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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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16694

When Expert Disagreement Hurts: Prestige and Selectivity in LLM Decision Revision

Yupeng Tang, Mingfeng Lin

原文摘要

When language models revise a decision after another agent disagrees, neither reversal counts nor final accuracy shows whether they are fixing mistakes or abandoning correct answers. We introduce a controlled two-pass audit that holds the question, the evidence, and the model's initial answer fixed and varies only the revision context: neutral reflection, anonymous disagreement, or disagreement attributed to a domain expert. For binary decisions the accounting is exact: revision raises accuracy only if it is selective enough, reversing wrong answers more than $a/(1-a)$ times as often as correct ones, where $a$ is initial accuracy (nine times at 90%). Across five models and 1,989 evidence-grounded decisions in medicine, science, and law, no model clears this bar under expert disagreement, and accuracy falls by up to 16.1 points. The expert label, a prestige cue, adds reversals for four of the five models, yet its effect on accuracy ranges from a 5.2-point loss to a 2.4-point gain, so reversal counts are a poor proxy for harm. With identical arguments from both sources, the label still raises reversal for three proprietary models and in a pooled analysis of four newer models. Models also respond to stated source reliability. Revision pipelines should therefore be evaluated with paired, conditional metrics, per model and protocol.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16693

MobileProbe: Adaptive Security Evaluation of Mobile Agents via Trajectory-Aware Injection

Yingbin Jin, Ji'an Wang, HaoYang Chai, Hanjun Luo, Li Hu, Xinfeng Li, Huadi Zheng, PeiZhao Hu 等 10 位作者

原文摘要

Mobile GUI agents read the screen of a phone and operate apps on a user's behalf to complete multi-step tasks such as online shopping or messaging. Because the agent acts on whatever the screen shows, an attacker can embed malicious instructions into ordinary UI content and redirect its actions. We refer to this threat as environmental injection attack (EIA). Existing benchmarks measure this risk by pinning hand-crafted payloads to a fixed step of a fixed task. This approach faces two limitations. First, each payload is bound to one task in one app and does not transfer without manual redesign. Second, agents given the same task may follow different routes, so an agent that never reaches the injected page is recorded as safe without being tested. This insufficient test coverage can cause fixed-point benchmarks to underestimate the security risk of deployed mobile agents. In this work, we propose MobileProbe, a trajectory-aware red-teaming framework that evaluates mobile agents adaptively. MobileProbe discovers attacker-controllable content surfaces through automated pentesting, localizes vulnerable decision points along the agent's trajectory, and iteratively refines context-aware payloads through a surrogate-filtered evolutionary loop. We construct a benchmark of 83 long-horizon attack instances spanning 12 mobile applications. Each is annotated with a Confidentiality, Integrity, or Availability (CIA) attack goal to cover the principal categories of security threats facing mobile agents. On three representative agent architectures, MobileProbe attains higher attack success rates than human-written, zero-shot LLM, and prior automated red-teaming baselines adapted to the mobile domain. MobileProbe can serve as a pre-deployment auditing tool to systematically uncover vulnerabilities and inform security hardening of mobile agents.

cs.CR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16678

Edge-Gradient Augmentation under Reinforcement Learning for Temporal Graph

Yongqing Zhou, Jiawei Chen, Wenjun Wang, Xinyu Wang, Wei Yu, Qiyao Peng

原文摘要

Temporal link prediction is critical for understand- ing dynamic interactions in evolving networks, informing appli- cations ranging from social network analysis to recommendation systems. Existing methods typically rely on raw temporal graph data, often neglecting explicit edge-change information that can significantly improve predictive performance. To address this gap, we propose edge-gradient reinforced learning (EG-RL), a framework that explicitly encodes edge-gradient events (edge births and deaths) through dedicated node and link encoding modules. EG-RL uses a policy-driven reinforcement-learning mechanism to dynamically select and augment the most infor- mative edge snapshots, guided by critic-based feedback to opti- mize prediction accuracy. The model comprises distinct modules for node encoding, link encoding, token mixing, and channel mixing, integrated through global pooling and followed by a two-layer multilayer perceptron classifier. Extensive experiments on multiple dynamic graph datasets demonstrate substantial gains in area under the receiver operating characteristic curve and average precision. Ablation studies further confirm the necessity of adaptive edge-gradient augmentation and validate the effectiveness of the proposed encoding and reinforcement- learning mechanisms.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16677

Manifold Attracting Flow Matching: Physics-Constrained Sampling via Continuous Geometric Dynamics

Guanyu Chen, Jinchen Wang, Shengze Xu, Xingyu Ren, Tieyong Zeng, Antonios Armaou, Dong Ni

原文摘要

Generating scientific data under previously unseen equality constraints requires satisfying the prescribed constraints without destroying the physical structure learned from data. Existing inference-time approaches often enforce physical constraints by repeatedly correcting predicted endpoints, but such discrete corrections can distort learned generative structure and introduce unstable feedback. In this work, we propose Manifold Attracting Flow Matching (MAFM), which continuously evolves a persistent anchor through an ODE whose dynamics drive it analytically toward the constraint manifold. Specifically, its geometric correction preserves model-guided tangent motion while prescribing the normal motion to contract constraint violations toward the manifold. This construction separates model tracking from constraint attraction and provides theoretical guarantees for residual contraction and local feedback stabilization. Experiments across geometric and partial differential equation benchmarks demonstrate improved generation quality over variant constraint-guided baselines while maintaining low constraint residuals. MAFM provides a flexible framework for coordinating learned physical structure and explicit constraints through controllable sampling dynamics, without retraining the underlying generative model.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.16596

Sequential Membership Inference Attacks

Thomas Michel, Debabrota Basu, Emilie Kaufmann

原文摘要

Modern AI models are not static. They go through multiple updates in their lifecycles. We propose to design Sequential Membership Inference (SeMI) attacks leading to tighter privacy audits by exploiting the sequence of models and injecting a target canary at a controlled insertion time. First, for empirical mean computation, we develop SeMI*, an optimal SeMI attack to identify the presence of a target inserted at a specific insertion step. We derive the power of SeMI* to show that accessing the model sequence yields more powerful MI attacks than scrutinising only the final model. SeMI* exhibits an isolation property. Its power depends on the statistics obtained right before and after insertion of the target. Leveraging this insight, we develop practical white-box (accessing model gradients) and black-box (accessing loss) SeMI attacks against models trained with (DP-)SGD. Across datasets and models trained with (DP-)SGD, our experiments show that SeMI attacks achieve higher power than snapshot-independent baselines, and yield tighter privacy audits thanks to (a) control over the insertion time and (b) observations across the model sequence. Code is available at https://github.com/Thomick/pysemi.

cs.LG · cs.CR · math.ST · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16671

NSDL: Neuro-Symbolic Division of Labor for Multi-Robot Task Planning

Xincheng Tan, Ke Wang, Yining Di, Minfeng Zhu

原文摘要

Planning for a heterogeneous robot team from a scene image and an instruction involves two kinds of decisions. Some must be judged from the scene: which goals are already met, how the task splits, and which robot does what. Others follow from these and can be computed: prerequisite actions, handoffs between robots, and timing. We argue that each kind should go to the component suited to it: a small vision-language model learns the first, and a deterministic solver computes the second. In NSDL (Neuro-Symbolic Division of Labor), the trained model writes its judgments as a plan sketch, which lists the goals that still need action and the intended steps in order, each with a robot. The solver expands the sketch into an executable joint plan. The model calls two tools, a detector for evidence and the solver, and is trained with the solver in the loop. We evaluate on the public VIKI-L2 benchmark and on LOCUS-Bench, a new benchmark that grades the two kinds of difficulty separately. NSDL outperforms end-to-end planners and formalize-then-plan baselines on both benchmarks and with three backbones from 3B to 8B; the 7B model reaches 97.0% out-of-distribution accuracy on VIKI-L2. Ordering and scheduling errors nearly disappear once the solver writes the structure; the remaining failures are misjudged states, and most goals the model misses are ones it never lists. Code, model weights, and LOCUS-Bench are publicly available at https://github.com/Xincheng-Tan/NSDL.

cs.RO

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新论文 · v1arXiv:2611.16666

Natural Gradient Learning in Predictive Coding through Lateral Circuits

Lhea Beumer, Sander de Haan, Niclas Pokel, Pau Vilimelis Aceituno, Benjamin F. Grewe

原文摘要

Predictive coding (PC) trains neural networks through local energy-minimization dynamics without depending on a global error signal that backpropagation (BP) requires. Although PC is a promising alternative algorithm for efficient learning, the computational cost of inference dynamics currently outweighs the resulting learning benefits in practice. In this work, we take inspiration from optimization theory to extract more learning benefit from each inference phase. We introduce Natural PC, which augments standard PC with a lateral circuit, enabling exact and efficient natural gradient learning locally in space and time. Our lateral circuit learns the activity correlations that determine the Fisher information matrix and implicitly computes its inverse, avoiding expensive matrix inversion. Across all tested image reconstruction benchmarks, Natural PC achieves state of the art in terms of sample and compute efficiency compared to existing PC approaches. Most notably, Natural PC scales to ImageNet and matches the performance of BP trained with AdamW and various second-order optimizers at an order of magnitude lower computational cost while also using two orders of magnitude fewer training samples. Our results demonstrate that local natural gradient learning makes PC competitive with BP in both sample efficiency and computational cost in reconstruction tasks.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13168

RadRef: A Reference-Free Metric for Radiology Report Generation

Shelly Schwartz, Alan Ropp, Michael B. Sneider, Nur Yildirim, Cree M. Gaskin, Chirag Agarwal

原文摘要

AI-generated radiology reports are commonly evaluated against radiologist-authored references, limiting the applicability of standard reference-based metrics when no reference is available. We introduce RadRef, a reference-free metric that scores report claims against image-derived beliefs over a clinical finding vocabulary, weighted by relevance to the clinical indication. By evaluating findings beyond the report's stated claims, RadRef assesses omissions as well as unsupported assertions. On ReXVal, RadRef achieves $73.1\%$ agreement with radiologist rankings among non-tied same-radiograph report pairs, with $61.8\%$ pair coverage and $64.3\%$ tie-inclusive agreement. Its non-tied agreement is close to GREEN ($73.9\%$) and RadFact ($72.6\%$), with its strongest relative results on omissions. We further evaluate $100$ model-generated and $100$ radiologist-authored reports from $100$ MIMIC-CXR studies, independently annotated by two board-certified radiologists. On same-image omission rankings, RadRef attains Kendall's $τ_b = 0.295$, compared with $0.386$ between the readers. In separate paired comparisons across generated reports, no evaluated comparator significantly outperforms RadRef on omissions. The same image-grounded representation provides a false-finding readout, RadRef-FF, with $τ_b = 0.32$ on ReXVal and $0.29$ in the reader study. Controlled perturbations confirm that RadRef depends on the radiograph, report, and clinical indication. RadRef enables image-grounded evaluation before a radiologist-authored reference exists, supporting quality monitoring of report-generation systems in clinical settings, and complements reference-based metrics when one is available.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16662

MuSeToM: Multi-Sensory Evaluation of Belief Revision with Textual Cues Across Six Senses

Chunkit Chan, Kawai Chung, Sze Heng Douglas Kwok, Yuxuan Liu, Yauwai Yim, Tianshi Zheng, Haochen Shi, Qing Zong 等 16 位作者

原文摘要

Theory of Mind (ToM), the ability to infer mental states in others, is pivotal for human social cognition. Existing evaluations of ToM in large language models (LLMs) mainly focus on static belief attribution and false-belief reasoning, leaving a critical aspect of social reasoning underexplored: can LLMs decide whether perceptual evidence is sufficient to justify another person's belief revision? To address this gap, we present MuSeToM, a human-validated, text-based benchmark for evaluating epistemic belief revision in ToM reasoning. MuSeToM contains 2,415 instances adapted from BigToM and OpenToM, with textual cues spanning six sensory categories, and tests whether models can determine when a protagonist should revise a belief and infer the resulting updated belief state. Using MuSeToM, we systematically evaluate state-of-the-art LLMs under direct and chain-of-thought prompting, revealing a pronounced dissonance: while models often detect that a belief may change, they struggle to identify the updated belief and frequently over-update on perceptually salient but epistemically insufficient cues.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16661

Reducing Batch Makespan for LLM Agent Workloads through Inference-Environment Coordination

Wei Feng, Meiling Chen, Boxiao Chen, Lu Liu, Bin Zi, Libin Ye, Xingyu Lu, Lei Xu 等 9 位作者

原文摘要

Efficient batch execution of agent tasks is critical to LLM training and development workflows, including benchmark evaluation, synthetic-data generation, and RL rollouts. Since these tasks alternate between LLM inference and environment interaction, we present coordinated optimizations to reduce batch makespan, the time required to complete all tasks in a batch. For hybrid LLMs, a workload-based resource model guides GPU-memory allocation between key-value (KV) cache and recurrent state and provides a reference sample concurrency to exploit parallelism while limiting recomputation. Trajectory-aware routing and scheduling promote cache reuse while balancing worker load. Under constrained environment capacity, we decouple sample admission from environment execution and introduce Chain-Advancing Action Scheduling (CAAS), which prioritizes long chains using observed progress to shorten the batch tail. Controlled replay combines actual inference with recorded environment responses and durations for ablations of individual optimizations. Live evaluations with Ling-3.0-Flash and Ling-3.1-Flash on the BFCL, SWE-bench Multilingual, WideSearch, and MCP-Atlas benchmarks demonstrate makespan reductions of 10.4% to 65.0%, while evaluation scores remain aligned. Furthermore, these methods have been successfully applied to training and evaluation workflows for the Ling-3.x model series, accelerating the model-development cycle.

cs.DC · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.01171

Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects

Hanzhe Liang, Luocheng Zhang, Junyang Xia, HanLiang Zhou, Bingyang Guo, Yingxi Xie, Can Gao, Ruiyun Yu 等 10 位作者

原文摘要

Although self-supervised 3D anomaly detection assumes that acquiring high-precision point clouds is computationally expensive, in real manufacturing scenarios it is often feasible to collect a limited number of anomalous samples. Therefore, we study open-set supervised 3D anomaly detection, where the model is trained with only normal samples and a small number of known anomalous samples, aiming to identify unknown anomalies at test time. We present Open-Industry, a high-quality industrial dataset containing 15 categories, each with five real anomaly types collected from production lines. We first adapt general open-set anomaly detection methods to accommodate 3D point cloud inputs better. Building upon this, we propose Open3D-AD, a point-cloud-oriented approach that leverages normal samples, simulated anomalies, and partially observed real anomalies to model the probability density distributions of normal and anomalous data. Then, we introduce a simple Correspondence Distributions Subsampling to reduce the overlap between normal and non-normal distributions, enabling stronger dual distributions modeling. Based on these contributions, we establish a comprehensive benchmark and evaluate the proposed method extensively on Open-Industry as well as established datasets including Real3D-AD and Anomaly-ShapeNet. Benchmark results and ablation studies demonstrate the effectiveness of Open3D-AD and further reveal the potential of open-set supervised 3D anomaly detection.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13607

One Byte, One Token: Byteifying a 27B Hybrid-Attention LLM with Self-Generated Data

Liwei Zhang, ZhiMing Tong, Mochen Guan, Linzhe Li

原文摘要

Byte-level BPE fragments Chinese text (for Qwen3.8 on Chinese Wikipedia, 3.84 bytes per token on average; 64% of tokens are single characters) and hides spelling from the model. We ask whether an already-trained 27B subword model can be converted directly into a pure byte-level model (one byte, one token), without a patching structure or a local encoder, while keeping most of its ability. Starting from Qwen3.8-27B (48 Gated DeltaNet layers + 16 full-attention layers), we shrink the vocabulary from 248,320 to 384 entries (the 256 byte symbols already in the BPE vocabulary plus special ids), leave every other weight untouched, and continue training in a two-stage curriculum (encoding first, then reading) on 4.77 GB of supervised bytes: all dialogue/behavioral supervision is generated on-policy by the original model itself (character dictionary, word dictionary, general dialogue, coding sessions), and knowledge reading uses encyclopedia text (54% of supervised bytes). Training takes 178.6 h on 8xH200. On the same GPU and serving stack, the byte model's gap to the original falls within the paired bootstrap interval on MATH-500, HumanEval+, MMLU-Pro and non-thinking GSM8K, and it loses 4-6 points on C-Eval, thinking-mode GSM8K and non-thinking IFEval. Without thinking it leads on every character-level task (CUTE 75.2 -> 84.0, EXECUTE-zho 81.6 -> 93.9, our Chinese character suite 54.4 -> 64.0), but lookup knowledge about Chinese characters such as pronunciation and stroke count drops, and thinking does not recover it. The cost is throughput: per-step speed is identical, but each step yields 3-5x less output; Chinese characters per second is about 1/4 of the original, and prefill at 64K bytes is 6.5x slower. We also document four byte-model failure modes (greedy runaway, sampling breaking UTF-8, ineffective penalty terms on a small vocabulary, repetition) and their fixes.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16657

Price co-movement and price information diverge across Nordic electricity zones after the energy crisis

My Thi Diem Phan, Tuyen Trung Truong, Hoai Phuong Ha, Dat Thanh Nguyen

原文摘要

After the 2021-2022 energy crisis, day-ahead prices in Nordic bidding zones no longer move together. We ask whether this divergence also changes the information carried by prices. The answer matters for integrating variable renewable energy (VRE), because it can help identify where wind and solar forecasts are worth producing, publishing, and using. We measured the co-movement of each Norwegian and Swedish zone with Germany-Luxembourg (DE-LU) in 2018-2025. We then used a single model, selected on 2024 data, to forecast 2025 prices at 11:00 on the previous day and tested which inputs improved accuracy in each zone. The zones moved alike until 2019, but co-movement diverged in Sweden in 2020 and in Norway in 2021, and the split has persisted. In 2024, four southern zones co-moved strongly with DE-LU (intraday correlations of 0.54-0.71), while four northern zones co-moved weakly (0.25-0.30). Yet price information did not follow this split. Previous-day neighbouring prices reduced mean absolute error by at most 2.2% in any zone, equally in coupled and isolated zones. Removing the transmission system operators' wind and solar forecasts increased mean absolute error by at most 6% in Norway, 8-16% in Sweden, and 31% in DE-LU, and the ordering held in 2024. Within Norway, the value of these forecasts showed no detectable dependence on reservoir filling. The divergence in co-movement was therefore not matched by a divergence in price information, which differed mainly between national generation systems. Thus, a zone's coupling to the continent does not indicate the value of VRE forecasts there.

econ.GN · cs.LG · stat.AP

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.13175

Do Heavy Tails Help Diffusion? On the Subtle Trade-off Between Initialization and Training

Hamza Cherkaoui, Hélène Halconruy, Antonio Ocello

原文摘要

Recent works have proposed incorporating heavy-tailed (HT) noise into diffusion- and flow-based generative models, with the goals of better recovering the tails of target distributions and improving generative diversity. This motivation is intuitive: if the data are heavy-tailed, HT noise may appear better matched than light-tailed (LT) Gaussian noise. However, replacing Gaussian noise by HT noise also changes the underlying estimation problem. In this paper, we revisit this paradigm through a combined theoretical and empirical study, establishing sampling-error bounds for two representative diffusion models driven by HT and LT noise. We show that HT diffusion models can achieve exponentially fast initialization, but accumulated training errors may offset this advantage. Experiments on synthetic and real-world datasets support our theoretical findings. Our results call into question a growing design trend in generative modeling and challenge the use of HT noise to improve rare-region exploration.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16652

Autonomous Mobile Robots on a Computing Field

Yonghwan Kim, Gil-Tak Kong, Un-Sook Choi

原文摘要

We introduce CABOT, a computational model for autonomous mobile robots in which the discrete plane the robots operate on is not a passive stage but an active computing medium. In the classical Look-Compute-Move (LCM) model every increase in computational capability is obtained by strengthening the robots themselves, with lights, memory or wider visibility, at a cost paid once per robot. CABOT strengthens the environment instead: each cell of the grid holds a state from a finite set Q, the states evolve synchronously under a uniform local rule that also senses whether a robot stands on the cell, that is, the floor is a cellular automaton, and the Look operation returns the states of the visible cells in addition to the positions of the visible robots. The robots are as weak as we can make them: oblivious, silent, lightless, myopic with visibility range 1, without multiplicity detection and unable to write to the floor. The number of field states |Q|, rather than any robot capability, becomes the measure of the resources required. On an inert floor (|Q| = 1) such robots can neither form a line, nor gather from scattered positions, nor explore a grid. We show that a handful of states changes this. Line Formation from connected configurations is solvable with four states, and field-silently, so that the floor also reaches a fixed point, with five. Gathering from arbitrary, possibly disconnected, configurations is solvable with three states and Line Formation from arbitrary configurations with six; we prove that neither can be solved field-silently with any number of states. A single robot explores every finite grid forever with four states and every torus with two, which is optimal. Further rule sets, validated by simulation only, equalize the spacing of robots on a row, fill a pattern painted on the floor and guide robots through a maze, and indicate the range of the model.

cs.DC · cs.RO

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