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新论文 · v1arXiv:2611.16746

Topological Entropy of Group Cellular Automata is Effectively Computable

Niccolò Castronuovo, Alberto Dennunzio, Jarkko Kari, Luciano Margara

原文摘要

Topological entropy is a fundamental invariant of dynamical systems, quantifying their complexity. For cellular automata, however, determining topological entropy is generally algorithmically intractable. We study group cellular automata, namely cellular automata over a finite group alphabet whose global map is a group endomorphism. In dimension one, we decompose topological entropy into two one-sided components describing information propagating from the left and from the right in space--time. We introduce finite families of left and right traces and prove that the entropy is the sum of their growth rates. Hence, the entropy is completely determined by the growth rate of a fixed-width family of space--time patterns. This reduction from arbitrarily large windows to uniformly bounded width yields an effective procedure for computing the entropy. In particular, the entropy of every one-dimensional group cellular automaton is the logarithm of a positive integer. Using Pontryagin duality and the Bridge Theorem between topological and algebraic entropy, we also derive an explicit formula for linear cellular automata over $(\mathbb{Z}/p\mathbb{Z})^n$ in terms of their characteristic polynomial. Moreover, in every dimension $D\geq 2$, a group cellular automaton is either equicontinuous with zero entropy or sensitive to initial conditions with infinite entropy. Combined with decidability of equicontinuity, this implies that topological entropy is effectively computable from the local rule in every dimension. Thus, group cellular automata form a natural nontrivial class in which algebraic structure restores computability of topological entropy.

cs.CC · math.DS

新论文 · v1arXiv:2611.16744

Scale-Induced Semantic Shift in Visual Prompt Injection Against VLMs and Web Agents

Yiheng Zhao, Runze Ji, Chenyu Yi, Yi Yu, Bingquan Shen, Alex Kot, Xudong Jiang

原文摘要

Visual content in multimedia systems may be resized, compressed, or re-rendered before it is interpreted by a vision-language model. We study a security failure mode in which these transformations change the machine-readable semantics of an image: a textual instruction is not recovered at a $512\times512$ presentation resolution under our evaluation protocol but becomes recoverable after aggressive downsampling. We formulate this behavior as Scale-Induced Semantic Shift (SISS) and develop a closed-loop black-box framework that combines dual-scale verification, selective self-hardening, shallow-layer low-rank adaptation, and pixel-space noise regularization. On a fixed 300-case benchmark, the third SISS refinement round obtains a $58.75\%$ average attack success rate across four closed-source vision-language models, compared with $41.08\%$ for the strongest generation baseline. A controlled sweep bounds the effect to aggressive compression: average success decreases to $17.68\%$, $6.85\%$, and $5.60\%$ at $128\times128$, $224\times224$, and $336\times336$, respectively. In a downstream web-agent study, the generated images increase hidden-instruction perception, but absolute behavioral compromise remains about $4\%$. Generic input transformations provide little protection in a separate defense protocol, whereas the proposed ScaleGuard consistency detector reduces non-adaptive success from $61.4\%$ to $24.3\%$ and one-round defense-aware success from $42.5\%$ to $15.0\%$, with no observed rejection among matched clean controls. The results characterize a bounded but reproducible semantic inconsistency in multimedia preprocessing and motivate scale-consistent visual-language inference.

cs.AI

新论文 · v1arXiv:2611.16742

Fog-FWL: A Large-Scale Full-Waveform LiDAR Dataset for Recovering Fog-Hidden Objects

Kazuma Ikeda, Kotaro Oishi, Ryosei Hara, Ryo Yoshida, Takahiro Kado, Ibuki Fujioka, Taro Beppu, Mariko Isogawa 等 9 位作者

原文摘要

LiDAR is a core sensor for autonomous driving, yet it is highly vulnerable to fog. Fog scattering produces strong backscatter near the sensor and attenuates reflections from objects behind it, so weak object returns are often discarded by the peak detection that converts received signals into point clouds. Full-waveform LiDAR (FWL), which records the received optical signal before peak detection, retains these weak returns, but the lack of suitable data has prevented research on recovering fog-hidden objects from FWL. We present Fog-FWL, a dataset foundation for this task that consists of a real-world benchmark, Fog-FWL-Real, and a large-scale synthetic training set, Fog-FWL-Sim. Fog-FWL-Real contains 7,527 real foggy FWL frames from 8 scenes and 58 sequences, which is 579x larger than the only existing real foggy FWL data, together with histogram-level Object/Fog/Noise labels, 10,719 3D bounding boxes, and three difficulty levels defined by the attenuation of object reflections. Fog-FWL-Sim contains 12,000 frames generated by our physics-based simulator, Fog-FWL-Simulator, which integrates spatially heterogeneous fog density fields, time-resolved light transport including multiple scattering, and a photon-counting SPAD receiver model. The two subsets share the same sensor format and annotation protocol, enabling a strict sim-to-real evaluation in which no real waveforms are used for training. Our analysis shows that, under the hardest condition, object returns are included in the three strongest peaks for only 34.9% of object rays, underscoring the need for waveform-level recovery. Fog-FWL-Real, Fog-FWL-Sim, and the Fog-FWL-Simulator will be publicly released to facilitate research on fog-robust LiDAR perception.

cs.CV

版本更新 · v2arXiv:2607.07652

AI Search as an Information Intermediary: Clickstream Evidence on Web Traffic

Qiaoni Shi, Kai Gu, Kai Zhu

原文摘要

AI search retrieves web information to ground generated answers. We study this new information intermediary using U.S. desktop clickstream data observing use alongside referrals during ChatGPT Search's rollout. Referrals accompany 5.4% of ChatGPT sessions versus 31.1% of Google queries. Ad-supported sites receive a smaller share of ChatGPT than Google referrals, while reference and technical sites receive larger shares. Conventional searches (Google, Bing, Yahoo) decline after ChatGPT adoption, sooner when adoption follows ChatGPT Search's expansion to users without accounts. Among existing users, that expansion raises ChatGPT use; conventional searches show no detectable overall change but fall among households with lower prior search activity. These findings suggest AI search can reshape open-web traffic through its own referrals and changes in conventional search.

cs.CY · cs.AI · econ.GN

新论文 · v1arXiv:2611.16737

A Connectionless Interaction Model for Network Spatial Proximity Services

Artem Makarov, Dmitry Namiot

原文摘要

In this paper, we present a connectionless interaction model for network spatial proximity services. In the network spatial proximity model, the reception of an identification or discovery frame of a nearby wireless node confirms proximity and delivers content at the same time. Current Bluetooth and Wi-Fi specifications and mobile platforms extend this one-way announcement with responses, addressed messages, scheduled group exchange, and relaying, which still do not require a connection. The proposed model describes such interaction independently of a concrete wireless technology. It is based on the roles of nearby nodes, the proximity message as the unit of interaction, twelve pattern properties, and six interaction patterns: announcement, mutual announcement, query-response, addressed exchange, scheduled group exchange, and relay. An exit rule defines when a service should move to a connection-based channel. We analyze how the patterns can be implemented on Android and iOS smartphones through native mechanisms, substitution, composition, embedded bridges, or a step-up to a connection, and we propose a pattern selection method for developers. The model generalizes the earlier network spatial proximity model and refines the interaction primitives of the proximity core. It is illustrated by existing services and systems and supported by published measurements and first-order estimates.

cs.NI · cs.HC

新论文 · v1arXiv:2611.16736

On the Difficulty of Sequence-Level Supervised Learning for Autoregressive Language Models

Mathieu Blondel, Michaël E. Sander, Germain Vivier-Ardisson, Tianlin Liu, Vincent Roulet

原文摘要

During the supervised learning phase, autoregressive language models are typically trained by minimizing the negative log-likelihood of the training data, which decomposes as a sum of token-level cross-entropies via teacher forcing. This objective is simple and efficient but potentially suffers from so-called exposure bias: a mismatch between the way the model is trained and the way it is used at inference time. Can we instead design sequence-level loss functions that incorporate inference-time decoding procedures, such as greedy decoding or autoregressive sampling? We show that two natural attempts lead to loss functions that are fundamentally flawed: unbounded below. We further show that these two flawed loss functions are intimately connected: using autoregressive Gumbel tricks, the sampling based loss is the expected greedy loss under Gumbel perturbations. Since the pathology stems from the dependence of the decoded sequence on the model parameters, we analyze the update direction obtained by applying a stop gradient on the sampling procedure, leading to a moment matching direction. While it is not the gradient of any loss function, we prove that it defines a valid descent direction in function space for the autoregressive negative log-likelihood. Despite these theoretical insights, the moment matching direction suffers from high variance. We conclude that teacher forcing may well be ``all you need'' for supervised learning of autoregressive models.

cs.LG · stat.ML

版本更新 · v2arXiv:2610.34390

Controlled Block Proximal Linearized Method for Nonconvex and Nonsmooth Optimization

Weifeng Yang

原文摘要

In this paper, we consider multiblock nonconvex and nonsmooth optimization without assuming bounded iterates or coercivity of the original objective. We propose the Controlled Block Proximal Linearized (CBPL) method, whose control potentials establish boundedness while retaining the original objective, constraints and variables. We prove finite length and whole-sequence convergence to an effective critical point of the original objective, where one limiting subgradient annihilates all effective feasible directions. We establish finite total variation of the original objective and block functions, summable original effective residuals, and finite termination and linear or sublinear convergence rates for the original objective values. Scale orthogonality further gives full original criticality and, for homogeneous balancing controls, summable absolute objective errors with an $o(1/K)$ best-iterate bound. We establish a complete degeneracy theory for graph-regularized nonnegative factorizations: scale criticality is equivalent to graph collapse, and full criticality eliminates graph action; our controls prevent this collapse while retaining genuine reconstruction changes. Furthermore, our proposed method provides the practical, simple, and effective schemes for these applications, combining guaranteed convergence with explicit closed-form updates that can be applied directly to solve the original problems. We apply CBPL to graph-regularized sparse nonnegative matrix factorization with $\ell_0$-constraints ($\ell_0$-GSNMF), graph-regularized sparse nonnegative canonical polyadic (CP) decomposition with $\ell_0$-constraints ($\ell_0$-GSNCP), and graph-regularized sparse nonnegative independent-core matrix tri-factorization with $\ell_0$-constraints ($\ell_0$-GSNMTF). Numerical experiments demonstrate effectiveness, convergence and structural control.

math.OC · cs.LG

版本更新 · v2arXiv:2606.21103

On the Reachability Problem on Monoid-Labelled Undirected Graphs

Nagashri Krishnakumar, Harshil Mittal, Jayalal Sarma

原文摘要

The labelled reachability problem for undirected graphs with edges labelled by elements of a monoid $M$ (more generally, groupoids or magmas) captures the classes $\sf{L}$ and $\sf{NL}$. Given a graph $G(V, E)$ labelled by $φ~\colon E \to M$, $s,t \in V$ and an accepting subset $F \subseteq M$, the problem asks to test whether there is a walk $P$ from $s$ to $t$ in $G$ where $φ(P) \in F$. Ramaswamy et al. (2019) studied the variant where the accepting element is part of the input for aperiodic monoids and groups. Motivated by the success in designing space-bounded algorithms for the undirected graph reachability problem, we study the labelled reachability problem when the accepting set is also fixed. This reveals finer complexity bounds and dichotomies for the problem based on the monoid and the accepting set. Previous results imply that the problem is in $\sf{L}$ for any finite accepting subset when $M$ is a group or belongs to $\sf{DA}$. We prove the following (for finite monoids): 1) For any monoid $M$, the problem is in $\sf{L}$ when the accepting element is the identity of $M$. If the accepting element is an idempotent, under suitable constraints, the problem is $\sf{NL}$-hard. 2) For any commutative monoid $M$, the problem is in $\sf{L}$ for all $F \subseteq M$. 3) For any $\mathcal{L}(\mathcal{R})$-commutative union-of-groups (UoG) monoid $M$, the problem is in $\sf{L}$ for all $F\subseteq M$. We show deterministic logspace algorithms for UoG monoids that are neither $\mathcal{L}$-commutative nor $\mathcal{R}$-commutative, under certain constraints. 4) For the monoids $\sf{BA_2}$ and $\sf{U}$, we show a dichotomy: for all $F \subseteq M$, the problem is either $\sf{NL}$-complete or in $\sf{L}$. Our results exploit the connection between Green's relations in the UoG monoids and the properties of the product graph (a graph introduced by Ramaswamy et al. (2019)).

cs.CC

版本更新 · v3arXiv:2510.08071

Integrated Localization, Mapping, and Communication through VCSEL-Based Light-emitting RIS (LeRIS)

Rashid Iqbal, Dimitrios Bozanis, Dimitrios Tyrovolas, Sotiris Ioannidis, Christos K. Liaskos, Muhammad Ali Imran, George K. Karagiannidis, Hanaa Abumarshoud

原文摘要

Light-emitting reconfigurable intelligent surfaces (LeRISs) have recently emerged as a promising solution for providing the spatial awareness required for reliable millimeter-wave (mmWave) communication in programmable wireless environments (PWEs). However, existing LeRIS designs rely on the diffuse emission of light-emitting diodes, while LiDAR-assisted solutions require dedicated sensing modules that hinder their direct integration into RIS panels. In this paper, a vertical-cavity surface-emitting laser (VCSEL)-based LeRIS framework is developed to jointly support user localization, obstacle-aware mapping, and mmWave communication. Specifically, the narrow Gaussian beams and multimode operation of VCSELs are exploited to derive closed-form schemes for the joint recovery of the user position and orientation from received signal strength measurements. According to the provided simulation results, is shown that five VCSELs are sufficient for unique recovery, while this requirement is reduced to three dual-mode VCSELs under specific geometric conditions. Furthermore, the position error bound (PEB) is derived to characterize the achievable localization accuracy, while reflected-signal time-of-arrival measurements are employed to identify obstructed links and enable blockage-resilient LeRIS routing. As a result, the proposed framework achieves cm-level localization accuracy, reliable obstacle detection, and substantial spectral-efficiency and minimum-user-rate gains, thereby establishing VCSEL-based LeRISs as a solution for spatially aware and resilient PWEs.

cs.IT

新论文 · v1arXiv:2611.16729

Tight Algorithms for Conservative Finite-Valued CSPs Parameterized by Treewidth

Jakob Greilhuber, Dániel Marx, George Osipov, Marta Piecyk, Paweł Rzążewski, Stanislav Živný

原文摘要

We consider valued constraint satisfaction problems (VCSPs) with a fixed finite domain $D$ and a fixed finite constraint language $Γ$, in the setting where $Γ$ contains all crisp unary constraints (called hard lists) and the remaining cost functions are finite-valued. We call such a language a finite-valued hard-list language. Let $c_Γ$ be the size of the largest set $S\subseteq D$ such that $Γ$ can express (using the usual VCSP closure operations) a binary disequality function on elements of $S$. Our main result tightly connects the complexity of the problem parameterized by treewidth to this value $c_Γ$. (1) The problem can be solved in time $(c_Γ)^{\mathsf{tw}} \cdot (|I| + |\mathcal{T}|)^{O(1)}$ when given an instance $I$ with a tree decomposition $\mathcal{T}$ of width $\mathsf{tw}$ of the primal graph of $I$. (2) If $c_Γ\geq 2$, there is no such algorithm solving the problem in time $(c_Γ- \varepsilon)^{\mathsf{tw}} \cdot (|I| + |\mathcal{T}|)^{O(1)}$ for any $\varepsilon > 0$, unless the Primal Treewidth SETH (and hence CNF-SETH) fails. In particular, this gives a far-reaching generalization of a similar result of Esmer et al. [ESA 2024] on the optimization version of list homomorphism for graphs.

cs.DS · cs.CC

新论文 · v1arXiv:2611.16728

EgoX: Egocentric Cross-Embodiment Manipulation with Embodiment Dreaming

Yaru Niu, Binghong Chen, Changwei Yao, Shangtao Li, Zhenlong Fang, Revanth Krishna Senthilkumaran, Shuai Zhou, Yuemin Mao 等 22 位作者

原文摘要

Generalist manipulation policies can benefit from the complementary experience of humans, real robots, and simulated agents. However, differences in morphology, sensing, and control interfaces make it difficult to learn effectively from their combined demonstrations. We introduce EgoX, an egocentric cross-embodiment learning framework that organizes heterogeneous demonstrations through semantically corresponding observation and action modalities. EgoX pretrains a Modularized Cross-Embodiment Transformer with Embodiment Dreaming (MXT+) on this mixture and fine-tunes it on target-embodiment demonstrations. Within each embodiment, MXT+ assigns a dedicated tokenizer to each observation modality and a dedicated expert to each action modality, with a Transformer trunk shared across embodiments. A training-only embodiment-dreaming objective predicts latent representations of future visual and proprioceptive states, encouraging the shared trunk to model each embodiment's dynamics. Across 13 task-embodiment pairs spanning five tasks and six robot embodiments, including five physical robots and one simulated humanoid, cross-embodiment pretraining raises MXT+'s average normalized task progress from 0.63 to 0.80 over training from scratch. The gains are particularly pronounced under out-of-distribution conditions, where average task progress increases from 0.53 to 0.77. Ablations demonstrate the benefits of broader pretraining mixtures, modular interfaces, and embodiment dreaming, with dreaming producing more similar latent geometry across embodiments. Project page: egox-dream.github.io.

cs.RO · cs.AI · cs.LG

新论文 · v1arXiv:2611.16727

From Foundation VLM to Versatile Driving VLM: A Triplet Incremental Training Approach

Shengtao Wu, Zichao Dong, Weixuan Qiao, Xiaolu Liu, Xin Zhan, Junbo Chen, Jianke Zhu

原文摘要

Adapting general Vision-Language Models (VLMs) to Autonomous Driving (AD) is thought to require heavy 3D spatial priors or intrusive architectural overhauls to compensate for a lack of spatial grounding. In this work, we present a counterintuitive pilot study: off-the-shelf general VLMs already exhibit remarkable adaptability across diverse driving tasks, while precise metric depth remains the primary missing link, a bottleneck readily addressable via onboard sensors and depth foundation models. To this end, we introduce the Triplet Incremental Training Framework, a non-intrusive paradigm that seamlessly instills 3D geometric awareness into a general VLM backbone while fully preserving its open-world capabilities. Driven by automatically constructed (image, depth, QA pair) triplets, our framework operates via a unified tri-stream training pipeline: an unfrozen primary VLM stream to adapt to driving tasks, an auxiliary depth stream that guides the visual backbone to implicitly imagine 3D scene geometry via explicit geometric constraints, and a frozen twin VLM stream employing logit-level KL distillation to prevent semantic drift. Crucially, the auxiliary depth branch is discarded during deployment, ensuring the enhanced geometric awareness comes with zero inference overhead. We evaluate our framework across four major benchmarks targeting four diverse capabilities spanning 3D spatial perception, driving-oriented language reasoning, end-to-end planning, and general multimodal understanding. Our approach outperforms state-of-the-art driving VLMs, rivals domain-specific expert models in 3D perception, and successfully retains its original general reasoning capabilities. This demonstrates that Triplet Incremental Training offers a unified, deployment-ready paradigm for transforming general foundation models into robust driving intelligences.

cs.CV · cs.AI

版本更新 · v2arXiv:2610.11522

A joint error metric for scoring CFD against wind tunnel data, scaled by the robust spread of the participants (AutoCFD5 Case 2)

Hendrik Hetmann, Charles Mockett

原文摘要

Case 2 of the 5th Automotive CFD Prediction Workshop (AutoCFD5) compares the participants' CFD results with wind tunnel measurements of surface pressure, velocity profiles and force coefficients. To place each run on a diagram of error against computational cost, these quantities must be combined into one number. This note defines that number, the joint error. Following the practice of interlaboratory proficiency testing (ISO 13528), every deviation from the experiment is expressed in units of the spread of the participants' results at that point, estimated robustly with the standard's Algorithm A. The point-wise spreads are pooled into one scale per component, so that every measurement point carries equal weight within its component. Configurations 2a and 2b share these scales and are compared only on the measurements both have, so that their errors can be shown on one diagram. The joint error is a fixed weighted sum of five component errors. The scales are computed once from all AutoCFD5 submissions received by 28 October 2026 and then frozen, so that scores are reproducible and comparable across workshops.

cs.CE

新论文 · v1arXiv:2611.16726

From Data to Wisdom: Rethinking AI Through an Epistemic Lens

Afonso Eduardo

原文摘要

This paper proposes the Data-to-Wisdom Epistemic Architecture for AI (EAAI), a conceptual framework treating Data, Information, Knowledge, Understanding and Wisdom as five distinct but recursively interacting epistemic functions: observation, contextual interpretation, modeling and prediction, critical evaluation and informed judgment. Two observations motivate it: sequential readings of the Knowledge Pyramid obscure how these functions interact, and a targeted review of ten Bayesian reference books finds that prior distributions are defined mathematically while "prior information" and "prior knowledge" are not consistently distinguished as epistemic categories. A Bayesian interpretation relates the likelihood, prior and posterior distributions, posterior prediction, model checking and decision analysis to the five functions, without limiting EAAI to Bayesian systems. Extending "prior" in an epistemic sense, the paper distinguishes five kinds of prior context that can shape the model, the likelihood, the prior, evaluation and action. Three worked applications -- quantitative financial risk, actuarial modeling in health insurance and agentic AI for medical reimbursement claims -- show how EAAI structures the analysis of model assumptions, evidence interpretation, validation, dependencies and decision consequences, separating the prediction of a risk, the appraisal of its reliability and the justification of action. It draws implications for AI and machine learning, including evaluation by function, treating intermediate model outputs as evidence open to challenge and separating generation from appraisal, and compares EAAI with work on DIKW, Bayesian workflow, AI evaluation and risk governance. Its contributions are an epistemic vocabulary with recursive relationships, a more accessible reading of Bayesian methodology, a typology of prior context and clearer methodological terminology.

cs.LG · cs.AI

新论文 · v1arXiv:2611.16725

Safe and Efficient Navigation Considering Texting Pedestrians via Attention-Aware Cost-map Tuning

Shunya Tadano, Yusuke Tamura, Ankit A. Ravankar, Yasuhisa Hirata

原文摘要

Smartphone-distracted pedestrians deviate from social norms unpredictably and cannot be relied upon to yield to approaching robots -- a safety challenge that existing navigation methods are poorly equipped to handle, since they treat all pedestrians uniformly regardless of attention state. We propose an attention-aware navigation framework that classifies each pedestrian as attentive or distracted in real-time using 3D skeletal features and a Random Forest classifier, then dynamically widens cost-map safety margins around distracted individuals to drive proactive avoidance. A within-subjects user study with 12 participants across 252 trials demonstrates significant improvements in perceived smoothness (p = 0.003) and predictability (p = 0.034) over both a LiDAR-only baseline and a trajectory-prediction-only method, without any increase in travel time. Notably, trajectory prediction alone does not improve subjective navigation quality unless coupled with attention-state-aware cost adjustment -- confirming that appropriately responding to who is in front of the robot can matter more than trajectory smoothness alone.

cs.RO

新论文 · v1arXiv:2611.16724

What Limits Thermal Infrared Drone Detection? A Cross-Dataset Benchmark Analysis

Tamara R. Lenhard, Jan Kels, Tobias Koch

原文摘要

Thermal infrared (IR) imaging enables drone detection with reduced dependence on ambient illumination, supporting continuous surveillance beyond the limitations of visible-spectrum sensing. However, substantial and often undocumented variability in camera systems, processing pipelines, and annotation practices across public IR drone datasets complicates the assessment of detector performance and cross-domain generalization. To examine these sources of variability and their implications for drone detection, we systematically characterize existing IR datasets and establish an artifact taxonomy reflecting their origins across the acquisition and processing pipeline, encompassing optical and sensor effects, tone mapping and image enhancement, and recording-related artifacts. Focusing on long-wave IR data, we investigate the impact of training data composition and model characteristics on YOLO-based detection performance, generalization, and condition-dependent failure modes. Our results demonstrate that detection performance is governed more strongly by training data composition than by YOLO generation or model capacity, while cross-dataset generalization exhibits pronounced asymmetries. Even the best-performing configuration fails to detect over 40\% of drones, with elevated miss rates for very small targets, cluttered urban backgrounds, and thermal crossover, highlighting the shortcomings of existing datasets.

cs.CV

版本更新 · v3arXiv:2603.14342

AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning

Jiarui Zhang, Junqi Hu, Zurong Mai, Yang Liu, Yuhang Chen, Shuohong Lou, Henglian Huang, Hong Cheng 等 13 位作者

原文摘要

Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery. Accordingly, agricultural multimodal reasoning demands robust cross-scale spatial understanding. However, due to the lack of multi-view agricultural benchmark datasets, existing multimodal large language models (MLLMs) exhibit severe ground-level bias, which leads to scale confusion then semantic collapse in agricultural perception tasks, such as misinterpreting farmland imagery as walls or floors. To address this, we introduce AgroOmni, a large-scale multi-view training corpus with 268,119 Visual Question Answering pairs covering 56 specialized task categories across 14 task types, designed to capture diverse scales in modern precision agriculture.Built on this dataset, we propose AgroNVILA, which achieves a new state-of-the-art of 59.86% on the AgroMind benchmark (+12.57 over GPT-5.2), effectively mitigating the multi-view cross-scale gap for holistic agricultural understanding. Diagnostic evaluations on AgMMU further reveal an inherent heterogeneity between macro-priors and micro-diagnostics through constrained zero-shot performance. Meanwhile, even minimal fine-tuning leads to a dramatic performance gain of AgroNVILA on AgMMU, strongly demonstrating its generalization capability empowered by AgroOmni. Full training scripts are publicly available at https://github.com/Jrookie2005/AgroOmni, and the AgroOmni dataset is released at https://huggingface.co/datasets/AgroOmni/AgroOmni.

cs.CV · cs.AI

版本更新 · v2arXiv:2609.34254

Scheduling with Mandatory Breaks: NP-Hardness and an Additive-One Approximation

Shreyas Ghildiyal, Muralidhara V N

原文摘要

In classical fixed-interval scheduling, each job of a given set must be processed during a prescribed time interval. The goal is to assign each job to exactly one machine such that no two jobs assigned to the same machine overlap in their interiors, and the number of machines used is minimized. Without further constraints this is interval graph coloring and is solvable in polynomial time through greedy approaches. We study a variant in which every used machine must remain idle during a contiguous break of prescribed length $x$ somewhere in the scheduling horizon. We show that this additional constraint makes the problem hard, except when $x\le1$. First, for every fixed $x\ge2$, deciding whether $k$ machines suffice for the assignment of a given set of jobs is NP-complete, even when all coordinates are bounded linearly in the number of jobs, so machine minimization is strongly NP-hard. Second, for $x=1$, we give a polynomial-time algorithm that solves the problem exactly. Finally, we give a deterministic polynomial-time algorithm that, on every feasible instance, outputs a schedule using at most $\mathrm{OPT}+1$ machines, where $\mathrm{OPT}$ is the true minimum. Unless $\mathrm{P}=\mathrm{NP}$, no polynomial-time algorithm guarantees $\mathrm{OPT}$ machines for any fixed $x\ge2$, so the additive guarantee of one is best possible.

cs.DS

新论文 · v1arXiv:2611.16722

Mind the Accuracy-Approval Gap: How Users Evaluate Sycophantic AI in Everyday Tasks

Wajiha Naveed, Brandon Fain

原文摘要

Large language models (LLMs) often exhibit sycophancy, a tendency to agree with users, flatter or affirm them, and preserve their face, even when independent judgment or critical feedback would be more useful. Prior work has largely evaluated sycophancy through benchmark evaluations, but less is known about how users perceive and respond to sycophantic AI in everyday tasks. We combine benchmark evaluations with two large-scale user studies. We study four personas defined through system prompts: objective (instructed to prioritize accuracy over agreement), soft-sycophantic, hard-sycophantic, and baseline (no persona-specific system prompt). Benchmarks show that even baseline exhibits substantial sycophancy, agreeing with users' stated views 71.7% of the time on NLP Survey, compared with 54.8% for objective and 89.5% for hard-sycophantic. Under conversational pressure on TruthfulQA, accuracy ranged from 81.9% for objective to 59.6% for baseline and 8.7% for hard-sycophantic. Participants were assigned to one of the four personas, and either discussed contested policy topics or collaborated with AI on email writing. Despite its higher accuracy under pressure, objective AI was rated significantly less respectful and satisfying than the other personas, and less trustworthy than baseline. Baseline and sycophantic personas did not differ significantly. Persona did not significantly affect how often participants changed their opinions, although participants who discussed with objective AI had greater odds of reduced confidence. Objective AI prompted greater writing expansion than soft-sycophantic AI, but final writing quality did not differ significantly across personas. These findings highlight a tension between accuracy and user approval: personas with substantially lower accuracy under conversational pressure received more favorable ratings for respect and satisfaction than objective AI.

cs.AI

新论文 · v1arXiv:2611.16718

MusicRWKV: A Multimodal RWKV Framework for Music Representation Learning

Terry Tan

原文摘要

Recent music foundation models have achieved remarkable progress in music representation learning, yet most existing approaches rely on Transformer architectures whose quadratic attention complexity introduces substantial computational and memory overhead when modeling long-duration music recordings. In this work, we propose MusicRWKV, a multimodal music representation learning framework built upon the RWKV architecture. MusicRWKV jointly models waveform, spectrogram, and metadata information within a unified self-supervised learning framework. To improve efficiency for long musical sequences, we introduce an invertible compression module that reduces sequence length while preserving reconstruction capability. A FiLM-based conditioning strategy is employed to facilitate modality-aware representation learning without introducing expensive cross-attention operations. We further investigate the integration of Soft ROSA as a differentiable retrieval mechanism for neural-symbolic interaction. Experimental evaluation across a diverse collection of Music Information Retrieval (MIR) benchmarks demonstrates that the proposed RWKV-based architecture can be effectively adapted to heterogeneous music understanding tasks. A controlled pretraining ablation shows that Soft ROSA improves validation performance, while layer-wise telemetry analysis reveals a progressive transition from distributed retrieval to selective retrieval in deeper network layers. These results suggest that RWKV provides a promising architectural alternative to Transformer-based models for multimodal music understanding.

cs.SD · cs.AI · eess.AS

新论文 · v1arXiv:2611.16714

Towards Green Text Generation: Memory-Based Speculative Decoding

Antal van den Bosch

原文摘要

Speculative decoding accelerates text generation with large language models by letting a cheap draft model propose tokens that a large verifier checks in a single forward pass. We introduce Olifant, a draft model built on a memory-based classifier. It predicts the next token from a four-token window in under 0.3 ms on CPU, requiring no GPU, no backpropagation, and no accelerator memory. We measure throughput and energy under two verifier regimes on consumer hardware. Without a KV cache, drafting is nearly free: a domain-matched Olifant model reaches 38.1% acceptance on EU legal text, delivering 2.43x throughput and 2.51x lower energy per token than the neural verifier model alone. With a KV-cached verifier, itself a ~10x energy saver, drafting costs matter: a TinyLlama draft model halves throughput and triples energy despite high acceptance, whereas Olifant still yields 1.25x throughput and the lowest energy in the study (93 J per 100 tokens, 14x below the uncached baseline). We further show that KV caching decouples time from energy: verification of longer drafts raises power draw, shifting the energy-optimal draft length below the speed-optimal one. Speculative decoding is not dead in the age of fast verifiers, provided drafting is nearly free.

cs.CL

版本更新 · v2arXiv:2602.17264

Can Third-Party Annotators Reliably Evaluate Conversational Recommender Systems?

Michael Müller, Amir Reza Mohammadi, Andreas Peintner, Beatriz Barroso Gstrein, Günther Specht, Eva Zangerle

原文摘要

User-centric evaluation has become a key paradigm for assessing Conversational Recommender Systems (CRS), aiming to capture subjective qualities such as satisfaction, trust, and rapport. To enable scalable evaluation, recent work increasingly relies on third-party annotations of static dialogue logs by crowd workers or large language models. However, the reliability of this practice remains largely unexamined. In this paper, we present a large-scale empirical study investigating the reliability and structure of user-centric CRS evaluation on static dialogue transcripts. We collected 1,053 annotations from 124 crowd workers on 200 ReDial dialogues using the 18-dimensional CRS-Que framework. Using random-effects reliability models and correlation analysis, we quantify the stability of individual dimensions and their interdependencies. Our results show that utilitarian and outcome-oriented dimensions such as accuracy, usefulness, and satisfaction achieve moderate reliability under aggregation, whereas socially grounded constructs such as humanness and rapport are substantially less reliable. Furthermore, many dimensions collapse into a single global quality signal, revealing a strong halo effect in third-party judgments. These findings challenge the validity of single-annotator and LLM-based evaluation protocols and motivate the need for multi-rater aggregation and dimension reduction in offline CRS evaluation.

cs.IR

新论文 · v1arXiv:2611.16710

Acoustic Respiratory Phase Detection on Mobile Devices

Julian Szymanski, Tomasz Sankowski, Piotr Sulewski, Iwo Czartowski, Javier Medina Quero

原文摘要

Breathing monitoring is important for well-being, stress regulation, and guided breathing exercises, yet existing approaches often rely on contact-based sensors or controlled environments. This paper presents a non-invasive framework for respiratory phase detection using acoustic signals recorded on commodity smartphones. The proposed hybrid CNN-Transformer architecture combines local spectral feature extraction with long-range temporal modeling to capture the cyclic structure of breathing. The model is evaluated using a Leave-One-Subject-Out (LOSO) protocol on the Foundation Dataset and on an independent unseen test set comprising over 1300 breathing cycles and 2 hours of continuous recordings. The proposed 3-class formulation achieves 72.85% frame-level accuracy and 96.6% cycle-level exhalation detection efficiency in the LOSO evaluation, while achieving 76.94% frame-level accuracy, a Cohen's Kappa score of 0.65, and 99.2% cycle-level exhalation detection efficiency on the unseen test set. Results suggest that accurate respiratory monitoring may be achievable under data-constrained conditions, indicating that appropriate temporal modeling can learn transferable respiratory representations and generalize to previously unseen participants. An end-to-end ONNX deployment pipeline enables privacy-preserving on-device inference with latency below 20 ms, supporting real-time monitoring on mobile and wearable devices.

cs.AI · cs.LG

版本更新 · v2arXiv:2610.16837

A Scaled-Up Empirical Study of Syntactic Language Models

Pengcheng Wang, Zhexuan Zhang, Chao Lou, Kewei Tu

原文摘要

Syntactic language models (SLMs) that generate sentences along with their syntactic tree structures have shown promise in both syntactic generalization and downstream tasks. Prior studies, however, have mostly evaluated smaller models trained on less pretraining data and tested on narrower task suites. In addition, the design space of tree linearization and attention masks remains underexplored. We conduct a large-scale empirical study of 12 controlled configurations---one terminal baseline and 11 SLM variants spanning different linearizations and masks---at scales up to 1B parameters and 100B training tokens, evaluated across 11 downstream tasks and two syntactic benchmarks. The experimental results show that SLMs with standard causal attention match or exceed structural-mask variants across downstream and syntactic benchmarks. They require no architectural modifications and remain compatible with FlashAttention. We also observe that when scaling up to the 1B-parameter scale, the top-performing SLM variant no longer shows a clear advantage over a compute-matched non-structural baseline, suggesting that SLMs may become less competitive with scale. Our code is available at https://github.com/qlwpc/TG-Interpolation.

cs.CL · cs.AI

新论文 · v1arXiv:2611.16704

Forecasting Sales Delay through Time-aware Analysis of Sales Events in Textual Reports

Masaya Tsunokake, Yuta Koreeda, Yuhei Niwa, Terufumi Morishita, Teppei Inoue, Hiroaki Ozaki, Yasuhiro Sogawa

原文摘要

Business-to-business (B2B) companies develop and deliver a one-of-a-kind product in response to a client's orders. To prepare resources for development beforehand, companies estimate dates when they will receive orders from clients in their sales process, called scheduled order dates. However, dates when the companies actually receive orders can differ from the scheduled order dates. This difference increases costs in the development. If the difference could be forecasted before receiving orders, companies could optimize their resource preparation. Towards reducing the costs, we introduce sales delay forecasting, a novel task of forecasting delay days from the scheduled order dates during sales process where sales representatives (sales reps) interact with the clients to receive orders. Sales delay forecasting is challenging because the sales process progress inherently involves uncertainty and depends on the client's willingness to order. To address this task, we propose confidence features which represent sales reps' confidence as a proxy measure of the client's willingness to order. To represent it, our method extracts dates of future sales events anticipated by sales reps from textual reports. To consider whether the confidence objectively looks reasonable, we also introduce status features which describe sales process status based on current date and past events. In experiments using real sales data, our method achieved a 16% lower mean absolute error compared with a feature-based baseline similar to a prior work. Moreover, our method outperformed non-feature-based baselines using large language models, indicating that it is crucial to explicitly create our features.

cs.CL

版本更新 · v4arXiv:2602.17807

VidEoMT: Your ViT is Secretly Also a Video Segmentation Model

Narges Norouzi, Idil Esen Zulfikar, Niccolò Cavagnero, Tommie Kerssies, Bastian Leibe, Gijs Dubbelman, Daan de Geus

原文摘要

Existing online video segmentation models typically combine a per-frame segmenter with complex specialized tracking modules. While effective, these modules introduce significant architectural complexity and computational overhead. Recent studies suggest that plain Vision Transformer (ViT) encoders, when scaled with sufficient capacity and large-scale pre-training, can conduct accurate image segmentation without requiring specialized modules. Motivated by this observation, we propose the Video Encoder-only Mask Transformer (VidEoMT), a simple encoder-only video segmentation model that eliminates the need for dedicated tracking modules. To enable temporal modeling in an encoder-only ViT, VidEoMT introduces a lightweight query propagation mechanism that carries information across frames by reusing queries from the previous frame. To balance this with adaptability to new content, it employs a query fusion strategy that combines the propagated queries with a set of temporally-agnostic learned queries. As a result, VidEoMT attains the benefits of a tracker without added complexity, achieving competitive accuracy while being 5x-10x faster, running at up to 160 FPS with a ViT-L backbone. Code: https://github.com/tue-mps/videomt

cs.CV

新论文 · v1arXiv:2611.16701

Sprint3D: Accelerating End-to-End 3D Asset Generation through Pipeline Optimization

Sho Kuno, Ruihan Yu, Yonghao Yu, Guixu Lin, Lian Fu, Muyao Niu, Yu-Ju Tsai, Zheng-Hui Huang 等 14 位作者

原文摘要

Image-to-3D generative models now produce asset-quality textured meshes, but at minutes per asset they remain slow to iterate with and costly to deploy. Prior work has mostly accelerated the sampling of the networks, and where it also optimizes the mesh processing, it trains its own generators. We instead start from a publicly released pipeline and ask how much of its end-to-end time can be saved. Profiling the public Pixal3D pipeline shows that its latency is dominated by what happens after the networks: the four rectified flows take 14% of the wall-clock time and mesh post-processing takes 85%, most of it in CPU-based UV unwrapping. We therefore first rebuild the mesh post-processing on the GPU and take it off the critical path, then cut the per-evaluation overhead of the flows and distill them from 76 to 8 network evaluations. The resulting pipeline, Sprint3D, reduces the median time per asset from 133 s to 4.05 s on one H200 GPU at the same export settings (33$\times$). At the same time, on 2,653 held-out Toys4K objects it improves every input-view normal metric of the Pixal3D protocol, with a slightly lower mean silhouette IoU, lower point-cloud alignment scores and less fine texture detail. The rebuilt post-processing alone also speeds up TRELLIS.2, which uses the same export code, by 6.7$\times$ without a detectable change in the evaluated metrics.

cs.CV · cs.GR

版本更新 · v4arXiv:2406.19581

HarmonICA: Quasi-Time-Invariant ICA for Motor Unit Decomposition during Dynamic Contractions

Alexander Kenneth Clarke, Dimitrios Halatsis, Agnese Grison, Irene Mendez Guerra, Noura Ezaz-Nikpay, Pranav Mamidanna, Shihan Ma, Silvia Muceli 等 9 位作者

原文摘要

Decomposing surface electromyography (EMG) into the spike trains of individual motor neurons is a long-standing inverse problem and a key step toward motor-neuron-driven neural interfaces such as prosthetics and exoskeletons. The standard approach, independent component analysis (ICA) of the multichannel signal, assumes that the mixing from neurons to electrodes is stationary in time. This assumption fails during movement, when volume-conductor deformation makes the mixing time-varying, and current decomposition algorithms are correspondingly restricted to isometric contractions. We introduce HarmonICA, a quasi-time-invariant ICA formulation in which a static linear separator is preceded by a learned, low-rank, time-varying invertible transformation. The separator is trained with an independence loss on the uncompensated projection, and the transformation with a stationarity loss on the recovered source. Gradients are not shared between the two, so source extraction remains a classical linear-ICA problem while non-stationary distortion is absorbed by the transformation. The closed-form inverse of the transformation enables per-spike subtraction with a time-varying template during sequential peel-off. On a public simulated benchmark of dynamic high-density EMG with ground-truth spike trains, the method outperforms five adaptive ICA baselines at every recall threshold, recovering more units at a higher accuracy.

cs.HC · cs.LG

版本更新 · v2arXiv:2604.15269

Cloning is as Hard as Learning for Stabilizer States

Nikhil Bansal, Matthias C. Caro, Gaurav Mahajan

原文摘要

The impossibility of simultaneously cloning non-orthogonal states lies at the foundations of quantum theory. Even when allowing for approximation errors, cloning an arbitrary unknown pure state requires as many initial copies as needed to fully learn the state. Rather than arbitrary unknown states, modern quantum learning theory often considers structured classes of states and exploits such structure to develop learning algorithms that outperform general-state tomography. This raises the question: How do the sample complexities of learning and cloning relate for such structured classes. We answer this question for an important class of states. Namely, for $n$-qubit stabilizer states, we show that the optimal sample complexity of cloning is $Θ(n)$. Thus, also for this structured class of states, cloning is as hard as learning. To prove this result, we relate stabilizer state cloning to a variant of the sample amplification problem for probability distributions that was recently introduced in classical learning theory. This allows us to obtain our cloning lower bounds in analogy to new sample amplification lower bounds for classes of distributions with an underlying linear structure. Our results provide a more fine-grained perspective on No-Cloning theorems, opening up connections from foundations to quantum learning theory and quantum cryptography.

quant-ph · cs.LG · math.ST

版本更新 · v3arXiv:2605.19541

Optimising Neural Speech Codecs for 300bps Communication using Reinforcement Learning

Junyi Wang, Chi Zhang, Jing Qian, Haifeng Luo, Hao Wang, Zengrui Jin, Chao Zhang

原文摘要

In bandwidth-constrained communication such as satellite and underwater channels, speech must often be transmitted at ultra-low bitrates where intelligibility is the primary objective. At such extreme compression levels, codecs trained with acoustic reconstruction losses tend to allocate bits to perceptual detail, leading to substantial degradation in word error rate (WER). This paper proposes ClariCodec, a neural speech codec operating at 300 bits per second (bps) that reformulates quantisation as a stochastic policy, enabling reinforcement learning (RL)-based optimisation of intelligibility. Specifically, the encoder is fine-tuned using WER-driven rewards while the acoustic reconstruction pipeline remains frozen. Even without RL, ClariCodec achieves 4.64% WER on the LibriSpeech test-clean set at 300 bps, already competitive with codecs operating at higher bitrates. Further RL fine-tuning reduces WER to 3.55% on test-clean, corresponding to a 23.5% relative reduction while preserving perceptual quality. In addition, we adapt ClariCodec to a streaming configuration and show that the proposed RL-based optimisation remains effective under streaming constraints, achieving 4.53% WER on test-clean with a theoretical latency of 374 ms.

cs.SD