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新论文 · v1arXiv:2611.15359

MetaCom: Meta Codebook Learning for Robust V2X Semantic Communications

Muge Yu, Danlan Huang, Xinyi Zhou, Shuqi Chai, Zhiyong Feng

原文摘要

Collaborative perception in Vehicle-to-Everything (V2X) networks faces dual challenges: strictly limited bandwidth and channel impairments. Existing static quantization schemes often suffer from feature manifold drift caused by highly non-stationary traffic environments. To address this, we propose MetaCom, a robust digital semantic communication framework featuring a Meta-Codebook Learning paradigm. First, we introduce a Differentiable Quantized Semantic Communications (DQSemCom) module trained with perturbations induced by the simulated digital modulation and channel via Gumbel-Softmax relaxation. Building on this, we design a Bi-Level Meta-Evolution Mechanism (Bi-MEM) that treats manifold drift as a sequence of rapid adaptation tasks, aligning the codebook via data-level smoothing and parameter-level meta-updates. Furthermore, we validate MetaCom under AWGN, Rayleigh, and Rician fading channels. Experimental results demonstrate that MetaCom achieves superior trade-offs between perception precision and communication overhead, outperforming state-of-the-art methods by a significant margin under low-SNR conditions.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15357

Multimodal Semantic Communication using Shared and Unique Representation Decomposition

Peng Yi, Ying-Chang Liang

原文摘要

Semantic communication (SemCom) has emerged as a promising paradigm for bandwidth-constrained mobile and vehicular networks. This paper proposes a unified representation learning framework for multimodal SemCom (MSC), which encodes heterogeneous data into a compact semantic space for efficient transmission and source recovery. The framework addresses the multimodal semantic representation gap, which arises when at least one modality contains semantic information that cannot be fully inferred from the other. To preserve this information for reconstruction, the framework decomposes each representation into shared and modality-specific components. We formulate a mutual information objective for source reconstruction, cross-modal alignment of the shared components, and decoupling of the shared and modality-specific components. To enhance transmission efficiency, we introduce a shared knowledge base implemented as a compact codebook of semantic vectors learned during training. The shared components are transmitted digitally as codebook indices, while the modality-specific components are sent in analog form. We apply knowledge distillation during training, using a pretrained vision foundation model to guide the learning of quantized shared features. On image--text pairs over additive white Gaussian noise (AWGN) and Extended Vehicular A (EVA) channels, the proposed framework improves image reconstruction over conventional separate source-channel coding (SSCC) and representative multimodal and unimodal SemCom systems. Further evaluations show gradual degradation under channel-estimation mismatch, complementary effects of alignment, decoupling, and knowledge distillation on the shared representation, and alignment of shared features across modalities with separation of the modality-specific features.

cs.IT · eess.SP

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.00950

Equation-Free Digital Twins for Nonlinear Structural Dynamics

Mohammad Mahdi Abaei, Ahmad BahooToroody, Arttu Polojärvi, Heikki Remes, Ulf Tyge Tygesen, Mikko Suominen, Michael Beer

原文摘要

Monitoring floating offshore wind turbines (FOWTs) under stochastic excitation and sensor failure requires computationally efficient and physically interpretable estimators. This paper introduces an equation-free, output-only digital twin framework combining Hankel dynamic mode decomposition (Hankel-DMD), sparse-sensor reconstruction, and a stochastic Kalman extension. A sufficient identifiability condition is derived for modal amplitudes recovered from delay-embedded measurements, while Tikhonov regularization addresses ill-conditioned sensor layouts. Correlation-domain identification improves damping estimates under stochastic forcing and distinguishes structural modes from rotor harmonics. The framework is validated using OpenFAST simulations of an NREL 5-MW spar turbine and experimental basin-test measurements from a 1:70-scale IEA 15-MW semi-submersible. In simulations, the identified tower modes agree with established operational modal analysis methods (MAC at least 0.99), while the Kalman estimator achieves R-squared values of 0.91 to 0.99 under severe sensor sparsity. Experimental results identify tower modes at 0.430 and 0.415 Hz with MAC of 1.00 against SSI-COV and reconstruct tower-base bending moments on unseen runs with R-squared values of 0.83 to 0.89 using three of four sensors. The framework provides a computationally efficient, sensor-failure-resilient structural-state estimation layer for digital twins.

eess.SP · cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.24921

CineWild: Balancing Art and Robotics for Ethical Wildlife Documentary Filmmaking

Pablo Pueyo, Fernando Caballero, Ana Cristina Murillo, Eduardo Montijano

原文摘要

Unmanned aerial vehicles (UAVs) have become powerful tools across domains from industry to the arts. In documentary filmmaking, they offer dynamic, otherwise unreachable perspectives, transforming how stories are told. Wildlife documentaries especially benefit, yet UAVs also raise ethical concerns like the risk of disturbing the animals they aim to capture. This paper introduces CineWild, a model predictive control (MPC) formulation for autonomous aerial cinematography that combines robotics, cinematography, and ethics. Building on an MPC solution that jointly controls drone motion and camera focal length, CineWild dynamically adjusts flight paths and camera settings to balance cinematic quality with animal welfare. Key features include adaptive zoom for filming from acoustic and visual safe distances, path-planning that avoids an animal's field of view, and smooth, low-noise maneuvers. CineWild exemplifies interdisciplinary innovation, bridging engineering, visual storytelling, and environmental ethics. We evaluate the formulation in simulation, with ablation studies and a comparison against a cinematographic baseline and a welfare-aware baseline under identical weights, and we demonstrate the perception and camera-actuation pipeline in real-world proof-of-concept trials. We will release the code.

cs.RO · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.03664

Morphology-Aware Implicit Super-Resolution Network for Pathological Images

Jiaming Liang, QiHui Han, Haolin Chen, Chengxin Ye, Jiawen Liu, Jiazhou Chen, Xiaoqi Sheng, Hongmin Cai

原文摘要

Accurate diagnosis in Digital Pathology (DP) relies on high-resolution whole-slide images, yet clinical deployment is often limited by hardware costs. Super-Resolution (SR) offers a promising alternative by computationally enhancing low-resolution acquisitions. However, existing SR methods frequently struggle to preserve fine-grained cellular morphology, leading to texture oversmoothing and blurred structural boundaries under complex tissue variability. To address this issue, we propose Morph-ISR, a morphology-aware implicit super-resolution framework for DP that restores diagnostically relevant details with sub-pixel precision. Morph-ISR reformulates SR as a continuous coordinate-based reconstruction problem and integrates an Implicit Position-aware Kernel Generator (IPKG) to adaptively model spatially varying tissue morphology. To further enhance structural fidelity, a Morphological Fidelity Prior (MFP) is introduced, leveraging semantic guidance from a pre-trained cell segmentation network to enforce boundary-preserving and region-aware reconstruction, thereby improving the representation of critical cellular boundaries and nuclear textures. Experiments on TCGA and SurGen datasets show that Morph-ISR achieves the best LPIPS and ST-LPIPS among the evaluated methods, reducing them by up to 38.37% and 39.55%, respectively, over the second-best methods while maintaining strong PSNR and SSIM. These results demonstrate superior preservation of diagnostically relevant cellular boundaries and nuclear textures, while compact parameterization and high throughput support efficient edge deployment. Code and trained models will be released upon publication.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13711

Hint, Not Hole: Localization-Robust Video Overlay Removal

Dongyang Zhang, Wei Zhang, Renjie Zou, Zhiwei Huang

原文摘要

Video overlay removal recovers the scene beneath burned-in subtitles, karaoke lyrics, titles, credits, logos and watermarks. Generative video models now fill a known region convincingly, but in practice the region comes from a detector or a segmenter, and it is wrong in both directions: it misses strokes, halos, whole frames and whole objects, and it covers background between characters, around boxes and on genuine scene text. Erasers that treat the mask as a hole, regenerating inside and copying outside, inherit every one of these errors. We present HintErase, which treats localization as a hint, not a hole. The model sees the full, unmasked video together with a hint channel; during training the hint is corrupted the way real localizers fail, while supervision follows the true overlay. The result is a single model that removes overlay the hint missed, keeps background the hint wrongly covered, and is trained to keep genuine scene text even when the hint lands on it. For full-length releases, a streaming pipeline handles interlaced sources, shot cuts, missed and jittering hints, paste-back and audio, with memory independent of the length of the video. This report describes the approach, the pipeline that turns it into full-length deliveries, and qualitative results; video results, with input, hint and output side by side, are on the project page.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15348

Breath-Dx: A Time Series Classification Benchmark for Disease Screening with eNose

Ivan Poddiakov, Ivan Kruzhilov, Galina Zubkova, Svetlana Erofeeva, Andrey Savchenko, Pavel Blinov

原文摘要

Electronic nose (eNose) technology offers a portable, non-invasive route for disease screening via exhaled breath analysis. However, progress in the field is hindered by the lack of systematic benchmarks that compare machine learning methods under realistic conditions, particularly sensor drift, a known but rarely quantified failure mode. Most existing studies rely on small, private datasets with inconsistent protocols, rendering results non-comparable and slowing progress. We introduce Breath-Dx, the first comprehensive benchmark of time series classification methods for breath-based disease screening. Our work leverages the Scent of Health (S-OH) dataset, the largest public clinical eNose collection to date, comprising 1,234 patients across nine diagnostic groups, each providing a 17-channel multivariate time series with explicit temporal train/test splits that account for sensor drift. We systematically evaluate 10 methods spanning four families: feature-based tabular learners, convolution-based models, graph-based, and time series architectures. All methods degrade significantly under temporal validation (0.05-0.12 AUC drop compared to random cross-validation), confirming that drift embeds a strong temporal signal. Under this realistic evaluation, the best methods achieve ROC AUC of 0.74-0.76 for some lung conditions, though others remain near chance. Recurrent architectures show the most consistent performance across diseases, while specialized time series methods achieve the highest peak results. Demographic confounders matter: breath signals provide independent diagnostic value for some conditions but add little beyond age for others. We release the S-OH dataset, all temporal splits, evaluation protocols, and code to enable reproducible research and accelerate progress in clinical breathomics.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15346

Towards Better Legal Reasoning LLMs: Signal Balancing and Reward Scheduling

Yinghao Hu, Qian Xiao, Yuting Huang, Kaisong Song, Yiquan Wu, Ang Li, Junjie Cao, Jun Lin 等 10 位作者

原文摘要

In the legal domain, LLMs can improve reliability and interpretability by generating explicit reasoning traces. However, training LLMs to produce high-quality reasoning traces remains challenging. Existing supervised fine-tuning methods struggle when reasoning traces are significantly longer than final answers, as the learning signal for the answer becomes diluted. Meanwhile, reinforcement learning methods such as Group Relative Policy Optimization also have several limitations, including costly reward design, performance plateaus, and reward hacking. We therefore propose a two-stage training framework. In Stage I, JurisCoT-SFT employs a length-normalized dual objective to balance learning signals between reasoning traces and final answers. In Stage II, Lifecycle-Aware Backtrackable Policy Optimization dynamically evaluates and retires auxiliary reward signals based on their impact on final evaluation objectives, enabling efficient reward utilization. Our framework substantially improves legal reasoning performance on relevant tasks selected from multiple legal benchmarks, including ChJGBench, LawBench, and LegalBench. Experimental results demonstrate consistent improvements in both reasoning trace quality and final answer accuracy, while our models remain competitive with larger models.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2407.10204

Improving Graph Out-of-distribution Generalization Beyond Causality

Can Xu, Yao Cheng, Jianxiang Yu, Haosen Wang, Jingsong Lv, Yao Liu, Xiang Li

原文摘要

Existing methods for graph out-of-distribution (OOD) generalization primarily rely on empirical studies on synthetic datasets. Such approaches tend to overemphasize the causal relationships between invariant sub-graphs and labels, thereby neglecting the non-negligible role of environment in real-world scenarios. In contrast to previous studies that impose rigid independence assumptions on environments and invariant sub-graphs, this paper presents the theorems of environment-label dependency and mutable rationale invariance, where the former characterizes the usefulness of environments in determining graph labels while the latter refers to the mutable importance of graph rationales. Based on analytic investigations, a novel variational inference based method named ``Probability Dependency on Environments and Rationales for OOD Graphs on Real-world Data'' (DEROG) is introduced. To alleviate the adverse effect of unknown prior knowledge on environments and rationales, DEROG utilizes generalized Bayesian inference. Further, DEROG employs an EM-based algorithm for optimization. Finally, extensive experiments on real-world datasets under different distribution shifts are conducted to show the superiority of DEROG. Our code is publicly available at https://github.com/LEOXC1571/DEROG.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.14821

The C-index Multiverse

Begoña B. Sierra, Colin McLean, Peter S. Hall, Catalina A. Vallejos

原文摘要

Quantifying out-of-sample discrimination performance for time-to-event outcomes is a fundamental step for model evaluation and selection in the context of predictive modelling. The concordance index, or C-index, is a widely used metric for this purpose, particularly with the growing development of machine learning methods. Beyond differences between proposed C-index estimators (e.g. Harrell's, Uno's and Antolini's), we demonstrate the existence of a C-index multiverse among available R and python software, where seemingly equal implementations can yield different results. This can undermine reproducibility and complicate fair comparisons across models and studies. Key variation sources include tie handling and adjustment to censoring. Additionally, the absence of a standardised approach to summarise risk from survival distributions, result in another source of variation dependent on input types. We demonstrate the consequences of the C-index multiverse when quantifying predictive performance for several survival models (from Cox proportional hazards to recent deep learning approaches) on publicly available breast cancer data, and semi-synthetic examples. Our work emphasises the need for better reporting to improve transparency and reproducibility. This article aims to be a useful guideline, helping analysts when navigating the multiverse, providing unified documentation and highlighting potential pitfalls of existing software. All code is publicly available at: www.github.com/BBolosSierra/CindexMultiverse.

stat.ML · cs.LG · stat.AP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15343

Hybrid Classical-Quantum Optimization for CVRPTW: A Systems Framework with Provable Feasibility and Empirical Hardware Limits

Syamasundar Gopasana, Swatantra Singh, Rohan Joydhar, Shubhashis Sengupta

原文摘要

Solving large-scale Capacitated Vehicle Routing Problems with Time Windows (CVRPTW) remains challenging because strict operational constraints and scale limit both exact and heuristic methods. We present a hybrid classical-quantum architecture combining Mixed Integer Linear Programming (MILP) with vehicle-level Quadratic Unconstrained Binary Optimization (QUBO), solved using Toshiba SQBM+ and QAOA on Rigetti Ankaa-3 and IQM Emerald quantum processors. The decomposition avoids the variable growth and constraint-enforcement difficulties of monolithic CVRPTW QUBO formulations. We evaluate the framework on five realistic benchmarks derived from London traffic and location data, spanning 10--70 customers, using a validation pipeline enforcing feasibility and monotone improvement over classical baselines. SQBM+ achieves verified fleet-distance reductions of 2.80% (7,927 m) for the 25-customer instance and 0.22% (1,186 m) for the 55-customer instance. QAOA experiments establish a current hardware boundary for reliable constraint satisfaction at subproblems of up to 4 customers (16 qubits). Diagnostic results identify assignment-level time-window tightness, rather than local route optimization, as the dominant scalability bottleneck. The results demonstrate a practical near-term role for hybrid classical-quantum optimization while establishing empirical limits for current quantum hardware.

quant-ph · cs.ET

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15342

MechBench: A Domain-Specific LLM Benchmark for Parallel Mechanisms

Zhihao Xia, Tingting Wang, Hui Li, Yongheng Xing, Chunxu Tian

原文摘要

Parallel mechanisms pose distinctive challenges to large language models (LLMs), as valid reasoning depends jointly on mechanism topology, loop closure, geometry, joint types, and modeling assumptions. Yet existing LLM benchmarks provide little systematic coverage of such domain-specific knowledge, constraint-sensitive analysis, and constructive design. To fill this gap, we introduce MECHBENCH, a 2,541-question benchmark for evaluating LLMs in parallel mechanisms across professional knowledge, mechanism analysis, and design. MECHBENCH contains standard and challenge tiers spanning six capability categories and nine executable analysis and design tasks. It is constructed through MECHFORGE, a domain-grounded framework that combines expert-organized knowledge, LLM-assisted question generation, programmatic instance generation, two-stage expert review, and task-aware scoring with exact matching, rubric-based evaluation, and program verification. We further develop MECHGPT as a diagnostic domain-adapted model. Experiments show that strong knowledge performance does not directly translate into executable mechanism reasoning: leading closed-service models achieve over 95 on the standard tier but only 47-60 on the challenge tier, while domain adaptation substantially improves MechGPT over its Qwen3-8B baselines but leaves a large gap in constrained analysis and design. These results establish MECHBENCH as a systematic testbed for measuring domain-specific LLM capabilities in mechanism science.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.02319

Optimal Privacy-Utility Trade-Offs in LDP: Functional and Geometric Perspectives

Seung-Hyun Nam, Hyun-Young Park, Si-Hyeon Lee

原文摘要

Local differential privacy (LDP) has emerged as a gold-standard framework for privacy-preserving data analysis. However, characterizing the optimal privacy-utility trade-off (PUT) and the corresponding optimal LDP channels remains largely fragmented, relying on problem-specific, case-by-case analyses. In this work, we develop a unified theoretical framework that systematically characterizes the optimal PUT and optimal LDP channels for general privacy-preserving statistical decision-making problems. We first identify key functional properties of Bayesian and minimax risks as functions of the LDP channel, including the data processing inequality (DPI), direct-sum quasi-convexity (or additivity), concavity, and symmetry invariance. Leveraging these properties, we reduce the optimization domain required to compute the optimal PUT. To this end, we reinterpret the existing result implying the one-to-one correspondence between maximal LDP channels under the Blackwell order and a finite-dimensional polytope, through an alternative convex geometric derivation. This result renders the optimal PUT computationally tractable via vertex enumeration or linear programming. Furthermore, when the underlying problem exhibits symmetries characterized by a transitive group action, we show that the optimal PUT can be computed by comparing finitely many values, rather than optimizing over a high dimensional polytope. Our framework applies broadly beyond risk minimization, encompassing the maximization of information-theoretic measures. We demonstrate the efficacy of our theoretical framework by recovering or strengthening several known results, and deriving exact analytic expressions for the optimal PUTs in specific tasks that were previously unaddressed.

cs.CR · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15341

Janus-MOPD: Tracing the Student Learning Dynamics in Multi-Teacher On-Policy Distillation

Zongji Yu, Ming Zhang, Dongcheng Zhang, Jiawei Zhou, Haotian Pang, Yiliu Sun, Xiang Wang, Youbang Sun 等 10 位作者

原文摘要

Multi-domain capabilities are critical for practically applicable Large Reasoning Models (LRMs). Therefore, Multi-teacher On-Policy Distillation (MOPD) has become a crucial part of the post-training process, integrating the capabilities of multiple domain-specific teacher models into a single student model. However, the student usually fails to acquire multi-domain capabilities simultaneously due to undesirable domain heterogeneity. Existing works have address this problem in view of temporal uneven efficiency and spatial hard allocation in external multi-teacher supervision. While in broader multi-domain training, varying degrees of performance degradation is still largely unexplained. In this paper, we shift the focus on how the student learns and integrates multi-domain capabilities internally, and then propose \textbf{\textsc{Janus-MOPD}}, a general learning dynamics framework of geometric representations for LRMs. Specifically, our main focus is on the internal performance of multi-domain capabilities on both temporal and spatial dimensions within the student. Temporally, our study shows that the student exhibits a Expansion-Consolidation-Optimization (ECO) three-phase learning dynamics, together with inherent limitations in learning efficiency and scaling. Spatially, we summarize two types of subspace locking within and across domains by tracing the representation space of the student, while explaining its clear learning directions but inherent cross-domain conflict. Therefore, \textsc{Janus-MOPD} yields a unified learning dynamics formulation, a geometric tracing methodology, and systematic findings of how the student learns and integrates multi-domain capabilities. Overall, we provide a \textsc{Janus} perspective to internally trace the continual evolution of the multi-domain capabilities within the model itself.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.16764

ReLU Networks for Model Predictive Control: Network Complexity and Performance Guarantees

Xingchen Li, Keyou You

原文摘要

Recent years have witnessed a resurgence in using ReLU neural networks (NNs) to represent model predictive control (MPC) policies. However, determining the sufficient network complexity to ensure closed-loop guarantees remains a fundamental open problem. This involves a critical precision-complexity trade-off: undersized networks may fail to capture the MPC policy, while oversized ones may outweigh the benefits of ReLU network approximation. In this work, we project the MPC actions onto a tightened constraint set to satisfy the hard constraints, and establish a state-dependent Lipschitz continuity property for the optimal MPC cost function, which enables sharp convergence analysis of the closed-loop system. For the first time, we derive explicit bounds on ReLU network width and depth for approximating MPC policies with closed-loop guarantees. To reduce network complexity for the convergence to user-specified target sets, we propose a non-uniform error framework with a state-aware scaling function to adaptively adjust both the input and output of the ReLU network. Our contributions provide a foundational step toward certifiable ReLU NN-based MPC.

eess.SY · cs.LG · math.OC

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2510.01720

Constructions of Efficiently Implementable Boolean Functions with Provable Nonlinearity/Resiliency/Algebraic Immunity Trade-Offs

Palash Sarkar

原文摘要

We describe new families of efficiently implementable Boolean functions achieving provable trade-offs between resiliency, nonlinearity, and algebraic immunity. Compared to one direction of research on balanced and 1-resilient functions with optimal algebraic immunity, the new functions provide lower algebraic immunity, but much lower -- in fact, almost optimal -- linear bias and importantly are much more efficient to implement. Compared to another direction of research based on constructions using direct sum, the new functions while achieving similar efficiency, has less number of variables for the same order of resiliency, same lower bound on algebraic immunity, and similar nonlinearity.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15339

Interactive Perception of Granular Materials via Robotic Scooping and Force-Torque Sensing

Samuli Hynninen, Mohammad Amman, Meri Mäkelä, Anirvan Dutta, Ville Kyrki

原文摘要

Granular materials exhibit complex and highly variable interaction dynamics, making their reliable manipulation challenging without knowledge of their physical properties. We present an interactive perception framework that infers granular material properties directly from force-torque measurements acquired during scooping. The proposed Latent Filter++ combines a bio-inspired frequency-temporal filter-bank representation with a causal latent dynamical model, allowing the representation to evolve as interaction observations become available while avoiding hand-crafted signal preprocessing. The learned latent representation captures physically meaningful variation in granular materials and supports estimation of properties including grain size and bulk density. Evaluated on 26 diverse granular materials, the approach generalizes to materials excluded from training and enables few-shot adaptation of property-specific mappings using a small number of additional interaction samples. Finally, we demonstrate the relevance of the inferred properties to downstream manipulation by examining their correlation with the maximum acceleration the materials can withstand without spilling from the spoon during transport.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15338

Matrix-Based Ant Colony Search with Batched Decoding for Hierarchical Replenishment Routing: A Stylized Case Study Based on Korean City Data

Jun Zhang, Wei-Jie Yu, Tong Qian, Eui Jeong Kwak, Sang-Woon Jeon, Hua Ding, Qijia Jiang, Hu Jin 等 10 位作者

原文摘要

We study a stylized single-vehicle replenishment-routing problem in which the cities of a national logistics network are both demand points and supply points: a vehicle of limited capacity delivers to every city once, and when its load is insufficient for the next city it must detour to a city already served that can still supply goods. A 100-node benchmark is built from Korean population and geographic data, with the cities divided into 10 levels; the per-visit supply coefficient and the allowed number of stops decrease geometrically with the level. Building on matrix-based ant colony optimization, we keep the states of the whole colony in arrays, couple route construction with a fixed greedy replenishment-decoding policy, and evaluate the candidate moves of a local search in batches. With frozen parameters and 30 runs on seeds never used for tuning, the matrix-based ant colony search with the batch local search (MACO) reaches a mean total distance of 3081.7 km; a genetic algorithm equipped with the same local search at the same frequency reaches 3074.9 km (no significant difference), and a MAX-MIN ant system with the same local search 3142.2 km. Ablation identifies the batch local search as the main contributor to solution quality; a load-aware transition factor gives no significant gain. Within the parameter ranges tested, raising the supply coefficients of the cities below the top level shortens the route more than relaxing the stop limits, which are not binding here.

cs.NE · cs.AI · cs.CE · math.OC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.02719

Large Language Model-Driven Context-Aware Eco-Feedback Generation and Evaluation

Wooyoung Jung, Prosper Babon-Ayeng

原文摘要

The objective of this study is to demonstrate the potential of generating context-aware eco-feedback - eco-feedback that reflects a household's contextual characteristics alongside its energy use patterns - through a large language model-integrated framework. Previous studies have introduced personalized eco-feedback, mostly relying on household energy use patterns; however, they frequently did not reflect distinct household characteristics, including their persona or non-negotiable routines, leaving eco-feedback ineffective and sometimes superficial. To address these limitations, we introduce a contextual engineering framework that generates eco-feedback using a self-consistency with chain-of-thought prompt, leveraging household energy analysis data, utility rate structures, and household characteristic information. We conducted a rigorous empirical validation and a combinatorial evaluation analysis to assess this framework systematically. The former tested the framework's ability to generate accurate and contextually grounded eco-feedback for three households by comparing its output against reference interventions independently derived from the same household data. The latter examined the framework's adaptability across 400 scenarios spanning 50 households, two utility rate structures, and four behavioral personas. Our framework generated eco-feedback that aligned with reference interventions at a mean rate of 92.0% and grounded its recommendations in the provided household data with 95.7% citation accuracy. It also proved highly adaptive, shifting both the appliances targeted and the energy-saving strategies recommended in response to rate structure and household context. Ultimately, this study contributes to realizing the next level of context-aware interactions between occupants and buildings which paves the way for higher occupant living quality and sustainability.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.05243

GUSH3R: Everyone Everywhere All at Once as Gaussians

Keito Abe, Kaede Shiohara, Takashi Otonari, Toshihiko Yamasaki

原文摘要

Reconstructing dynamic human-scene environments from monocular videos is a challenging problem that requires jointly modeling scene geometry, camera motion, and non-rigid human dynamics while enabling photorealistic rendering. Recent feed-forward methods can efficiently predict geometry, but they are often limited to non-photorealistic representations such as point clouds and meshes, or they fail to handle non-rigid objects, particularly dynamic humans. To fill this gap, we present GUSH3R (Gaussian-Unified Scene Human 3D Reconstruction), a feed-forward framework for online dynamic human-scene reconstruction. From a monocular human-scene video, our method reconstructs dynamic humans (everyone) and static scenes (everywhere) in a single forward pass (all at once) as 3D Gaussian Splatting (3DGS) primitives (as gaussians), which are geometrically consistent and capable of novel view synthesis. Experiments on monocular human-scene datasets demonstrate that our approach achieves competitive novel view synthesis quality while significantly improving inference efficiency compared to optimization-based methods.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.11465

What Score Distillation Transfers to Compact Dense Retrievers

Kirill Dubovikov, Martin Takac, Salem Lahlou

原文摘要

Compact retrievers are often specialized by distilling a large embedding model's similarity scores on hard negatives mined by the teacher model. We argue that this common setup calls for a different loss than the scale-dependent ones usually used, such as MarginMSE and PairMSE. When the teacher discards candidates scored too close to the labeled positive, the negatives it keeps get nearly identical scores. These losses then mostly ask the student to reproduce this tiny score range rather than the teacher's ranking. A per-row Pearson correlation loss, invariant to the shift and scale of the scores in each row (a query and its 32 candidates), avoids this failure, and removing PairMSE's scale term alone recovers most of Pearson's lead. We propose row scale dispersion, computed from the untrained student before training, to tell when this matters. With this loss, on rows mined around a labeled document, the teacher's scores substitute for relevance labels rather than add to them. Training on the binary label alone works as well, with the same loss or, for one student, a contrastive loss: the teacher's value lies in choosing the negatives. Without labels, the teacher's scores on its own top-ranked documents recover most of the labeled gain, largely through its top document as a pseudo-label. Experiments with two compact students on three BEIR datasets support both conclusions, including, for one student, comparisons with MarginMSE and a tuned listwise KL. We suggest a simple practical rule: distill teacher-mined negatives with a scale-free loss and, on such rows, use the teacher's scores where labels are missing rather than on top of them.

cs.IR

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新论文 · v1arXiv:2611.15329

TabOmics: A Small, Open Tabular Foundation Model for Wide Omics Data

Andrea López-García, Luis Bote-Curiel, Francisco Javier Gimeno-Blanes, José Luis Rojo-Álvarez, Sergio Muñoz-Romero

原文摘要

Omics studies often produce tables with thousands to tens of thousands of molecular features and few samples. Tabular foundation models use diverse strategies to predict labels for such tables, with some relying on in-context learning, while approaches to wide tables may involve subsampling, grouping, or compressing features. We present TabOmics, a small, open family of tabular foundation models with 7.3M parameters, pretrained exclusively on synthetic tables. TabOmics processes all features retained after preprocessing jointly and derives predictions, attention-derived feature-importance scores, and test sample embeddings from the same forward pass. On 46 classification tasks from three benchmarks of high-dimensional, low-sample-size tables, TabOmics v2.5 with eight ensemble members achieves the highest mean area under the receiver operating characteristic curve (AUROC) among the evaluated models (0.943), outperforming TabPFN-3.5 with eight members on 32 tasks despite having about 30 times fewer parameters. With a single member, TabOmics v2.5 reaches a mean AUROC of 0.941, versus 0.942 for TabPFN-3.5 with eight members, with no significant paired difference under the reported task-level analysis. On synthetic tables, its feature-importance scores achieve feature recovery similar to that of TabPFN-Wide, and its predictions remain above chance at 30,000 features. On breast cancer tasks, the top ranked genes include PAM50 genes, which define the subtype labels, indicating concordance with the label definition rather than independent biological validation. The code, model weights, training recipes, and experimental artifacts are publicly available.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15328

Vision-Grounded Semantic Learning and Alignment for Scalable Multilingual Text-Based Person Retrieval

Xinyu Zhou, Fan Li, Shasha Chen, Min Cao, Ahtamjan Ahmat, Qiaoming Zhu, Mang Ye

原文摘要

Multilingual Text-Based Person Retrieval (TBPR) aims to retrieve individuals with texts in multiple languages. Despite its practical importance, it has been overlooked by existing research, which mainly focuses on English-only retrieval. The only prior attempt at multilingual TBPR suffers from (1) the absence of a large-scale, multilingual pretraining dataset and (2) a suboptimal model architecture for multilingual text encoding and cross-modal alignment, which employs a shared encoder that overlooks linguistic heterogeneity and relies on English as a pivot for cross-modal alignment, introducing English-centric bias. For this, we contribute MultiPedes, the first large-scale, pedestrian-centric multilingual pretraining dataset. It contains 10 million image-text pairs across four languages, constructed by a multilingual quality-aware data construction pipeline. We also propose ViSeLA (Vision-grounded Semantic Learning and Alignment), a novel framework featuring: (1) a semantic-aware multilingual learning module that extends the mixture-of-experts architecture by routing language-invariant semantics to specialized experts for improved multilingual feature learning; and (2) a vision-grounded cross-modal alignment module that employs a semantic probe to capture fine-grained semantics across modalities, enabling direct cross-modal alignment without relying on a pivot language. Extensive experiments on both multilingual and English-only TBPR datasets demonstrate the effectiveness of ViSeLA. The dataset and code are available at https://github.com/Flame-Chasers/ViSeLA

cs.CV · cs.CL · cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.14244

An AI Assistant for Automating Anti-Corruption Expertise of Draft Legislation Based on the TRACE Framework

Serhii Zabolotnii, Alina Tarasova

原文摘要

We present an AI assistant for automating anti-corruption expertise of draft legislation based on the TRACE architecture, designed to screen bills for corruption risks: pinpoint provisions an expert would criticise and name the regulatory defect in each. The pipeline combines a deterministic parsing core, model-guided single-provision qualification, rule-based factor assignment, and external human oversight. To evaluate the assistant, we constructed an open benchmark from the published conclusions of Ukraine's National Agency on Corruption Prevention (NAZK), comprising 132 draft laws with 474 model-extracted findings. A system flag agrees with a finding when it targets a compatible provision and an LLM judge rates the risk as the same (strict) or partly the same (lenient). On 100 draft laws, the pipeline matches 0.28 of the findings within its top ten flags using the local Gemma 4 31B model, 0.30 with Sonnet 5.5, and 0.43-0.46 across all flags (non-blind; the frozen blind baseline achieves 0.21, 95% CI 0.16-0.26). Across all runs, most misses occur on provisions that the system located but qualified as a different risk, and none of the 32 legal-collision findings was matched. On 21 acts held out from tuning, the pipeline outperforms a monolithic prompt containing the full methodology under the strict measure, though not under the lenient one, and Opus 5.5 adds nothing over Sonnet. The reported scores reflect agreement with model-extracted findings rated by an LLM judge whose two access routes agree on 73 of 74 labels, without human expert validation.

cs.CL · cs.AI

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新论文 · v1arXiv:2611.15325

CaseScope: A Multi-Field Benchmark for Case-Level Pathology Diagnosis with Multimodal LLMs

Wenchuan Zhang, Chengyang Zhang, Fengling Li, Ting Yin, Chen Shu, Jie Chen, Shuwan Zhang, Xiaoyan Wu 等 12 位作者

原文摘要

Pathologists rarely diagnose from a single image: they scan a section at low power for architecture, zoom into selected fields for cytology, and integrate the evidence across views and scales. Pathology benchmarks for multimodal large language models (MLLMs), however, mostly ask about one image or require whole-slide pipelines, and the few that show several fields draw nested regions from cancer slides of one public cohort. We introduce CaseScope, a benchmark of diagnosis from several curated fields of one lesion: CaseScope-Teaching, 436 cases from a residency-training image library, reconstructed, de-identified, harmonised against a WHO-based ontology and re-examined by a pathologist wherever an automated pre-review raised a doubt, and CaseScope-Atlas, 800 cases from 88 published atlases (387 of them figure series of one disease), labelled from the captions by two LLMs that must agree. They cover 843 entities across 18 organ systems and pose 4,679 items in six tasks, from open-ended and ten-way diagnosis with ontology-guided hard negatives under circular evaluation to the verification and location of morphological findings. Across twenty-eight open and four proprietary MLLMs, diagnoses are unstable -- the best open and proprietary models are correct under all ten option rotations on 17.9% and 61.7% of the teaching cases -- and wrong answers favour the classic look-alike; open models make little use of the additional fields on teaching cases, where the one-line specimen site matters more, although on atlas cases with three or more fields the fields count about as much as the site; and most open models verify findings barely above chance (median 62.0% vs. 50%, best 81.5%), gaining a median +3.4 points from images; proprietary models reach 83.2-93.2%. Rankings broadly replicate between the sources. The evaluation code is available at https://github.com/Wenchuan-Zhang/CaseScope.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.10543

Flexible Flows for Biological Sequence Design

Yogesh Verma, Dani Korpela, Harri Lähdesmäki, Vikas Garg

原文摘要

Designing functional biological sequences requires navigating vast discrete spaces under strict evolutionary and biophysical constraints. Discrete Flow Matching (DFM) offers a generative framework over such spaces, but existing approaches rely on biologically uninformative couplings and offer limited flexibility for variable-length sequence generation and fine-grained control. We propose a structured coupling that encodes domain-specific preferences among sequence elements, biasing the source distribution toward plausible regions without modifying the flow objective or training procedure. Building on this, we introduce a latent edit-based rate parameterization that models variable-length generation via edit operations conditioned on a shared global latent, akin to a latent variable model, while remaining tractable. We further introduce a latent classifier-free guidance mechanism that steers generation coherently in continuous latent space, along with Dirichlet-prior temperature scaling for test-time control over edit operations. Our method achieves state-of-the-art performance across diverse biological sequence tasks, including density estimation, unconditional and conditional DNA sequence generation, and peptide sequence generation. Our code is available at https://github.com/Aalto-QuML/FlexFlow.

cs.LG · cs.AI · cs.ET · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15320

Delaunay-Based Nearest Neighbor Search with Grid-Accelerated Queries and Parallel Construction

Pengfei Wang, Shiqing Xin, Changhe Tu

原文摘要

Nearest-neighbor search is a fundamental primitive in computer graphics, with applications in distance field computation, surface reconstruction, and point cloud registration. In many graphics applications, data points lie on a surface manifold embedded in 3D space, whereas queries are distributed throughout the surrounding volume, a setting in which spatial partitioning structures such as k-d trees and grids can become inefficient. Dynamic-programming-based nearest-neighbor search (DP-NNS) offers an alternative: it traverses a successor DAG that records the history of incremental Delaunay construction and often outperforms spatial partitioning structures. However, its sequential construction is slow, and naive parallelization degrades query performance, limiting its practical adoption. We accelerate both queries and construction in DP-NNS. For queries, we introduce a grid-based initialization strategy that precomputes valid starting points on the successor DAG, shortening search paths while preserving exactness and enabling a twofold speedup. For construction, we analyze how parallel insertion affects query cost and develop a parallel algorithm that preserves logarithmic expected query cost under stated geometric assumptions. Our parallel construction algorithm achieves a 10x speedup over the sequential baseline while maintaining nearly the same query cost. We further reorder query points to improve cache locality. Extensive experiments demonstrate that our method outperforms widely used libraries in nearest-neighbor query performance.

cs.GR · cs.CG

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新论文 · v1arXiv:2611.15317

BiPOP: Partial-Order Planning for Long-Horizon Bimanual Robotic Manipulation

Kun Chu, Xufeng Zhao, Jan-Gerrit Habekost, Cornelius Weber, Stefan Wermter

原文摘要

Bimanual robotic manipulation is versatile, yet its central challenge lies not only in executing dexterous motion with each hand, but also in coordinating the two arms for long-horizon manipulation under coupled spatial and temporal constraints. Prior work has largely focused on endowing robots with human-level manipulation skills, paying less attention to the structure of bimanual behaviour itself: many practical tasks are a coordinated composition of reusable unimanual skills and a small set of synchronous bimanual skills. This makes high-level spatio-temporal coordination, rather than skill acquisition alone, a key bottleneck for efficient bimanual execution. We propose BiPOP, a framework that couples Large Language Models (LLMs) with Temporal Partial-Order Planning for long-horizon bimanual manipulation. At its core is an explicit spatio-temporal model that partitions the workspace by arm reachability and captures inter-arm dependencies, arm assignment, synchronization requirements, and opportunities for safe concurrent execution. Building on this model, an LLM translates natural-language instructions into a formal PDDL specification, while a temporal planner enforces the constraints and schedules actions, yielding plans that are logically consistent at the symbolic level and efficient through parallel execution. On simulated long-horizon tasks of varying complexity, BiPOP outperforms open-loop planning with strong reasoning models and closed-loop ReAct-style planning in success rate, planning time, and plan efficiency, and the full pipeline runs end to end on the real bimanual robot NICOL. A failure analysis further shows that LLM-only planners mainly violate spatial and temporal coordination constraints, which motivates delegating these constraints to a symbolic planner. Code: https://github.com/Kchu/BiPOP

cs.RO

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新论文 · v1arXiv:2611.15316

IoTPwnBench: A Synthetic Benchmark for IoT Firmware Security Analysis

Luca Ferrari, Marco Alecci, Luca Verderame, Jacques Klein, Francesco Pagano

原文摘要

Building and evaluating security tools for IoT firmware requires targets whose weaknesses are known in ad- vance and can be reproduced in a running device. Two problems remain: (1) firmware collected from real-world products provides realistic software, but hardware dependencies and undocumented defects make it difficult to establish what a security tool should be expected to find; and (2) existing benchmarks typically collect sets of vulnerable or misconfigured firmware images without providing purpose-built vulnerable services with explicit weaknesses that a security analysis tool can target. We present IoTPwnBench, a synthetic, modular benchmark that brings these requirements together. IoTPwnBench contains 716 vulnerable firmware images covering 86 weakness scenarios and 28 CWE classes across 11 device profiles, including industrial controllers, health monitors, and household devices. Each vulnerable image runs a plausible device application with simulated sensors and actuators that generate live readings over its native interfaces while exposing one target weakness mapped to a primary CWE. A documented exploitation payload and observable signal establish the expected behavior independently of the evaluated tool. Every vulnerable image is paired with a corrected twin that preserves the device and service while removing that weakness. Researchers can therefore compare the same target before and after correction to assess detection and false positives for the target weakness. The benchmark produces kernel and filesystem binaries for static inspection and dynamic testing under QEMU. Device profiles and weakness modules remain separate, allowing the same scenario to be reused across device contexts. Vulnerable targets were validated manually, and the open source build workflow regenerates the firmware bit-for-bit.

cs.CR · cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.07922

Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT

Giulia Marchiori Pietrosanti, Giulio Rossolini, Giorgio Buttazzo

原文摘要

Vision Transformers (ViTs) remain vulnerable to localized adversarial attacks, e.g., adversarial patches, while recent test-time defenses mitigate them by suppressing image tokens with abnormally high attention scores. These defenses exploit a strong coupling between attention and adversarial effectiveness: adversarial tokens often need to attract substantial attention to influence the prediction. We introduce adversarial decoys, image patches optimized to redirect attention, and therefore related defenses, towards selected target tokens. Rather than jointly optimizing misclassifications and defense evasion, our approach decouples the two objectives: the original adversarial region induces the incorrect prediction, while a separate decoy manipulates the attention ranking used by the defense. A layer-wise objective increases target-token attention and promotes these tokens above competing non-target ones. Since the decoy optimization is not tied to a specific attack formulation, the method can be easily integrated with any existing adversarial patch attack. Experiments on ImageNet across multiple ViT architectures and adversarial patch attacks show that decoys consistently redirect high attention scores away from the true adversarial region while preserving much of the attack effectiveness against attention-ranking defenses. These results reveal a fundamental limitation of using attention alone as an indicator of adversarial relevance.

cs.CV · cs.AI

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