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新论文 · v1arXiv:2610.38086

From Next Day Wildfire to Next Days Wildfire: A New Multi-Continent Dataset of Active-Fire Spread for Sequence-to-Sequence Forecasting

Jose Guillen, Renzo Zanca, Valentin Barriere

原文摘要

Benchmarks such as WildfireSpreadTS (WSTS) have enabled data-driven forecasting of wildfire spread from multimodal satellite time series, but they suffer from limitations: size restricts them to large fires that burn for weeks, and they are from similar environments. We introduce AFSeq, a multimodal dataset of 1,194 recent fire events (2023 to 2026) from 8 different countries across three regions, dominated by the short, small fires that existing benchmarks underrepresent. Our dataset is the largest and most diverse to date for time-series-based wildfire spread prediction. We also introduce UTAE-AR, an extension of a classical U-Net with Temporal Auto Encoder, enhanced with recurrent modules before the decoder. This allows the model to not only predict the next day but the whole sequence in an autoregressive way. Moreover, our model can feed its own predicted fire mask back one day at a time, producing the day-by-day forecasts that single-step models cannot. Our results show that UTAE-AR surpasses state-of-the-art on both WSTS, WSTS+ and AFSeq for next-day accuracy while having 21.5% of their parameters, and being able to predict confidently at a longer horizon.

cs.CV

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首次观测 · 记录 #1软件学报:jos:7669软件学报

在轨环境预测驱动的卫星计算调度方法

黄天泽, 张其阳, 徐梦炜, 李晴, 王尚广, 黄罡, 刘譞哲

原文摘要

星地传输限制已成为当前制约卫星应用时效性的主要瓶颈, 导致海量遥感数据无法及时回传. 卫星计算通过在轨预处理与智能分析将原始数据压缩为轻量化的语义结果, 从而缓解下行压力. 然而, 通过在两颗在轨卫星上进行长期实测发现, 传统的地面静态调度难以适应高动态的太空环境, 导致卫星计算的实际部署面临以下核心矛盾: 卫星在阳照区的太阳能利用率极低, 而激进提升计算负载又易触发系统热失控. 为此, 提出一种环境预测驱动的卫星计算调度方法. 该方法通过模型预测控制, 利用轻量级的环境预测模型(太阳能采集功率、电池及温度模型)在轨构建实时约束边界, 并通过卫星数据处理流水线解耦与主动计算分载策略实现资源的动态优化. 基于在轨实测数据的模拟实验结果表明, 该方法在严格满足卫星能源与温度约束的前提下, 可显著提升在轨计算的端到端数据交付时效性与太阳能利用率.

cs.*

软件学报 · 2026 · 正式发表

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首次观测 · 记录 #1软件学报:jos:7670软件学报

基于比特切片的SM4-BGV转密技术

谭清泉, 彭聪, 李小红, 何德彪

原文摘要

随着数据安全需求的提升与云计算场景的广泛应用, 如何在不解密的前提下对加密数据进行高效计算已成为密码学研究的重要方向. 全同态加密(fully homomorphic encryption, FHE)能够支持密态计算, 但较高的计算与存储开销限制了其在高吞吐或实时场景中的应用. 为此, 业界提出转密(transciphering)的技术, 即利用同态加密对已有对称加密体系(如AES、SM4)的密文进行同态解密和再计算, 从而有效结合对称密码的紧凑性和同态加密的密态计算能力. 相较于已较为成熟的AES转密研究, 现有SM4转密方案普遍面临乘法深度大、噪声增长快和整体性能受限的问题. 针对上述挑战, 提出一种基于比特切片(bit-slicing)的高效SM4转密实现方案, 并在BGV同态加密框架下完成设计与实现. 该方案通过对SM4 S盒进行布尔建模与算子层级的结构化优化, 显著提升S盒性能; 同时利用比特切片的特性高效实现线性变换, 避免额外的代价较高的同态操作. 最后, 为应对32轮轮函数迭代所需的高乘法深度和噪声积累问题, 设计动态噪声管理模块, 可在噪声预算不足时自动实现密文批量自举操作, 从而保证计算的正确性和可扩展性. 实验结果表明, 所提方案较当前公开的SM4转密实现均取得更优性能: 相较于基于BGV的有限域实现, 吞吐量提升2.35倍; 相较于基于CGGI的布尔电路实现, 吞吐量提升7.04倍.

cs.*

软件学报 · 2026 · 正式发表

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首次观测 · 记录 #1软件学报:jos:7671软件学报

融合多智能体系统与检索增强的芯片分区布局框架

孙可钦, 杨秋松, 李明树

原文摘要

在超大规模集成电路物理设计流程中, 芯片分区直接决定后续布局布线的效率与质量. 构建融合多智能体与检索增强的芯片分区布局框架ChipMASRAO, 将多智能体强化学习(multi-agent reinforcement learning, MARL)与检索增强(retrieval-augmented optimization, RAO)技术深度融合. 该系统利用RAO机制从知识库检索历史分区结果作为高质量初始化, 通过MARL进行增量优化和多智能体协同评估, 最终驱动OpenROAD完成布局. 与传统方法每次从零计算不同, ChipMASRAO通过复用历史方案避免重复计算. 在ISPD 2015测试集8个设计上的实验表明: 相较于Baseline和ChipDRAG中性能较好的方法, 半周线长(half-perimeter wirelength, HPWL)总平均改善21.6%; 多智能体评估机制使测试集设计负载平衡度平均提升3.47%. 消融实验证实了RAO的关键作用: 引入RAO后平均超边切割数(Cutsize)降低86.5%, HPWL改进30.4%. 提出“检索-复用-修正”的新型范式, 验证了知识驱动方法在芯片分区问题上的有效性.

cs.* · cs.AI · cs.MA

软件学报 · 2026 · 正式发表

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首次观测 · 记录 #1软件学报:jos:7743软件学报

人工智能保险箍: 发展现状与未来展望

李金海, 李若涵, 胥迤潇, 李默涵, 孙彦斌, 田志宏, 方滨兴

原文摘要

近年来, 以大语言模型和具身智能为代表的面向通用人工智能发展的相关技术快速演进, 其广泛应用在带来巨大潜力的同时, 也引入了复杂的安全风险. 这些风险总体可分为两类: 一类是源于模型缺陷、系统设计或环境不确定性的自发性风险, 另一类是由外部对抗攻击或恶意诱导触发的诱发性风险. 为保障人工智能系统在开放环境中的安全可靠运行, 人工智能保险箍通过风险感知、风险量化与风险熔断这3类核心能力, 为人工智能行为体构建独立的安全保障机制, 使系统具备在行为偏离安全边界时及时识别、风险评估与干预控制的能力. 以人工智能保险箍为研究对象, 系统梳理相关研究进展与关键技术. 首先界定人工智能行为体的基本概念及其潜在失控风险类型. 随后围绕风险感知、风险量化与风险熔断这3个技术维度, 对相关研究进行系统分类与综述, 并分析国内外代表性工作的研究现状. 区别于以威胁检测或单点防御技术为核心的综述, 从构建面向人工智能行为体的底线安全机制这一视角出发, 对人工智能保险箍的技术体系进行整体分析. 最后讨论该领域面临的关键挑战与未来发展方向, 为构建安全可控的人工智能系统提供参考.

cs.* · cs.CL · cs.AI

软件学报 · 2026 · 正式发表

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新论文 · v1arXiv:2610.38084

CADENCE: Distributional Next-Transaction Forecasting for Business Cash-Flow Underwriting

Alexander Wu, Jingchao Huang

原文摘要

Recurring cash flows are the backbone of cash-flow underwriting: they separate durable revenue from one-off deposits, expose standing obligations, and ultimately determine ability to pay. For small businesses, recurrence is both indispensable and hard to estimate, because much of their income arrives as ambiguous transfers, most payment streams are short, and schedules follow calendar conventions that frequency-domain methods treat as noise. We present CADENCE, Calendar-Aware Distributional Event Network for Cash-Flow Estimation, a compact causal Transformer that treats each transaction as a token and predicts the amount and a full, nonparametric timing distribution of every cash flow's next payment. Inheriting transaction semantics from an external labeler and a frozen text encoder keeps CADENCE compact yet general across the long tail of small-business counterparties, while predicting a calibrated distribution rather than an opaque embedding makes its outputs interpretable enough to feed existing underwriting models and power credit decisions. Ablations confirm the role of each design choice, with counterparty semantics driving recall on short histories, calendar inputs driving timing accuracy, and no-event supervision driving precision. Against the production FFT baseline, CADENCE improves one-day timing accuracy by 93.3% and next-event F1 by 6.7%, while producing calibrated distributions that serve directly as a confidence gate for downstream decisions. In an end-to-end credit evaluation on production applications, replacing FFT recurrence features with CADENCE features raises KS by 4.8%, raises the approval rate at a fixed loss rate by 18.2%, and lowers the loss rate at a fixed approval rate by 13.1%.

cs.LG · q-fin.RM

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新论文 · v1arXiv:2610.38082

Interpretable Feature Discovery from Unstructured Data Using LLMs and Statistical Choice Models

Baptiste Arnaudo, Amine Bennouna, Sébastien Martin

原文摘要

Feature extraction from unstructured data typically relies on opaque embeddings unsuited to interpretable decision-making. Interpretable features, on the other hand, typically require costly, and often infeasible, hand-engineering. Large language models (LLMs) offer a natural bridge, as they can read raw data and describe it in interpretable terms. One could simply ask an LLM to extract features from a large sample of the dataset, but context limitations and diluted attention can lead to unsatisfactory results. We introduce a method that automatically discovers interpretable, decision-relevant features from unstructured data. Our key idea is to perform pairwise comparisons between items, asking an LLM to articulate, in interpretable terms, the features distinguishing each pair. We treat these explanations, or rationales, as data: we develop a statistical choice model that combines these rationales across the comparisons to identify relevant features and estimate per-item, per-feature scores. We first test our method on a synthetic hiring dataset of cover letters with known ground-truth features and show that it significantly outperforms direct LLM feature extraction in feature recovery. We then apply it to the ChangeMyView (Reddit) debate dataset, where it discovers several interpretable features associated with persuasion while reaching predictive accuracy comparable to established baselines. We also show how the same rationales can be used to audit LLM decisions, yielding a holistic score whose decomposition over interpretable features reveals what the LLM rewards.

stat.ML · cs.AI · cs.LG

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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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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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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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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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新论文 · v1arXiv:2610.38076

Prompt Lottery in LLM-as-a-Judge: Measuring and Mitigating Sensitivity to Syntactic Rephrasing

Ilija Subasic, Brett Levert, Andrew Rabinovich, Sajjad Pakdamansavoji, Zhao Chen

原文摘要

LLM-as-a-Judge has become a standard for evaluating large language models, yet most approaches implicitly assume that a single judge prompt functions as a stable evaluation instrument. Prior work has primarily explored deliberate prompt modifications such as scoring rubrics, role descriptions, or chain-of-thought reasoning in order to optimize accuracy. In contrast, we investigate whether validated prompt variants intended to preserve evaluation semantics while differing in surface realization (\textit{paraphrastic rephrasings}) yield consistent judgments under a fixed evaluation task. We introduce \textit{Prompt Lottery}, a phenomenon in which paraphrastic rephrasings produce different LLMaJ judgments. Across a large-scale study spanning diverse evaluation tasks, judge families, and model backbones, we find that these variations can alter evaluation outcomes and, in some settings, reverse relative model rankings. A user study with 10 human annotators shows that LLMaJ judges exhibit 6-9$\times$ larger agreement degradation under prompt rephrasings than humans, falling below the generally accepted threshold in usability studies. This indicates that single-prompt evaluation is insufficiently robust for model ranking. Finally, we show that multi-prompt aggregation substantially improves robustness by emphasizing judgments that remain consistent across prompt variants.

cs.CL · cs.AI

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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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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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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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arXiv ↗PDF ↗