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版本更新 · v2arXiv:2606.12801

Evaluating open-weight LLMs for scoring L2 essays: Accuracy, prompt transfer, and deployment of AiAWE

John Maurice Gayed

原文摘要

This study presents AiAWE, an open-source automated writing evaluation system that scores argumentative essays with a LoRA-adapted large language model deployed on local hardware. Using 480 TOEFL Independent Writing essays from a restricted-access Educational Testing Service (ETS) dataset, we fine-tuned Gemma-3-27B and LLaMA-3.3-70B under identical LoRA configurations on 120 essays from one prompt, with all hyperparameters fixed in advance, and evaluated them on the remaining 360 essays. Gemma achieved a root mean square error (RMSE) of 0.474 and a quadratic weighted kappa (QWK) of 0.828 on the half-point grid against the ETS scores. In paired comparisons, it significantly outperformed a fine-tuned GPT-3.5 baseline from prior work on every metric tested, showing that a locally deployable open-weight model can achieve competitive scoring performance in this setting. It also had significantly lower error than the larger LLaMA configuration, although, because the two models differ in family as well as size, this comparison does not isolate the effect of scale. Neither open-weight model showed a statistically detectable increase in error on the 240 essays written to a second, unseen prompt from the same task family. On a single consumer-grade GPU, the scoring model returned a complete response in a median of 14.2 s, demonstrating technical feasibility under the tested conditions. Only the scoring component was evaluated, not the generated feedback. The system is publicly accessible at https://app.awade.gec.waseda.ac.jp, and its code, LoRA adapters, and training configurations are openly released.

cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.16912

PepEVOLVE: Position-Aware Dynamic Peptide Optimization via Group-Relative Advantage

Trieu Nguyen, Hao-Wei Pang, Shasha Feng

原文摘要

Macrocyclic peptides are an emerging modality that combines biologics-like affinity with small-molecule-like developability, yet their vast combinatorial space and multi-parameter objectives make lead optimization slow and challenging. Prior generative approaches for peptide lead optimization (e.g., PepINVENT) require chemists to pre-specify mutable positions for optimization, choices that are not always known a priori, and rely on static pretraining and optimization algorithms that limit the model's ability to generalize and effectively optimize peptide sequences. We introduce PepEVOLVE, a position-aware, dynamic framework that learns both where to edit and how to dynamically optimize peptides for multi-parameter improvement. PepEVOLVE (i) augments pretraining with dynamic masking and CHUCKLES shifting to improve generalization, (ii) uses a context-free multi-armed-bandit router that discovers high-reward residues, and (iii) couples a novel evolving optimization algorithm with group-relative advantage to stabilize reinforcement updates. During in silico evaluations, the router policy reliably learns and concentrates probability on chemically tractable sites that influence the desired peptide's properties. On a therapeutically motivated Rev-binding macrocycle benchmark, PepEVOLVE outperformed PepINVENT by reaching higher averaged permeability-weighted optimization scores of approximately 0.8 vs. 0.6, achieving best candidates with score of 0.97 (vs. 0.89), and converging in fewer steps under the task of optimizing permeability and lipophilicity with structure constraints. Overall, PepEVOLVE offers a practical, reproducible path to linear and macrocyclic peptide lead optimization when optimal edit sites are unknown, enabling more efficient chemical space exploration and improving design quality across multiple objectives to accelerate the design cycles.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36219

Bringing Hardware Security Education to All: A Seamless Curriculum Integration Approach

Tanvir Hossain, Mahmudul Hasan, Kareem El-Araby, Lorenzo R. Godinez, Swarup Bhunia, Md Tauhidur Rahman, Tamzidul Hoque

原文摘要

Cybersecurity education has predominantly focused on software and network security, under the assumption that the underlying hardware is trustworthy. Threats introduced through the global semiconductor supply chain, together with vulnerabilities arising during design and integration, challenge the assumption that hardware can serve as an unquestioned root of trust. Sensor-enabled systems and machine-learning accelerators further expand the hardware attack surface. The undergraduate curriculum of electrical and computer engineering and computer science programs has not evolved accordingly. Core courses cover the design and the functionality of hardware while the security aspects remain overlooked, and dedicated hardware security (HS) courses are elective, scarce, and often restricted to graduate students. At the University of Florida, fewer than 7% of the undergraduate electrical and computer engineering students register for the introductory HS course each year. We present an integrated and economical approach that disseminates fundamental HS concepts within existing core courses through contextual learning. Nine hands-on modules were developed such that each security concept exercises an associated design concept of the core course, as a component of that course and not as a course of their own. Doing the security work is how the student exercises the design topic, so the modules are built to strengthen the host course and not only to add to it. Three were assessed across three offerings of the embedded systems course through one course survey and two conducted by an external evaluator, with 25 respondents. We report what those instruments measured and the limitations of the approach.

cs.CR · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38091

Jailbreak Connectivity: Towards Diverse, Transferable, and Universal Multimodal Large Language Model Jailbreak

Xu Zhang, Ziqing Hu, Shuo Han, Ren Wang

原文摘要

Multimodal large language models (MLLMs) are vulnerable to visual jailbreak attacks that bypass safety safeguards. Existing methods typically optimize isolated adversarial examples, limiting attack diversity, transferability, and generalization. We propose Jailbreak Connectivity (JC), a framework based on the observation that effective adversarial inputs can be connected through low-loss regions in image space. JC optimizes continuous adversarial paths: path sampling yields diverse jailbreak images, lightweight surrogate classifiers guide cross-model transfer, and a distribution-level objective enables universal attacks across harmful queries. On MM-SafetyBench, JC achieves average attack success rates of 84.01%, 85.77%, and 97.95% on Qwen3-VL-8B-Instruct, InternVL3.5-8B, and Gemma-3-12B-IT, respectively, exceeding the strongest baselines by up to 68.36 percentage points. In black-box evaluations, JC improves attack success rates over FigStep by 24.69 and 27.34 percentage points on GPT-5.6 and Gemini 3.1 Pro, respectively. These findings reveal structured vulnerabilities in MLLM safety alignment and inform more comprehensive security evaluation.

cs.LG · cs.AI · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38090

Predicting What Answer Engines Cite: Open Decision Models, Rerankers and a Distilled 4B Student on Ten Engines under Controlled Retrieval

Elisha Bajemon, Andre-Louis Rochet

原文摘要

Generative engine optimization needs a scorer that ranks candidate pages by the share of an answer they will receive as citations. We measure pretrained rerankers, a hosted typed decision model (Jev), eight open decision models and distilled students on GEO-Bench queries answered by ten engines that receive the same candidate sources in their prompt. Where several engines answer the same query, they agree with one another at a within-group Spearman correlation of 0.704. Jev and two open 27B decision models, used without training, are the strongest scorers at 0.575 to 0.578. A Qwen3-Reranker-4B fine-tuned with low-rank adapters on teacher labels for other GEO-Bench queries, released as GEO Citation Ranker 4B, reaches 0.542 to 0.549 across three seeds, and its differences with the 27B models and with Jev are not distinguishable from zero on 229 queries. Most of the remaining distance between the student and Jev disappears when Jev scores one passage at a time, as the student does. Students trained on teacher labels and on observed engine citations reach the same level, and labels from Jev alone match the two-teacher target at 0.6B. We release the model, the benchmark, the predictions of every reported scorer and the code.

cs.IR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.15742

Collective Kernel EFT for Pre-activation ResNets

Hidetoshi Kawase, Toshihiro Ota

原文摘要

In finite-width deep neural networks, the empirical kernel $G$ evolves stochastically across layers. We develop a collective kernel effective field theory (EFT) for a simplified pre-activation residual model. Exact conditional Gaussianity of the residual increment gives finite-width discrete identities for $G$ and its sigma-kernel source. A projected $G$-only closure and a continuous-depth approximation define reference equations for the mean kernel $K_0$, the kernel covariance $V_4$, and the next-to-leading mean correction $K_1$. Numerical tests show that this closure captures the leading mean-kernel evolution, whereas its covariance and next-to-leading accuracy depend on sigma-kernel modes omitted from the $G$-only state. For the scalar ($N=1$) model with $\tanh$ activation, a Hermite--Galerkin extension agrees with the extrapolated $K_1$ value and reduces the $V_4$ endpoint error by $85$--$93\%$ relative to the $G$-only reference. These results clarify the range of validity of the equal-time Markov $G$-only description and identify the additional collective information required beyond it.

cs.LG · hep-th · stat.ML

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版本更新 · v3arXiv:2603.14681

Generalized Hierarchical Bayesian Segmentation with Irregular Designs, Multi-Sequence Hierarchies, and Grouped/Latent-Group Designs

Omid Shams Solari

原文摘要

Bayesian change-point and segmentation models provide uncertainty-aware piecewise-constant representations of ordered data, but exact inference is often limited to narrow likelihood classes, single sequences, or index-uniform designs. We present BayesBreak, a modular offline Bayesian segmentation framework that separates local block scoring from global inference: each candidate block supplies a marginal likelihood and any needed moment numerators, while a dynamic program combines these scores to compute posteriors over segment counts, boundaries, and latent signals. For weighted exponential-family likelihoods with conjugate priors, block evidences and posterior moments are available in closed form from cumulative sufficient statistics, enabling exact sum-product inference for p(y|k), p(k|y), boundary marginals, and Bayes regression curves. We distinguish these summaries from the joint MAP segmentation, recovered by a separate max-sum recursion. BayesBreak supports design-aware partition priors for irregular observations, exact pooling across replicates with shared boundaries, and latent-template mixtures with exact EM updates. For non-conjugate GLM blocks, the same DP layer can use deterministic local approximations such as Laplace, variational methods, EP, or quadrature. We prove a posterior-odds stability bound: uniform per-block log-evidence error ε perturbs k-odds and boundary-odds by at most (k+k')ε and 2kε. Validation includes synthetic recovery, calibration, and scaling experiments, plus four real-data illustrations: well-log geology, array-CGH copy number, equity-return volatility, and CpG-atlas methylation.

cs.LG

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新论文 · v1arXiv:2610.38088

What Can Firms Do in the Face of Model Distillation?

Diptangshu Sen, Jibang Wu, Juba Ziani

原文摘要

Model distillation has become a major concern in the rapidly growing generative AI discourse. In its simplest form, distillation is the phenomenon where smaller firms can use data distilled from transcripts of conversations with powerful proprietary closed-weight models to train their own models, thus achieving reasonably high model quality but at much lower costs. Besides the legitimate safety and security concerns associated with distillation, another major issue is how it can also distort marketplace outcomes. This motivates us to initiate an exploration of the problem from an economic theory lens. We consider a setting with two players: an incumbent and a distilling competitor and characterize how the incumbent's model characteristics drive the incentives around how much the competitor should distill. We also consider the impact of distillation on key market outcomes like user-side surplus and the incumbent's utility. Our key contribution, however, is to study interventions for preventing or limiting distillation attacks. In this context, we study two settings: i) when it is possible, with a certain probability (which grows in the extent of distillation conducted), to detect an attack on the incumbent's model; and ii) when a distillation attack cannot be reliably detected. In the first setting, we show that penalty-based regulations can be very effective in completely deterring distillation, although it can have potentially adverse effects on market competition and user surplus. In the latter setting, we show that the incumbent can use self-distillation as a mitigation strategy. Importantly, the effectiveness of self-distillation depends on how cost-efficiently the incumbent can deploy a new self-distilled model on the market. On the positive side, self-distillation can preserve market competition thus protecting user surplus to some extent.

cs.GT · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.19134

Introducing M: A Modular, Modifiable Social Robot

Victor Nikhil Antony, Zhili Gong, Yoonjae Kim, Chien-Ming Huang

原文摘要

We present M, an open-source, low-cost tabletop social robot platform aimed at lowering barriers to social robotics research by making robots easier to reproduce, modify, and deploy in real-world settings. M combines a modular mechanical design, multimodal sensing, and expressive yet mechanically simple actuation architecture with a ROS2-native software package that cleanly separates perception, expression control, and data management. The platform includes a simulation environment with interface equivalence to hardware to support easy sim-to-real transfer of interaction behaviors. We demonstrate extensibility through additional sensing/actuation modules and provide example interaction templates for storytelling and two-way conversational coaching. Lastly, we report real-world use of M in interaction design and week-long in-home deployments, showing how M can serve as a practical platform for longitudinal, reproducible social robotics research.

cs.RO · cs.HC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.11547

Sublinear Algorithms for Estimating Single-Linkage Clustering Costs

Pan Peng, Christian Sohler, Yi Xu

原文摘要

Single-linkage clustering (SLC) is a fundamental method for hierarchical data analysis. In the distance setting, a $k$-clustering produced by SLC can be obtained by computing a minimum spanning tree (MST) and deleting its $k-1$ heaviest edges. This naturally induces a cost profile for the SLC hierarchy: for each $k\in[n]$, we define $\mathrm{cost}_k$ to be the weight of the resulting $k$-component spanning forest, equivalently, the minimum total weight of any spanning forest with exactly $k$ connected components. The corresponding \emph{SLC cost profile} is $(\mathrm{cost}_1,\ldots,\mathrm{cost}_n)$, and the scalar quantity $\mathrm{cost}(G)=\sum_{k=1}^{n}\mathrm{cost}_k$ is the area under this profile. We study the problem of approximating these quantities in sublinear time. We assume that the input is a weighted graph $G$ of average degree $d$ with edge weights in $\{1,\dots,W\}$, accessed through adjacency-list queries; missing edges are treated as having infinite distance. Our main result is a sampling-based algorithm that outputs a succinct sketch of the entire SLC cost profile in the distance setting. The algorithm runs in $\widetilde{O}(d\sqrt{W}/\varepsilon^3)$ time and returns a sketch from which one can derive estimates $(\widehat{\mathrm{cost}}_1,\ldots,\widehat{\mathrm{cost}}_n)$ satisfying $\sum_{k=1}^{n}\bigl|\widehat{\mathrm{cost}}_k-\mathrm{cost}_k\bigr| \le \varepsilon\,\mathrm{cost}(G)$. Thus, we obtain an $\ell_1$ approximation to the full profile whose error is at most an $\varepsilon$-fraction of the area under the true profile. In particular, this yields a $(1\pm\varepsilon)$-approximation to $\mathrm{cost}(G)$ within the same running time. We also prove a nearly matching lower bound of $Ω(d\sqrt{W}/\varepsilon^2)$ queries for estimating $\mathrm{cost}(G)$.

cs.DS

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版本更新 · v3arXiv:2608.30076

Budget-Aware Compression Pipeline for Single-GPU LLM Inference: Methods, Trade-offs, and Coupling Effects

Hongyu Yu, Yifei Shen

原文摘要

Single-GPU deployment of 70B-parameter language models on an NVIDIA GPU is constrained by device memory, long-context throughput, and engineering integration cost. We cast single-GPU inference as a budget-aware design problem over these three axes and study how pruning, quantization, and KV-cache compression interact under realistic execution. Controlled ablations show that layer-wise pruning makes weight quantization more robust. KV-cache sparsification complements INT8 KV quantization by reducing memory without hurting decoding speed, while static vector quantizers often conflict with dynamic caching. Guided by these coupling results and explicit budget tracking, we assembled a practical pipeline and compressed a 70B model to about 33 GB, sustained about 57 tokens/s on 10k token prompts on a single A40, and kept absolute accuracy within 5% on common and reasoning benchmarks. We contribute design rules and a reproducible evaluation protocol that jointly report quality, memory, and end-to-end speed, and we provide a foundation for automated pipeline search under realistic single-GPU constraints.

cs.CL

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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:2610.38074

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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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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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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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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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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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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