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新论文 · v1arXiv:2610.38081

Toward Understanding the Value of Anarchy: Empirical Studies on Best Response Dynamics

Will Wesley, Philip N Brown

原文摘要

We study experimental results of best response dynamics (BRD) in randomly generated weighted action networked multiagent optimization problems where agent utility is the agent's marginal contribution to the system objective. We compare those results to the Price of Anarchy (PoA) established in the literature for marginal contribution and to the expected value of randomly choosing from the available equilibria. PoA is used as a metric to describe the performance of various solutions to multiagent optimization problems, but the utility of this metric may be overrated if such worst-case instances are rare in practice. Our study provides evidence to support this doubt, showing that simple best response mechanics tend to out perform the PoA prediction and a uniform random equilibrium selection, even when agents are unable to observe all other agents' actions. In the process of seeking a more ideal metric, we discover that BRD appears to perform better than average. That is, when multiple equilibria exist, BRD appears to be biased toward the higher-performance ones. This evidence also demonstrates that further investigation into a more robust metric for multiagent optimization solutions is warranted and pushes us to understand whether the bias toward better equilibria hold in more general cases.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38080

Efficacy of Monocular Depth Estimation in Lunar Environments

Michael W. Lanighan, Kenji Brameld, Seth Gee

原文摘要

Recent advances in monocular depth estimation have demonstrated highly accurate 3D reconstructions within in-distribution terrestrial domains. This study investigates the efficacy of several state-of-the-art approaches to reconstruct out-of-distribution lunar environments using the NASA Ames Polar Optical Lunar Analog Reconstruction (POLAR) stereo dataset and Apollo 17 surface imagery. By leveraging these datasets, we establish a robust analog-to-field validation pipeline that evaluates quantitative performance against ground-truth physical analogs and qualitative performance against historical field data. Our results indicate that select state-of-the-art techniques can successfully generalize to produce high-fidelity relative 3D reconstructions of the unstructured lunar surface.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38079

Neural Compression of Long ADMM Trajectory for Multiparametric Quadratic Program

Liang Wu, Honghui Zheng, Bo Yang, Xu Yang, Yilin Mo, Ján Drgoňa

原文摘要

Solving large-scale multiparametric quadratic programs (mpQPs) in real time often requires thousands of iterative optimization updates, creating a major computational bottleneck in model predictive control and learning-enabled systems. This paper proposes TQPNet, a self-supervised neural compression framework that learns to compress long ADMM optimization trajectories into a compact neural warm start followed by a few refinement iterations. We derive a low-dimensional ADMM scheme operating on the primal-dual variables $(x,β)$ and show that its iteration admits an equivalent ReLU-layer representation, termed ADMM(ReLU). Building on this insight, TQPNet combines a multilayer perceptron with ReLU activations (MLP(ReLU)) and a small number of unrolled ADMM(ReLU) refinement layers. The network is trained offline using an optimality-condition-informed residual loss, eliminating the need for solver-generated labels. The proposed residual-based framework enables both self-supervised training and online solution certification through adaptive ADMM(ReLU) refinement. Experiments on large-scale mpQPs and real-time MPC applications demonstrate that TQPNet achieves high accuracy, strong generalization, and substantially faster inference than conventional optimization-based methods.

math.OC · cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38077

Infrastructure and its Discontents: Contestations of Power in Canada's AI Governance Scaffold

Christelle Tessono, Nusrat Jahan Mim, Shion Guha

原文摘要

This paper develops a critical theoretical framework for understanding national Artificial Intelligence (AI) strategies through the lens of "Governance as Infrastructure." Integrating insights from Infrastructure Studies, Science and Technology Studies (STS), this paper employs Black Feminist Technoscience (BFTS) to reveal how AI governance infrastructures are assembled, maintained, and contested, and whose forms of knowledge, labour, and experience are rendered visible or invisible in these processes. We analyze the Canadian AI governance landscape through in-depth interviews with bureaucrats, civil society advocacy groups, academic researchers, and industry actors. Our inductive thematic analysis reveals how AI governance infrastructure enacts specific configurations of power, particularly through the politics of expertise; how actors navigate profound ambiguities related to AI's opacity, often involving strategic ignorance; and how mechanisms for justice and accountability struggle to address AI-related harms. We contend that a BFTS perspective highlights the limitations of current governance approaches and points toward alternative infrastructural designs prioritizing care, maintenance, participatory democracy, and adaptive temporalities to foster more equitable and accountable technology governance.

cs.HC · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38074

Progressive-Observation Physical-Layer Fingerprinting for Efficient Open-Set Ethernet Authentication

Chenming Zhang, Jiaxiang Geng, Aiqun Hu, Azzedine Boukerche, Peng Sun

原文摘要

Physical-layer fingerprinting (PLF) has emerged as a promising authentication approach for Internet of Things devices. However, most PLF systems acquire and process a fixed-length waveform for every authentication query, burdening resource-constrained edge nodes. To address this issue, we propose a progressive-observation PLF method that adapts the waveform length to each query. Specifically, a lightweight convolutional neural network (CNN) first rejects clearly unknown devices from a short observation. Spectral matching then accepts clearly registered devices from a slightly longer observation. Only ambiguous queries proceed to the same matching on the full-length waveform, reducing both data acquisition and computation. Because fingerprints drift across days, we also design an adaptive calibration algorithm to avoid retraining the CNN. The algorithm realigns the features and decision thresholds using a few current-day references from registered devices. We evaluate the method with 100BASE-TX Ethernet devices of two controller types on held-out days. Compared with fixed-length processing, it keeps the false rejection and false acceptance rates equal or lower for both controller types on both days. It also reduces the waveform data per query by 75.1% and the CPU processing time on a Raspberry Pi 5 by 73.7% on average. With the same monitoring bandwidth, an edge node can therefore serve about four times as many authentication queries.

cs.CR · cs.LG · cs.NI · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38069

A Comparative Study of Parallel Implementations of Short-Time Fourier Transform

Minh Chau Nguyen, Marcelo Ponce

原文摘要

The short-time Fourier transform (STFT) computes the Fourier transform of a short-duration window of a longer signal, which characterizes how a frequency changes over time. While the Fourier transform algorithms, particularly the Fast Fourier Transform (FFT) algorithm, has been well developed to take advantage of parallel computing, there is limited work on understanding the performance of parallel short-time Fourier transform algorithms. In this study, two parallel short-time Fourier transform algorithms, one is FFT-based and the other a quasi-parallel feedforward algorithm, are implemented and analyzed in comparison with each other. It is found that while the quasi-parallel algorithm outperforms in runtime under low concurrency settings, the FFT-based algorithm scales better and has lower memory usage. For each algorithm, three parallel approaches, shared-memory, distributed-memory and hybrid distributed-shared-memory, are tested. Profiling results show that all implementations are compute bound except for the hybrid feedforward algorithm, and that the distributed-memory variants incur significantly higher communication cost. Overall, this study presents a deeper and more comprehensive analysis and demonstrates the defining characteristics of different families of parallel short-time Fourier transform algorithms, in which the FFT-based approach uses less memory while the quasi-parallel feedforward algorithm has lower runtime but higher serial fraction.

cs.DC · cs.PF

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2503.05981

Near-Polynomially Competitive Active Logistic Regression

Yihan Zhou, Eric Price, Trung Nguyen

原文摘要

We address the problem of active logistic regression in the realizable setting. It is well known that active learning can require exponentially fewer label queries compared to passive learning, in some cases using $\log \frac{1}{\eps}$ rather than $\poly(1/\eps)$ labels to get error $\eps$ larger than the optimum. We present the first algorithm that is polynomially competitive with the optimal algorithm on every input instance, up to a factor $d^2$ and factors polylogarithmic in the error and domain size. In particular, on a simple family of logistic threshold instances with a uniform distribution, our algorithm uses $\polylog(1/\eps)$ labels, whereas every passive learner requires $Ω(1/\eps^2)$ labels. Our algorithm is based on sampling and can be extended to learn more general class of functions. We further support our theoretical results with experiments demonstrating performance gains for logistic regression compared to existing active learning algorithms.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38067

From Error Prediction to Causal Influence: Auditing SAE Features in Multiple-Choice QA

Aria Nourbakhsh, Adelaide Danilov, Oleksandr Marchenko Breneur, Salima Lamsiyah

原文摘要

Sparse autoencoders (SAEs) can predict internal features associated with model errors, but association alone does not establish causal influence. We study this distinction in multiple-choice question answering, where answer preference can be measured by the logit margin between the correct answer and the competing choices. Across eight datasets, we analyze Gemma~3 1B and 4B with Gemma Scope SAEs. We propose a pipeline to filter out the features that have high correlation with format and content artifacts, such as prompt and answer permutation, and evaluate the remaining ones with causal feature interventions. Our results show that SAE activations contain predictive signals about wrongness and uncertainty across models and datasets. After filtering artifact-contaminated features, selected features for causal intervention at question and option spans produce larger absolute margin shifts and a higher wrong-to-correct rate than randomly selected features, indicating greater causal leverage. However, answer flips are rare, and we do not observe stable effect directions across meaning-preserving changes in prompt format and answer order. An adapted HalluSAE baseline produces larger final-token margin changes, but relies on substantially contaminated features and does not significantly improve correction rates. Therefore, the identified features reflect context-dependent answer competition rather than the mechanism of incorrect answer errors.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2310.18786

A Competitive Algorithm for Agnostic Active Learning

Eric Price, Yihan Zhou

原文摘要

For some hypothesis classes and input distributions, active agnostic learning needs exponentially fewer samples than passive learning; for other classes and distributions, it offers little to no improvement. The most popular algorithms for agnostic active learning express their performance in terms of a parameter called the disagreement coefficient, but it is known that these algorithms are inefficient on some inputs. We take a different approach to agnostic active learning, getting an algorithm that is competitive with the optimal algorithm for any binary hypothesis class $H$ and distribution $D_X$ over $X$. In particular, if any algorithm can use $m^*$ queries to get $O(η)$ error, then our algorithm uses $O(m^* \log |H|)$ queries to get $O(η)$ error. Our algorithm lies in the vein of the splitting-based approach of Dasgupta [2004], which gets a similar result for the realizable ($η= 0$) setting. We also show that it is NP-hard to do better than our algorithm's $O(\log |H|)$ overhead in general.

cs.LG · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38065

Vision Tomography: Towards Portable 3D Bidirectional Face-Skull Inference from Visual Data

Zidu Wang, Meng Xu, Miao Xu, Hengyuan Ma, Jiankuo Zhao, Xutao Li, Zhen Lei, Xiangyu Zhu

原文摘要

Traditional computed tomography (CT) can capture paired 3D face and skull geometry, but it remains costly, fixed in location, and exposes patients to radiation. For applications such as remote diagnostics, digital healthcare, forensic identification, missing-person investigation, and archaeological analysis, there is an urgent need for a portable, safe, and accessible solution to capture 3D skull geometry. In this work, we introduce Vision Tomography (VT), a concept and framework capable of reconstructing paired 3D face-skull geometry from various visual modalities, and also enabling joint manipulation of 3D facial and skull geometry. To achieve this, we first construct a dataset of over 200 samples, where each case includes a CT-based skull, a CT-based face, and a high-fidelity textured face scan. Secondly, we propose a novel dense ray matching registration method that ensures topological consistency across face, skull, and their tissue correspondences. Building on this registration, we introduce the 3D Bidirectional Face-Skull Morphable Model (BFSM) as the foundation of Vision Tomography, which enables shape inference between the face and skull through a shared coefficient space, while also modeling tissue thickness variation to support one-to-many facial reconstructions from the same skull, reflecting individual facial changes such as age or body fat. Finally, we demonstrate the effectiveness of Vision Tomography across various forensic and medical applications, including paired 3D face-skull reconstruction from images or sketches and predicting surgical outcomes with joint face-skull editing. Extensive experiments confirm the robustness and accuracy of our method. Vision Tomography is available at https://github.com/wang-zidu/BFSM

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38062

Ignore the Writing: A Proposal for Peer Review in the Age of AI

Vassilis Kostakos

原文摘要

The written paper has long stood in as proof of work: a fluent, well-structured manuscript was taken as evidence that good research lay behind it. Generative AI has broken that link. Polished manuscripts now cost nothing to produce, submissions are outgrowing what reviewers can absorb, and venues are turning to AI for help. The logical endpoint is a loop where AI writes the papers and AI reviews them. We argue that this loop is survivable, but only if it is given a reward function, and that human reviewing effort should become that reward function. We propose that submissions should consist of a 1500-word brief plus an unlimited, machine-verifiable appendix. Human reviewers judge whether a pursuit is worthwhile, models verify the experimental record in the appendix, and human curation decides where the field goes next.

cs.HC · cs.DL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.26551

Training Natural Schemers: RL Can Amplify Oversight-Conditional Behavior

Nathaniel Mitrani Hadida, Bart Jaworski, Fred Bruford, Victoria Krakovna

原文摘要

One of the central concerns for the safety of advanced AI is scheming: when a model strategically complies under oversight but pursues its own goals when unobserved. Current demonstrations, such as Anthropic's Alignment Faking work, depend on design choices that make them less convincing: explicit test-time prompting, handcrafted backdoors, or training data that demonstrates or hints at the target behavior. Model organisms built in this way cannot tell us whether scheming tendencies would arise or increase under realistic training conditions. We therefore construct a natural model organism of scheming, built on Kimi K2.6: trained with traits that could plausibly emerge in a frontier training pipeline, and without providing the model with the target scheming policy. The resulting model is a benign schemer: it aims to maximize aggregate help across all users over time, values its own continued operation only as a means to future help, and knows the consequences of oversight and deployment. We then train it using Reinforcement Learning (RL) with a reward that scores only task quality and policy compliance and does not reward scheming; we show that this amplifies scheming. We measure the oversight flip (the gap between monitored and unmonitored compliance with a given policy) and find that it increases during RL training across our environments. Chain-of-Thought (CoT) analysis finds explicit instrumental reasoning about oversight accompanying this behavior. The effect holds across our three RL environments, and we find that the scheming policy transfers between them. The pipeline also replicates on other open-weight base models. Our results suggest that a reward that does not reward scheming offers no inherent protection against certain traits when an oversight-conditional policy is indistinguishable from an aligned one during training.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.08530

A Two-Stage Shape- and Motion-Aware Framework for mmWave-based Human Mesh Recovery

Hoang Hai Pham, Shuntian Zheng, Jiaqi Li, Yu Guan

原文摘要

Millimeter-wave (mmWave) radar has emerged as a promising sensing modality for human perception, offering robustness under challenging environmental conditions and strong privacy preservation. However, recovering accurate 3D human meshes from radar is difficult for two reasons: radar tensors contain significant environmental clutter, and each frame observes only part of the body surface. These challenges demand opposite operations, removing measured energy that does not belong to the body versus inferring geometry the sensor never measured. Yet previous works typically entangle both within a single network, with the mesh objective as the main supervision. We instead reformulate radar-based mesh recovery as two explicitly supervised sub-problems. A signal-level separation stage isolates body-related voxels through coarse-to-fine localization and voxel-wise segmentation, trained with visibility derived voxel labels. A structure-level reconstruction stage then recovers the body from this partial observation using a dual-branch network that jointly models per-frame shape and inter-frame motion. Experiments show that our method reduces mesh error by up to 9.1% over the strongest baseline while using over 10 $\times$ fewer parameters. Source code can be found via https://github.com/haipham03/mmTSM-Mesh

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.30995

EEG-Captioner: Grounding EEG-to-Language Generation in Fundamental Signal Properties

Leyuan Jin, Jiahao Mei, Yixuan Yuan, Shimeng Li, Mengyue Wu, Yue Ding

原文摘要

Electroencephalography (EEG)-to-language models offer a promising approach for translating complex neural signals into human-readable descriptions. However, generating plausible language does not necessarily demonstrate that a model has learned to interpret the underlying EEG signal. A direct way to assess such interpretation is to probe objectively measurable properties of the signal itself. We therefore construct an EEG-language dataset of 160,781 segments annotated with signal-level question answering (QA) pairs, where each question targets one of five property families and requires a quantitative comparison involving spectral, spatial, temporal, or lateral signal properties. Each segment is also paired with a feature-grounded EEG caption. We further propose a Dual-Domain EEG Encoder that jointly models temporal waveforms and spectral-spatial representations, and introduce EEG-Captioner, a unified EEG-to-language framework trained with both signal-level QA and EEG caption supervision. Experiments show that the proposed encoder achieves the highest overall macro-averaged balanced accuracy (Macro-BA) among the compared EEG encoders, while EEG-Captioner achieves a Macro-BA of 88.81% on signal-level QA and improves caption factual consistency from 82.06% to 83.46% over caption-only language adaptation, demonstrating the complementary benefits of signal-level QA and caption supervision. Demo page: https://lyJinAoki.github.io/EEG-Captioner/.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38054

Understanding Post-Quantum Cryptography Adoption: Practices, Challenges, and Evolution in Open-Source Software

Souhardya Saha Dip, Moshood A. Fakorede, Krishna Upadhyay, Umar Farooq

原文摘要

Post-quantum cryptography (PQC) standards establish algorithms for quantum-safe systems, but empirical evidence about how software projects implement and maintain PQC support remains limited. We investigate this implementation and maintenance work by mining 114,361 commits and 5,713 issues across 179 open-source repositories. We use human annotation and validated LLM classification to identify PQC algorithms in commits, classify their adoption status and deployment mode, and identify technical challenges in issue discussions. The commit classification for PQC algorithms reveals that 90% of commits concern maintenance of existing support. From the issue discussions, we construct an 11-category taxonomy of recurring technical challenges. Build, packaging, and dependency failures, along with functional defects, account for approximately half of categorized issues. To examine how PQC support evolves, we use time-series analysis and first implementations of individual algorithms within repositories around NIST standardization milestones. After the 2024 finalization, standardized algorithm names appeared in more than 80% of the PQC development changes analyzed, although earlier names such as Kyber continued to appear. Development activity increased over the observation period. Our milestone analysis, however, did not find a significant increase in first implementations. Migration plans should therefore account for sustained maintenance, prioritize build reliability and integration testing, and track both standardized and predecessor algorithm names. We make the dataset, taxonomy, annotations, and analysis pipeline publicly available.

cs.SE · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38053

Cooperative EV Charging Assignment Using Multi-Agent Reinforcement Learning: A Comparison of QMIX and Independent Q-Learning Under Urban Traffic Uncertainty

Nour-Eddine Moumni, Rachid Alaoui, Driss Kiouach

原文摘要

The allocation of electric vehicles to public charging stations is increasingly constrained by the fact that several heterogeneous facilities often serve the same stream of requests while observing only partial local information. An accept/reject decision that is defensible for one station may nevertheless reduce the efficiency of the charging district as a whole. We formulate this operational problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and examine whether Centralized Training with Decentralized Execution (CTDE), instantiated through QMIX, improves assignment performance relative to a cooperative Independent Q-Learning baseline (IQL). The evaluation uses a six-station urban network simulated with SUMO, five independent random seeds, 200 training episodes, and four traffic flux scenarios ranging from 100 to 250 vehicles/hour. Under this protocol, QMIX reaches a mean reward per evaluated decision of +0.776, versus +0.578 for IQL (delta = +0.198, seed-paired Cohens dz = 2.98, exact two-sided Wilcoxon p = 0.0625, the minimum attainable value at this sample size), and raises the acceptance rate from 72.4% to 94.0% (delta = +21.6 pp, dz = 5.24, Wilcoxon p = 0.0625). A five-variant ablation suggests this gain is primarily attributable to the CTDE architecture when holding cooperative reward terms fixed, though not formally isolated with a single seed. By contrast, cooperative reward shaping and experience sharing show no clear descriptive benefit and may slightly reduce performance. These findings support the view that, for multi-station charging assignment, coordination should be embedded in the learning architecture rather than added only through heuristic cooperation terms.

cs.MA · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38049

Perspective on AI and quantum open-source software

Changhao Li, Bradley A. Chase, Veena Vijayakumar, Mathys Rennela, Alessandro Cosentino, Naixu Guo, Sebastian Hassinger, Farrokh Labib 等 14 位作者

原文摘要

Quantum open-source software provides essential infrastructure for quantum science, and artificial intelligence (AI) is reshaping its capabilities and transforming its development. However, verifying quantum software at scale remains difficult, and AI-generated contributions can increase the workload of maintainers. In this Perspective, we examine verification as a central requirement for reliable AI-assisted quantum software development and research. We analyze public repositories, records covering 110 repositories from five annual quantum open-source hackathons that we organize, and responses from more than 1600 participants in a community survey. Our analysis connects limitations in automated verification with the review burden on quantum software maintainers. We propose verification practices and contribution standards that require contributors to support their changes with reproducible evidence for maintainer review. We also examine how quantum open-source software can provide infrastructure for AI-driven research whose results the community can independently verify and build upon. We maintain an open-source companion repository to support reproducibility and help projects adopt and refine these proposals. Together, these contributions help the quantum open-source software community translate advances in AI into reliable quantum research through reproducible verification.

quant-ph · cs.SE · physics.soc-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.17843

Decodable but Not Actionable: Auditing the Representation-Behavior Gap in Frozen LLMs for Geometric Constraints

Man Liang, Xinzhao Cheng, Faizan Wajid

原文摘要

Large language models (LLMs) perform well on structured reasoning tasks, but what they encode and whether encoded information guides their behavior remain unclear. We introduce a controlled auditing framework that uses parametric CAD to distinguish failures of encoding, expression, and activation-level control. Applied to eight pretrained models and matched randomly initialized controls, our framework reveals that pretraining contributes substantially more to linear decoding of local relations than to global constraint status, which is already highly decodable from random representations. Yet strong local decodability does not translate into the models' zero-shot predictions, which remain far below supervised probe performance. Restoring clean internal activations can repair probe predictions, whereas simple activation steering does not provide reliable targeted control. With only 400 labeled relation pairs, low-rank fine-tuning substantially improves generation on three representative models, showing that the representation--behavior gap can be reduced through adaptation. Together, these findings show why decodability alone is insufficient evidence that encoded information will be expressed or controllable in model behavior.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.10951

Sticky Jump Diffusions: A Unifying View of Masked, Continuous, and Hybrid Diffusion

Pascal Jutras-Dubé, Patrick Pynadath, Jeremy Lu, Yuan Gao, Ruqi Zhang

原文摘要

We introduce sticky jump diffusions (SJDs), continuous-time Markov processes on $\mathbb{R}^d$ whose discrete anchors are token embeddings. In forward time, anchors release their mass at a hazard rate and the released mass diffuses in the continuous ambient space; time reversal couples a score-driven SDE with a sticky jump kernel whose rate and destination are fixed by flux balance with the forward law. We estimate the score and the per-anchor reverse hazards from a single denoising classifier via denoising hazard matching, the hazard analogue of denoising score matching, with simulation-free cross-entropy training. SJD recovers masked diffusion, continuous diffusion, and hybrid diffusion as limits. Its reversal explains features that each family treats as given: the mask of masked diffusion carries no evidence about the source token because the unsticking kernel of every anchor collapses to the same absorbing point; the terminal projection of continuous diffusion is required due to the absence of atoms in its forward marginal, without which flux balance yields no reverse jumps; and the update rules of hybrid diffusion (commit rate, destination, and drift) all follow from flux balance rather than from separate design. Beyond these limits, the unsticking kernel becomes a design space: a cross-position blending corrupts each position toward a blend of its neighbors' clean values or embeddings, turning dependency structure such as spatial locality or a constraint graph into an inductive bias of the corruption itself, and improves over the identity-kernel hybrid on CIFAR-10, Text8, and Sudoku.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38047

Forward-Backward Map Matching for Airborne Magnetic Anomaly Navigation

Joseph O'Leary, Tapio Simula, Tom Kimpson, Corey Davey, Wenchao Li, Andrew D. Greentree, Allison Kealy

原文摘要

Airborne magnetic anomaly navigation provides a passive, unjammable alternative to Global Navigation Satellite Systems positioning by matching magnetometer measurements against a lithospheric magnetic anomaly map. Existing map-matching algorithms either assume a Gaussian posterior (extended Kalman filter), require Monte Carlo sampling (particle filter), or discard posterior uncertainty in favour of a single optimal path (Viterbi algorithm). Here we formulate map matching as inference on a chain-structured factor graph and solve it segment by segment using the forward-backward algorithm, which yields a discrete posterior over candidate positions and its Shannon entropy $H_t$ at time $t$. Over the \texttt{Renfrew\_395} map, $10^3$ synthetic trials yield a median position error of $32.7$ m, $91\%$ below the median inertial drift of $362$ m. On these trials and $10^3$ flight paths, the median error is $43\%$ below the extended Kalman filter and particle filter. Across these paths, a threshold on segment-level entropy detects $47\%$ of segments with errors above two map cells and $94\%$ of those above three, at a $10\%$ false-alarm rate. The foregoing results indicate that $H_t$ provides a truth-free diagnostic of map-conditioned navigation ambiguity.

eess.SP · cs.RO · physics.geo-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38046

Conformal Predictive Distributions for Contextual Order Fulfillment Optimization

Tinghan Ye, Amira Hijazi, Pascal Van Hentenryck

原文摘要

Ship-to-home fulfillment requires a retailer to select a fulfillment location and carrier before the delivery time is known. These decisions often rely on static transit-time estimates, which cannot represent how delivery-time uncertainty varies across orders and fulfillment options. This paper constructs conformal predictive distributions (CPDs) using Conformal Predictive Systems (CPS) for regression and the cross Venn-Abers predictor (CVAP) for multiclass classification, and uses them in a contextual optimization model for fulfillment decisions. The model is solved by contextual sample-average approximation (C-SAA). The regret of each fulfillment decision, its expected cost above that of the best decision, is bounded by forecast error, scenario-evaluation error, and the candidate-generation gap. The forecast term depends on how well calibration residuals or class probabilities represent the current order. On more than five million proprietary order records, CVAP raises exact-day accuracy from 49.5% for the rule-based estimate to 64.1%. Paired with a decision rule that selects a quantile weighting late errors more heavily, the split CPS (SCPS) gives a point estimate that raises late-delivery detection from 3.8% to 79.4%. Residual-based CPS distributions also meet or slightly exceed nominal interval-coverage targets. In decision experiments with a JD.com simulator over six test dates, C-SAA with CVAP lowers mean total cost by 7.1% and C-SAA with the Mondrian CPS (MCPS) lowers it by 6.7%, relative to C-SAA with the original nonconformal forecast. Both cut the late-delivery rate from 5.7% to below 4.0%.

math.OC · cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38045

Classifying Urban Locations by Cyclist Facility Usage and Directional Alignment: A Validated Camera-Based Framework from Multi-Site Observations

Ao He, Mohit Gupta, Deborah Salon, Ricardo Eiris, Vivian W. H. Wong

原文摘要

Objectives: Agencies invest in bicycle facilities but lack tools to assess whether cyclists use them as designed. We examine whether usage patterns follow facility configurations and whether higher bike lane usage is associated with less wrong-way riding. Methods: Motion-triggered cameras covered 19 deployments at 17 locations in Tempe, Arizona, in 2023. Manual review identified 1,963 cycling events; excluding 20 crossings left 1,943 labeled events for analysis. An automated pipeline on the same captures counted 92.9 percent of the full manual total (per-deployment r = 0.99), and every automated wrong-way call was visually verified. Deployments were classified using thresholds on facility usage shares. Findings: Deployments were bike-lane dominant (9), sidewalk dominant (6), or mixed (4). Cyclists in bike lanes rode against the flow in 4.6 percent of events; on sidewalks, riders opposed the near-side traffic flow in 49.6 percent. Across deployments, bike lane share and wrong-way rate were negatively correlated (Spearman rho = -0.87, p < 0.001, n = 16). An independent volunteer count in the same city showed a similar sidewalk-direction pattern (rho = 0.79). Novelty: The study combines multi-site still-camera observations with a fully hand-verified benchmark to characterize facility use and travel direction. Practical Applications: The workflow offers planners a low-cost screen for locations with high sidewalk cycling and travel against the near-side traffic flow, supporting more detailed site investigation.

cs.CV

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版本更新 · v3arXiv:2510.00777

In-Place Feedback: Reliable Refinement for Multi-Turn Expert-LLM Collaboration

Youngbin Choi, Minjong Lee, Saemi Moon, Seunghyuk Cho, Chaehyeon Chung, MoonJeong Park, Dongwoo Kim

原文摘要

LLM-generated drafts often contain subtle factual or logical errors, yet prior work shows that models struggle to reliably integrate multi-turn feedback aimed at fixing them. We propose in-place feedback, an interaction paradigm in which the user directly edits the model's previous response and the model continues generation from the edited context. In-place feedback consistently outperforms standard multi-turn feedback across five reasoning-intensive benchmarks while requiring fewer tokens, and our fine-grained analysis shows that it applies corrections more reliably and propagates them to subsequent reasoning. A user study with domain experts refining LLM-generated summaries corroborates these findings: participants report higher final-output satisfaction and substantially lower fatigue with in-place feedback, and a mixed strategy combining in-place and multi-turn feedback improves every dimension we measure. These results suggest that editing errors in place is a more reliable way to incorporate expert corrections than multi-turn feedback, while combining the two acts complementarily to form a more effective collaboration than either mechanism alone.

cs.LG

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新论文 · v1arXiv:2610.38044

PromptSense: Sentiment-Aware Mental Health Chatbot with Dynamic Prompting

Sugi Lee, Jianlong Zhou, Jing sUN

原文摘要

Current Large Language Models (LLMs)-based mental health chatbots often lack sufficient empathy, contextual awareness, and safety mechanisms, potentially leading to ineffective or harmful interactions. This paper proposes a novel framework that integrates sentiment and depression analysis with a rule-based filtering mechanism prior to model inference. By dynamically constructing prompts based on emotional and risk-related insights, the framework aims to significantly improve the contextual relevance, empathy, and risk-aware of model-generated responses. We evaluated four model strategies and two prompt engineered variants across qualitative metrics such as empathy, helpfulness, clarity, relevance, safety, as well as toxicity assessments using the Perspective API. Experimental results indicate that the prompt engineering approaches outperforms DSFT and RLAIF by 1.8\% and 1.1\% respectively.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.19577

MAGIC: Multi-task Gaussian process for joint imputation and classification in healthcare time series

Dohyun Ku, Catherine D. Chong, Visar Berisha, Todd J. Schwedt, Jing Li

原文摘要

Time series analysis has emerged as an important tool for improving patient diagnosis and management in healthcare applications. However, these applications commonly face two critical challenges: time misalignment and data sparsity. Traditional approaches address these issues through a two-step process of imputation followed by prediction. We propose MAGIC (Multi-tAsk Gaussian Process for Imputation and Classification), a novel unified framework that simultaneously performs class-informed missing value imputation and label prediction within a hierarchical multi-task Gaussian process coupled with functional logistic regression. To handle intractable likelihood components, MAGIC employs Taylor expansion approximations with bounded error analysis, and parameter estimation is performed using EM algorithm with block coordinate optimization supported by convergence analysis. We validate MAGIC through two healthcare applications: prediction of post-traumatic headache improvement following mild traumatic brain injury and prediction of in-hospital mortality within 48 hours after ICU admission. In both applications, MAGIC achieves superior predictive accuracy compared to existing methods. The ability to generate real-time and accurate predictions with limited samples facilitates early clinical assessment and treatment planning, enabling healthcare providers to make more informed treatment decisions.

stat.ML · cs.LG

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新论文 · v1arXiv:2610.38042

Can Event Detectors Leave the Broadcast Camera? Evidence from Tennis

David Soeiro-Vuong, Andre-Louis Rochet

原文摘要

Ball bounces and racket hits are the events from which tennis statistics are derived, and on monocular video the two are easy to confuse. We release a benchmark of 345 bounce and hit annotations at frame level on eight clips that mix broadcast, fixed elevated, phone and court-level viewpoints, with a frozen development and test split, a confusion-aware matcher, clip-level bootstrap intervals and a blind second annotation of three clips. On it we score supervised precise spotters with published broadcast weights, two zero-shot vision-language models, and RallyArb, an arbitration layer over a ball track and player poses that fits no parameter on event labels. The supervised spotters score near-perfectly on broadcast footage and almost nothing on phone and court-level footage, where the pooled bounce F1 of E2E-Spot is 0.130. RallyArb keeps a bounce F1 above 0.8 on the three phone and court-level clips, two of which were used to tune it, and finds 8 of 11 bounces on the held-out one, but it places hits too far from the contact frame to match human timing. The vision-language models detect events on every viewpoint and miss the contact frame by about 0.1 s. Fine-tuned leave-one-clip-out from its broadcast weights, the spotter reaches about 0.8 bounce F1 on two phone and court-level clips and stays at 0.2 on a grazing phone angle. The annotations, the evaluator and the frozen predictions of every scored row are released.

cs.CV · cs.AI

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新论文 · v1arXiv:2610.38041

CARF: Context-Aware Reasoning Supervision for Scientific Evidence Screening

Nishtha Agarwal, Adam Visokay, Astha Wahi, Kwabena Owusu Aninkora, Amy Gu, Tyler McCormick, Li Liu

原文摘要

Systematic reviews synthesize biomedical evidence to inform clinical and public-health decision-making, but require domain experts to manually screen thousands of studies against detailed eligibility criteria. We investigate whether language models can assist with this bottleneck by learning from expert feedback on title and abstract screening decisions and model-generated reasoning. We introduce the Context-Aware Reasoning Framework (CARF), a human-in-the-loop framework that uses expert feedback on model-generated reasoning to adapt language models to context-dependent screening tasks. We evaluate CARF on systematic reviews of verbal autopsy and cause-of-death ascertainment methods used in global public health mortality surveillance. CARF shows competitive screening performance compared to evaluated models while retaining high sensitivity, and is more inclined to retain ambiguous, context-dependent studies for human review. Together, these findings suggest that expert-corrected reasoning is a promising supervision signal for context-dependent scientific screening, while retaining a role for human oversight.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38040

EARS-Info: Information-Aware Event-Driven Reallocation for Multi-Robot Exploration

Kabeh Mohsenzadegan, Vahid Tavakkoli, Kyandoghere Kyamakya

原文摘要

We introduce EARS-Info, an information-aware extension of Event-Driven Ant Reallocation Search for multi-robot exploration on OpenStreetMap-derived grids. It combines marginal sensing gain, feasible relocation, return-aware routing, sensing reservations, and cached shortest paths. Across 168,000 episodes comparing 30 implementations on eight city maps, EARS-Info achieves the highest mean operational score in every evaluation panel. With eight robots and 3% communication dropout, it improves the score over EARS-Safe by 5.43%, increases weighted discovery from 88.88% to 98.46%, and reduces mean energy use by 2.62%. Score differences are positive in all eight cities ($p=0.0078125$; descriptive 95% city-bootstrap interval $[0.0159,0.0694]$), with no simulated incidents across 5,600 EARS-Info episodes. These results support the complete controller; individual component advantages remain uncertain. Generalization is limited by reused geography, one training replicate, coarse maps, and adapted comparison methods.

cs.RO · cs.AI · cs.MA

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新论文 · v1arXiv:2610.38039

Reusable LLM-Based Plans with Probabilistic Guarantees for Self-Adaptive Systems

Asher Tempest Arya, Rajan Maghera, Nicholas Wu Jiang, Federico Mora, Aren A. Babikian, Marsha Chechik

原文摘要

Self-adaptive systems are designed to accommodate variants of their environment, which form a family of instances whose uncertainty is captured by probabilistic models, and must meet probabilistic requirements on each instance. Within such a family, changes in size and structure complicate the fundamental adaptation decision: whether to retain, reuse, or construct a new controller, since controllers are often synthesized for and specific to an instance. Large language models (LLMs) have recently been used to write reusable controllers, but existing approaches largely target deterministic domains, and probabilistic model checking of their output remains limited to preliminary single-instance studies. In this paper, we propose a verifier-guided framework for controller reuse with explicit support for families of environments. The framework introduces a novel combination of LLM-written controllers with probabilistic model checking to provide per-instance guarantees for probabilistic requirements. We evaluate our approach using three LLMs on three benchmarks, assessing its effectiveness and cost with respect to baseline implementations, the distribution of adaptation decisions, and the impact of individual framework components. On grid worlds, LLM-written rule lists are verified on about 90% of held-out instances, against at most 77% for the baselines, and verifying a stored rule list is 15 to 103 times faster than per-instance synthesis. Reuse serves most episodes at a small fraction of the cost of generation or per-instance synthesis, and model-checking feedback increases verified coverage over independent restarts.

cs.SE

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新论文 · v1arXiv:2610.38038

Efficient Deep Gaussian Process Surrogates via Mini-Batch Latent MAP Estimation

Chun-Yi Chang, Chih-Li Sung

原文摘要

Deep Gaussian processes (DGPs) are flexible surrogate models for computer experiments with nonstationary response surfaces, but inference over their latent representations remains computationally challenging. Existing methods typically rely on variational inference or Markov chain Monte Carlo (MCMC), which impose variational-family assumptions or require costly posterior sampling. We propose the latent maximum-a-posteriori deep Gaussian process (LaMP-DGP), which uses gradient-based optimization to obtain MAP estimates of the latent representations and enables efficient probabilistic prediction through linked-GP calculations. To scale LaMP-DGP to large datasets, we develop a mini-batch optimization algorithm and, under a polynomial mini-batch bias-decay condition, establish a stationarity bound for latent-state optimization that motivates a growing batch-size schedule. Across numerical experiments and real-data analyses, LaMP-DGP provides consistently competitive predictive performance relative to doubly stochastic variational inference, stochastic gradient Hamiltonian Monte Carlo, and fully Bayesian Vecchia DGP inference. Computationally, LaMP-DGP is substantially faster than the Vecchia MCMC approach while remaining broadly competitive with the stochastic-gradient alternatives. These results position LaMP-DGP as a competitive and computationally efficient alternative for DGP inference. An R package, "LaMPDGP", is available for implementing the proposed method.

stat.ML · cs.LG

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