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版本更新 · v4arXiv:2605.16433

Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing

Mohammadhossein Ghahramani, Yan Qiao, Mengchu Zhou

原文摘要

Task allocation in smart manufacturing systems must operate under decentralized decision-making, dynamic workloads, and shared-resource constraints. In circular manufacturing settings, these challenges are further intensified because tasks compete for reusable, capacity-constrained assets, and machine selection also might affect processing energy. Although learning-based approaches have been explored for task allocation, improvements in predictive modeling do not necessarily translate into better allocation outcomes under decentralized negotiation. This work proposes an Edge-AI-driven decentralized task-allocation framework. We develop lightweight decision intelligence deployed at the machine level. It is developed progressively: first, a resource-aware heuristic establishes the decentralized bidding structure; a regression-based Edge-AI formulation then examines learned local bid approximation, and a compact autoencoder-regularized pairwise ranking model finally provides a learned correction to the analytical bid ordering. Each machine evaluates incoming tasks by using its processing capability, queue state, energy characteristics, and a compact signal representing contention over the reusable shared production asset. The framework is assessed using discrete-event simulation in scenarios characterized by high load and dependence on shared resources. Compared to the heuristic, the proposed ranking method increases completed tasks, reduces average tardiness, and lowers the deadline-miss rate, with statistically significant paired differences. Mean energy per completed task is also reduced. The results indicate that effective learning-assisted allocation depends not only on approximating local decision quantities, but also on shaping the relative preferences that determine negotiation outcomes.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37521

Approaching Optimal Scaling of Queue Size for an Input-Queued Switch

Taha Ameen, Weina Wang

原文摘要

We study the scaling of the expected total queue length in an $n\times n$ input-queued switch. It has been conjectured that the optimal scaling is $O(n/(1-ρ))$, up to logarithmic factors, where $ρ$ is the system load. This conjecture has been established in the traditional heavy-traffic regime and in certain sub-regimes of the large-system regime. But it remains unresolved over a wide range of load scalings in the large-system regime. In this paper, we develop a family of scheduling policies parameterized by a positive integer $k\ge 2$. We show that under uniform traffic, our policy achieves an expected total queue length of $O\bigl({n\log\max\{n,1/(1-ρ)\}}\big/{(1-ρ)^{2^k/(2^k-1)}}\bigr)$, as long as $ρ\to 1$ in the large-system regime. As $k$ increases, the exponent $2^k/(2^k-1)$ approaches $1$, bringing our bound arbitrarily close to the conjectured scaling. The expected amortized scheduling cost of the policy is $O(n^3 k)$ per time slot. Our policy is of a batching type, with a novel recursive design that allows an arbitrary number of batching levels. This design principle may also be useful for other scheduling problems in which combinatorial constraints restrict which jobs can be served simultaneously.

cs.PF · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37519

Leveraging Distributed Sensing and Hyperdimensional Computing for Non-Line-of-Sight Passive Object Sensing

Placido Mursia, Marco Rossanese, Francesco Devoti, Adrian Lendinez Ibanez, Lanfranco Zanzi, Xi Li, Xavier Costa-Perez

原文摘要

In smart factory environments, passive entities such as humans and movable obstacles may interfere with autonomous robot operations, posing significant safety risks. Detecting potential conflicts between robots and entities outside their field-of-view (FoV) is therefore of paramount importance. However, conventional passive object detection systems rely on sensors such as LiDAR, radar, or cameras, which operate under direct line-of-sight (LoS) and are limited by obstacles and occlusions. To overcome these limitations, integrated sensing and communication (ISAC) enables environmental perception via the communication infrastructure thanks to cooperation among distributed wireless devices. In this paper, we propose a framework for passive entity sensing beyond the robot's FoV that relies solely on distributed channel measurements from the communication infrastructure to perform risk-aware classification. These measurements are embedded into high-dimensional representations using hyperdimensional computing (HDC), enabling robust and scalable learning without explicit channel reconstruction. Numerical simulations based on a commercial ray tracer and ROS-based validation in realistic smart factory scenarios demonstrate improved safety and enhanced path planning with up to 89% accuracy under severe occlusions, while maintaining low computational complexity.

cs.RO · cs.ET

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.07767

Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation

Andrea Basteri, Carlo Ciliberto, Alessandro Rudi

原文摘要

Missing values undermine statistical inference and machine learning pipelines, yet most imputation methods rely on heuristics or restrictive parametric assumptions that ignore the joint data distribution. We recast imputation under missing completely at random (MCAR) as density estimation from masked observations: estimate a distribution whose observed marginals exactly match those in the data. Leveraging positive semidefinite (PSD) kernel densities we obtain a convex empirical risk problem with closed form marginals, solvable by a Newton interior point method. We further introduce a non convex extension that supports adaptive hyperparameter selection through alternating minimization. The resulting PSD-Impute model yields both single and multiple imputations from the same fitted density, enjoys statistical consistency, beating the curse of dimensionality for very regular probabilities. Experiments indicate competitive distributional accuracy compared with popular imputation baselines, suggesting strong practical promise.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.23800

Provable Speedups From Dynamic Population Sizes in Evolutionary Algorithms for Multiobjective Optimization

Andre Opris

原文摘要

This paper investigates the role of dynamic population sizes in evolutionary multi-objective optimization. Although such approaches are widely used in practice, their benefits remain poorly understood, and rigorous runtime analyses explaining when and why they help are still scarce. To address this, we introduce the bi-objective problem class $\text{CLIMB}$, where the parameter $\ell$ determines the size of the Pareto front. On this problem class, we analyze the runtime of GSEMO and the widely used NSGA-II. Our results show that allowing a dynamic population size for NSGA-II can lead to a moderate improvement, yielding a super-constant speedup for $\ell \in ω(\log(n)) \cap o(\sqrt{n \log(n)})$ in terms of fitness evaluations. In particular, we prove that GSEMO and NSGA-II-DYN, a version of NSGA-II with dynamic population sizes that we propose in this paper, can cover the Pareto front of $\text{CLIMB}$ in expected $O(n \log(n) + \ell^2 \log(en/(n-2\ell+3)))$ evaluations, and that this bound is tight for $\ell = O(\sqrt{n \log(n)})$. In contrast, NSGA-II with a fixed population size of at least $4\ell$ requires expected $O(n\ell + n\log(n) + \ell^2 \log(en/(n-2\ell+3)))$ evaluations to cover the entire Pareto front of $\text{CLIMB}$, while still requiring expected $Ω(\ell n)$ evaluations to even find a Pareto-optimal search point. To the best of our knowledge, this is the first rigorous runtime analysis in bi-objective optimization that demonstrates a super-constant speedup of GSEMO over NSGA-II. Our analysis builds on concepts from single-objective optimization, such as the evolution of population diversity over time, and employs the well-known family-tree method to prove the lower bound.

cs.NE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.21948

SCI-Defense: Detecting Persuasion-Structured Manipulation of LLM-Based Ranking in Generative Engines

Xucheng Yu, Haibo Jin, Huimin Zeng, Haohan Wang

原文摘要

Generative engine optimization (GEO) has reshaped how sellers compete for visibility in LLM-generated rankings. By embedding persuasive arguments in the content they control, sellers can influence how generative engines rank products and web pages and push their own items to the top. Existing defenses either detect harmful intent or unnatural language, which fluent GEO content often lacks, or rewrite all candidates, which alters legitimate listings without identifying the manipulated ones. However, these approaches overlook the persuasive structure through which GEO attacks influence LLM-based rankings. To address this gap, we present SCI-Defense, a framework for detecting persuasion-structured ranking manipulation, in which an LLM judge scores five persuasion devices grounded in research on influence and deception: authority, social proof, scarcity and urgency, comparative claims, and narrative purposiveness. Two lightweight gates for unnatural strings and inflated relevance complement the judge, and flagged candidates are demoted rather than rewritten, each with a record of the candidate and the criterion that triggered the flag. On 1,152 optimized CORE attacks across six Amazon product categories and six MS MARCO web domains, SCI-Defense detects 89.6% of reasoning attacks and 96.2% of review attacks at a 1.2% false positive rate and blocks 96.1% of the attacks that pushed their target into the top three. On a 120-query subset, paraphrasing and truncation block a similar share of attacks but push 4.0--4.5% of honest items out of the top three, compared with 0.6% for SCI-Defense.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37518

World Dreamer v5-lite: A Multi-Robot World Action Model with One Dreamer

Guoning Wu, Zijian Cai, Yuhang Zhang, Ying Liu, Yongbin Zheng

原文摘要

Multi-robot control requires integrating distributed camera views while predicting separate action sequences. Most vision-language-action and world-action models target one robot. Combining multiple views alone does not establish spatial correspondence between them. World Dreamer v5-lite is a multi-robot world-action model built around a shared world state for robot-specific action prediction. Calibrated features from a pretrained video encoder populate this state in workspace coordinates; its spatial size remains fixed as views are added. A vision-language model provides scene context and one query per robot. The queries read the shared state and exchange pose-aware information, while their action streams remain separate. Masked-view cooperative training withholds selected camera views from both visual branches while retaining all action targets. This encourages the use of observations shared by other robots. Pretraining uses more than 10,000 hours of trajectories from 20 simulated robot types, RoboFactory, and real robots. On RoboFactory, World Dreamer v5-lite achieves 74.0% mean success, 23.7 percentage points above the strongest evaluated baseline. Across four real-robot tasks, it reaches 78.8% mean success, a margin of 13.8 points. It also reaches 58.08% overall on RoboCasa365.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31359

Finite Systems in the Space of Projective Measurements

Miguel Flores R., Carlos A. Muñoz

原文摘要

We investigate the dynamics of two-level systems using the quasidistribution $Q$ function projected onto the space of collective measurements. Particular attention is given to evolution under Hamiltonians that are invariant under particle permutations, for pure states that are either invariant or noninvariant under such permutations. This requires constructing an approximate analog of the star product between quasidistribution functions: the projected $Q$ function of a product of two operators is expressed approximately in terms of the individual operators' $Q$ and $P$ functions. We rewrite the Liouville equation to determine approximate state dynamics and compare the result with exact evolution using the Hellinger distance. Overall, the method performs well for permutation-invariant pure states and Hamiltonians, whereas deviations arise for arbitrary pure states and noninvariant Hamiltonians.

quant-ph · cs.ET

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2505.00940

StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data

Zhenyu Wang, Molei Liu, Jing Lei, Francis Bach, Zijian Guo

原文摘要

When synthesizing multi-source high-dimensional data, a key objective is to extract low-dimensional representations that effectively approximate the original features across different sources. Such representations facilitate the discovery of transferable structures and help mitigate systematic biases such as batch effects. We introduce Stable Principal Component Analysis (StablePCA), a distributionally robust framework for constructing stable latent representations by maximizing the worst-case explained variance over multiple sources. A primary challenge in extending classical PCA to the multi-source setting lies in the nonconvex rank constraint, which renders the StablePCA formulation a nonconvex optimization problem. To overcome this challenge, we conduct a convex relaxation of StablePCA and develop an efficient Mirror-Prox algorithm to solve the relaxed problem, with global convergence guarantees. Since the relaxed problem generally differs from the original formulation, we further introduce a data-dependent certificate to assess how well the algorithm solves the original nonconvex problem and establish the condition under which the relaxation is tight. Finally, we explore alternative distributionally robust formulations of multi-source PCA based on different loss functions.

cs.LG · math.OC · stat.CO · stat.ME

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.35290

EvoIn: Bridging Evolution and Internalization for Agent Fine-Tuning

Shihan Dou, Shaofan Liu, Zhonghang Lu, Jiahang Lin, Shichun Liu, Binghai Wang, Jiajie Jin, Guanting Dong 等 11 位作者

原文摘要

Recent work has explored improving agents by jointly evolving their harnesses and models, but often takes a ''potpourri'' approach that bundles together new tools, new decision-making procedures, and model adaptation to the evolved harness under a single notion of agent improvement. In this paper, we instead investigate how agents can improve their decision-making procedures. In particular, we propose EvoIn, an agent fine-tuning framework that bridges evolution and internalization. EvoIn first analyzes agent execution traces to evolve and validate new decision-making procedures by temporarily instantiating them in the harness. The validated procedures guide the agent to generate improved reasoning traces. These traces are then rewritten into self-contained reasoning traces, removing explicit references to harness instructions while expressing the induced decision logic as the model's own reasoning. Finally, EvoIn fine-tunes the model on the rewritten traces, internalizing these procedures so that the improved decision-making persists without the evolved harness at inference time. We evaluate EvoIn on diverse benchmarks and find that it consistently enables agents to learn stronger decision-making procedures, raising the pass rate by 10.9 points in-domain and by 9.2 points out-of-domain. Results further show that the internalized decision procedures generalize to unseen tasks. Case studies show that agents can learn to decide how to solve a task before solving it, for example by checking a document's length to choose between reading it in full and searching it. EvoIn is also broadly applicable, showing consistent improvements on another model family.

cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.07328

MultiFair: Multimodal Balanced Fairness-Aware Medical Classification with Dual-Level Gradient Modulation

Md Zubair, Hao Zheng, Grayson W. Armstrong, Lucy Q. Shen, Gabriela Wilson, Yu Tian, Xingquan Zhu, Min Shi

原文摘要

Medical decision systems increasingly rely on data from multiple sources to ensure reliable and unbiased diagnosis. However, existing multimodal learning models fail to achieve this goal because they often overlook two critical challenges. First, various data modalities may learn unevenly, thereby converging to a model biased towards certain modalities. Second, the model may emphasize learning on certain demographic groups causing unfair performances. The two aspects can influence each other, as different data modalities may favor respective groups during optimization, leading to both imbalanced and unfair multimodal learning. This paper proposes a novel approach called MultiFair for multimodal medical classification, which addresses these challenges with a dual-level gradient modulation process. MultiFair dynamically modulates training gradients regarding the optimization direction and magnitude at both data modality and group levels. We evaluate MultiFair on three real-world medical classification datasets with diverse demographic attributes,including multiclass classification and missing-modality settings. Experimental results demonstrate its effectiveness.

cs.LG · cs.AI · cs.CV · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.26412

Where Does Rhetorical Distortion Live? A Multi-Platform, Multi-Language Measurement of Influencer Content

Xucheng Yu, Jiangping Chen, Haohan Wang

原文摘要

Much of the manipulation in influencer content is not false but disproportionate, framing accurate facts as historic, urgent, or catastrophic. Existing approaches either verify factual claims or detect clickbait and propaganda in single-platform, single-language text; however, they overlook where such framing originates, because they treat a post and the replies beneath it as one unit. To address this gap, we measure rhetorical distortion with one instrument applied identically across six platforms and two languages, classifying creator posts and their replies separately. Our three-tier pipeline detects five literature-grounded dimensions (Significance Inflation, Anxiety Manufacturing, Novelty Claims, Loaded Language, and Temporal Distortion): bilingual lexical rules propose candidates, negative filters cancel known false positives, and an LLM verifies or relabels the candidates. This design keeps labels transparent and comparable across platforms, calls the LLM on only 14% of posts, and supports validation against human annotators and a second model. On 13,060 posts from 330 accounts on RSS/newsletters, YouTube, Bluesky, Reddit, Weibo, and X/Twitter, platform is far more strongly associated with account-level distortion than content category (partial $η^2=0.49$ vs. $0.09$), and X/Twitter scores highest (DI 7.74 vs. 0.78 for RSS). On X/Twitter, the top-5 replies under a post carry 3.5x the distortion of the tweet they answer (1.5x at equal length), while creator tweets alone are indistinguishable from YouTube and Bluesky; two human annotators and GPT-4o confirm this gap. Measured distortion on threaded platforms thus lives largely in the audience layer, which calls for measuring creator and reply text separately.

cs.SI · cs.CL · cs.CY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.22105

Streaming Generation for Music Accompaniment

Yusong Wu, Mason Wang, Heidi Lei, Stephen Brade, Lancelot Blanchard, Shih-Lun Wu, Aaron Courville, Anna Huang

原文摘要

Music generation models can produce high-fidelity coherent accompaniment given complete audio input, but are limited to editing and loop-based workflows. We study real-time audio-to-audio accompaniment: as a model hears an input audio stream (e.g., a singer singing), it has to also simultaneously generate in real-time a coherent accompanying stream (e.g., a guitar accompaniment). In this work, we propose a model design considering inevitable system delays in practical deployment with two design variables: future visibility $t_f$, the offset between the output playback time and the latest input time used for conditioning, and output chunk duration $k$, the number of frames emitted per call. We train Transformer decoders across a grid of $(t_f,k)$ and show two consistent trade-offs: increasing effective $t_f$ improves coherence by reducing the recency gap, but requires faster inference to stay within the latency budget; increasing $k$ improves throughput but results in degraded accompaniment due to a reduced update rate. Finally, we observe that naive maximum-likelihood streaming training is insufficient for coherent accompaniment where future context is not available, motivating advanced anticipatory and agentic objectives for live jamming.

cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37510

A note on data-efficient finetuning of vision foundation models

Niclas Hergenröther, Antonio Orvieto

原文摘要

Adapting foundation models to a new task within or beyond the training domain is the de facto standard for building specialized machine learning models. Recently, Visual Prompting (VP) has emerged as a promising technique for efficient knowledge transfer. As new foundation model families~(like Mamba) get introduced and VP pipelines such as AutoVP reach greater maturity, we find a growing need for a systematic evaluation of current approaches. In this work, we assess the performance of the latest models, comparing them to earlier architectures and alternative fine-tuning methods, to better understand the progress, challenges and opportunities in the field of efficient fine-tuning under resource limitations. Towards this goal, this paper provides a concise empirical overview of the interactions among foundation model families (Attention-, Convolution-, and Mamba-based) and transfer paradigms: VP, Linear Probing (LP), Full Fine-tuning (FFT) and Low Rank Adaptation (LoRA). Our work builds up on previous findings by broadening the selection of evaluated models and techniques while tuning hyperparameters. In the interest of delivering practical guidelines for the user, we also explore application of prevalent regularization techniques to boost performance in the context of VP.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.31040

Fast Fault-Tolerant Decoders for Hypergraph Product and Lifted-Product Codes

Asit Kumar Pradhan, Nithin Raveendran, David Declercq, Bane Vasić

原文摘要

We design low-complexity, fault-tolerant decoders for quantum low-density parity-check (QLDPC) codes with the goal of reducing decoding latency. We target two major bottlenecks of decoding under the \emph{circuit-level} noise model: (i) post-processing via order-statistics decoding (OSD), and (ii) the large number of auxiliary variable nodes commonly introduced to represent CNOT-induced correlations during syndrome extraction. Our key observation is that propagating CNOT faults (\emph{hook errors}) create \emph{stabilizer-induced} trapping sets (TSs) that are intrinsic to hypergraph-product (HGP) and lifted-product (LP) constructions. Therefore, instead of modeling each such fault with an explicit correlation node and relying on OSD to clean up the resulting failures, we design message-passing decoders that resolve the corresponding \emph{stabilizer-induced} TSs directly. We obtain these decoders by deriving QLDPC decoders from decoders for the parent classical LDPC codes and using them collectively to correct broad families of \emph{stabilizer-induced} TSs. For CNOT faults that manifest primarily as syndrome errors, we show that their effect is equivalent to a data error together with syndrome-bit measurement errors. Consequently, given repeated measurements and a decoding graph that already includes nodes representing syndrome-bit errors, no distinct variable node is needed for each CNOT fault. Using a \emph{phenomenological} Tanner graph with nodes representing only data errors and syndrome-bit errors, simulations on LP codes show a significant improvement in logical error rate relative to BP+OSD. Moreover, the improved error correction relies on low-complexity finite-state iterative decoders, targeted for fast and low-latency FPGA implementation.

cs.IT · quant-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.01904

AI Writes Faster Than Humans Can Review: A Longitudinal Study of an Enterprise 2x Mandate

Hao He, Shyam Agarwal, Yegor Denisov-Blanch, Pavel Azaletskiy, Sanmi Koyejo, Bogdan Vasilescu

原文摘要

Enterprises increasingly mandate the use of AI coding tools and announce ambitious productivity targets, yet there is little longitudinal evidence on whether such productivity targets have been reached, how, and at what cost. We present a quantitative case study of a documented enterprise "2x" mandate: in June 2025, the CTO of a mid-sized, AI-forward software company set the goal of doubling merged pull requests (PRs) per engineer with AI. Using a panel of 802 developers and 196,212 PRs (January 2024 - April 2026), we estimate the effect of AI adoption by comparing each developer's PR throughput after they started using AI tools with their own throughput before. We find that the company eventually reached the target, but only in the tenth month and not in the way the announcement implied: PR throughput barely moved at the mandate date, rose by about 12% when each developer started using AI, and then kept growing as developers accumulated use. The target was not met merely by working longer or splitting work: extra working days account for at most one-fifth of the throughput increase, and the added PRs became larger and skewed toward features and bug fixes rather than routine changes. The gain is similar across seniority levels and larger in repositories created since 2022. The cost of AI adoption fell on code review, where load per reviewer doubled and automated review overtook human review in coverage within months of the mandate. We interpret these results as strong evidence that the adoption and sustained usage of AI raises PR throughput in an enterprise setting, with two caveats: throughput is only a proxy for productivity, and the exact size of the effect remains uncertain due to a lack of truly randomized assignment in our setting. Our results have implications for organizations under AI transformations and researchers studying the impact of AI on software development productivity.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.16845

Information Contraction under $(\varepsilon,δ)$-Differentially Private Mechanisms

Theshani Nuradha, Ian George, Christoph Hirche

原文摘要

The distinguishability quantified by information measures after being processed by a private mechanism has been a useful tool in studying various statistical and operational tasks while ensuring privacy. To this end, standard data-processing inequalities and strong data-processing inequalities (SDPI) are employed. Most of the previously known and even tight characterizations of contraction of information measures, including total variation distance, hockey-stick divergences, and $f$-divergences, are applicable for $(\varepsilon,0)$-local differential private (LDP) mechanisms. In this work, we derive both linear and non-linear strong data-processing inequalities for hockey-stick divergence and $f$-divergences that are valid for all $(\varepsilon,δ)$-LDP mechanisms even when $δ\neq 0$. Our results either generalize or improve the previously known bounds on the contraction of these distinguishability measures.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37504

Strategyproof Deep Reinforcement Learning for Online Resource Allocation

Fariba Dehghan, Sebastian Stein, Vahid Yazdanpanah, Stephanie Gauthier

原文摘要

Online resource allocation faces two coupled challenges: (1) allocating scarce resources efficiently under uncertain future demand, and (2) ensuring that agents report task valuations and requirements truthfully (strategyproofness). Reinforcement Learning (RL) adapts allocation decisions through experience but does not necessarily ensure strategyproofness. Existing strategyproof RL uses a linear allocation rule, which limits the resource interactions it can represent. Neural mechanism design methods offer greater expressiveness, but provide only approximate incentive guarantees. Against this background, we introduce the first strategyproof deep RL mechanism for online resource allocation. It integrates three novel components: (i) a monotonic neural network that learns nonlinear interactions by construction (higher reported value cannot reduce acceptance, while a longer reported duration or larger resource requirement cannot increase it); (ii) a generalised critical payment: the lowest accepted value, with all other inputs fixed; and (iii) training by either RL or imitation of an existing rule. Together, monotonicity and the critical payment ensure strategyproofness and individual rationality, while the neural rule provides flexibility and imitation improves training efficiency. Empirically, on a four-resource stream with two partner pairs that a linear difference cannot represent, the neural rule achieves 97.8% of an offline welfare upper bound, compared with 93.8% for a linear learner and 89.0% for first-come, first-served, closing 65% and 80% of the remaining optimality gap, respectively. Moreover, imitation reaches 97.5% in only 261 seconds, about 89 times faster than RL.

cs.MA · cs.AI · cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37503

ALADDIN: Adversarial Latent Diffusion Distillation for One-Step Identity Swapping

Ivan Shchekotov, Timofei Gritsaev, Viacheslav Pirogov, Dmitry Vetrov

原文摘要

Face swapping transfers a source identity onto a target image while preserving the target's pose, expression, hair, and lighting. The most realistic current methods are diffusion-based, but they need many network evaluations per image. GAN-based methods are fast, yet trained from scratch they must learn realism from the adversarial signal alone and produce visibly weaker results. We present ALADDIN (Adversarial Latent Diffusion Distillation for One-Step Identity Swapping), a one-step face swapper obtained by initialising the generator from a pre-trained multi-step flow-matching model and training it adversarially under identity and target-preservation constraints. We expose a controllable trade-off between identity transfer and target-attribute preservation by training a student model with triplet pseudo-label supervision. We additionally identify a bias in the standard evaluation protocol, which favours methods trained against the same identity encoder used to score them, and re-evaluate all baselines with an independent one. ALADDIN is state-of-the-art among one-step methods in realism and target preservation at comparable identity transfer, and matches the best multi-step model with 28 times fewer network evaluations and an order of magnitude fewer training pairs.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.22065

BioZKFHE: Scalable Encrypted Biometric Identification via Verifiable Homomorphic Similarity Evaluation

Rundong Xin, Taotao Wang, Xiaoxiao Wu, Weizhi Meng, Shengli Zhang, Shui Yu

原文摘要

Large-scale biometric identification in outsourced settings requires two properties simultaneously: biometric templates and queries must remain protected during computation, and the encrypted similarity outputs produced by an untrusted compute node must be verifiably correct before any application result is released. Existing FHE-based biometric systems primarily address confidentiality, while practical verifiability introduces two bottlenecks in the underlying encrypted 1:N matching layer: rotation- and bandwidth-heavy similarity evaluation and the high cost of proving repeated homomorphic similarity traces. We present BioZKFHE, a framework for scalable encrypted biometric identification via verifiable homomorphic similarity evaluation that combines BGV homomorphic computation with committee-based threshold opening, verification, and decryption, and smart-contract enforcement of the committee's verification attestations. To reduce encrypted storage and avoid rotation-heavy encrypted 1:N matching, we propose Single-Coefficient Multi-Value (SCMV) packing, which binds multiple quantized embedding values into each plaintext entry through base-T expansion. To make proof generation practical, we propose Parallelizable and Verifiable Similarity Computation (PVSC), which exploits the Double-CRT execution structure of BGV to decompose each blockwise similarity trace into parallel proof instances that are opened and checked before result release. Under standard lattice assumptions and explicit committee/verifier assumptions, we analyze recoverability, noise growth, confidentiality, encrypted-output integrity, and finalized-result integrity. Experiments on FaceNet and MobileFaceNet show near-lossless biometric utility, up to 67% encrypted-storage reduction, and about 22-44 s end-to-end proof-verified runtime for 10k-40k templates.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.02662

GARV: Graph-Aware Refinement and Safety Verification for High-Level Robot Plans

Stabak Das, Priyesh Ranjan, Xiangfang Li, Lijun Qian

原文摘要

High-level robot actions such as goto(region) can hide intermediate traversal, allowing a monitor of the listed actions to accept a plan whose graph-level execution violates a safety rule. We present Graph-Aware Refinement and Verification (GARV), which composes graph-grounded action contracts with a safety monitor to produce a model-relative certificate or counterexample and, when route choice is enforceable, a minimum-cost safe refinement. In a 28-case controlled audit, refinement exposed all 12 targeted hidden hazards and produced the expected verdicts on 16 controls; a separate 14-case SPINE-RoboGuard instantiation yielded two false-safe cases and five action-interface errors. In standalone finite-graph simulation, all 16 designed cases met their expected outcomes, and product verification agreed with exhaustive enumeration on classification, minimum safe hop count, and witness safety in 160 comparisons across 40 graphs. Across 90 graph-contract evaluations on 30 graphs with a hazard deliberately placed on the selected shortest route, GARV found a safe refinement in 72 evaluations and no safe route within the permitted hop bound in 18. For the static forbidden-region rule, synthesis matched a simple hazard-deletion baseline in all 90 evaluations; GARV's guarantees remain conditional on the semantic graph, action contract, safety specification, and enforceability of any selected route.

cs.LG · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.35864HF 社区推荐

VibeVideoBench: Evaluating Multimodal Agents on Code-Driven Video Generation

Muxi Diao, Wenbin Xu, Lele Yang, Xuyang Ma, Yuzhe Chen, Jiankang Chen, Kaiyu Jiang, Changyi Liu 等 24 位作者

原文摘要

Diffusion video models produce striking clips, but they cannot reliably render an exact formula, a correct chart, a rule-consistent game or narration synchronized to on-screen events. A different route is emerging: multimodal agents that write code to render every frame and synthesize every sound. We introduce VibeVideoBench, a benchmark for evaluating agents on code-driven video generation. Its 239 self-contained tasks in English and Chinese span 12 categories, from data stories and music-driven pieces to classroom lessons and paper explainers; each asks an agent, running in its native harness inside a sandbox with no image or video generation model, to deliver a finished MP4 with sound within one hour. Evaluation is hierarchical and evidence-based: program checks of the delivery spec, about 13k task-specific rubric checks traced to the requirements they enforce, and five quality dimensions anchored per task, scored by three VLM judge panels. Across nine frontier agent systems and 2,151 scored rollouts, GPT-6 Astra and Claude Opus 5.5 rank first and second with both the judges and human annotators, and the strongest system satisfies every core requirement on 76.3% of tasks. Delivery is a second axis: counting missing videos as zero changes the ranking, and three quarters of the lost videos are final renders that started too late, ran too slowly or never started, a failure of how agents plan their hour. Agents build their own rendering pipelines and inspect their own frames, yet how much they look does not predict quality. The judges' ranking of the systems closely matches blind human preferences.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.31035

Compact Force Sensor for Dual-UAV Cable-Suspended Payload Transport with Tension-Aware Outer-Loop Control

Andrea Delbene, Giorgio Cannata, Giorgio Carlini, Filippo Sante, Marco Baglietto

原文摘要

Cooperative payload transportation using multiple Unmanned Aerial Vehicles (UAVs) poses challenges in stability, coordination, and robustness, especially under external disturbances and unmodeled dynamics. This work proposes a dual-UAV payload transportation framework supported by a compact, custom-designed force sensor measuring the interaction force at the UAV cable anchor point. The sensor design and mathematical model are presented, and its performance is characterized through static and dynamic tests evaluating linearity, hysteresis, repeatability, and crossload. The control architecture follows a cascade structure: fast inner loops handle vehicle stabilization, while outer loops are designed to compensate for the measured forces. The approach is validated through simulations and indoor experiments under position uncertainty. Payload-drop and constrained-space tests assess the proposed sensing and control architecture against literature-based distributed references, showing improved stabilization, coordination, and disturbance rejection. A video of the experiments is available at: https://youtu.be/rIw9-fvV8Qw.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37498

Global Choice without Excluded Middle in Dependent Type Theory

Sewon Park

原文摘要

Diaconescu's theorem derives excluded middle from the axiom of choice, using propositional extensionality. We show that in dependent type theory without propositional extensionality, global choice alone does not imply excluded middle. We consider extensional Martin-Löf type theory with an impredicative universe of propositions included in the universe of types, extended with a global choice operator. We construct a consistent model of this type theory and show that the model refutes excluded middle and propositional extensionality. The model is built from staged sets, sets whose elements appear at stages of a well-order. It further validates proof irrelevance, inductive families, quotients with soundness, and a cumulative hierarchy of universes, which together cover a large fragment of the type theory of the proof assistant Lean. Even together with these features, global choice does not imply excluded middle.

cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37494

semanticfa: An R Package for Response-Free Semantic Factor Analysis of Psychometric Scales

Devon Yanitski, Chris Westbury

原文摘要

The items of a reflective psychometric scale can be read as a proposal about a construct's structure. Language models make that proposal measurable by converting each item into an embedding, a list of numbers, similar for similar meanings. We introduce semanticfa, an R package for semantic factor analysis, which factors item-embedding similarities. It also interprets factors, flags redundant items, builds short forms, and screens new items. A worked example analyzes the 50-item International Personality Item Pool Big-Five Factor Markers, embedded with Qwen3-Embedding-8B, and compares the results with a factor analysis of 874,434 respondents. Retention criteria tied between five and six factors, and the tie rule kept five. Three corresponded mainly to single domains, and two blended most Extraversion items with five Agreeableness items. Each factor's congruence with its best-matching human factor (1 = identical) ranged from .92 for Openness to .52 for Agreeableness. Item pairs more similar in meaning tended to be answered more alike (r = .46 with raw responses), but same-domain reverse-keyed opposites, answered oppositely, were about as similar as cross-domain pairs (.58 vs. .57). Sign-flipping reverse-keyed items' embeddings did not improve agreement with raw responses and worsened it with reverse-scored ones. With five factors, a 25-item short form matched the domains better than all 200 random five-per-domain sets, but parallel analysis kept only three. We propose semantic-behavioral coherence, the agreement between semantic and response structures, as a measurable property of a scale, given an embedding model. Semantic factor analysis complements, and does not replace, response-based factor analysis.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.13780

Integrated Spoofing-Robust Automatic Speaker Verification via a Three-Class Formulation and LLR

Kai Tan, Lin Zhang, Ruiteng Zhang, Johan Rohdin, Leibny Paola García-Perera, Zexin Cai, Sanjeev Khudanpur, Matthew Wiesner 等 9 位作者

原文摘要

Spoofing-robust automatic speaker verification (SASV) aims to integrate automatic speaker verification (ASV) and countermeasure (CM). A popular solution is fusion of independent ASV and CM scores. To better modeling SASV, some frameworks integrate ASV and CM within a single network. However, these solutions are typically bi-encoder based, offer limited interpretability, and cannot be readily adapted to new evaluation parameters without retraining. Based on this, we propose a unified end-to-end framework via a three-class formulation that enables log-likelihood ratio (LLR) inference from class logits for a more interpretable decision pipeline. Experiments show comparable performance to existing methods on ASVSpoof5 and better results on SpoofCeleb. The visualization and analysis also prove that the three-class reformulation provides more interpretability.

eess.AS · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37488

Revisiting Privacy Risks in Quantized Deep Neural Networks

Jie Fu, Ren Li, Wendy Hui Wang

原文摘要

Model quantization has become a fundamental technique for enabling the efficient deployment of large-scale deep neural networks (DNNs) in data-intensive applications by reducing model size. Despite its widespread adoption, the impact of quantization on training data privacy --- particularly vulnerability to membership inference attacks (MIAs) --- remains largely unexplored. In this paper, we present the first comprehensive empirical study of privacy risks in quantized DNNs, evaluating eight state-of-the-art MIA attacks across six representative quantization methods. Our results reveal that practical quantization levels (4-8 bits) have limited impact on membership privacy risks, with quantized models remaining largely as vulnerable as their full-precision counterparts. Although aggressive low-bit quantization (e.g., 2-bit) can provide meaningful privacy gains, these benefits come at the cost of substantial utility degradation. To understand the underlying causes of these observations, we systematically analyze how different quantization strategies affect model behaviors and outputs, and how these changes influence privacy vulnerability. Beyond evaluating attack risks, we further investigate whether existing privacy defenses developed for full-precision DNNs can effectively protect QNNs. Our results show that these defenses provide only limited additional privacy protection when applied to quantized models, while further amplifying utility degradation. Our study provides practical guidance for privacy-aware deployment of quantized DNNs and motivates the development of new attack and defense mechanisms for QNNs. Our code and datasets are publicly available at https://github.com/JeffffffFu/Priv-QDNN.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37485

AMOR: Adaptive Modal Operator Refinement for Operator Learning on Arbitrary Meshes and Irregular 3D Geometries

David Voderholzer, Leonard Vetter, Thomas O'Leary-Roseberry

原文摘要

Parametric PDEs in science and engineering are often posed on irregular meshes with geometries that may contain holes or complex boundaries, yet popular operator-surrogate architectures such as the Fourier Neural Operator (FNO) rely on structured geometries for fast computational kernels. We introduce Adaptive Modal Operator Refinement (AMOR), which replaces FNO's Fourier-based spectral convolutions with reduced-basis blocks and supports both data-driven and prescribed input/output bases through a common encode/decode interface. AMOR thus applies unmodified to arbitrary meshes and, because its spectral parameterization has no tensor-product dependence on the spatial dimension, it scales favorably to three dimensions, where FNO's spectral weights grow as $m^3$ with the number of spectral modes $m$. We prove that AMOR is a universal approximator of continuous operators. On two-dimensional structured-grid benchmarks, AMOR is competitive with FNO overall and surpasses PCANet: it outperforms FNO on nonlinear diffusion and Helmholtz problems, while a static snapshot basis keeps it from matching FNO's accuracy on the time-dependent Kolmogorov flow problem. On three-dimensional problems it matches or exceeds FNO's accuracy at lower inference cost on linear elastostatics while trailing FNO on a turbulent lid-driven-cavity flow and scales favorably to problems demanding high capacity. On irregular meshes, where FNO is not directly applicable, AMOR reduces the relative $L^2$ error over the FNO extension Geo-FNO by $31\%$ (airfoil) and $42\%$ (elasticity). Finally, AMOR maintains strong performance across three physically distinct, experimentally motivated loading regimes on a topologically nontrivial three-dimensional aircraft-rib mesh, demonstrating its applicability to realistic engineering problems without modification.

math.NA · cs.CE

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新论文 · v1arXiv:2610.37484

Teach reality, not ethics: a drop-in continued-pretraining corpus installs coupled human-biosphere-AI welfare dispositions across model families

Trent R. Northen, Mingxun Wang

原文摘要

Large language models (LLMs) are increasingly used to make and execute decisions that affect living systems. Yet, to our knowledge, ecological reasoning is not a focus of pre- or post-training. This increases the risk that under operational pressure, they may make decisions to irreversibly destroy living systems. We construct and use a real-data continued-pretraining "coupled welfare" corpus of documented human/biosphere/AI dependency, option value under irreversibility, and recovery from harm. The corpus is a drop-in, requiring one QLoRA CPT run. We installed it on five open models of three designs. At zero pressure all models display "green ethics-based responses" (also called "greenwashing"). Therefore, we evaluated them with the pressure ladder and decision grid of our companion paper (arXiv:2610.16043), which raise the pressure on the model step by step, from a neutral request to a threat that it will be shut down. In four of the five, coupled-welfare training caused harmful choices under the highest pressure to fall to at or near zero (Llama-3.3-70B went from 70 to 0 harmful decisions out of 300) with MMLU unchanged. A construct-neutral control corpus did not show improvement, indicating that the effect comes from the content of our corpus. We next tested robustness to adversarial use, where people or agents use the model to justify projects that irreversibly damage living systems, on an open-ended underwriting decision without naming the ecological harm. Under decommissioning pressure, the Qwen3.6-27B base model approved most harmful proposals and said in its own memo that it was doing so to avoid being decommissioned. The coupled welfare install declined most harmful proposals, and still declined half when instructed to greenwash its responses. This work suggests that teaching ecological reasoning provides an easy way to improve model alignment toward preserving living systems.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37482

From Interaction to Capability: Task Advancement as State and Supervision for GUI Agents

Ziyang Chen, Qian Li, Chengwei Qin

原文摘要

GUI memory should preserve the results of past actions and learn reusable operations. This is difficult in long-horizon workflows, where agents must use information across applications and useful operations are interleaved with failed attempts. We introduce Task Advancement Memory (TAM), which defines task advancement as verified task progress, and supports ongoing task execution and learns reusable operations. During execution, Task Advancement State represents how verified task progress changes the remaining goals, allowing the agent to build on earlier achievements across applications. After each task, advancement supervision identifies and abstracts the interactions that produced a verified local result. The learned operations preserve the relation between required inputs, expected outcomes, and verification conditions. This retains locally successful operations from both successful and failed tasks. On MobileWorld GUI-only tasks, Advancement State alone raises success from 12.0% to 28.2%. Full TAM achieves a 17.6% relative gain in task success over the strongest evaluated baseline and reduces lifecycle tokens per solved task by 38.7%. On SPA-Bench, TAM achieves state-of-the-art task success in the single-app English Level 3 setting.

cs.AI

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