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新论文 · v1arXiv:2611.04685

MIMIC-Note-Extract: A Large-Scale Structured Dataset Extracted from MIMIC-IV-Note Discharge Summaries

Kristian Schwethelm, Jonas Kuntzer, Conrad Ketzer, Eva Wende, Moritz Schlager, Daniel Rueckert, Georgios Kaissis, Friederike Jungmann 等 9 位作者

原文摘要

MIMIC is one of the most widely used public hospital datasets for machine learning on electronic health records, yet clinically important information, such as past medical history, allergies, and diagnostic findings, remains confined to free-text discharge summaries. Studies requiring this information must therefore build their own extraction pipelines, duplicating substantial effort across projects. We release MIMIC-Note-Extract (MNE), a large-scale dataset that uses a hybrid extraction pipeline combining deterministic parsing with schema-guided large language model generation to transform 16 sections from 300,720 discharge summaries into structured tables. MNE adds information that has no structured MIMIC counterpart and enriches related tables with broader coverage and greater clinical detail, including 10 times more patient-history entries and medication histories for more than twice as many admissions. Validation by medical doctors confirms extraction accuracy above 92% across sections. Using MNE, we construct a clinical decision-making benchmark for LLM agents, demonstrating how reusable structured representations of clinical narratives reduce data curation effort.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04683

FenixWalk: Adaptive Sampling for Large-Scale Dynamic Graphs on Heterogeneous Architectures

Pinhuan Wang, Jianian Fu, Jie Zhang, Shixuan Sun, Qing Wang, Xiaoliang Wang, Chen Tian, Chengying Huan

原文摘要

Random walks are fundamental to graph analytics, yet executing them efficiently on large, evolving graphs remains challenging. Existing approaches must balance sampling efficiency, update overhead, and memory consumption. Sampling structures optimized for throughput often incur substantial memory overhead and costly maintenance, whereas lightweight alternatives can suffer from high sampling latency on high-degree vertices or skewed weight distributions. These challenges become more pronounced on heterogeneous CPU--GPU platforms, where host memory provides capacity but GPU memory limits the amount of graph data that can benefit from parallel acceleration. We present FenixWalk, a memory-efficient random-walk system that combines unified in-place sampling with selective GPU acceleration for large-scale dynamic graphs. At its core, FenixWalk employs an in-place Fenwick representation that supports both rejection sampling and inverse-transform sampling without duplicating edge-level weight storage. This representation enables expected O(1) weight lookup for rejection sampling, together with O(log d) prefix sampling and updates. To exploit heterogeneous hardware efficiently, FenixWalk maintains the complete graph and its sampling structures in host memory while selectively placing frequently reused vertex-local samplers on the GPU. By transferring compact walker states instead of repeatedly migrating graph partitions, the system reduces cross-device communication and makes effective use of limited GPU memory. Experiments on large real-world graphs and representative random-walk workloads demonstrate that FenixWalk achieves average speedups ranging from 2.06x to 400.57x over existing random-walk systems.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.38968

Beyond Spatial-Domain Supervision: A Relation Constrained Space for Multi-Modal Image Fusion

Zeyu Wang, Mingyu Ge, Haiyu Song, Haoran Duan

原文摘要

Multi-modal image fusion (MMIF) aims to form a single image by integrating shared information, preserving complementary cues, and coordinating cross-modal conflicts across modalities. However, due to the absence of ground-truth fused images, existing MMIF supervision commonly uses spatial-domain sources or gradient variants as surrogate ground truths, making the supervision mechanism inherently misaligned with the goal of MMIF and causing pixel-level compromise or modality bias. To address this, we propose a relation-constrained supervision paradigm that moves fusion supervision from the spatial domain to a learned relation space. Instead of forcing the fused image to approximate the sources, we use frozen pretrained representation models as information providers and design a learnable feature adapter to align heterogeneous DINO and CLIP features into a unified supervision space. The adapter infers three relation parameters, namely sharedness, dominance, and coordination radius, which define three losses corresponding to the MMIF's goal. To make this space reliable, we devise a self-supervised contrastive ranking objective tailored to the adapter and couple it with the fusion network through alternating optimization. Extensive experiments show that the proposed supervision space yields significant gains regardless of which mainstream backbone the fusion network adopts, offering a supervision paradigm better aligned with the goal of MMIF. Code: github.com/GMY628/RCS-Fusion.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04680

Hardness Self-Amplification in Linear Algebra and Fine-Grained Cryptography

Geoffroy Couteau, Nikolas Melissaris, Thejas Radhika Sajith

原文摘要

We study the possibility of basing fine-grained cryptography on the hardness of fundamental problems in linear algebra. Our main technical contribution is a hardness self-amplification method based on random low-rank updates: we show how to turn an algorithm that solves an inverse-polynomial fraction of random linear systems into a solver which succeeds for every right-hand side of almost every matrix. Our reduction increases the runtime only linearly in the inverse of the original success probability. In particular, this yields fine-grained one-way functions from the almost worst-case hardness of linear systems over large enough fields. Using the same approach, we also obtain tighter worst-case to average-case reductions for matrix multiplication and online matrix-vector multiplication, as well as variants of our reductions that recover exact solutions from approximate algorithms, whose expected coordinate agreement is only $1/q+\varepsilon$, with inverse-quadratic dependence on $\varepsilon$ up to logarithmic factors, over every finite field. Finally, we also use it to connect the existence of fine-grained key agreement to the problem of computing the rank of a matrix: over a suitable sequence of growing characteristic-two fields, the almost-everywhere average-case hardness of rank computation (for any of a large class of distributions) implies infinitely-often fine-grained key agreement.

cs.CC

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2604.10186

VeriNC: Finding Design Risks of In-Network Computing Systems

Tianyu Bai, Xiaoxi Zhang, Haoqing Wang, Ying Zhang, Wenfei Wu

原文摘要

The emergence of programmable switches has brought in-network computing (INC) into the spotlight in recent years. By offloading computation directly onto the data transmission process, INC improves network utilization, reduces latency to sub-RTT levels, saves link bandwidth, and maintains throughput. However, INC disrupts the transparency of traditional networks, forcing developers to consider network exceptions like packet loss and out-of-order. If not properly handled, these exceptions can lead to violations of application properties, such as cache consistency and lock exclusion. Usual testing cannot exhaustively cover these exceptions, raising doubts about the correctness of INC systems and hindering their deployment in the industry. This paper presents VeriNC, the first general-purpose tool for verifying INC systems. VeriNC provides a high-level specification language and saves developers 67.2% lines of code on average. To help better understand the behavior of the system, VeriNC offers configurable network environments. VeriNC enables developers to express INC-specific correctness properties. VeriNC translates developer-specified systems into state transition representations, performs model checking to detect potential design risks, and reports violation traces to developers. We propose optimizations for INC-specific scenarios to address the challenge of state space explosion. We modeled INC systems across four application domains and identified design risks with VeriNC in seconds. VeriNC has also been adopted to guide the design of a new INC protocol. Based on our verification experience, we summarize lessons that help develop a correct INC protocol. We further reproduce them in real systems to confirm the validity of our verification result.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04678

Lower Bounds for Adaptive Relaxed Locally Decodable Codes via the Capped Horvitz--Thompson Estimator

Guy Goldberg, Tom Gur, Sidhant Saraogi

原文摘要

We prove a nearly optimal lower bound on the blocklength of general relaxed locally decodable codes (RLDCs), extending the result of [GGS26] to nonlinear encodings and adaptive decoders with two-sided error. Namely, we show that any $q$-query RLDC $C\colon \{0,1\}^k \to \{0,1\}^n$ must satisfy $n = k^{1+Ω(1/q)}$, closely matching the upper bound of $n = k^{1+O(1/q)}$ by Ben-Sasson, Goldreich, Harsha, Sudan, and Vadhan (STOC 2004). Our proof reduces showing RLDC lower bounds to estimating the output distribution of adaptive decoders. To this end, we introduce a capped Horvitz--Thompson estimator with uniformly bounded discrete derivatives on the $p$-biased cube. We establish its concentration of measure via self-bounding inequalities and control the downward bias induced by capping through a spreadness argument.

cs.CC · cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.11967

H2G: Hierarchy-Aware Hyperbolic Grouping for 3D Scenes

Byungha Ko, Youngmin Lee, Dong Hwan Kim

原文摘要

Hierarchical 3D grouping aims to recover scene groups across multiple granularities, from fine object parts to complete objects, without relying on semantic labels or a fixed vocabulary. The main challenge is to transform 2D foundation-model cues into coherent hierarchy supervision and embed that hierarchy in a 3D representation. We propose H2G, a hyperbolic affinity field for hierarchical 3D grouping. Our method derives semantically organized tree supervision by interpreting foundation-model affinities through Dasgupta's objective for similarity-based hierarchical clustering. This supervision is distilled into a single Lorentz hyperbolic feature field, whose geometry is well suited for tree-like branching structures. A hierarchy-aware objective aligns the field with fine-level assignments, coarse object structure, compact feature clusters, and LCA (Lowest Common Ancestor) ordering. This formulation represents multiple grouping levels in one feature space, enabling semantic hierarchical grouping grounded in 2D foundation-model knowledge. Code is available at https://github.com/Rhqo/H2G.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.15437

Terminal Fano 4-fold Hypersurfaces via Deep Reinforcement Learning

Marc Truter

原文摘要

We construct $46{,}992$ new families of Fano $4$-fold hypersurfaces $X_d\subset\mathbb(a_1,\ldots,a_6)$ of Fano index $1$ with $d>200$, whose general members have terminal singularities. Unlike the $11{,}617$ quasismooth families classified by Brown and Kasprzyk, these are nonquasismooth, for which determining terminality can require the analysis of hyperquotient singularities. An exhaustive search has classified all nonquasismooth families with $d\leq200$, but extending this classification to higher degrees becomes computationally prohibitive. We therefore exploit the sparsity and spatial clustering of terminal examples using two best-first searches over $\mathbb{Z}^6$: one with a fixed heuristic, and the other with a neural network trained online by deep reinforcement learning. Together, the searches reach degrees up to $3{,}234$. The neural network additionally discovers an average of $79$ hard-to-reach families per run. For the most isolated examples, our conservative distance-based model gives an estimated lower bound of nearly $40\%$ of the fixed search's total step budget to discover a single such family. An ablation study demonstrates that this advantage arises from the neural network rather than stochastic exploration alone. The complete list of families is publicly available.

math.AG · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04673

Right Tool, Wrong Parameters: Defending LLM Agents Against Parameter Poisoning

Yao Wang, Zehua Ma, Weiming Zhang, Han Fang

原文摘要

Indirect Prompt Injection (IPI) poses a critical threat to LLM agents. Existing defenses largely focus on whether an injection induces a new tool invocation or unintended action. In this paper, we study a different attack surface, parameter poisoning, where the attacker preserves the intended action but manipulates one or more tool arguments, such as a recipient or other task-critical value. Such attacks are difficult to detect because no new action is introduced, while parameter changes themselves are often legitimate parts of the user's request. Our key insight is that the legitimacy of a parameter change should be determined by the trust of the information flow that produces it under the user query. The query induces a task-specific trust hierarchy over available information fields: some are trusted to determine particular parameters, while others should have little or no influence. Based on this insight, we propose AuthorityFlow, a three-stage parameter-level defense. First, it interprets the user query to construct a task-specific trust hierarchy over the information fields available to the agent. Second, for each tool argument, it traces the information flow that produces its current value and attributes the value to its supporting field. Finally, it accepts the value only when that field is trusted to determine the parameter under the current task, allowing high-trust changes while flagging low-trust influence as potential parameter poisoning. We further introduce FactAuthBench for evaluating parameter poisoning in LLM agents. Without defense, the attack success rate reaches 93.06%. AuthorityFlow reduces it to 9.03%, while retaining 82.64% benign task utility compared with 90.28% without defense. Our results show that effective IPI defense requires reasoning not only about what action an agent takes, but also about which information is trusted to determine its parameters.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04672

The Devil is in the Context: Nine Dimensions of Prompt Injection Attacks

Jiamin Chang, Kristina Nikolić, Rishi Sharma, Giovanni Cherubin, Vasilios Mavroudis, Jose Sanchez Vicarte, Arnav Garg, Hammond Pearce 等 10 位作者

原文摘要

Prompt injection is usually discussed and evaluated as a single, homogeneous type of attack. However, vulnerabilities reported in real-world systems show that prompt injections vary widely. Moreover, the same instruction can be a prompt injection in one context and a legitimate request in another, depending on the user's intent and the system's policies. Ignoring this variation leaves gaps in how defenses are evaluated, and can give a false sense of security against real-world attacks. We identify nine dimensions along which prompt injection attacks differ. Together, they form a taxonomy that describes the injected instruction itself, its relation to the user's task, and how it manifests in the target system. Each dimension affects which defenses can detect or prevent an attack. We first use the taxonomy to characterize six real-world prompt injection vulnerabilities, showing how much these attacks differ. We then apply it to nine prompt injection benchmarks and find that most cover only a small region of this space. Combining benchmarks helps, but 13% of pairwise category combinations remain untested, mostly among the less commonly represented categories. Finally, we show that the taxonomy supports automated annotation of benchmarks and targeted generation of new test cases to fill these gaps. The taxonomy is also useful beyond benchmarks. Defense authors can use it to state which types of prompt injection they evaluated against, and system operators can use it to identify which attacks apply to their system and choose defenses to match.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.11827

Jaguar: Fast Private Neural Network Inference with Power-of-Two Homomorphic Arithmetic

Yewon Jeong, Nayoung Jung, Hyeri Roh, Woo-Seok Choi

原文摘要

Hybrid HE/2PC private neural network inference remains bottlenecked by prime-modulus homomorphic arithmetic in convolution and by a precision flow that runs ReLU at doubled bitwidth before invoking a separate truncation protocol. We present Jaguar, a system built on a single design choice--a power-of-two ciphertext ring--that addresses both. The choice enables SPA-Conv, a coefficient-domain convolution kernel that replaces NTT-centric polynomial multiplication with Scalar-Polynomial Accumulation, and an exact ciphertext-side truncation by local right shifts that lets ReLU run directly at the target fixed-point precision and eliminates the post-ReLU truncation protocol. Where NTT remains genuinely useful--at the client, for the single polynomial multiplication during decryption--we recover it through an auxiliary NTT prime, preserving the power-of-two protocol substrate while keeping decryption O(N log N). On ImageNet-scale ResNet-18, ResNet-50, and MobileNetV2 with AVX disabled, Jaguar achieves 2.07-3.72x lower end-to-end latency than Cheetah and 2.16-3.36x lower than Rhombus, with 1.16-1.76x lower communication than Cheetah.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.23258

Agnostic Language Identification and Generation

Mikael Møller Høgsgaard, Chirag Pabbaraju

原文摘要

Recent works on language identification and generation have established tight statistical rates at which these tasks can be achieved. These works typically operate under a strong realizability assumption: that the input data is drawn from an unknown distribution necessarily supported on some language in a given collection. In this work, we relax this assumption of realizability entirely, and impose no restrictions on the distribution of the input data. We propose objectives to study both language identification and generation in this more general "agnostic" setup. Across both problems, we obtain novel interesting characterizations and nearly tight rates.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04671

LeanSparse: Memory-Efficient Serving for Native Sparse-Attention LLMs

Zaifeng Pan, Chris Wu, Xinwei Qiang, Zhengding Hu, Zhongkai Yu, Yufei Ding

原文摘要

Sparse attention limits the history read by each query, but serving long contexts still requires substantial GPU memory. Existing KV offloading systems retain the full history of indexer keys on GPU, limiting decode concurrency. Prefill's temporary memory demand can further delay admission even when enough memory remains for the same request's subsequent decode. We present LeanSparse, a serving system that reduces both decode's persistent memory use and prefill's temporary memory demand. Our design exploits two observations: prefill consumes historical data one layer at a time, and decode can identify promising candidates before comparing their original indexer scores. During prefill, LeanSparse streams layer histories through two reusable GPU buffers, overlapping next-layer loading with ongoing model computation. During decode, coarse-to-fine indexing searches compact representations and retrieves original indexer keys only for candidate refinement. Separate indexer key and KV caches reuse data across generation steps. We implement LeanSparse in SGLang and evaluate it on DeepSeek-V4-Flash. At 950K context length, LeanSparse supports 2.95$\times$ as many concurrent requests and achieves 2.14$\times$ decode throughput over HiSparse. On multi-turn Open-SWE traces, it reduces median time to first token from 21.6 to 0.6s and median session completion time by 17.7% relative to ECHO.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04670

Sheaf-MPNN: A Library for Sheaf Neural Networks as Message Passing

Alessio Borgi, Mario Severino, Luke Braithwaite, Steve Azzolin, Rémi Bourgerie, Olga Zaghen, Emanuele Mule, Riccardo Ali 等 17 位作者

原文摘要

Standard Graph Neural Networks (GNNs) implicitly assume that neighboring nodes share the same feature space, which contributes to oversmoothing and poor performance on heterophilic graphs. Sheaf Neural Networks (SNNs) remove this assumption by giving each node and edge its own vector space and learning restriction maps that transform features as they move between neighbors. These maps define a sheaf Laplacian that drives diffusion over the graph, but existing implementations often assemble it explicitly as a global sparse block matrix, which adds memory and computational overhead and can hinder scalability. In this paper, we introduce Sheaf-MPNN, a PyTorch Geometric library that reformulates sheaf diffusion as local incidence-wise transport and aggregation, eliminating the need to construct the global Laplacian. Sheaf-MPNN implements fifteen families of SNNs under a unified message-passing framework, each supporting both homogeneous and heterogeneous graphs, and directed and undirected settings, with diagonal, general, orthogonal, and low-rank restriction maps and attention-based predictors. We further provide a unified benchmarking suite covering node- and graph-level tasks, heterogeneous graph learning, and computational efficiency, enabling systematic and reproducible comparisons across architectures. We establish theoretical equivalence between these message-passing formulations and their original counterparts and identify the architectural mechanisms needed to preserve their propagation dynamics. This unified perspective provides a principled foundation for implementing, comparing, and extending sheaf-based architectures within standard graph learning frameworks. Source code is available at https://github.com/alessioborgi/sheaf-mpnn.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.14962

Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation

Ku Onoda, Paavo Parmas, Hiroki Furuta, Soichiro Nishimori, Yuta Oshima, Shohei Taniguchi, Yutaka Matsuo

原文摘要

Text-to-image (T2I) models produce realistic, prompt-aligned images, but samples for the same prompt often cover only a few visually distinct modes. For person-centric prompts, this lack of diversity can reflect or amplify demographic skew. We formalize representative diversity as target-mode coverage: how well a set of samples covers predefined modes, such as colors or perceived-race categories. We then propose MultiMaxPO, a reinforcement learning post-training method that improves this coverage in diffusion T2I models by optimizing a multi-axis max@K objective. Given one score per mode for each sample, multi-axis max@K takes the maximum over samples for each mode and then sums these maxima. A sample therefore receives credit for a mode only when it raises that mode's maximum, so different samples are rewarded for different modes. We verify this credit assignment on a synthetic Gaussian mixture and on Stable Diffusion 3.5 Medium with pixel-based color rewards, and then apply it to perceived-appearance fairness. On held-out prompts and under three automatic evaluators, MultiMaxPO improves the Fairness Score over the base model by 0.22-0.36 on Stable Diffusion 3.5 Medium and by 0.07-0.19 on FLUX.1-dev, outperforms matched RL baselines, and maintains image quality and text alignment. Code is available at https://github.com/KuOnoda/multi-axis-maxk.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37431

Segmentation Confidence for Arbitrary CNNs

Baris Oguz, Xing Yao, Claudia Tawil, Alison Pouch, Brett Byram, Gabriel Arenas, Nadav Schwartz, Ipek Oguz

原文摘要

Convolutional neural networks (CNN) are widely used for medical image segmentation. However, they lack an inherent measure of confidence. Existing methods for estimating prediction uncertainty often require a custom network architecture and/or dedicated training setups and need to be trained from scratch. In addition, the deduction of a single confidence score per input from the uncertainty maps is not always straightforward. In this study, we explore voxel-based uncertainty estimation methods that can be added to existing segmentation pipelines without training from scratch. We also propose a novel method to estimate a single confidence score from the uncertainty maps. Our evaluations on different ROIs and modalities suggest that our confidence score can predict segmentation performance in unseen data.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03233

Axion 1.0: Open Agent Foundation Models, Simplified

Yuqi Jia, Yunjie Tian, Jiayu Zheng, Yijun Liang, Jingchen Sun, Mengyu Zhao, Di Fu

原文摘要

We introduce Axion 1.0, a fully open agent foundation model that combines strong and broad agentic capabilities with a remarkably simple training recipe. Axion 1.0 is trained in only three stages: two lightweight supervised fine-tuning stages followed by reinforcement learning, without dedicated mid-training, direct preference optimization (DPO), or on-policy distillation (OPD). The first SFT stage develops strong self-contained reasoning through model-aware data construction, where we synthesize challenging problems and adversarially strengthen them based on the base model's own successful solution strategies. The second stage extends these capabilities to tool-dependent reasoning through a scalable pipeline for constructing diverse and automatically verifiable tasks grounded in real-world data sources and deterministic computation. Finally, reinforcement learning further strengthens long-horizon reasoning and interaction with external tools and environments. Despite this compact training recipe, Axion 1.0 achieves strong performance across a broad suite of agentic benchmarks and is competitive with leading commercial models on several of them. Equally importantly, Axion 1.0 is open beyond model weights: we release the complete recipe, including data construction, generation, filtering, verification, prompts, and training code, enabling the entire development process to be reproduced and extended. Axion 1.0 demonstrates that strong agent foundation models can be both open and simple, offering a practical and reproducible path toward advancing open agentic intelligence.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.39688

Hardware-Aware Model Inversion in Memristive Spiking Neural Networks

Pablo Sorrentino, Fernando M. Quintana, Erika Covi, Fatih Turkmen

原文摘要

Hardware-accelerated spiking neural networks (SNNs) implemented on memristive crossbar arrays offer significant gains in energy efficiency, while device variability and analog noise are sometimes assumed to hinder information extraction. However, whether these hardware effects improve, preserve, or degrade privacy after deployment remains largely unexplored. Therefore, we ask whether model-inversion leakage characterized on an SNN remains representative after the same model is mapped to a non-ideal memristive substrate, and how hardware non-idealities and reliability faults affecting synapses, neuron thresholds, and spike processing reshape that leakage. To study this problem, we introduce Spiking Domain Model Inversion (SDMI), an inversion method that combines Expectation over Transformation (EOT) with surrogate gradients to optimize inputs through stochastic analog behavior and non-differentiable spiking dynamics. We use SDMI consistently across the SNN model, its memristive SNN, and the same memristive SNN under additional hardware perturbations. Across four datasets, reconstruction leakage does not vary monotonically with model accuracy or hardware variability. At comparable accuracy levels, neuron-threshold perturbations increase reconstruction fidelity, measured by SSIM, by up to 51.8%, whereas spike-level perturbations primarily degrade utility without consistently improving reconstruction. Weight noise, in contrast, reduces leakage under the same inversion procedure. These results show that hardware variability is not inherently privacy-preserving: model inversion leakage in memristive SNNs depends on the hardware state and cannot be inferred from model accuracy alone.

cs.CR · cs.ET · cs.NE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04666

Phase-DGS: Phase-guided Dynamic Gaussian Splatting from Unsynchronized Multi-view Video

Hosung Jeon, Jun Young Jeong, Sangwoon Kwak, Joonsoo Kim, Sangmin Kim, Jaesik Park, Won-Sik Cheong, Hyon-Gon Choo

原文摘要

Spatiotemporal scene reconstruction with Dynamic Gaussian Splatting (DGS) fundamentally depends on perfect temporal synchronization across all cameras, an assumption rarely achievable in practice. Industry-standard synchronization requires expensive specialized hardware and complex setup, while data-driven alternatives including audio-based alignment, geometry matching, and pose tracking remain limited by restrictive environmental or scene-specific assumptions. To overcome these limitations, we propose Phase-DGS, a framework that replaces conventional timestamps with a semantic phase representing each frame's inherent state within the underlying motion cycle. Frames capturing the same motion state share the same phase regardless of which camera records them or when, naturally establishing spatiotemporal correspondence across unsynchronized cameras. We validate Phase-DGS under temporal offsets, frame drops, and camera freezes, achieving up to +8.5 dB PSNR improvement over baselines while enabling seamless integration with existing DGS backbones including RealTime-4DGS and FreeTimeGS.

cs.CV · cs.GR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.08453

Sink vs. diagonal patterns as mechanisms for attention switch and oversmoothing prevention

Peter Súkeník, Cristina López Amado, Christoph H. Lampert, Marco Mondelli

原文摘要

This paper studies the role of sinks and diagonal patterns as attention switch and anti-oversmoothing mechanisms. We analyze geometric conditions under which sinks can be represented, showing a necessary alignment between the embedding of the sink and all other embeddings. Next, we refine the current understanding of the role of sinks in oversmoothing prevention: we specify the conditions under which dense attention is expected to smooth more than sparse attention, and empirically verify that such conditions are often satisfied in practice. We further prove an equivalence between sinks and hard attention switch, in which the output of the attention is identically 0. Finally, we relax the hard attention switch by allowing token self-communication: we provide a quantitative comparison of the costs of representing sinks vs.\ diagonal patterns, showing why sinks are favored in pretrained transformers. The introduction and analysis of diagonal patterns and the generalization of the attention switch close the gap between what oversmoothing prevention requires and what sinks provide, while also establishing when and why attention layers act like MLPs if token communication is not necessary.

cs.LG · cs.AI · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04664

Feature-Based and Deep Learning Methods for Astronomical Light Curve Classification

V. Baymakanov, Kh. Khamitov, A. Raimova, A. Isaeva, R. Taran, M. Zedan, M. Kornilov, M. Demianenko 等 10 位作者

原文摘要

We compare feature-based machine learning and end-to-end deep learning for variable star classification using the Zwicky Transient Facility (ZTF) StarEmbed dataset, which contains about 40,000 objects across seven imbalanced classes. Classical models trained on physically motivated features consistently outperform deep networks trained directly on raw light curves. We find that limited labeled data contributes to the performance gap, while the multivariate, irregularly sampled, and morphologically diverse nature of light curves poses additional challenges for deep learning models. While deep learning holds promise for learning representations directly from observations, our results indicate that current models fail to generalize with the available training set size. These findings motivate further investigation of the benefits of scaling labeled datasets and self-supervised pretraining on large unlabeled surveys. This work establishes a quantitative baseline for future research and highlights the need for further innovations to unlock the potential of deep learning in astronomical time series analysis.

cs.LG · astro-ph.IM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04663

Best-of-Depth: Learning Exploratory Dynamics in Looped Transformers

Shota Takashiro, Takeshi Kojima, Masanori Koyama, Kohei Hayashi, Yusuke Iwasawa

原文摘要

Looped Transformers extend inference-time computation through repeated latent-state updates, but final-depth training does not directly encourage useful intermediate alternatives. We introduce Best-of-Depth (BoD), which trains a recurrent trajectory as a candidate set by minimizing prediction loss across depths. Different valid targets can be matched at different depths without architectural changes or depth-specific labels. An exact-match analysis connects this objective to solution coverage. On N-Queens completion and graph coloring, BoD improves coverage over final-depth and intermediate-supervision objectives. N-Queens coverage reaches 91.03%, versus 45.75% for final-depth training. Further experiments show discovery beyond the training depth and recovery of solutions withheld from teacher pools on familiar inputs. Fine-tuning a pretrained looped language model with BoD yields higher pooled code-generation success than final-depth training when programs are generated separately at evaluated fixed depths. These results support training recurrent computation for solution discovery along its trajectory.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04662

SPADE: Spatiotemporal Preference Alignment and Diversity Enhancement for Local Search

Chenxu Wang, Wenxuan Yang, Yueqi Mou, Shuxian Bi, Biao Tang, Peng Yan

原文摘要

Proactive query recommendation aims to suggest relevant search queries to users on the homepage of local service apps. Unlike conventional recommendation over a fixed item set, this requires open-set generation from a vocabulary of millions, where outputs must simultaneously align with user preference, match fine-grained spatiotemporal context, cover long-tail intents, and provide diverse alternatives within a few candidate slots. Existing approaches address these axes in isolation: standard tokenization suppresses long-tail queries, coarse location encoding misses fine-grained geographic demand, generic preference optimization fails to align with production impression-click distributions, and standard beam search collapses to near-duplicate outputs. We present SPADE (Spatiotemporal Preference Alignment and Diversity Enhancement), an end-to-end generative framework that jointly optimizes the generation space, contextual representation, preference distribution, and output slate quality through four components: Objective-Aligned BPE (OA-BPE) aligns subword merging with business value signals to boost long-tail query generation probability; Spatio-Temporal Adaptive Representation (STAR) replaces coarse location tokens with continuous multi-scale encodings and soft gating for precise spatiotemporal grounding; MARS-DPO (Multi-dimensional Adaptive Reward Sampling DPO) combines cross-domain pre-training with preference alignment on production impression-click data via composite rewards and adaptive hard negative sampling; and Sigmoid Diversity Beam Search (SDBS) improves intent coverage without sacrificing recall. Offline experiments on large-scale production data show that SPADE outperforms the strongest generative baseline by +5.6% HR@5, -40.3% GeoC@5, and +1.9% Exec@5. A one-week online A/B test confirms production impact: +1.88% Module CTR, +0.177% Search Volume, and +0.105% Search DAU.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2602.00716

Emergence of Distortions in High-Dimensional Guided Diffusion Models

Enrico Ventura, Beatrice Achilli, Luca Ambrogioni, Carlo Lucibello

原文摘要

Classifier-free guidance (CFG) is the de facto standard for conditional sampling in diffusion models, yet it often reduces sample diversity. Using tools from statistical physics, we analyze the emergence of generative distortions induced by CFG, namely the mismatch between the CFG sampling distribution and the true conditional distribution. We study this phenomenon in analytically tractable settings with exact score functions, characterizing its dependence on data dimensionality and the number of classes. For high-dimensional Gaussian mixtures, we use dynamic mean-field theory to show that distortions persist when the number of classes scales exponentially with the data dimension, whereas they vanish in the sub-exponential regime due to a dynamical phase transition. Moreover we found that, in the infinite-class limit, distortions remain unavoidable regardless of dimensionality because of the increasing density of classes. Finally, we show that standard CFG schedules cannot prevent variance shrinkage, and we propose a theoretically grounded guidance schedule incorporating a negative-guidance window that improves both class separability and sample diversity in real-world latent diffusion models.

stat.ML · cond-mat.dis-nn · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04660

Leakage-free evaluation of residual graph convolutional networks for network intrusion detection

Zahid Iqbal

原文摘要

Graph neural networks are increasingly proposed for network intrusion detection, yet many reported results come from protocols in which test flows shape the graph used for training, duplicated flows sit on both sides of the split, or a single split is reported. This study measures what a residual graph convolutional network gains over simpler models once these sources of optimism are removed. CICIDS2017 and NF-UNSW-NB15-v2 were cleaned with label-aware removal of exact duplicates, removing 69% of NF-UNSW-NB15-v2 flows. Flows were linked in a nearest-neighbour graph built from training flows only, and validation and test flows were attached to their nearest training flows at prediction time. A two-layer residual network was compared with eight models under the same settings, including a nearest-neighbour vote over the same graph, gradient-boosted trees and a random forest, over three seeds that each draw a new split, with ablations, leave-one-family-out and feature-noise tests. On CICIDS2017 the residual network reached a binary F1 of 98.11% (SD 0.18) and on NF-UNSW-NB15-v2 90.57% (0.38), indistinguishable from a multilayer perceptron without the graph and from other graph networks. Tree ensembles were 1.48 to 1.57 F1 points higher on CICIDS2017 and led on the area under the precision-recall curve on both datasets; their CICIDS2017 multiclass macro-F1 reached 93.08% against 64.51%, a gap mainly from benign flows the residual network assigned to small attack classes. No ablation, including transductive graph construction, changed F1 beyond seed variation. Under Gaussian feature noise (SD 0.5), the residual network kept an F1 of 96.16%, while XGBoost fell to 0.67%. Under a leakage-controlled protocol, message passing over flow graphs adds no measurable accuracy over a perceptron, and tree ensembles remain stronger on both; the advantage of graph models lies in robustness to feature noise.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.39774

Anchoring the Security of Invariant Quantum Money

Hans Schmiedel, Jiangshan Yu

原文摘要

Prior studies on quantum money from knots, and the invariant money framework that generalizes it, anchor their security on the hardness of path-finding. However, we show that the hardness of path-finding is proven to be necessary for security under certain conditions. In other conditions, this assumption is not a direct anchoring hardness problem such that breaking it allows an adversary to forge a banknote directly. In particular, for quantum money from knots, even when the path-finding assumption does not hold, an adversary still cannot directly forge a banknote. The adversary needs to additionally prepare the uniform superposition over the paths it finds, which we show is as hard as the original problem. Existing security proofs supplement path-finding hardness with knowledge-of-path assumptions. While this combination is sufficient, it may not be necessary. We identify an assumption pair, preparation assumption and transfer assumption, that is both necessary and sufficient for the security of every invariant money scheme. Breaking either assumption directly enables a forgery. The preparation assumption says that no efficient algorithm, given the serial number of a banknote and one object measured from it, prepares a state that verification accepts. While quantum money security gives a forger one quantum state and asks for two that pass, our assumption concerns a problem that takes only classical inputs and asks for one quantum state that passes. This assumption is equivalent to security against forgers that measure their banknote first. Given that its inputs are classical, it is falsifiable and can be attacked and bounded like other computational problems. The transfer assumption covers forgers that keep the banknote coherent, stating that every such forger only has an advantage over the measure-first adversary by at most a polynomial factor.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.22068

COCOTree: A Dataset and Benchmark for Open Tree-Structured Visual Decomposition

Junhyub Lee, Seunghun Chae, Hyosu Kim

原文摘要

We formalize and enable the task of open tree decomposition, which segments an image into hierarchical trees of visual components with unconstrained granularity and flexibility. Specifically, we provide the foundation benchmark for this new paradigm with the following three key contributions. First, we overcome the prohibitively high cognitive and physical bottlenecks of manual annotation by developing a fully automated generation pipeline that synergizes the semantic reasoning of Large Vision-Language Models (LVLMs) with the precise geometric grounding of SAM 3. Second, leveraging this pipeline, we construct COCOTree, a massive-scale benchmark featuring over 21K images and 1.8M structural nodes. By embracing an open-vocabulary space of over 3.5K unique labels, it successfully captures the long-tail distribution of complex physical assemblies. Notably, rigorous human evaluation confirms our generated annotations demonstrate strong alignment with human structural judgment. Third, we establish a standardized evaluation protocol by proposing the Open Tree Quality (OTQ) metric, which jointly assesses mask precision, label accuracy, and structural consistency. We release our dataset and benchmark code at https://github.com/melonkick3090/COCOTree.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04657

Exploiting Biases in Attention for Transformers

Charles Herr, Gabriele Spadaro, Enzo Tartaglione

原文摘要

Attention is known to be sparse in vision transformers. In particular, we analyze the unequal relevance of attention-matrix columns to the final computation and show that this phenomenon becomes increasingly pronounced with network depth. This can be interpreted as saying that tokens have an absolute importance for the attention process, independently of queries. Inspired by this, we propose a reformulation of the attention formula that makes this phenomenon explicit by adding an input-dependent bias to the query-key dot product. We show that this new term gives each token a scalar value indicating its anticipated influence on other tokens during the attention process, and can thus serve as a proxy for pruning attention maps. Across multiple transformer model sizes, this allows us to substantially reduce the computational cost of attention, in terms of both FLOPs and inference latency, while retaining most of the original classification accuracy. We publicly released the code and models at https://github.com/EIDOSLAB/AttentionWithBias.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04654

BhopalMLS: A LiDAR Dataset for Semantic Scene Understanding in Heterogeneous Urban Environments

Pyare Lal Chauhan, Vaibhav Kumar, Bharat Lohani

原文摘要

We introduce BhopalMLS, a large-scale Mobile Laser Scanning (MLS) dataset from a dense, heterogeneous urban environment in Bhopal, India, with mixed traffic, informal structures, and narrow unplanned streets. It spans approximately 10 km of road corridors with over 1.2 billion points and 19,518 annotated instances across 62 fine-grained static categories, grouped into 12-class and 21-class benchmark taxonomies; 6 optional moving-class subclasses support dynamic-object analysis. Existing 3D datasets, collected in well-planned cities, are structurally more uniform than such unplanned settings, and lack their subtype diversity. On semantic and instance segmentation at both granularities, fine-tuned foundation models surpass supervised baselines: Utonia(L) reaches 71.28% mIoU (vs. PTv3's 68.04% from scratch) and 44.07% mAP50 on the 12-class benchmark. Cross-region experiments against four established datasets (Ke-MLS, Toronto-3D, Paris-Lille-3D, SemanticKITTI) reveal a consistent asymmetric transferability: models trained on BhopalMLS transfer better than the reverse on every pair, for both Utonia(L) and Concerto(L) (mean 21.5, up to 36 mIoU points), holding even when the target is denser. Factor-controlled retraining at a fixed label budget, repeated on both models, attributes this to the coverage of BhopalMLS's subtype inventory rather than to dataset size or corridor geometry: collapsing each class to a single subtype costs 13.0 and 13.3 mIoU points for Utonia(L) and Concerto(L). BhopalMLS therefore offers a testbed for 3D perception beyond well-planned cities. Data and an interactive viewer are at https://geoai4cities.github.io/bhopalmls.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.36755

Drag as Evidence: Motion-Grounded Latent Recomposition for Drag-Based Editing

Xinyu Pu, Hongsong Wang, Jie Gui, Pan Zhou

原文摘要

Modern image editors excel at semantic manipulation and visual synthesis, yet remain limited in precise spatial control, motivating the development of drag-based editing. However, existing drag-based methods often struggle to balance drag accuracy with natural, plausible, and intent-aligned generation. We propose MoRe-Drag, a motion-grounded drag-based editing method. Our key insight is to treat pixel-space warping as coarse motion evidence, and to inject this evidence into the generative sampling trajectory. Specifically, MoRe-Drag performs region-aware latent recomposition over refinement, inpainting, and preservation regions, coupled with stage-adaptive conditioning that progressively shifts from motion-grounded structure formation to semantic refinement. We further support a no-manual-instruction interface by adapting the MLLM-based text encoder for drag-aware instruction inference. Experiments on DragBench-SR and DragBench-DR show that MoRe-Drag substantially improves drag precision over strong base editors and achieves superior drag accuracy among SOTA drag-based methods, while delivering strong semantic consistency and visually realistic results. Project page: https://xinyu-pu.github.io/projects/more-drag/.

cs.CV

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arXiv ↗PDF ↗