignment training and rejection-sampled fine-tuning to enhance interpretability and control during inference.",
  "results": [
    "• Achieves an average accuracy improvement of 2.3 points across five benchmarks.",
    "• Reduces the number of generated tokens by 37.5% compared to the baseline model.",
    "• On GSM8K, SPOT improves accuracy by 3.1 points while reducing output length by 52.1%.",
    "• On AIME2025, SPOT achieves 39.33% accuracy (+3.3 points) with a 15.8% reduction in tokens.",
    "• Generalizes well to out-of-domain tasks, achieving 54.55% accuracy on GPQA-Diamond (+4.5 points)."
  ],
  "limitations": [
    "• The framework's performance may degrade with overly dense <pause> insertions, leading to redundancy.",
    "• Further research is needed to explore generalizing the framework beyond heuristic span definitions.",
    "• The interpretability of latent states may still be challenging in complex reasoning scenarios."
  ],
  "why_useful": "SPOT enhances the efficiency of reasoning in large language models by reducing computational overhead while maintaining accuracy. Its interpretability features make it suitable for applications requiring transparent decision-making processes, such as educational tools and automated reasoning systems.",
  "provenance": {
    "page_citations": [],
    "code_data_availability": "Not provided."
  }
}
            

arXiv Page | PDF

Published: 2026-03-06 12:32:37

Authors: Reda El Makroum, Sebastian Zwickl-Bernhard, Lukas Kranzl, Hans Auer

Categories: cs.AI, cs.MA, eess.SY

Abstract:
Residential demand response depends on sustained prosumer participation, yet existing coordination is either fully automated, or limited to one-way dispatch signals and price alerts that offer little possibility for informed decision-making. This paper introduces Conversational Demand Response (CDR), a coordination mechanism where aggregators and prosumers interact through bidirectional natural language, enabled through agentic AI. A two-tier multi-agent architecture is developed in which an aggregator agent dispatches flexibility requests and a prosumer Home Energy Management System (HEMS) assesses deliverability and cost-benefit by calling an optimization-based tool. CDR also enables prosumer-initiated upstream communication, where changes in preferences can reach the aggregator directly. Proof-of-concept evaluation shows that interactions complete in under 12 seconds. The architecture illustrates how agentic AI can bridge the aggregator-prosumer coordination gap, providing the scalability of automated DR while preserving the transparency, explainability, and user agency necessary for sustained prosumer participation. All system components, including agent prompts, orchestration logic, and simulation interfaces, are released as open source to enable reproducibility and further development.

arXiv Page | PDF

Published: 2026-03-06 12:32:24

Authors: Miriam Jäger, Boris Jutzi

Categories: cs.CV

Abstract:
We present a novel Eigenentropy-optimized neighboorhood densification strategy EntON in 3D Gaussian Splatting (3DGS) for geometrically accurate and high-quality rendered 3D reconstruction. While standard 3DGS produces Gaussians whose centers and surfaces are poorly aligned with the underlying object geometry, surface-focused reconstruction methods frequently sacrifice photometric accuracy. In contrast to the conventional densification strategy, which relies on the magnitude of the view-space position gradient, our approach introduces a geometry-aware strategy to guide adaptive splitting and pruning. Specifically, we compute the 3D shape feature Eigenentropy from the eigenvalues of the covariance matrix in the k-nearest neighborhood of each Gaussian center, which quantifies the local structural order. These Eigenentropy values are integrated into an alternating optimization framework: During the optimization process, the algorithm alternates between (i) standard gradient-based densification, which refines regions via view-space gradients, and (ii) Eigenentropy-aware densification, which preferentially densifies Gaussians in low-Eigenentropy (ordered, flat) neighborhoods to better capture fine geometric details on the object surface, and prunes those in high-Eigenentropy (disordered, spherical) regions. We provide quantitative and qualitative evaluations on two benchmark datasets: small-scale DTU dataset and large-scale TUM2TWIN dataset, covering man-made objects and urban scenes. Experiments demonstrate that our Eigenentropy-aware alternating densification strategy improves geometric accuracy by up to 33% and rendering quality by up to 7%, while reducing the number of Gaussians by up to 50% and training time by up to 23%. Overall, EnTON achieves a favorable balance between geometric accuracy, rendering quality and efficiency by avoiding unnecessary scene expansion.

arXiv Page | PDF

Published: 2026-03-06 12:26:42

Authors: K. A. Kukkadapu, P. Li, H. Van Haevermaet, A. N. Koushik, W. Beaumont, N. van Remortel

Categories: physics.ins-det

Abstract:
A detailed characterisation of a combined Linear Variable Differential Transformer (LVDT) position sensor and voice-coil (VC) actuator designed for seismic isolation systems in gravitational wave detectors is presented. A dedicated experimental setup and a FEMM-based finite-element simulation framework were developed to measure and model a representative ETpathfinder Type-A LVDT+VC assembly. The setup employs a precision translation stage and balance to quantify LVDT displacement response and VC force output under controlled conditions. We found good agreement between experiment and simulation: the measured LVDT response was determined with an uncertainty of 0.5% and differed by only 1.3% from the FEMM model prediction, demonstrating high linearity over a +/- 5 mm range. In addition, the VC force measurements agreed within the total uncertainty: the maximum normalised force was determined with a precision of 2.3% and matched the simulated value with only a 0.6% discrepancy. These results validate the combined sensor-actuator design and our measurement methodology. The demonstrated linear response and stable actuation confirm that this LVDT+VC device can be used for low-frequency suspension control. Our framework therefore provides a validated tool to optimise existing sensor and actuator designs and to study novel prototypes for next-generation gravitational wave detectors.

arXiv Page | PDF

Published: 2026-03-06 11:58:46

Authors: Yacin Ameur, Ena Jahic

Categories: math.CV, math-ph, math.PR

Abstract:
We consider two-dimensional Coulomb systems for which the coincidence set contains an outpost in the form of a suitable Jordan curve. We study asymptotics for correlations along the union of the outpost and the outer boundary of the droplet. These correlations turn out to have a universal character and are given in terms of the reproducing kernel for a certain Hilbert space of analytic functions, generalizing the Szegő type edge correlations obtained recently by Ameur and Cronvall. There are several additional results, for example on the effect of insertion of an exterior point charge in the presence of an outpost.

arXiv Page | PDF

Published: 2026-03-06 11:56:31

Authors: Mohammad Reza Farhadinia, Nicolas Olivier

Categories: physics.optics

Abstract:
Providing quantitative interpretation of coherent nonlinear microscopy images, such as third-harmonic generation (THG), is generally hampered by the complex phase-matching conditions, especially in the presence of sample linear heterogeneity. We recently presented a numerical pipeline using the finite-difference time-domain (FDTD) method to take this heterogeneity into account. However, due to software restrictions, we only considered nonlinear materials with diagonal nonlinear susceptibilities. We now expand the recently developed FDTD approach to model nonlinear microscopy for anisotropic materials that obey Kleinman Symmetry, organized in layers along the optical axis, and validate our simulations on well-described geometries.

arXiv Page | PDF

Published: 2026-03-06 11:46:33

Authors: Shuailin Xue, Jun Wan, Lihua Zhang, Wenwen Min

Categories: cs.CV

Abstract:
Accurate detection of cancer tissue regions (CTR) enables deeper analysis of the tumor microenvironment and offers crucial insights into treatment response. Traditional CTR detection methods, which typically rely on the rich cellular morphology in histology images, are susceptible to a high rate of false positives due to morphological similarities across different tissue regions. The groundbreaking advances in spatial transcriptomics (ST) provide detailed cellular phenotypes and spatial localization information, offering new opportunities for more accurate cancer region detection. However, current methods are unable to effectively integrate histology images with ST data, especially in the context of cross-sample and cross-platform/batch settings for accomplishing the CTR detection. To address this challenge, we propose SpaCRD, a transfer learning-based method that deeply integrates histology images and ST data to enable reliable CTR detection across diverse samples, platforms, and batches. Once trained on source data, SpaCRD can be readily generalized to accurately detect cancerous regions across samples from different platforms and batches. The core of SpaCRD is a category-regularized variational reconstruction-guided bidirectional cross-attention fusion network, which enables the model to adaptively capture latent co-expression patterns between histological features and gene expression from multiple perspectives. Extensive benchmark analysis on 23 matched histology-ST datasets spanning various disease types, platforms, and batches demonstrates that SpaCRD consistently outperforms existing eight state-of-the-art methods in CTR detection.

arXiv Page | PDF

Published: 2026-03-06 11:39:20

Authors: Claire Roman, Philippe Meyer

Categories: cs.CV, cs.AI, cs.CL, cs.LG

Abstract:
Learning similarity metrics for glyphs and writing systems faces a fundamental challenge: while individual graphemes within invented alphabets can be reliably labeled, the historical relationships between different scripts remain uncertain and contested. We propose a two-stage framework that addresses this epistemological constraint. First, we train an encoder with contrastive loss on labeled invented alphabets, establishing a teacher model with robust discriminative features. Second, we extend to historically attested scripts through teacher-student distillation, where the student learns unsupervised representations guided by the teacher's knowledge but free to discover latent cross-script similarities. The asymmetric setup enables the student to learn deformation-invariant embeddings while inheriting discriminative structure from clean examples. Our approach bridges supervised contrastive learning and unsupervised discovery, enabling both hard boundaries between distinct systems and soft similarities reflecting potential historical influences. Experiments on diverse writing systems demonstrate effective few-shot glyph recognition and meaningful script clustering without requiring ground-truth evolutionary relationships.

arXiv Page | PDF

Published: 2026-03-06 11:38:05

Authors: Sachin Ballal, Tushar Halder

Categories: math.GR

Abstract:
In this paper, we study the posets of classes of subgroups of finite group having same set of orders of elements. We show that this poset is a chain only in the case of p-groups and moreover, we characterize all finite groups for which this poset is C2, the chain with two elements. We also show that this poset forms a lattice in the case of finite cyclic and dihedral groups and give a characterization when this lattice is distributive and modular.

arXiv Page | PDF

Published: 2026-03-06 11:35:37

Authors: Benyuan Meng, Qianqian Xu, Zitai Wang, Xiaochun Cao, Longtao Huang, Qingming Huang

Categories: cs.CV

Abstract:
As powerful generative models, text-to-image diffusion models have recently been explored for discriminative tasks. A line of research focuses on adapting a pre-trained diffusion model to semantic segmentation without any further training, leading to what training-free diffusion segmentors. These methods typically rely on cross-attention maps from the model's attention layers, which are assumed to capture semantic relationships between image pixels and text tokens. Ideally, such approaches should benefit from more powerful diffusion models, i.e., stronger generative capability should lead to better segmentation. However, we observe that existing methods often fail to scale accordingly. To understand this issue, we identify two underlying gaps: (i) cross-attention is computed across multiple heads and layers, but there exists a discrepancy between these individual attention maps and a unified global representation. (ii) Even when a global map is available, it does not directly translate to accurate semantic correlation for segmentation, due to score imbalances among different text tokens. To bridge these gaps, we propose two techniques: auto aggregation and per-pixel rescaling, which together enable training-free segmentation to better leverage generative capability. We evaluate our approach on standard semantic segmentation benchmarks and further integrate it into a generative technique, demonstrating both improved performance broad applicability. Codes are at https://github.com/Darkbblue/goca.

arXiv Page | PDF

Published: 2026-03-06 11:35:16

Authors: Rosa Cascella, Silvia Properzi, Arne Van Antwerpen

Categories: math.GR, math.QA

Abstract:
A finite non-degenerate set-theoretic solution $(X,r)$ of the Yang-Baxter equation gives rise to a structure skew brace $B(X,r)$ that is a $λ_f$-skew brace, i.e. every element has finitely many $λ$-images, and whose additive group is $FC$. This motivates the study of finiteness conditions on skew braces. We first study the general class of $λ_f$ skew braces and the subclass where the additive group is $FC$, showing that these properties share a resemblance to finite conjugacy, having an analog of the $FC$-center and several analogous structural results. Furthermore, by passing through the structure skew brace of a solution, this property measures whether elements are contained in a finite decomposition factor, identifying a class of infinite solutions that may exhibit similar properties to finite ones. Finally, we show that for a sub skew brace where both groups have finite index, both indices need to coincide and that such a sub skew brace contains a strong left ideal of finite index.

arXiv Page | PDF

Published: 2026-03-06 11:34:18

Authors: Niklas Dexheimer, Natalia Jeszka

Categories: math.ST

Abstract:
We study high-dimensional drift estimation for Lévy-driven Ornstein--Uhlenbeck processes based on discrete observations. Assuming sparsity of the drift matrix, we analyze Lasso and Slope estimators constructed from approximate likelihoods and derive sharp nonasymptotic oracle inequalities. Our bounds disentangle the contributions of discretization error and stochastic fluctuations, and establish minimax optimal convergence rates under suitable choices of tuning parameters in a high-frequency regime. We further quantify the sample complexity required to attain these rates depending on the Lévy noise. The results extend the theory of high-dimensional statistics for stochastic processes to a substantially broader class of noise mechanisms, in particular pure jump processes. They also demonstrate that Lasso and Slope remain competitive for jump-driven systems, providing practical guidance for inference in applications where Lévy processes are a natural modeling choice.

arXiv Page | PDF

Published: 2026-03-06 11:33:08

Authors: Kazimierz Musiał

Categories: math.PR

Abstract:
Let $(X, \mfA,P)$, $(Y, \mfB,Q)$ be two arbitrary probability spaces and $¶:=\{(\mfA,P_y):y\in{Y}\}$ be a regular conditional probability on $\mfA$ with respect to $Q$. Denote by $R$ the skew product of $P$ and $Q$ determined by $\{P_y:y\in{Y}\}$ on the product $σ$-algebra $\mfA\otimes\mfB$ and by $\wh{R}$ its completion. I prove that a process $\{ξ_y:y\in{Y}\}$ possesses an equivalent $\wh{R}$-measurable version if and only if it is measurable with respect to a certain particular $σ$-algebra, larger than $\mfA\otimes\mfB$ and uniquely determined by $¶$. It is known that not every process possesses an equivalent measurable version (cf. \cite[§19.5]{St}). My approach is essentially different from earlier trials. It reverts to \cite[Theorem 3]{ta1}, where Talagrand proved existence of an equivalent separable version of a measurable process (in case of $R=P\times{Q}$), provided $Y$ is endowed with a separable pseudometric. The theorem is a strong generalization of \cite[Theorem 6.1]{smm} and \cite[Theorem 5.1]{mms1} where it was proved only that a suitable class of liftings transfer a measurable process into a measurable process.

arXiv Page | PDF

Published: 2026-03-06 11:19:33

Authors: Andrew Caunes, Thierry Chateau, Vincent Fremont

Categories: cs.CV

Abstract:
Semantic and panoptic occupancy prediction for road scene analysis provides a dense 3D representation of the ego vehicle's surroundings. Current camera-only approaches typically rely on costly dense 3D supervision or require training models on data from the target domain, limiting deployment in unseen environments. We propose FreeOcc, a training-free pipeline that leverages pretrained foundation models to recover both semantics and geometry from multi-view images. FreeOcc extracts per-view panoptic priors with a promptable foundation segmentation model and prompt-to-taxonomy rules, and reconstructs metric 3D points with a reconstruction foundation model. Depth- and confidence- aware filtering lifts reliable labels into 3D, which are fused over time and voxelized with a deterministic refinement stack. For panoptic occupancy, instances are recovered by fitting and merging robust current-view 3D box candidates, enabling instance-aware occupancy without any learned 3D model. On Occ3D-nuScenes, FreeOcc achieves 16.9 mIoU and 16.5 RayIoU train-free, on par with state-of-the-art weakly supervised methods. When employed as a pseudo-label generation pipeline for training downstream models, it achieves 21.1 RayIoU, surpassing the previous state-of-the-art weakly supervised baseline. Furthermore, FreeOcc sets new baselines for both train-free and weakly supervised panoptic occupancy prediction, achieving 3.1 RayPQ and 3.9 RayPQ, respectively. These results highlight foundation-model-driven perception as a practical route to training-free 3D scene understanding.

arXiv Page | PDF

Published: 2026-03-06 11:17:37

Authors: Zikai Zhou, Muyao Wang, Shitong Shao, Lichen Bai, Haoyi Xiong, Bo Han, Zeke Xie

Categories: cs.CV, cs.AI

Abstract:
The growing demand for text-to-image generation has led to rapid advances in generative modeling. Recently, text-to-image diffusion models trained with flow matching algorithms, such as FLUX, have achieved remarkable progress and emerged as strong alternatives to conventional diffusion models. At the same time, inference-time enhancement strategies have been shown to improve the generation quality and text-prompt alignment of text-to-image diffusion models. However, these techniques are mainly applicable to conventional diffusion models and usually fail to perform well on flow models. To bridge this gap, we propose Reflective Flow Sampling (RF-Sampling), a theoretically-grounded and training-free inference enhancement framework explicitly designed for flow models, especially for the CFG-distilled variants (i.e., models distilled from CFG guidance techniques), like FLUX. Departing from heuristic interpretations, we provide a formal derivation proving that RF-Sampling implicitly performs gradient ascent on the text-image alignment score. By leveraging a linear combination of textual representations and integrating them with flow inversion, RF-Sampling allows the model to explore noise spaces that are more consistent with the input prompt. Extensive experiments across multiple benchmarks demonstrate that RF-Sampling consistently improves both generation quality and prompt alignment. Moreover, RF-Sampling is also the first inference enhancement method that can exhibit test-time scaling ability to some extent on FLUX.

arXiv Page | PDF

Published: 2026-03-06 11:16:55

Authors: Ajinkya Kulkarni, Sandipana Dowerah, Atharva Kulkarni, Tanel Alumäe, Mathew Magimai Doss

Categories: cs.SD, cs.AI, cs.CL

Abstract:
Self-supervised learning (SSL) underpins modern audio deepfake detection, yet most prior work centers on a single large wav2vec2-XLSR backbone, leaving compact under studied. We present RAPTOR, Representation Aware Pairwise-gated Transformer for Out-of-domain Recognition a controlled study of compact SSL backbones from the HuBERT and WavLM within a unified pairwise-gated fusion detector, evaluated across 14 cross-domain benchmarks. We show that multilingual HuBERT pre-training is the primary driver of cross-domain robustness, enabling 100M models to match larger and commercial systems. Beyond EER, we introduce a test-time augmentation protocol with perturbation-based aleatoric uncertainty to expose calibration differences invisible to standard metrics: WavLM variants exhibit overconfident miscalibration under perturbation, whereas iterative mHuBERT remains stable. These findings indicate that SSL pre-training trajectory, not model scale, drives reliable audio deepfake detection.

arXiv Page | PDF

Published: 2026-03-06 11:04:21

Authors: Anna Fitriana, Katsuya Tanaka, Lukas Raam Jaeger, Martin Hafermann, Thomas Pertsch, Carsten Ronning, Isabelle Staude

Categories: physics.optics

Abstract:
Resonant chiral dielectric metasurfaces can support circular dichroism exceeding that of natural materials, but their small dissipative losses simultaneously limit the maximization of circular dichroism, which inherently relies on absorption. Importantly, while the condition for optimal circular dichroism in resonant structures can be rigorously formulated based on the concept of critical coupling, controlling the amount of absorption experimentally, and ideally tuning it to the optimal value post-fabrication, remains elusive. Here, we experimentally tailor the dissipative losses of chiral bilayer dielectric metasurfaces post-fabrication using energetic ion beam irradiation. Specifically, we study the transmission characteristics of C4-symmetric chiral metasurface consisting of silicon nanocuboid arrays embedded in silica glass using polarization-resolved spectroscopy. We enhance the circular dichroism from 0.70 in the pristine, unirradiated metasurface to 0.85 after irradiation. Our experimental results are complemented by numerical simulations allowing us to retrieve the refractive index changes induced by the ion beam irradiation in the constituent materials of the metasurface. Our work offers a new approach to globally maximize optical chirality in engineered nanostructures, paving the way towards chiral emission and advanced polarization control applications

arXiv Page | PDF

Published: 2026-03-06 11:04:12

Authors: Alejandro J. González-Santana, Giovanny A. Cuervo-Londoño, Javier Sánchez

Categories: cs.LG, cs.AI, physics.geo-ph

Abstract:
Accurate regional ocean forecasting requires models that are both computationally efficient and capable of representing predictive uncertainty. This work investigates ensemble learning strategies for sea surface temperature (SST) forecasting using Graph Neural Networks (GNNs), with a focus on how input perturbation design affects forecast skill and uncertainty representation. We adapt a GNN architecture to the Canary Islands region in the North Atlantic and implement a homogeneous ensemble approach inspired by bagging, where diversity is introduced during inference by perturbing initial ocean states rather than retraining multiple models. Several noise-based ensemble generation strategies are evaluated, including Gaussian noise, Perlin noise, and fractal Perlin noise, with systematic variation of noise intensity and spatial structure. Ensemble forecasts are assessed over a 15-day horizon using deterministic metrics (RMSE and bias) and probabilistic metrics, including the Continuous Ranked Probability Score (CRPS) and the Spread-skill ratio. Results show that, while deterministic skill remains comparable to the single-model forecast, the type and structure of input perturbations strongly influence uncertainty representation, particularly at longer lead times. Ensembles generated with spatially coherent perturbations, such as low-resolution Perlin noise, achieve better calibration and lower CRPS than purely random Gaussian perturbations. These findings highlight the critical role of noise structure and scale in ensemble GNN design and demonstrate that carefully constructed input perturbations can yield well-calibrated probabilistic forecasts without additional training cost, supporting the feasibility of ensemble GNNs for operational regional ocean prediction.

arXiv Page | PDF

Published: 2026-03-06 11:03:44

Authors: Yezhang Li, Stephan C. Kramer, Matthew D. Piggott

Categories: physics.ao-ph, physics.comp-ph, physics.flu-dyn

Abstract:
This study investigates the use of machine learning based mesh adaptivity, specifically mesh movement methods (UM2N), with depth integrated non-hydrostatic shallow water models. Motivation for this comes from the need for models which balance efficiency and accuracy for use in probabilistic coastal hazard assessment. Implementations are built on the discontinuous Galerkin finite-element (DG-FE) based software, Thetis, which leverages the partial differential equation (PDE) framework Firedrake for automated code generation. Verification on benchmark test cases and validation against laboratory measurements of coastal hazards, focusing on tsunami propagation, run-up, and inundation is performed. In these tests, the UM2N-driven meshes help resolve key non-hydrostatic dynamics and yield numerical solutions in close agreement with reference computations and measured data. Numerical results indicate that the UM2N surrogate based approach significantly accelerates conventional mesh movement techniques and has high robustness over long integration periods and under strongly nonlinear wave conditions.

arXiv Page | PDF

Published: 2026-03-06 11:00:34

Authors: Wenze Li, Zhichen Pan, Lei Qian, Liyun Zhang, Yujie Chen, Dejiang Yin, Baoda Li, Yinfeng Dai, Yaowei Li, Dongyue Jiang, Qiaoli Hao, Menglin Huang, Xingyi Wang, Xianghua Niu, Minglei Guo, Jinyou Song, Shuangyuan Chen

Categories: astro-ph.HE

Abstract:
As most of the companions in the double neutron star systems should be normal pulsars, the Fast Folding Algorithm (FFA), which is suitable for finding these long spin period pulsars, was used to search their possible radio signals. A time domain resampling code PYSOLATOR was used to maximize the available data length by removing the orbital modulation. We collected and processed 272.2 hours observational data taken by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) for the 13 double neutron star systems in its sky. The signal-to-noise ratios of known pulsar signals are obviously improved by this search method, including the detection of a faint pulsar signal which only saw by folding the data. Unfortunately, no companion signals were found among all the 197962 candidates. Geodetic precession of the orbit could enhance detectability in future observations.

arXiv Page | PDF

Published: 2026-03-06 10:54:01

Authors: Yu-Jui Huang, Xiang Yu, Keyu Zhang

Categories: math.OC

Abstract:
For a general entropy-regularized time-inconsistent stochastic control problem, we design a policy iteration algorithm (PIA) and establish its convergence to an equilibrium policy with an exponential convergence rate. The design of the PIA is based on a coupled system of non-local partial differential equations, called the exploratory equilibrium Hamilton--Jacobi--Bellman (EEHJB) equation. As opposed to the standard time-consistent case, policy improvement fails in general and the target value function (now an equilibrium value function) is not even a priori known to exist. To overcome these, we prove that the value functions generated by the PIA form a Cauchy sequence in a specialized Banach space, hence admit a limit, and the rate of convergence is exponential, on the strength of the Bismut--Elworthy--Li formula of stochastic representation. The limiting value function is then shown to fulfill the EEHJB equation, and thus yields an equilibrium policy in a Gibbs form. Such convergence in value implies uniform convergence of the generated policies to the eventual equilibrium policy, again with an exponential rate. As a byproduct, the PIA gives a constructive proof of the global existence and uniqueness of a classical solution to our general EEHJB equation, whose well-posedness has not been explored in the literature.

arXiv Page | PDF

Published: 2026-03-06 10:52:49

Authors: Michael Grimes, Clifford J. Allington, Hiroki Ueda, Carl P. Romao, Kurt Kummer, Puneet Kaur, Li-Shu Wang, Yao-Wen Chang, Jan-Chi Yang, Shih-Wen Huang, Urs Staub

Categories: cond-mat.mtrl-sci, cond-mat.str-el

Abstract:
Lattice vibrations carrying angular momentum, known as chiral phonons, have emerged as a promising route to control and understand complex material properties, yet their deterministic manipulation remains largely unexplored. Here we demonstrate electric-field switching of phonon angular momentum in the technologically relevant ferroelectric BaTiO3. Using circularly dichroic resonant inelastic X-ray scattering (CD-RIXS) at the oxygen K edge, we directly probe the phonon angular momentum and compare the measured dichroism with first-principles predictions of phonon-mode chirality. We find excellent agreement, revealing a momentum-dependent circular-dichroism contrast that exhibits a reversible gyroelectric effect, stable for at least 15 hours. Our results establish a robust mechanism for non-volatile control of chiral phonons and point towards new opportunities for phonon-based information and energy technologies.

arXiv Page | PDF

Published: 2026-03-06 10:50:41

Authors: Björn van Zwol

Categories: cs.LG, cond-mat.dis-nn, cs.AI, cs.NE, stat.ML

Abstract:
Predictive coding graphs (PCGs) are a recently introduced generalization to predictive coding networks, a neuroscience-inspired probabilistic latent variable model. Here, we prove how PCGs define a mathematical superset of feedforward artificial neural networks (multilayer perceptrons). This positions PCNs more strongly within contemporary machine learning (ML), and reinforces earlier proposals to study the use of non-hierarchical neural networks for ML tasks, and more generally the notion of topology in neural networks.

arXiv Page | PDF

Published: 2026-03-06 10:39:49

Authors: Stefan Kolb, Milen Yakimov

Categories: math.QA, math.RT

Abstract:
We prove that the star product for quantum symmetric pair coideal subalgebras is short. We apply this result to obtain new conceptual proofs, from first principles, of several fundamental facts about quantum symmetric pairs. In particular, we establish the existence of the algebra anti-automorphism $σ_τ$ and of the bar involution, without making use of the quasi K-matrix. We give a new elementary proof of a conjecture by Balagović and Kolb, sometimes referred to as the fundamental lemma for quantum symmetric pairs. We obtain a conceptual formula expressing the tensor quasi K-matrix in terms of the much studied quasi R-matrix and the Letzter map. This also allows for a new independent proof of the intertwiner property of the quasi K-matrix.

arXiv Page | PDF

Published: 2026-03-06 10:38:51

Authors: Yuewei Li, Dalin Zhang, Huan Li, Xinyi Gong, Hongjun Chu, Zhaohui Song

Categories: cs.LG

Abstract:
Time series anomaly detection has achieved remarkable progress in recent years. However, evaluation practices have received comparatively less attention, despite their critical importance. Existing metrics exhibit several limitations: (1) bias toward point-level coverage, (2) insensitivity or inconsistency in near-miss detections, (3) inadequate penalization of false alarms, and (4) inconsistency caused by threshold or threshold-interval selection. These limitations can produce unreliable or counterintuitive results, hindering objective progress. In this work, we revisit the evaluation of time series anomaly detection from the perspective of detection semantics and propose a novel metric for more comprehensive assessment. We first introduce a partitioning strategy grounded in detection semantics, which decomposes the local temporal region of each anomaly into three functionally distinct subregions. Using this partitioning, we evaluate overall detection behavior across events and design finer-grained scoring mechanisms for each subregion, enabling more reliable and interpretable assessment. Through a systematic study of existing metrics, we identify an evaluation bias associated with threshold-interval selection and adopt an approach that aggregates detection qualities across the full threshold spectrum, thereby eliminating evaluation inconsistency. Extensive experiments on synthetic and real-world data demonstrate that our metric provides stable, discriminative, and interpretable evaluation, while achieving robust assessment compared with ten widely used metrics.

arXiv Page | PDF

Published: 2026-03-06 10:36:18

Authors: Dorothee D. Haroske, Susana D. Moura, Leszek Skrzypczak

Categories: math.FA

Abstract:
We study embeddings within different scales of generalised smoothness Morrey spaces defined on bounded smooth domains, i.e., in $\mathcal{N}^s_{\varphi,p,q}(Ω)$, $\mathcal{E}^s_{\varphi,p,q}(Ω)$, $B^{s,\varphi}_{p,q}(Ω)$ and $F^{s,\varphi}_{p,q}(Ω)$ spaces. We prove sufficient conditions for continuity and compactness of the embeddings. In some cases the conditions are also necessary. We generalise and even improve some earlier results known for the classical smoothness Morrey spaces. Our approach is based on wavelet characterisation of the function spaces.

arXiv Page | PDF