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arXiv 最近完成扫描：2026-11-16 01:34 · 全站最近入库：2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

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版本更新 · v3arXiv:2604.22904

Triple-Phase Sequential Fusion Network with Contrast Enhancement Kinetics for Hepatobiliary-Phase Liver MRI Synthesis

Qiuli Wang, Xinhuan Sun, Fengxi Chen, Yongxu Liu, Jie Cheng, Lin Chen, Jiafei Chen, Siwei Miao 等 12 位作者

原文摘要

Gadoxetate disodium-enhanced MRI is essential for the detection and characterization of hepatocellular carcinoma. However, acquisition of the hepatobiliary phase (HBP) requires a prolonged post-contrast delay, which reduces workflow efficiency and increases the risk of motion artifacts. In this study, we propose a Triple-Phase Sequential Fusion Network (TriPF-Net) with contrast enhancement kinetics to synthesize HBP images by leveraging the sequential information from pre-HBP sequences: while T1-weighted imaging serves as the indispensable baseline, the model adaptively integrates arterial-phase (AP) and venous-phase (VP) features when available. By modeling the tissue-specific contrast uptake and excretion dynamics across these three phases, TriPF-Net ensures robust HBP synthesis even under the stochastic absence of one or both dynamic contrast-enhanced sequences. In addition, clinical variables, including age, sex, total bilirubin, and albumin, are incorporated to enhance physiological consistency. Compared with conventional methods, TriPF-Net achieved superior performance on datasets from two centers. On the first-center dataset, the model achieved an MAE of 10.65, a PSNR of 23.27, and an SSIM of 0.76. On the second-center dataset, the model was trained independently, and the corresponding values were 12.41, 23.11, and 0.78, respectively. Moreover, lesion-level evaluation showed that the synthesized images achieved contrast comparable to that of real HBP images, with no significant difference in CNR (P = 0.2699). Blinded evaluations confirmed synthesized images were visually comparable to real HBP, with TriPF-Net remaining robust to missing input phases. This flexible solution enhances clinical workflow and lesion depiction, and may also serve as an automated quality control reference to flag real HBP acquisitions that warrant closer inspection.

eess.IV · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.23599

Partition-of-Unity Gaussian Kolmogorov-Arnold Networks

Amir Noorizadegan

原文摘要

Gaussian basis functions provide an efficient and flexible alternative to spline activations in Kolmogorov-Arnold networks (KANs), but their performance is sensitive to the Gaussian scale parameter ε. We introduce the partition-of-unity Gaussian KAN (PU-GKAN), a kernel-based normalization framework that transfers the Shepard partition-of-unity principle to trainable KAN edge functions. Each Gaussian edge basis is normalized by its local sum over fixed centers, preserving the standard edge-based architecture and essentially the same trainable coefficient structure as GKAN. The resulting feature map reproduces constants exactly at the edge level and admits an explicit normalized finite-feature kernel interpretation. Numerical experiments show that PU-GKAN reduces sensitivity to ε, improves validation accuracy for most smooth and moderately non-smooth targets, and provides more stable training behavior. These benefits persist across variations in sample size and number of centers, higher-dimensional architectures, and Matern RBF bases. PU-GKAN also improves physics-informed neural networks for Helmholtz and wave equations, with validation-error reductions of up to 90%. These results establish partition-of-unity normalization as a mathematically grounded and broadly applicable framework for improving the reliability and accuracy of radial basis function KANs.

cs.CE · cs.AI · math.AP

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2508.18741

Statistical Complexity of Soft Bellman Residual Minimization

Enoch H. Kang, Kyoungseok Jang

原文摘要

Offline reinforcement learning and offline inverse reinforcement learning aim to recover near-optimal value functions or reward models from a fixed batch of logged trajectories, yet current practice still struggles to enforce Bellman consistency scalably. Bellman residual minimization (BRM) has emerged as an attractive remedy. While globally convergent gradient-based soft (i.e., entropy-regularized) BRM methods for neural networks have recently been established, their statistical complexity under stochastic gradient descent remains largely unknown. In this paper, we address this theoretical gap for soft BRM. Through a Lyapunov-based analysis under a uniform PL condition in the auxiliary variable, we establish an $\mathcal{O}(1 / n)$ primal argument stability bound up to optimization error and an $\mathcal{O}(1 / n)$ statistical complexity for the soft BRM objective under the stated sampling conditions.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.09123

Look Closer, Align Better: Explicit Visual Attributes for Robust EEG-to-Image Decoding

Ke Xiao, Banghua Yang, Wenbin Guo, Fenqi Rong

原文摘要

EEG-to-image retrieval commonly aligns neural responses with representations from pretrained vision encoders. However, these representations may underrepresent fine-grained visual details needed to distinguish semantically similar images. To address this limitation, we first investigate the EEG decodability and complementary predictive value of explicit visual attributes, identifying visual cues that capture EEG-relevant information beyond pretrained representations. Building on these findings, we propose AttrEcho, a framework that enriches pretrained visual representations with complementary, spatially aligned visual attributes. Specifically, Region-Aligned Visual Attribute Fusion (RAVF) integrates EEG-relevant local visual attributes with spatially corresponding pretrained features through a learnable signed residual gate. We further employ Feature Token Aggregation (FTA), which learns dimension-specific weights across regions to aggregate refined visual features for contrastive alignment with EEG representations. Experiments on THINGS-EEG demonstrate that AttrEcho achieves 94.8% Top-1 and 99.7% Top-5 accuracy in intra-subject 200-way retrieval, while maintaining 82.1% Top-1 accuracy as the gallery expands to 1,600 images with same-concept distractors. AttrEcho also achieves 25.3% Top-1 and 53.0% Top-5 accuracy in inter-subject retrieval. These results highlight the importance of EEG-relevant visual attributes and spatially aligned fusion for fine-grained EEG-to-image retrieval.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.17739

GeneZip: Region-Aware Compression for Long Context DNA Modeling

Jianan Zhao, Xixian Liu, Zhihao Zhan, Xinyu Yuan, Hongyu Guo, Jian Tang

原文摘要

Long-context DNA modeling is computationally expensive, motivating existing approaches to reduce sequence length through uniform downsampling. However, functional information and sequence redundancy are unevenly distributed across the genome, calling for finer resolution in functionally dense regions and stronger compression of redundant sequence. We introduce GeneZip, a region-aware DNA compression framework that learns where to allocate a limited token budget. Building on H-Net-style dynamic routing, GeneZip combines a Region-Aware Ratio objective, which uses regional labels derived from GENCODE gene annotations to specify compression targets during training, with bounded routing for stable token counts. At inference, it compresses raw DNA without annotations. GeneZip provides three main benefits. First, it preserves predictive utility under high compression: GeneZip-70M achieves 137.6-fold compression with a validation perplexity increase of 0.31 relative to uncompressed Mamba2-84M, and the best average rank across four DNALongBench tasks among compression models with leakage-controlled pretraining. Second, it learns region-aware allocation with emergent repeat-aware compression. Alternative regional priors yield task-dependent performance trade-offs. Without repeat-specific supervision, GeneZip compresses transposable-element-derived and tandem repeats more strongly than matched non-repeat sequence; its tandem-repeat compression preference transfers to a nine-species evaluation panel without further training. Third, it enables efficient long-context modeling: on 450-kb inputs, GeneZip-70M achieves 36.1-fold higher inference throughput than JanusDNA-7.7M on a single H100 GPU.

q-bio.GN · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.13804

Attention in Constant Time: Vashista Sparse Attention for Long-Context Decoding with Exponential Guarantees

Vashista Nobaub

原文摘要

Large language models spend most of their inference cost on attention over long contexts, yet empirical behavior suggests that only a small subset of tokens meaningfully contributes to each query. We formalize this phenomenon by modeling attention as a projection onto the convex hull of key vectors and analyzing its entropic (softmax-like) relaxation. Our main theoretical contribution is a face-stability theorem showing that, under a strict complementarity margin (a support gap (Δ) certified by KKT multipliers), entropic attention concentrates on a constant-size active face: the total mass assigned to inactive tokens decays exponentially as (\exp(-Ω(Δ/\varepsilon))), while the error on the active face scales linearly in the temperature/regularization parameter (\varepsilon). This yields a practical criterion for when sparse long-context decoding is safe and provides a principled knob to trade accuracy for compute. Building on these guarantees, we introduce Vashista Sparse Attention, a drop-in mechanism that maintains a small candidate set per query through a paging-style context selection strategy compatible with modern inference stacks. Across long-context evaluations, we observe stable constant-size effective support, strong wall-clock speedups, and minimal quality degradation in the regimes predicted by the support-gap diagnostics. Finally, we discuss deployment implications for privacy-sensitive and air-gapped settings, where interchangeable attention modules enable predictable latency and cost without external retrieval dependencies.

cs.AI · cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.09214

Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability

Qingyue Zhao, Kaixuan Ji, Heyang Zhao, Quanquan Gu

原文摘要

\emph{Kullback-Leibler} (KL) regularization is ubiquitous in reinforcement learning algorithms in the form of \emph{reverse} or \emph{forward} KL. Recent studies have demonstrated $ε^{-1}$-type fast rates for decision making under reverse KL regularization, in contrast to the standard $ε^{-2}$-type sample complexity. However, for forward-KL-regularized objectives, existing statistical analyses are either not applicable or result in $\tilde{O}(ε^{-2})$ slow rates. We take the first step towards addressing this problem via a streamlined analysis of forward-KL-regularized offline CBs. We give the first $\tilde{O}(ε^{-1})$ upper bounds in tabular and general function approximation settings, both under notions of \emph{single-policy concentrability}. In particular, our convex-analytical pipeline unifies these settings by exploiting the pessimism principle in a novel way and completely bypasses the proof routines in previous works based on the mean value theorem, which might be of independent interest. Moreover, we provide rate-optimal lower bounds, manifesting the tightness of our upper bounds in terms of statistical rates. Our lower bounds also demonstrate that the forward-KL-regularized sample complexity recovers the unregularized slow rate in the low-regularization regime, similarly to the reverse-KL regularization.

cs.LG · cs.AI · cs.IT · math.ST · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.22490

Who Pays More for Safety? Measuring the Disparate Cost of Safety Alignment across Languages

Chanwoong Yoon, Jungsoo Park, Alan Ritter

原文摘要

Safety alignment helps models adhere to human values, but it often reduces response utility. We ask a critical but understudied question: Does safety alignment impose the cost equally across language groups? To answer this, we introduce a rigorous protocol to measure the utility loss imposed solely by safety alignment, which we term Safety Cost. Through direct pairwise comparisons between safety-aligned models and their unaligned counterparts, we find a systematic inequity: non-English users consistently bear a higher Safety Cost than English users. We further identify three underlying patterns. First, multiple languages lie in a double-penalty zone, experiencing both weaker safety protection and larger utility loss. Second, certain languages exhibit apparent utility gains that are in fact a consequence of safety filters failing to engage. Third, even high-resource languages pay a larger Safety Cost than English to reach the same level of safety. We show that these disparities arise from both explicit refusals and implicit qualitative differences across multiple dimensions. By accurately measuring the disparate effects of safety alignment, our findings expose a systematic disparity in current safety alignment practices.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.34768

DynEdit: Dynamic Entropy-Guided Sequential Editing for Large Language Models

Jinhu Fu, Yan Bai, Yihang Lou, Li Sun, Jiahong Wu, Xiangxiang Chu, Sen Su

原文摘要

Knowledge editing aims to update outdated or incorrect knowledge in large language models without full retraining. Existing parameter-editing methods remain limited for long-sequence knowledge: single-point editors provide insufficient coverage for extended targets, while fixed chunk-based editors distribute editing effort uniformly across rigid predefined windows, failing to prioritize difficult, information-dense tokens. In this work, we show that token entropy is closely associated with both editing difficulty and semantic importance: high-entropy regions are harder to edit and often contain key factual content. Motivated by this observation, we propose DynEdit, a dynamic framework for long-sequence knowledge editing. DynEdit uses token-level entropy to guide non-uniform sliding, increasing the coverage of difficult high-entropy regions, and further applies entropy-guided token-level optimization to strengthen updates on information-dense tokens within each editing window. Experiments on three benchmarks and two backbone models show that DynEdit consistently outperforms competitive baselines across diverse long-form editing settings, achieving gains of up to +14.0 BLEU points on challenging paraphrase queries and near-saturated semantic scores on several diverse knowledge domains.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.37161HF 社区推荐

WorkFactory: Turning Professional Intents into Executable Work for Scalable Agent Training

Ziyang Zeng, Tingyang Chen, Jia Deng, Qiyao Wang, Shuo Tang, Yike Yang, Tao Ren, Wayne Xin Zhao 等 13 位作者

原文摘要

Scaling supervised fine-tuning for working agents requires not only broad coverage of professional tasks, but also expert demonstrations that faithfully carry out professional workflows to completion. However, missing or inconsistent task information can disrupt expert demonstrations, weakening the resulting supervision. We introduce WorkFactory, a scalable task-construction framework that turns professional intents into executable task environments through hybrid intent sourcing, semantic workflow planning, and execution-based repair. Starting from these intents, WorkFactory constructs a Semantic Workflow Graph that explicitly represents information requirements, dependencies, and locations within the task environment. The graph guides the instantiation of task environments, while an adversarial execution-refinement loop exposes missing or inconsistent information and steers targeted repairs that preserve the original intent. Using WorkFactory, we construct 112,686 tasks spanning 612 occupations and train the WorkPilot family at 4B, 27B, and 35B-A3B scales on the resulting teacher trajectories. All three models improve over their respective base models across four professional-work benchmarks, with WorkPilot-35B-A3B rising from 879 to 1565 Elo on GDPval-AA v2.1. Scaling experiments with up to 110k tasks further show that larger models benefit more from additional task data but exhibit diminishing returns at smaller data scales, while WorkPilot's long-horizon execution evolves from verbose trajectories toward more efficient task completion. Controlled component studies quantify the negative effects of task-environment defects on downstream performance and show how semantic workflow planning and execution-based repair yield downstream gains through improved task-environment quality. The subset of data are available at https://github.com/IQuestLab/WorkFactory.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02267

Overcoming Demand Censoring through Randomization for Risk-Sensitive Newsvendor Learning

Zhengchao Wang, Nam Ho-Nguyen

原文摘要

We study online newsvendor learning with bounded demand, censored observations, and convex nonlinear shortage costs. Because stockouts can conceal the shortage magnitudes needed by gradient-based algorithms, we use randomized ordering to construct unbiased subgradient estimates. We characterize admissible randomization distributions within this framework and develop an online inventory policy that combines the resulting subgradient estimates with gradient-based updates. Our regret analysis quantifies the cost of randomization via the estimation variance and an additional loss term. We characterize the randomization distributions that minimize this objective. For piecewise-linear shortage costs, the optimal distribution is computed through a linear-time recursion; for costs with bounded curvature density, it is characterized by a scalar ordinary differential equation. The policy achieves expected regret of $O(T^{2/3})$ against the best fixed order in hindsight over $T$ periods. Under independent demand and suitable curvature of expected losses, the bound improves to $O(\sqrt{T})$. Matching minimax lower bounds establish rate optimality over the corresponding classes with a fixed marginal-cost bound. Both rates extend to nonperishable inventory under uniformly positive conditional expected demand. Numerical experiments on synthetic and retail data show improvements over alternative policies using censored observations.

cs.LG · math.OC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.34716

From Pixel Generation to Topological Inference: Structural Dual Super-Resolution for Trustworthy Cross-Physical-Domain Trabecular Morphology Learning

Fan Zhang, Yi Zhang, Yuhui Dong, Ling Wang, Xin Xu

原文摘要

Clinical CT and UHRCT cannot resolve individual trabeculae, whereas synchrotron radiation microCT (SRuCT) provides high-resolution references but is not applicable for in vivo imaging. The two domains differ by a 32x resolution gap, are only coarsely paired, and exhibit severe physical differences including partial volume effects, noise, and artifacts. Existing super-resolution networks and pretrained-prior methods (GLEAN/StyleGAN2, Stable SR/LDM) underperform because they target pixel generation---diverse details and SSIM/PSNR---and do not model these physical differences. Pixel generation for a 32x resolution gap is intrinsically ill-posed. We propose a paradigm shift from pixel generation to topological inference: deterministically predicting invariant microstructures from macro-scale low-resolution inputs, evaluated by morphological parameters. The core of our 2D morphology learning lies in training on 2D slices while evaluating on 3D morphological parameters, ensuring that the learned representations capture true three-dimensional trabecular topology rather than 2D pixel statistics. We realize this paradigm via structural dual super-resolution, coupling forward physical degradation (micro-to-macro) with inverse structural inference (macro-to-micro) through structural duality constraints. The method is an end-to-end, few-shot, compact structural dual network (SDN), comprising a bidirectional modeling network, a multi-scale structural consistency discriminator, and four structural duality constraints. On the test set, SDN achieves morphological parameters largely consistent with SRuCT across six metrics, with SSIM reaching 0.8. Trained on 3.2 um SSRF data, the model generalizes well to 3.25 um isotropic BSRF data from an independent source, validating cross-source generalization and confirming trustworthy structural inference rather than pixel generation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.04380

Transferring Visual Explainability from Self-Explaining to Prediction-Only Vision Transformers via Task Arithmetic

Yuya Yoshikawa, Ryotaro Shimizu, Takahiro Kawashima, Yuki Saito

原文摘要

In image classification scenarios where both prediction and explanation efficiency are required, self-explaining models that perform both tasks in a single inference are effective. However, for users who already have prediction-only models, training a new self-explaining model from scratch is costly in both labeling and computation. This study proposes a method to transfer the visual explanation capability of self-explaining Vision Transformer (ViT) models learned in a source domain to prediction-only, zero-shot-capable ViT models based on vision-language models (VLMs) in a target domain via task arithmetic, without target-side explanation supervision. The proposed method endows target models with explanation capability by adding an explainability vector induced by explanation supervision in the source domain based on the task arithmetic framework. Experiments on 10 diverse target datasets show that a single explainability vector learned on ImageNet-1k augmented with patch-level explanation supervision transfers consistently, improving explanation quality while largely preserving classification accuracy. Within-model faithfulness evaluation and comparisons with control vectors (e.g., random vectors of the same norm) show that the transferred explanations are faithful to the model's own predictions and that the effect is specific to the explainability vector. Beyond the transfer itself, we further investigate whether transfer success can be anticipated a priori from model-internal features alone, and find that, for a fixed source, a small set of such features carries non-trivial signal about the transfer outcome on held-out targets. Code is available at https://github.com/yuyay/explainability_vector.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.05243

CORP: Closed-Form One-shot Representation-Preserving Structured Pruning

Boxiang Zhang, Baijian Yang

原文摘要

Transformers achieve strong accuracy but incur high compute and memory cost. Structured pruning reduces inference cost, but most methods rely on retraining or multi-stage optimization, which limits post-training deployment. We propose CORP, a closed-form one-shot structured pruning method that removes MLP dimensions and attention substructures using only unlabeled calibration data without gradients or fine-tuning. CORP formulates structured pruning as a representation recovery problem. It models removed components as affine functions of retained components and derives closed-form ridge regression solutions that fold compensation into model weights. This minimizes a layer-local affine/logit reconstruction objective under the calibration distribution. Experiments on ImageNet with DeiT reveal strong redundancy in both MLP and attention representations. With CORP, models retain high accuracy under aggressive sparsity. On DeiT-Huge, CORP achieves 83.24% Top-1 accuracy after pruning 50% of both MLP and attention structures.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.01563

CHATDIFY: Automating End-to-End Dify Workflow Synthesis from Natural Language via Multi-Agent Collaboration

Tao Fan, Guoqiang Ma

原文摘要

The proliferation of Large Language Models has catalyzed powerful application development platforms like Dify, which enable users to build sophisticated LLM-powered workflows through modular node orchestration. However, manually configuring these workflows requires deep platform expertise and remains time-consuming and error-prone, creating a significant barrier for non-expert users. To address this challenge, we propose \textsc{ChatDify}, a novel multi-agent framework that automates end-to-end Dify workflow synthesis from natural language descriptions. \textsc{ChatDify} employs an Actor-Critic architecture comprising three specialized agents: the \textit{Planner Agent} that decomposes user requests into structured execution plans, the \textit{Executor Agent} that performs context-aware parameter synthesis and schema-augmented DSL compilation, and the \textit{Reviewer Agent} that ensures reliability through hybrid verification with closed-loop refinement. We evaluate \textsc{ChatDify} on a curated benchmark of 100 production-inspired tasks across three state-of-the-art LLM backends, achieving 0.96, 0.91, and 0.75 success rates with Qwen-3.7, GLM-5.1, and DeepSeek-v4, respectively. These results demonstrate its robustness and practical applicability for lowering the technical barrier to creating sophisticated LLM applications.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.13625

How to Interpret Agent Behavior

Jie Gao, Kaiser Sun, Jen-tse Huang, Katherine Van Koevering, Sijie Ji, Heyuan Huang, Weiyan Shi, Zhuoran Lu 等 11 位作者

原文摘要

Autonomous agents such as Claude Code and Codex now operate for hours or even days. Understanding their runtime behavior has become critical for downstream tasks such as diagnosing inefficiencies, fixing bugs, and ensuring better oversight. A primary way to gain this understanding is analyzing the reasoning trajectories and execution traces these agents generate. Yet such data remains in unstructured natural-language form, making it difficult for humans to interpret at scale. We introduce Act*ONOMY, a taxonomy for describing and analyzing agent behavior at runtime. Act*ONOMY has two components: (1) the taxonomy itself, developed through Grounded Theory and structured as a three-level hierarchy of 10 actions, 46 subactions, and 120 leaf categories; and (2) an open repository that hosts the living taxonomy, provides an automated analysis pipeline that applies it to agent trajectory analysis, and defines an extension protocol for customization and growth. Our experiments show that Act*ONOMY can be used to compare behavioral profiles across agents and characterize a single agent's behavior across diverse trajectories, surfacing patterns indicative of failure modes. By providing a shared vocabulary, Act*ONOMY aims to help agent researchers, designers, and deployment engineers interpret agent behavior more effectively and consistently, supporting further study of oversight and control.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.17403

Pseudometric-Weighted Correlation Clustering via Spectral Preclustering

Chenglin Fan, Dahoon Lee, Euiwoong Lee

原文摘要

We study \emph{pseudometric-weighted correlation clustering}, in which pairs have arbitrary positive or negative labels and nonnegative disagreement weights satisfying the triangle inequality. For every fixed $\varepsilon>0$, we give a randomized polynomial-time $(2+\varepsilon)$-approximation, improving the previous factor of $10/3$. The algorithm constructs an explicit feasible solution to the cluster LP with polynomial support and value at most $(1+\varepsilon)\mathrm{OPT}$, where $\mathrm{OPT}$ is the minimum integral clustering cost. Our main contribution is a weighted preclustering construction that witnesses a near-optimal clustering while bounding the total weight of admissible edges by $\operatorname{poly}(1/\varepsilon)\mathrm{OPT}$. We obtain admissibility certificates from random walks, where the factor-two Ptolemy's inequality for pseudometrics reduces the required length of walks from $O(\log n)$ to a constant. We then bound the rounding error separately at each vertex and use the triangle inequality to control its weighted cost. Sampling and coverage corrections produce an exactly feasible solution, and the factor-two rounding completes the approximation algorithm.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.15234

Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays

Hanbin Ko, Gihun Cho, Inhyeok Baek, Donguk Kim, Joonbeom Koo, Changi Kim, Dongheon Lee, Chang Min Park

原文摘要

Multimodal learning from paired medical images and clinical text is a central challenge in medical data-driven informatics, where effective cross-modal alignment is critical for scalable analysis and retrieval. In chest radiography, vision-language pretraining is constrained by heterogeneous radiology reports that contain abbreviations, impression-only notes, and institution-specific writing styles. Unlike general-domain settings, naively aggregating large collections of noisy reports can plateau or even degrade multimodal learning when reporting styles differ substantially. We propose a domain-adapted bidirectional large language model text encoder for chest radiograph reports, trained with masked token prediction and supervised contrastive learning on stylistically diverse but clinically equivalent report variants to produce robust, generalizable text embeddings. We then integrate this encoder into a dual-tower contrastive vision-language framework using parameter-efficient adaptation to improve image-text alignment. Across 1.6 million paired studies from public datasets and a de-identified hospital cohort, the proposed models improve bidirectional retrieval accuracy and external generalization, achieving GREEN scores of 0.308 on MIMIC-CXR and 0.618 on Open-I, while reducing the degradation observed when abbreviation-rich, impression-only hospital reports are added to training.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2605.27991

Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning

Minhao Yao, Ruoyu Wang, Xihong Lin, Lin Liu, Zhonghua Liu

原文摘要

Gradient-flow optimization is usually viewed as an algorithmic procedure for minimizing empirical loss, with training duration selected by validation or heuristic early stopping rules. We develop a statistical inference framework for gradient-flow training. We show that whenever fitted values evolve through a time-invariant positive semidefinite training operator, the output at each time is equivalent to the best linear unbiased predictor under a corresponding random-effects model. Training time then becomes a variance-component parameter governing variance reallocation from residual noise to structured signal. This turns two training decisions into inferential problems: whether training is needed becomes a variance-component test for signal beyond initialization, and how long to train becomes restricted maximum likelihood (REML) estimation of the training-time variance component. We show that the REML-guided early stopping rule selects the time at which optimized spectral losses become decorrelated from the training-operator eigenvalues. The asymptotic prediction optimality of the REML-guided early stopping time is established for fixed-design in-sample risk and random-design out-of-sample risk. Deep learning models in fixed-kernel gradient regimes provide canonical instantiations for our results. Numerical experiments and a UK Biobank proteomics application show competitive accuracy of the REML-guided early stopping time with reduced reliance on validation splits and repeated checkpoint evaluation.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2412.19439

Paleoinspired Vision: From Exploring Colour Vision Evolution to Inspiring Camera Design

Yijie Lu, Zhimin Zong, Junjie Zhang, Shenghan Su, Zeyun Zhao, Ziteng Cui, Yan Pu, Jing Lu 等 11 位作者

原文摘要

The evolution of colour vision is captivating, as it reveals the adaptive strategies of extinct species while simultaneously inspiring innovations in modern imaging technology. In this study, we present a simplified model of visual transduction in the retina, introducing a novel opsin layer. We quantify evolutionary pressures by measuring machine vision recognition accuracy on colour images shaped by specific opsins. Building on this, we develop an evolutionary conservation optimisation algorithm to reconstruct the spectral sensitivity of opsins, enabling mutation-driven adaptations to more effectively spot fruits or predators. This model condenses millions of years of evolution into seconds using GPU, providing an experimental framework to test long-standing hypotheses in evolutionary biology, such as vision of early mammals, primate trichromacy from gene duplication, retention of colour blindness, blue-shift of fish rod and multiple rod opsins with bioluminescence. Beyond validating existing paleontological hypotheses, our model further enables speculative explorations of hypothetical species, such as organisms with visual systems adapted to Martian conditions. These constitute our contributions to AI for life science and neuroscience. Besides, the principles derived from these biological studies can translate into practical engineering solutions. In practice, our findings suggest a minimalist yet effective approach to task-specific camera filter design, optimising the spectral response function to meet application-driven demands. Code repository: https://github.com/luyijie-research/PaleVision.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2512.00053

Ten-Four: An Open-Source Fused Dot Product Unit for Mixed-Precision GPGPU Tensor Cores

Nikhil Rout, Blaise Tine

原文摘要

Efficient mixed-precision MMA operations are critical for accelerating deep learning workloads on GPGPUs. However, existing open-source GPGPU Tensor Cores rely on discrete arithmetic units causing high latency, accumulated rounding errors, and poor resource utilization. To address these challenges, we propose Ten-Four, a configurable mixed-precision fused dot product (FEDP) unit microarchitecture implemented as part of the open-source RISC-V Vortex GPGPU's Tensor Core Unit extension. It supports low-precision multiplication in TF32, FP16, BF16, FP8, BF8, INT8 and INT4 with FP32/INT32 accumulation, native OCP Microscaling (MX), and sparse lane clock-gating for dynamic power reduction, while matching NVIDIA Tensor Core numerical accuracy. Ten-Four achieves 4-cycle latency at 300 MHz on the AMD Alveo U55C FPGA, delivering 2.7$\times$--7.9$\times$ speedup over Berkeley HardFloat and FPnew at less than 60% the area cost. We further demonstrate its format extensibility by prototyping LNSF4, an NVFP4-variant matching its perplexity with 6.7% fewer LUTs and 11.1% lower dynamic power.

cs.AR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29061

OccJEPA: Occlusion as Masking for Amodal Video Segmentation

Genpei Zhang, Runmin Jiang, Xingzu Zhan, Yibo Zhong, Ruien Li, Zhou Zhang

原文摘要

Amodal video segmentation asks for the complete mask of an object in every frame, including the parts hidden behind other objects. The best current methods fine-tune video diffusion models to generate these masks; they need a depth network and tens of denoising steps, and their outputs vary from sample to sample. Self-supervised video world models of the JEPA family are pre-trained on a closely related task, predicting the representations of masked space-time regions from the visible ones, yet their predictor is usually discarded after pre-training. We reuse it by treating occlusion as masking. Tokens not covered by the object's visible mask are dropped, the V-JEPA 2.1 predictor is fine-tuned to fill them in, and a lightweight decoder turns all features into amodal masks. The resulting model, OccJEPA (Occlusion-as-masking JEPA), is deterministic and 32x faster per frame than Diffusion-VAS, the diffusion-based state of the art. It is also more accurate on SAIL-VOS (+3.70 occluded-region mIoU over all occluded frames), on TAO-Amodal (+4.00 AP50 on a held-out test split, +4.32 with SAM2-tracked masks) and on Kubric. Controlled experiments trace most of the gain to the predictor's pre-trained weights rather than its capacity, and the predictor helps at every encoder size tested (87M to 1.85B parameters).

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.37364HF 社区推荐

EmbeddingGemma 2: Unified and Efficient Multimodal Representations

Henrique Schechter Vera, Sahil Dua, Jovan Andonov, Sara Smoot, Sebastian Russo, Ivan Llanos, Max Matveev, Jae Hyeon Yoo 等 77 位作者

原文摘要

We present EmbeddingGemma 2, an open multimodal embedding model which natively represents text, images, audio, video, and combinations thereof in a unified space. Our approach introduces improvements such as simplified per-layer embeddings and a multi-stage curriculum with modality and objective progression which ensure strong quality across unimodal, cross-modal, and mixed-modal evaluations. Notably, in comprehensive embedding benchmarks across all modalities, EmbeddingGemma 2 establishes a new Pareto frontier, outperforming both specialist models and models two to twelve times larger. Furthermore, our design offers flexibility for resource-constrained environments, supporting selective modality encoder loading, adjustable vision token budgets, truncatable embedding dimensions, and quantized weights. We present ablations analyzing key decisions and release EmbeddingGemma 2 to promote further research.

cs.LG · cs.CL · cs.CV · cs.SD

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.30631

Large language models show systematic failures in incorporating evolving medical knowledge

Min Zeng, Zaifu Zhan, Kai Yu, Rui Zhang

原文摘要

Medical knowledge evolves, yet whether large language models (LLMs) faithfully incorporate revisions into clinical recommendations remains unclear. We introduce ChronoEvo, comprising 1,709 recommendation-level revisions across 12 forms of medical knowledge change and 275 guideline lineages, to distinguish recognition of revised guidance from its full incorporation. With updated guideline evidence explicitly provided, revision recognition exceeded full incorporation across 14 models, with a 20.1-percentage-point gap in the strongest model. For Qwen3-4B, 90.8% of responses recognized revisions, 80.2% were clinically correct, and 58.5% fully incorporated them, yielding a 32.3-percentage-point gap. Two clinicians' adjudicated assessment of 300 responses yielded a 40.3-percentage-point gap in this enriched validation sample; 61 of 144 responses with incomplete incorporation were clinically incorrect. Prompting Qwen3-4B to prioritize revised evidence reduced outdated-content retention and improved clinical correctness without significantly improving full incorporation. These findings identify faithful incorporation of revised guidance as a distinct challenge for clinical LLM reliability.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.03180

GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations

Jonggwon Park, Seongeun Lee, Junhyun Park, Hannah Yun, Hyunwoong Kim, Sohyun Jeong, Hyewon Kang, Byungmu Yoon 等 9 位作者

原文摘要

Vision-language models (VLMs) for radiology have emerged as a scalable paradigm by leveraging image-report pairs naturally produced in clinical workflows. However, this pairing reveals a mismatch in scale: each finding occupies only a small region of the image, yet supervision is provided only at the global image-report level. This poses a central challenge: prior approaches spread weight densely across all patches rather than concentrating on the sparse subset relevant to a given query. To address this, we present GLINT (Gated Language-Image alignmeNT), a framework that models this sparse correspondence. On the alignment side, we introduce Sparsely Gated Alignment, a novel architecture in which a sigmoid gate over a separate gate embedding space activates only the patches relevant to each textual query, yielding sparsity. On the representation side, we add Dense Feature Regularization, which anchors the trainable encoder's intermediate features to a frozen self-supervised learning (SSL) teacher, preserving the fine-grained patch features that the gate relies on. The same recipe applies to both 2D chest X-ray (CXR) and 3D chest computed tomography (CT), built with DINOv3 and V-JEPA 2.1, respectively. GLINT enables zero-shot classification, grounding, and segmentation from free-text queries, and to our knowledge is the first to demonstrate zero-shot segmentation on 3D CT volumes without mask supervision. Notably, the most pronounced gains arise on zero-shot grounding and segmentation, where sparse, query-specific localization is required, consistent with our design intent. In downstream evaluation, GLINT outperforms both SSL encoders and medical VLMs on classification, report generation, and segmentation. Code is available at https://github.com/deepnoid-ai/GLINT.

cs.CV · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2512.00727

Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning

Sizhe Wei, Xulin Chen, Fengze Xie, Garrett Ethan Katz, Zhenyu Gan, Lu Gan

原文摘要

Reinforcement learning has enabled impressive locomotion skills on articulated robots, but common policy representations remain only weakly aligned with robot physics. Generic networks ignore kinematic structure, while graph-based policies encode connectivity without specifying how physical quantities transform across symmetric body parts. We introduce a morphological symmetry graph representation for locomotion policy learning, instantiated as MS-PPO with PPO on Go2 and MS-SAC with FastSAC on G1. Starting from the robot's topological graph, our representation explicitly incorporates the permutations and sign changes induced by morphological symmetry on observations and actions, yielding a graph actor with an equivariant action mean and an invariant critic. We evaluate the representation on Unitree Go2 command tracking and asymmetric joint-failure tasks, and on Unitree G1 29-DoF whole-body locomotion, including zero-shot sim-to-real deployment. Our approach achieves the lowest tracking errors in both simulation and hardware, and the highest mean simulation returns across all three joint-failure types on Go2 among the evaluated baselines. On Go2, MS-PPO uses only 34.6% of the parameters of the PPO baseline. These results support the effectiveness of jointly encoding topology and morphological symmetry across robot embodiments and learning frameworks.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2410.03756

The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability

Judah Goldfeder, Victoria Dean, Zixin Jiang, Xuezheng Wang, Bing dong, Hod Lipson, John Sipple

原文摘要

Commercial buildings account for 17\% of U.S. carbon emissions, with roughly half of that from Heating, Ventilation, and Air Conditioning (HVAC). HVAC devices form a complex thermodynamic system, and while model predictive control and reinforcement learning have been used to optimize control policies, scaling to thousands of buildings remains a significant unsolved challenge. Most current approaches are over-optimized for specific buildings and rely on proprietary data or hard-to-configure simulators. We present the Smart Buildings Control Suite, the first open source interactive HVAC control benchmark with a focus on solutions that generalize across building. It has 3 components: real-world data from 11 buildings over 6 years, a lightweight data-driven simulator, and a modular Physically Informed Neural Network (PINN) building model. The buildings span multiple climates, management systems, and sizes, and both the simulator and PINN easily transfer to new buildings, ensuring solutions using this benchmark are robust to these factors and only reliant on fully scalable building models. This represents a major step towards scaling HVAC optimization from the lab to buildings everywhere. To facilitate use, our benchmark is compatible with the Gym standard, and our data is available at our webpage. Code at https://github.com/google/sbsim

cs.AI · cs.CY · cs.DC · cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.11885

Flash-Fusion: Fast, Expressive Queries over Sensor Streams with LLMs

Kausar Patherya, Ashutosh Dhekne, Francisco Romero

原文摘要

Pervasive sensor deployments have generated significant interest in IoT data analysis across fields such as healthcare, transportation and urban planning. At the same time, the rise of Large Language Models (LLMs) offer a new tool for exploring IoT data through their expressive, natural language interface. Unfortunately, natural language querying of IoT sensor streams is impractical today: raw telemetry exhausts LLM context windows in fractions of a second at scale, incurs prohibitive per-token costs, and produces hallucinated responses. Existing systems work around these limits with lossy text summarization or unconstrained code synthesis, producing expensive, unverified computations. Multi-step queries compound this problem: logical errors in intermediate operations can derail the final answer. We present Flash-Fusion, an agentic framework for natural-language querying of tabular IoT data. Flash-Fusion identifies which available data fields and supported derivations can answer the user's query, constructs a plan using typed, domain-specific operators, and validates its structure and schema compatibility before executing. Because constructing plans still requires costly LLM reasoning, Flash-Fusion caches validated operator sequences for reuse across compatible queries. A smaller LLM adapts a cached plan's parameters to each query, cutting latency and cost. We evaluate Flash-Fusion across human activity recognition, ECG monitoring, and vehicular telematics, covering direct lookups, multi-step reasoning, predictive tasks, and out-of-scope requests. Flash-Fusion achieves 92.2% query accuracy versus 76.1% for ReAct, the strongest baseline, while completing queries 7x faster and consuming 5x fewer tokens. Flash-Fusion enables users across disciplines to explore IoT data without manually constructing queries.

cs.DC · cs.AI · cs.DB

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29153

Cycle structure of best-response dynamics in random bimatrix games

Georgios Nikolaos Karelas, Gábor Lugosi, Rui-Ray Zhang

原文摘要

We study the best-response dynamics in games with random payoffs. We consider two-player bimatrix games, with $n$ strategies each. We assume that payoffs come from a continuous distribution, and study the best-response dynamics of these games, after imposing some structure on their payoff matrices. In particular, we focus on symmetric games, antisymmetric games, common-interest games, zero-sum games, and games with independent payoffs. When two players take turns in playing the best response to the opponent's action, they eventually end up cycling through the same sequence of actions. The main focus of the paper is the cycle structure of large random games. In particular, we study the number of cycles, the number of cycles of a certain length, the length of the longest cycle, and related quantities. We show that the structure of the game has an important impact on the typical cycle structure. Our arguments are based on a representation of the best-response dynamics of these games as directed bipartite graphs, and on the cycle structure that arises in them.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.16121

Double circulant LCD codes over $\mathbb{F}_2+u\mathbb{F}_2$ and their related binary LCD codes

Xingxing Xu, Bo Wu, Jon-Lark Kim

原文摘要

Linear complementary dual (LCD) codes are linear codes that intersect their dual trivially. We study double circulant (Euclidean) LCD codes over the finite chain ring $R=\mathbb F_2+u\mathbb F_2$, where $u^2=0$. Using the Chinese Remainder Theorem and the factorization of $x^n-1$, we derive an explicit enumeration formula, with a separate treatment of the constituent corresponding to $x-1$, which is not obtained by a formal specialization of the previously known counting formula over $\mathbb F_q[u]/(u^k)$. Assuming Artin's primitive root conjecture for $2$, we show that the binary Gray images contain an asymptotically good family of rate $1/2$ which attains the asymptotic Gilbert--Varshamov bound, i.e., whose relative distance can approach $H_2^{-1}(1/2)$ from below. More importantly from the constructive viewpoint, by combining Gray images with a known extension construction, we obtain a binary LCD code with previously unknown parameters $[29,15,7]$ and construct two explicit permutation-inequivalent binary LCD codes with parameters $[45,23,9]$ and nine binary LCD codes with the best known parameters.

cs.IT · math.RA

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arXiv ↗PDF ↗