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版本更新 · v3arXiv:2601.22679

Stabilizing Consistency Training: A Flow Map Analysis and Self-Distillation

Youngjoong Kim, Duhoe Kim, Woosung Kim, Jaesik Park

原文摘要

Consistency models have been proposed for fast generative modeling, achieving results competitive with diffusion and flow models. However, these methods exhibit instability and limited reproducibility when training from scratch, motivating subsequent work to explain and stabilize these issues. While these efforts have provided valuable insights, the explanations remain fragmented, and the theoretical relationships remain unclear. In this work, we provide a theoretical examination of consistency models by analyzing them from a flow map-based perspective. This joint analysis clarifies how training and convergence behavior can give rise to degenerate solutions. Building on these insights, we revisit self-distillation as a practical remedy for certain forms of suboptimal convergence. To avoid excessive gradient norms during its optimization, we reformulate its objective empirically, yielding improved Self-Distillation (iSD). We further extend iSD with training-time classifier-free guidance, yielding iSD-T, which improves performance and reproducibility in the studied settings. We demonstrate that iSD extends beyond image generation to diffusion-based policy learning, without reliance on pretrained diffusion models for initialization.

cs.LG · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01685

Assessing Temporal Dispersion of Time-Varying Channel at 38 GHz for Different Reference Signals

Jarosław Wojtuń, Bartosz Czaja, Tomasz Grasza, Rafał Przesmycki, Jan M. Kelner, Cezary Ziółkowski, Radek Zavorka, Josef Vychodil 等 12 位作者

原文摘要

This paper investigates the influence of reference waveform selection on temporal dispersion estimation in a time-varying radio channel at 38 GHz. Three reference waveforms-numerically generated in MATLAB, measured through a conducted connection, and recorded in an anechoic chamber -are compared using the same wideband channel measurements conducted in a dynamic road environment. Their effects on the estimated power delay profiles (PDPs), mean delay, and root mean square (RMS) delay spread are evaluated. Despite noticeable differences in the time-domain and spectral characteristics of the reference waveforms, the resulting PDPs exhibit similar multipath structures, while the estimated temporal dispersion parameters show relatively small differences. The results indicate limited sensitivity to reference waveform selection under the investigated measurement conditions.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01684

GaitFM: A Cross-Modal Foundation Model for Wearable Gait Assessment

Yize Cai, Zhiqing Hong

原文摘要

Gait assessment supports the diagnosis, monitoring, and prognosis of diverse movement-related conditions, such as neurological and musculoskeletal disorders. Recent advances in deep learning using everyday wearable inertial measurement units (IMUs) offer an accessible and low-cost approach to automated gait assessment. However, IMU measurements vary substantially across individuals, body locations, and device configurations, while existing deep learning models are typically developed using small, homogeneous datasets for specific clinical tasks, limiting their robustness and generalizability in diverse real-world contexts. Here we present GaitFM, a cross-modal foundation model for gait assessment using IMU sensors. GaitFM improves the generalizability of gait assessment by transferring knowledge from large vision models and pretraining on unlabeled IMU sensor data. We pretrain GaitFM on a large curated dataset comprising about 4,000 participants and 1.23 million gait windows across diverse clinical and daily conditions, sensor modalities, and body locations. GaitFM demonstrates strong performance across 18 gait-related tasks, including 3 gait parameter estimation tasks, 14 disease and gait abnormality detection tasks, and 1 clinical outcome prediction task. Furthermore, GaitFM generalizes across different sensor configurations and body locations, including those commonly used in clinical settings and everyday wearable devices. These findings demonstrate the effectiveness of transferring visual knowledge in improving sensor-based gait assessment performance, offering a new promising pathway for addressing data scarcity in the sensor and medical domains.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.09737

Mosquito acoustic identification at unseen recording sites: practical limits of common transfer remedies for vector surveillance

Yucheng Gong, Rui Zhou, Binbin Zeng, Qiang Ren, Hongjin Hui

原文摘要

Passive acoustic monitoring could widen mosquito surveillance, but a classifier trained under one set of recording conditions often fails when moved to a new site, device or enclosure. On a nine-species, five-condition corpus of about 271,000 recordings, the published baseline identifies species at 0.881 balanced accuracy under conditions it has seen and 0.175 under held-out ones. Using that corpus's leave-domain protocol, we asked which transfer strategy is most reliable when a program brings a new site online. Using fixed features from a pre-trained bioacoustic model and a small classifier improved held-out-condition balanced accuracy to 0.305, without updating backbone weights and in minutes of training per site. Four widely used additions did not improve transfer: rebalancing training across recording conditions, fine-tuning the backbone and fusing a weaker second modality each reduced accuracy, and a six-term objective reduced to a single alignment term. Rebalancing reversed direction across four encoders, helping weak representations and harming strong ones. Genus-level aggregation was more accurate than species-level classification (about 0.50 against 0.30), suggesting genus-level screening may be the more realistic operating point here. These results come from one corpus under one protocol, and are dataset-bounded guidance rather than validated general rules.

eess.AS · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.08983

StretchTime: Adaptive Time Series Forecasting via Symplectic Attention

Yubin Kim, Viresh Pati, Jevon Twitty, Vinh Pham, Shihao Yang, Jiecheng Lu

原文摘要

Transformer architectures have established strong baselines in time series forecasting, yet they typically rely on positional encodings that assume uniform, index-based temporal progression. However, real-world systems, from shifting financial cycles to elastic biological rhythms, frequently exhibit "time-warped" dynamics where the effective flow of time decouples from the sampling index. In this work, we first formalize this misalignment and prove that rotary position embedding (RoPE) is mathematically incapable of representing non-affine temporal warping. To address this, we propose Symplectic Positional Embeddings (SyPE), a learnable encoding framework derived from Hamiltonian mechanics. SyPE strictly generalizes RoPE by extending the rotation group $\mathrm{SO}(2)$ to the symplectic group $\mathrm{Sp}(2,\mathbb{R})$, modulated by a novel input-dependent adaptive warp module. By allowing the attention mechanism to adaptively dilate or contract temporal coordinates end-to-end, our approach captures locally varying periodicities without requiring pre-defined warping functions. We implement this mechanism in StretchTime, a multivariate forecasting architecture that achieves state-of-the-art performance on standard benchmarks, demonstrating superior robustness on datasets exhibiting non-stationary temporal dynamics.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01682

Leveraging 2D Foundation Priors for Generalized Category Discovery under Test-Time Adaptation in 3D LiDAR Segmentation

Hanqi Chen, Chaoqi Chen, Tian Ji, Hui Huang

原文摘要

In autonomous driving, 3D LiDAR semantic segmentation is expected to adapt to evolving environments and recognize previously unseen categories, making generalized category discovery under test-time adaptation (GCD-TTA) an important problem. However, existing works treat GCD and TTA separately, while the limited semantic information in 3D point clouds makes GCD-TTA under domain shift more prone to feature collapse when combining GCD with TTA. To address this issue, we propose a two-stage framework for GCD-TTA in 3D LiDAR semantic segmentation. Specifically, in the first stage, we adaptively aggregate local 3D features according to geometric density variations and obtain reliable pseudo labels under a global class-wise prototype consistency constraint. In the second stage, we cast category discovery as an optimal transport assignment process for globally consistent grouping of novel categories. Meanwhile, semantic features from 2D vision foundation models are fused with 3D point cloud features to enrich 3D semantic information and promote a more separable feature space. Extensive experiments on Synth4D to SemanticKITTI, SynLiDAR to SemanticKITTI, and Synth4D to nuScenes demonstrate the effectiveness of our framework. Under a unified evaluation protocol, our method achieves the best overall mIoU on 9 out of 10 benchmark splits and improves the overall mIoU by up to 12.21 points over the strongest re-implemented baseline.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01678

Communication with Adversarial Users: Byzantine Models in Multi-User Information Theory

Minglai Cai, Christian Deppe, Neha Sangwan

原文摘要

We consider communication systems operating in environments where some participating users cannot be trusted. This motivates the study of Byzantine communication models, in which users may behave adversarially. In this survey, we provide a unified information-theoretic perspective on communication with adversarial users in multi-user settings. We review recent advances in Byzantine multiple access channels, including reliable communication, adversary identification, and distributed function computation. Furthermore, we discuss extensions to classical-quantum channels and highlight fundamental differences between classical and quantum models. Finally, we outline key open problems and future research directions at the intersection of communication, computation, and security.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01677

Xiaomi Auto World Model 2.0: Causal World Models for Real-World Driving and Embodied Intelligence

Zhenxin Zhu, Zhijian Huang, Cheng Chi, Yuru Wang, Jianhua Wu, Jiayi Guan, Yonggang Li, Mingfei Tu 等 23 位作者

原文摘要

We present Xiaomi Auto World Model 2.0, a unified streaming causal world model for Physical AI. It models and generates causal physical evolution in the real world, and unifies heterogeneous conditioning inputs for view-consistent generation. The architecture integrates View-Temporal Attention (VTA) to capture cross-view spatial dependencies and temporal dynamics, together with Multi-view Rotary Position Encoding (RoPE) to encode positional relationships across views and time. WorldRec 2.0 reconstructs static and dynamic 3D Gaussians from multi-view, multi-temporal observations in a single forward pass and uses their renderings as explicit scene context to preserve geometry and appearance during long-horizon generation. Conditioning inputs are fused through VAE latent concatenation in a shared representation space. We incorporate Gated Linear Attention (GLA) into the temporal layers of VTA for efficient streaming inference, reducing computation and KV-cache memory during multi-view rollouts. A physical-interaction data pipeline covers diverse interactions and long-tailed events. From Pre-train onward, these data are combined with large-scale videos and heterogeneous visual modalities to learn visual, geometric, and dynamic priors. During Mid-train, training across diverse view configurations further improves the model's ability to capture how actions and object states shape subsequent scene evolution. After distillation-based Post-train, we apply the model to closed-loop simulation, closed-loop training, and World Action Model (WAM) applications. These applications generate physically consistent future states and support world-state prediction, policy learning, and action generation for Physical AI.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03724

Does the Trust Boundary Hold over Time? A Long-Horizon Benchmark for Receiver-Directed Federated Learning

Ting Xu, Henry Leung

原文摘要

In receiver-directed federated learning, each site draws a trust boundary. For every source and model component it judges, on its own data, whether the transfer helps (admit), harms (reject) or cannot yet be told (abstain). We ask whether this boundary holds over hundreds of rounds. Our benchmark has a federation of thirteen semi-synthetic sites, an exact oracle for every evaluated transfer, paired seeds, an attack protocol, fourteen methods over 600 rounds and Qwen3-1.7B LoRA sites over 500. The answer depends on which boundary is measured. Across 20 paired CPU seeds, a gate judging against a fixed reference better identified transfers useful relative to the site's starting model. Yet it admitted 29% of transfers harmful to the model the site now has. A gate judging against the current model admitted 2% of harmful transfers on that target in the CPU tier, but missed many transfers useful to the starting model. Two short propositions explain these distinct failures. We also evaluate a held out audit that leaves gate verdicts intact. After training, it decides whether each final model is deployed or the site falls back to local training, and reports admissions that have gone stale. With independent audit data its guarantees hold after any training history. At 600 rounds the audit deployed 65 to 72% of models, none with negative benefit on its target, and kept 98% of the gain. No stale report had the wrong sign in our runs. With 20,000 audit observations, a variance-adaptive bound found 99% of stale transfers that cost at least 0.01 in log loss. A common-target Qwen3 comparison on 60 selected transfers shows the same tension over 100 rounds. With 38% adversarial sites, FedFomo and CS-Gate were the only evaluated methods with positive mean gain in every honest group.

cs.LG · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01676

MMCoT$^3$: Empowering 3D Scene Understanding with 3D-Text Interleaved Multi-Modal Chain-of-Thought Reasoning

Yongqi Shan, Yifan Wang, Yunzhi Zhuge, Lijun Wang, Huchuan Lu

原文摘要

Understanding and reasoning over 3D scenes from point clouds is essential for a broad range of AI applications. Recent 3D large language models have shown promising results, yet they still face two fundamental limitations: (i) insufficient capability for grounded multi-step reasoning in complex 3D scenes, and (ii) a text-centric inference paradigm that lacks effective interaction between textual reasoning and visual evidence. As a result, these models often fail to verify intermediate inferences, disambiguate similar object instances, and avoid hallucinations. To address these limitations, we propose MMCoT$^3$, the first 3D-Text Interleaved Chain-of-Thought (CoT) framework for 3D scene understanding. The core idea is to explicitly interleave textual reasoning with task-relevant 3D evidence, enabling the model to consult scene observations throughout the reasoning process. In addition, since a textual mention in 3D scenes may correspond to multiple candidate objects, naive object-mention alignment can inject incorrect evidence and mislead subsequent reasoning. To mitigate this multi-instance ambiguity, we design an ambiguity-aware evidence aggregation mechanism that uses query-guided cross-attention to fuse candidate object tokens and inject both evidence and ambiguity cues into the reasoning process. To better equip the model with multi-step reasoning capability, we further develop PointCoT, a large-scale 3D-specific CoT instruction tuning dataset with approximately 176.2K instances spanning captioning, question answering, dialogue, and planning tasks. Extensive experiments on six benchmarks show that our method consistently improves performance and achieves state-of-the-art results, validating the effectiveness of our proposed ambiguity-aware 3D-text interleaved reasoning for 3D scene understanding.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.09319

PGID: Progressive Guided Inversion and Denoising for Robust Watermark Detection

Minh Quoc Duong, Chun Tong Lei, Chun Pong Lau

原文摘要

With the proliferation of AI-generated images, digital watermarking has become an essential safeguard for protecting intellectual property and mitigating malicious exploitation. Recent works on semantic watermarking have enabled efficient copyright protection for diffusion models. However, the dependence of semantic watermarking on diffusion inversion for watermark detection creates a critical vulnerability. Imprint removal and forgery attacks exploit this weakness to produce deceptive results. Our analysis reveals that these attacks succeed by displacing watermarked latents into the unwatermarked region, while guiding unwatermarked latents into the watermarked region. Based on that, we propose Progressive Guided Inversion and Denoising (PGID), the first plug-and-play, training-free noise extraction framework designed to defend against both attack strategies. PGID effectively defends by projecting perturbed latents back to the region where they originally belong. The projection is achieved by eliminating intermediate latent deflections and mitigating adversarial perturbations through progressive inversion-denoising cycles. Comprehensive evaluations across multiple schemes demonstrate that PGID successfully restores detection reliability by recovering removed watermarks and identifying forged instances.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01675

Learned optical compression for single-pixel quantitative phase imaging

Ary Portes, Yuntian Wang, Che-Yung Shen, Shiqi Chen, Aydogan Ozcan

原文摘要

Quantitative phase imaging (QPI) reveals optical path-length variations in transparent objects but typically relies on spatially resolved intensity measurements. Here we numerically and experimentally demonstrate learned optical compression for single-pixel QPI, jointly optimizing a diffractive encoder and a digital neural decoder to concentrate reconstruction-relevant phase information into a compact measurement vector. A single reconfigurable phase-modulation plane sequentially displays the learned encoding patterns, which are independent of the input object, each yielding one scalar intensity measurement. Operating at a visible wavelength, the experimental system reconstructs quantitative phase images of unknown objects with ~40-fold dimensional compression, using only 2.55% as many intensity measurements as reconstructed pixels. Experimental blind testing on handwritten digits and grating-like phase objects achieves mean Pearson correlation coefficients of 0.945 and 0.848, respectively. Benchmarking under the same measurement budget demonstrates substantially higher reconstruction fidelity than other encoding strategies, highlighting the advantages of learned optical compression. With the optical encoder fixed, decoder-only neural adaptation enables experimental transfer to previously unseen data distributions, extending the utility of the learned measurement representation. These results establish learned optical compression as a measurement-efficient and reusable framework for QPI, integrating phase-information compression into single-pixel intensity measurements while retaining adaptability to new data. This approach paves the way for low-data-rate phase imaging and sensing, with the potential to extend QPI to spectral regions where high-quality detector arrays are costly or unavailable.

physics.optics · cs.CV · physics.app-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.18677

Sanyu Studio: A Multi-Agent System for Art-Historical Narrative Construction

Zhaoxi Wei, Hongye Yang, Shuyuan Tian

原文摘要

Amid concerns that generative AI may standardize art interpretation, this paper examines whether LLM-based interaction can support plural art-historical narrative construction. We present Sanyu Studio, a multi-agent dialogue system that models 321 Sanyu oil paintings as agents with fact, interpretation, organization, and memory-filtering mechanisms. Based on a seven-day workshop with eight art-university participants, the study shows that user prompts, evidence organization, and cognitive tendencies shaped divergent yet coherent versions of digital Sanyu. The findings suggest that, under conditions of limited historical evidence, AI can amplify human agency and offer public audiences an interactive entry point into art-historical interpretation.

cs.AI · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.13879

Map Users and Mapmakers: The Scope of Cognitive Attribution from Acquired Representations

Yiling Wu

原文摘要

An acquired representation can enlarge a system's cognitive repertoire without transferring the capacities exercised in producing that representation. This paper develops a framework for specifying that enlargement and its limits. Its central contribution is a five-part attribution table distinguishing effective tracking, application of acquired structures, acquisition from explicit specifications, acquisition from identifying observations, and retention and reuse. Each entry identifies a positive capacity commitment and a further claim requiring additional support. The argument deliberately grants meaningful content, causal efficacy, and productive inference, so that its conclusion does not depend on treating representations as inert encodings. Map and category examples show why even complete application competence leaves acquisition capacity undetermined, and why acquiring a criterion from its description differs from finding it in examples. Short formal proofs appear in an appendix. The framework is applied to Andrew Ng's world-model interpretation of Othello-GPT and to the specific indicators discussed in contemporary accounts of machine concepts. It preserves demonstrated recognition, classification, inference, and qualified acquisition while specifying what remains unestablished about criterion discovery and accumulation. The result concerns the scope of cognitive attribution rather than the constitutive conditions of concept possession: cognitive achievements deserve credit for the capacities they establish, without silently importing a broader repertoire through the labels attached to them.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01669

How Much Does Random Rotation Help GPTQ? Sharp Worst-Case Rate Gaps in the High-Rate Regime

Runguo Li

原文摘要

GPTQ is Babai's nearest-plane algorithm on the lattice of the Cholesky factor of the layer's input second moment $Σ$. In the high-rate, entropy-coded regime it needs $\tfrac12\log_2(\AM/\GM)$ of its Cholesky pivots more bits than WaterSIC, which is within $0.255$ bit of the rate-distortion limit for every $Σ$; random rotations close most of this gap in practice, and Ordentlich and Polyanskiy asked for the worst-case gap between rotated GPTQ and WaterSIC. We show that after a Haar rotation the $k$-th pivot converges almost surely, uniformly in $k\geδn$ and over spectra $ν$ of bounded condition number, to $1/S_ν(-k/n)$, a classical formula from the theory of Lyapunov exponents, and use it to answer the question. Over spectra with condition number $κ$ the worst rotated gap is $\tfrac12\log_2κ-\log_2\lnκ+\tfrac12-\tfrac1{\ln2}+o(1)$ bits, attained only by two-point spectra, just $\tfrac12\log_2\lnκ+O(1)$ bits below the worst case without rotation, so rotation cannot replace waterfilling in general. It nearly can on real layers: rotation cuts the cost of an $η$-fraction of outlier eigenvalues of size $μ$ from about $ημ$ to $η^2μ/2$ to leading order, but cannot remove spectral spread on a logarithmic scale; for non-Gaussian weights it also adds a weight-side rate cost, which we characterize exactly under a standard high-rate assumption. On $96$ Hessians of Qwen3 and Llama-3.1 the rotated gap has median $0.04$ bit and maximum $0.36$ bit, against $2$--$5$ bits in the worst case for the same condition numbers, and the limit matches it to within $0.009$ bit.

cs.IT · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.21745

The Presupposition Problem in Representation Genesis

Yiling Wu

原文摘要

Large language models are the first systems to achieve high cognitive performance without clearly undergoing representation genesis: the transition from a non-representing physical system to one whose states guide behavior in a content-sensitive way. Prior cognitive systems had already made this transition before we could examine it, and philosophy of mind treated genesis as a background condition rather than an explanatory target. LLMs provide a case that does not clearly involve this transition, making the genesis question newly urgent: if genesis did not occur, which cognitive capacities are affected, and why? We currently lack the conceptual resources to answer this. The reason, this paper argues, is structural. Major frameworks in philosophy of mind, including the Language of Thought hypothesis, teleosemantics, predictive processing, enactivism, and genetic phenomenology, share a common feature when applied to the genesis question: at some explanatory step, each deploys concepts whose explanatory purchase depends on the system already being organized as a representer. This pattern, which we call the Representation Presupposition structure, generates systematic explanatory deferral. Attempts to explain the first acquisition of content-manipulable representation within the existing categorical vocabulary import resources from the representational side of the transition itself. We call this the Representation Regress. The paper offers a conceptual diagnosis rather than a new theory, establishing the structure of the problem and deriving two minimum adequacy conditions for any account that avoids this pattern. LLMs make the absence of such a theory consequential rather than merely theoretical.

cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.10575

Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality

Melanie Wille, Dimity Miller, Tobias Fischer, Scarlett Raine

原文摘要

Underwater object detection is strongly affected by domain shift, where performance can vary significantly across different locations, habitats, and deployment conditions. However, detector performance is typically evaluated using aggregate metrics that hide failures in specific environments, while existing domain generalization benchmarks often rely on synthetic variations that do not reflect real-world conditions. To address these limitations we introduce a framework that characterizes underwater images by appearance, scene composition, and acquisition geometry to assign domain labels. Using this framework, we perform the first systematic study of how domain factors influence both human annotation quality in underwater object detection datasets and deep learning-based detector performance, revealing substantial domain-dependent discrepancies. By incorporating physically meaningful domain labels, domain shift becomes something we can characterize, measure, benchmark, and act on. We highlight how our framework can be used to guide data collection and annotation, design more informative benchmarks, and assess detector robustness across diverse underwater environments.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.40029

Segment Anything Model Based Context Guided Mamba Unet for Medical Image Segmentation

Chenchen Wang

原文摘要

Medical image segmentation is crucial for clinical diagnosis and treatment planning, requiring both global contextual understanding and fine-grained structural precision. While the Segment Anything Model (SAM) demonstrates strong generalization capabilities, its direct application to medical images often lacks domain-specific adaptability. Meanwhile, State Space Models (SSMs)-based methods, such as Mamba, efficiently capture long-range dependencies but may struggle with preserving fine anatomical details. To address these challenges, we propose a segment anything model based context guided mamba unet, SAMBA-UNet, a novel framework that integrates SAM's context knowledge with Mamba's hierarchical feature learning to enhance both global representation and local detail refinement. Specifically, we introduce the Hierarchical Context Enhancement Module (HCEM), an attention-based fusion module that deeply incorporates the multi-level features of SAM and Mamba to improve detail perception and segmentation precision. Additionally, we employ LoRA and Adapter-based fine-tuning to adapt SAM to medical imaging tasks, further enhancing its domain-specific segmentation capability and robustness. We conducted a comprehensive study on several medical segmentation datasets, including Synapse, ISIC17, and ISIC18, evaluating our method across multiple metrics. Experimental results demonstrate that SAMBA-UNet achieves state-of-the-art performance, effectively balancing generalization, segmentation accuracy, and computational efficiency. Our code will be released upon acceptance.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01666

Stand-In Effects and Pooling Amplification: Design-Based Tests of Whether Biological World Models Use Actions

Bowen Xu, Boyu Chen

原文摘要

Action-conditioned world models are increasingly trained on data from randomized experiments, and whether their predictions respond to actions is usually checked by replacing or removing the actions and measuring the loss in accuracy. We show that such diagnostics report action effects where there are none: under autocorrelated designs, future actions stand in for history the model does not see, and a better model learns the stand-in better. When the design law is known, a conditional randomization test written as an e-value is valid in finite samples whatever the model, provided the resampler conditions on the full state the design used; resampling from a simpler law reproduces the false effect. We identify a second failure, specific to world models: when many sites share one action sequence (cells in a microscope field, pixels in a frame), the natural pooled score turns small model errors along the action contrast into overwhelming evidence against a true effect. In a pixel world, its median log e-value fell from $-17.3$ to $-6832$ as frames grew from 64 to 4096 pixels, while the oracle's rose. Tempering the score by a design effect estimated on training data changes the bet, not the validity. Our own preregistered analysis of optogenetic ERK experiments failed in exactly this way; a tempered statistic, selected on synthetic data and registered before the data were obtained, then rejected the null on a never-opened field ($E\approx 1.1\times10^{10}$). Every analysis is preregistered, fixed in a dated protocol before it was run, or labelled exploratory, and every reported result is generated from hashed result files.

cs.LG · q-bio.QM · stat.ME

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.31466

Antenna Orientation Optimization for Rotatable Antenna-Enabled ISAC Systems

Qingjie Wu, Beixiong Zheng, Guangchi Zhang, Robert Schober

原文摘要

Non-fixed flexible antenna architectures, such as fluid antenna system (FAS), movable antenna (MA), and pinching antenna, have garnered significant interest in recent years. In this paper, we deploy a rotatable antenna (RA) array at the base station (BS) to improve the integrated sensing and communication (ISAC) performance by exploiting the additional spatial degrees of freedom (DoFs) introduced by antenna rotation. To enhance the sensing performance over an extended region containing a potential target while meeting the communication requirements of multiple users, we aim to maximize the minimum echo signal power within the sensing region, subject to required minimum communication rates of the users. For the special case of a single user and a point target, we develop a tractable approximation of the original problem, leading to independent per-RA subproblems. We characterize the geometric structure of these subproblems and derive closed-form solutions. Under the far-field approximation, the per-RA subproblems become approximately identical, allowing a common pointing vector to be adopted for all RAs. For the general multi-user and regional sensing case, we propose an alternating optimization (AO) algorithm that alternately optimizes the transmit beamforming for communication, the covariance matrix of the probing signal, and the pointing vectors of the RAs in an iterative manner. Simulation results demonstrate that the proposed RA-enabled ISAC system significantly outperforms various benchmark schemes with restricted antenna-orientation flexibility, while the evaluation of the Cramér-Rao bound (CRB) further verifies its benefits for target-position estimation accuracy.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.40085

Morphology-Aware Channel-Decoupled Mamba Network for Medical Image Segmentation

Chenchen Wang

原文摘要

Accurate multi-organ segmentation in computed tomography (CT) is essential for computer-aided diagnosis, treatment planning, and disease monitoring. However, substantial variations in organ shape and size, together with low tissue contrast and ambiguous boundaries, complicate the modeling of heterogeneous anatomical structures. To address these challenges, we propose a Morphology-Aware Channel-Decoupled Mamba Network (MCDNet), which integrates adaptive local structural enhancement with channel-decoupled long-range modeling. Specifically, we introduce a Morphology-Aware Channel-Decoupled State-Space (MCSS) block that divides feature channels into separate processing groups, allowing differentiated structural representations to be learned within an efficient state-space framework. Within each group, a Morphology-Aware Enhancement Module (MAEM) combines deformable convolutions with channel attention to adapt local feature extraction to anatomical shape variations. Group-wise state-space modeling then captures long-range spatial dependencies, and channel gating adaptively integrates the resulting features. Experiments on the Synapse abdominal CT dataset demonstrate the effectiveness of MCDNet, achieving an average Dice Similarity Coefficient of 81.61% for multi-organ segmentation.

cs.CV

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新论文 · v1arXiv:2611.01663

GatedVLA with Camera-Based Emergency Braking: AlpaSim E2E Closed Loop Challenge Technical Report

Kaneyoshi Hiratsuka, Naoki Takada, Makoto Kawano

原文摘要

This report presents GatedVLA, our entry for the Physical AI AV (PAI-AV) track. Our learned vision-language-action (VLA) policy pairs the driving-pretrained Qwen-Drive-1.0-4B vision-language model with a layer-wise flow-matching action expert that outputs a 2.5 s horizon of acceleration and curvature controls, augmented with a soft visual mask applied to the expert's cross-attention. Compared to an identically trained baseline, the masked policy achieves comparable overall open-loop error while reducing error on left turns. In local closed-loop evaluation across the same 150 scenes (two rollouts per scene), the visual mask increases the mean scene score from 0.479 to 0.517 (paired difference +0.038, 95% bootstrap CI [-0.018, +0.093], wins/losses 37/26, sign test p = 0.21). Incorporating a rule-based automatic emergency braking (AEB) module at deployment further increases the mean score to 0.599 (+0.082, CI [+0.023, +0.143]) and reduces the mean number of scenes with at-fault collisions from 34.0 to 11.0.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01661

A Hybrid Anomaly Detection Method for Multivariate Time Series

Ali Babalhavaeji, Hui Song, Mahdi Jalili

原文摘要

Multivariate time series are ubiquitous in monitoring smart systems such as power plants, smart houses, and servers. These systems use multiple sensors to collect the system's status over time. Those sensors represent the status of the whole system as one entity. Anomaly detection in such smart systems is of critical importance to prevent system failure and maintain functionality. But anomaly detection in such multivariate time series data is very challenging since this type of data includes both temporal and spatial information. Capturing both of these pieces of information is important to detect anomalies. In this paper, we propose a hybrid deep learning-based anomaly detection method, which captures both temporal and spatial information. We propose to take advantage of the feature extraction capabilities of 2d convolutional network in a variational autoencoder, alongside a prediction-based deep neural network of 2d convolutional networks and a gated recurrent unit network to capture both spatial and temporal information. Experiments on public data against state-of-the-art show the potential of our method.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.21736

Representational Acquisition Under Bounded Resources

Yiling Wu

原文摘要

A learner's obligation to keep acquired content usable imposes a storage-access tradeoff, with exact endpoints that force different physical organizations. For an exact concept-learning task, we determine the storage-read curve within a constant factor. One-read compound queries force answer-specific cells and exponentially costly updates; one-bit, order-independent updates instead force fact-specific cells. At minimum storage capacity, a robust counterpart holds: when update stretch is averaged uniformly over all semantic cube edges, small excess stretch bounds expected distance from a coordinate encoding under the uniform content distribution. It also bounds the information inaccessible to a one-read consumer. Preservation imposes further constraints. On a common invariant ready-state domain, old content preserved by ordinary zero-read overwrites must factor through the unwritten coordinates. Distributed encodings can require reading every cell to choose a preserving update. For conditional Horn reasoning, we obtain a second pair of exact endpoints: maintaining answers makes use local, while minimum storage and one-change named revision force a rule vector whose optimal query-read cost is determined exactly. These results explain how acquired distinctions, subsequent uses, and update constraints jointly determine the costs and organization of a retained resource.

cs.AI · cs.CL

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新论文 · v1arXiv:2611.01659

HuG: Learning Humanoid Whole-Body Affordance-Aware Grasping with Active Perception

Xuanbin Peng, Lai Wei, Yuze Zhai, Qingyuan Yu, Keyi Wang, Jianglong Ye, Xiaolong Wang

原文摘要

Acquiring objects in human environments is hard for humanoids: targets are partially occluded under egocentric viewpoints, and reaching them demands coordinated whole-body motion that keeps the target visible and reachable while the base stays stable. We present HuG, a framework for whole-body affordance-aware grasping with active perception that couples perception and action through a Perceive-Align-Grasp pipeline: belief-driven active perception resolves occlusion by treating viewpoint as an explicit decision variable; a point-cloud-conditioned affordance policy proposes whole-body grasps; and an affordance-aware alignment stage repositions the body to the most executable configuration. A key insight is that, on a floating base, kinematic validity is not enough: we curate training data with controller-in-the-loop dynamics simulation, rejecting 73% of kinematically valid grasps that would destabilize the deployment controller, so the policy learns kinodynamically feasible affordances. Trained entirely in simulation with zero real demonstrations, HuG attains 87.5% real-world success on a multi-level shelf task, generalizes to novel objects, and holds at least 73% success at every tested height. Beyond generalizing within its training distribution, the policy extrapolates spatially: at scale in simulation it succeeds at table heights well below any seen during training, indicating that kinodynamically filtered synthetic data grounds affordances in object geometry and whole-body feasibility rather than in memorized configurations.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01655

A Hybrid Anomaly Detection Method for Electric Vehicle Identification from Smart Meter Recordings

Ali Babalhavaeji, Hui Song, Mohammadreza Radmanesh, Mahdi Jalili

原文摘要

In recent years, electric vehicles (EVs) have attracted much attention due to their potential to decarbonize the transport system. EV charging imposes challenges on power grids, such as load imbalance and voltage instability. Therefore, electricity distributors need to identify which users have an EV. Smart meter recordings can be effectively used for this purpose. The major issue associated with such data is the imbalance problem, where the majority of the data belongs to users without EVs. The existing methods for EV identification from smart meter recordings have framed it as a classification task. In this paper, we frame the problem as an unsupervised problem and propose a hybrid anomaly detection method that combines the power of prediction-based and reconstruction-based anomaly detection methods. The proposed model considers temporal and stochastic representation information of smart meter time series via prediction-based and reconstruction-based components, respectively. The performance of the proposed model is compared with state-of-the-art supervised and unsupervised methods. Its superiority is verified on real-world data.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.10156

Inference for Batched Adaptive Experiments

Jan Kemper, Davud Rostam-Afschar

原文摘要

The advantages of adaptive experiments have led to their rapid adoption in economics, other fields, and among practitioners. However, adaptive experiments pose challenges for causal inference. This note suggests a BOLS (batched ordinary least squares) test statistic for inference on treatment effects in adaptive experiments. The statistic provides a precision-equalizing aggregation of per-period treatment-control differences under heteroskedasticity. The combined test statistic is a linear combination of heteroskedastic per-period z-statistics and can be used to construct asymptotically valid confidence intervals. We provide simulation results comparing rejection rates in the typical case with few treatment periods and few (or many) observations per batch.

econ.EM · cs.LG · stat.ME

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新论文 · v1arXiv:2611.01652

EEG-HealthLLM: Hierarchical Adaptation of Large Language Models for Unified Heterogeneous EEG Health Assessment

Ruidong Ma, Jisheng Dang, Ronghao Lin, Bimei Wang, Jingwen Zhao, Wenjun Ma, Wei-Shi Zheng, Meng Wang 等 10 位作者

原文摘要

Electroencephalography (EEG) provides a non-invasive means for neurological and mental health assessment, yet existing EEG models are typically developed for individual tasks and are difficult to unify across heterogeneous signal configurations and assessment objectives. Large language models (LLMs) offer a common interface for multiple tasks, but adapting heterogeneous EEG signals to a shared LLM raises a key challenge: how to preserve task-specific signal characteristics while enabling effective language-side parameter sharing. To address this problem, we propose EEG-HealthLLM, a unified and parameter-efficient framework for heterogeneous EEG health assessment. We introduce an Asymmetric Hierarchical Adaptation Strategy: task-specific EEG adapters preserve heterogeneous spatio-temporal dynamics, while a unified shared LoRA is injected into the LLM backbone (e.g., Phi-3) to capture shared high-level assessment semantics across tasks. We further establish EEG-HealthBench by reorganizing five existing EEG resources into paradigms covering epileptic seizure detection, major depressive disorder assessment, anxiety risk assessment, depressive symptom risk assessment, and emotional state assessment under a unified subject-independent protocol. Extensive experiments show that EEG-HealthLLM achieves an average balanced accuracy (BAC) of 69.58%, ranking first among the evaluated methods in both average BAC and macro-F1. Compared with independent single-task LoRA adaptation, it improves average BAC from 67.70% to 69.58% while reducing the aggregate language-side adapter footprint by 80.0%. Further analyses reveal task-dependent cross-task interactions and trade-offs, supporting the proposed hierarchical adaptation strategy. The implementation is publicly available at https://github.com/Marui-88/EEG-HealthLLM.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01651

Physics-Informed One-Step Generation on Long-short flow maps

Zhiqi Li, Duowen Chen, Jinjin He, Bo Zhu

原文摘要

Physics-informed generative models should produce samples that are both data-aligned and physically valid, yet existing methods are often iterative and too slow for practical use. Achieving this with a one-step flow-map model is difficult because both data-distribution and physics-constraint learning are enforced only indirectly through surrogate objectives, making their joint learning fragile for long-range flow maps. We propose \textbf{Physics-Informed Long-Short Flow Maps with Direct Guidance (LSFM-DG)}, which makes direct target computation feasible by decomposing one-step generation into a long flow map for global evolution and a near-terminal short flow map with computable closed-form solutions and physics potentials. As the short flow map shrinks to zero, it provides both direct distributional and physical targets for learning the long flow map. This yields a unified one-step flow-map model that removes Jensen's gap, improves training stability, and achieves stronger data fidelity and physical consistency without distillation.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.02180

Predictive Scheduling and Model Sharing for Continuous UAV Edge Inference

Houyi Qi, Minghui Liwang

原文摘要

Continuous edge inference for unmanned aerial vehicles (UAVs) faces uncertain wireless connectivity, competition for shared computing resources, and the overhead of model loading and session-state migration. These interdependent factors complicate request admission and the continuity of successive inference services. This paper investigates joint request admission, edge-server assignment, and model selection under an admission-priority objective that strictly favors any feasible increase in admitted requests over bounded losses in delay, interruption risk, model quality, migration, and deployment. To address this problem, we propose Service-Window-Guided Admission and Model Placement (SWAP), which integrates predictive service-window information with shared model deployment and coordinated scheduling. Quantile regression and probability calibration estimate the remaining service window and interruption risk, while a bounded residence factor characterizes the potential reuse of shared model instances. We then formulate a constrained exact potential game based on marginal-contribution utilities and develop a solution procedure combining unilateral improvement, coordinated admission, and finite binary logit refinement. Theoretical analysis establishes NP-hardness, the existence of a constrained pure Nash equilibrium, and finite descent, together with feasibility and approximate unilateral equilibrium guarantees for the returned assignment. Simulation results demonstrate improved service continuity and reduced request rejection under increasing resource contention, while characterizing the accompanying tradeoffs in model-deployment traffic, inference quality, and execution delay.

cs.NI

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arXiv ↗PDF ↗