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新论文 · v1arXiv:2611.15614

Learning-Based Interpolation of Detector Gaps in Photon-Counting X-ray Imaging

Johannes B. Thalhammer, Patrick Ilg, Tina Dorosti, Sebastian Peterhansl, Theresa Hiu, Daniel Frey, Zijin Huang, Florian Schaff 等 10 位作者

原文摘要

Image detectors assembled from tiled modules have physical gaps in the field of view that must be filled before downstream processing such as phase retrieval or tomographic reconstruction. Unlike natural-image inpainting, detector-gap interpolation must leave all other pixels unmodified and preserve the intensity statistics of the filled region, since even a small shift in mean intensity can cause ring artifacts after reconstruction. We adapted two generative models, a Wasserstein GAN with gradient penalty (WGAN-GP) and a conditional Pix2Pix model, to this task. We implemented a masking strategy that restricts all changes to the gap pixels and loss terms that constrain the mean intensity and the intensity distribution of the filled region. Both models were trained on nanoCT projections with simulated gaps and compared with linear interpolation, Navier--Stokes inpainting, and the deep image-inpainting network MADF, quantitatively on simulated gaps and visually on real detector gaps. WGAN-GP and Pix2Pix achieved the best perceptual similarity (LPIPS) and histogram distance and, apart from linear interpolation, the shortest inference time. Pix2Pix required about five times fewer training GPU-hours than WGAN-GP. Ablation studies showed that masking the network output contributes by far the most to plausible inpainting results. Applied to sinograms, Pix2Pix produced the most natural gap filling but improved the reconstruction only slightly compared with linear interpolation. The source code and the weights of the final Pix2Pix model are available at https://github.com/J-3TO/detectorGapRemoval_Pix2Pix.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.17100

The SAGES Critical View of Safety Challenge: A Global Benchmark for AI-Assisted Surgical Quality Assessment

Deepak Alapatt, Jennifer Eckhoff, Zhiliang Lyu, Yutong Ban, Jean-Paul Mazellier, Sarah Choksi, Kunyi Yang, Po-Hsing Chiang 等 41 位作者

原文摘要

Advances in artificial intelligence (AI) for surgical quality assessment promise to democratize access to expertise, with applications in training, guidance, and accreditation. This study presents the SAGES Critical View of Safety (CVS) Challenge, the first AI competition organized by a surgical society, using the CVS in laparoscopic cholecystectomy, a universally recommended yet inconsistently performed safety step, as an exemplar of surgical quality assessment. A global collaboration across 54 institutions in 24 countries engaged hundreds of clinicians and engineers to curate 1,000 videos annotated by 20 surgical experts according to a consensus-validated protocol. The challenge addressed key barriers to real-world deployment in surgery, including achieving high performance, capturing uncertainty in subjective assessment, and ensuring robustness to clinical variability. To enable this scale of effort, we developed EndoGlacier, a framework for managing large, heterogeneous surgical video and multi-annotator workflows. Thirteen international teams participated, achieving up to a 17% relative gain in assessment performance, over 62% reduction in calibration error, and a 17% relative improvement in robustness over the state-of-the-art. Analysis of results highlighted methodological trends linked to model performance, providing guidance for future research toward robust, clinically deployable AI for surgical quality assessment.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03685

LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends

Zhaojian Yu

原文摘要

Visual agents need to coordinate reasoning and action while their environment evolves during inference. We introduce LOLPlay, a benchmark in native League of Legends with 36 champion-specific tasks spanning resource management, combat, cooperation, and objectives. Agents interact through screenshots and mouse-keyboard actions; automated initialization and 82 executable subgoal checks distinguish partial progress from completion. Across 324 task evaluations of three models at three reasoning-effort settings, GPT-6 Astra achieves the highest task success rate of 25.0% at low and medium effort, and the highest subgoal success rate of 40.3% at medium effort. However, high effort improves neither metric over low effort for any model. For Astra, longer requests leave fewer interaction rounds, suggesting a cost to additional reasoning in real-time control. Trajectories reveal incomplete subgoal composition and actions executing after the observed scene changes, motivating agents that combine visual reasoning with timely action.

cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15610

Contrastive Bootstrap Representation Learning for Label-Efficient Agricultural Image Classification

Hazem Ahmed, Ali Hamdi, Ammar Mohammed

原文摘要

Expert annotation remains a major bottleneck in agricultural image classification, where collecting images is often easier than obtaining reliable labels from domain specialists. Self-supervised learning can reduce this dependence by pretraining transferable representations on unlabelled imagery. Contrastive and bootstrap methods provide two established but distinct training signals, motivating an empirical study of a formulation that applies both signals within one representation path under severe downstream label scarcity. We present Contrastive Bootstrap Representation Learning (CBRL), which jointly optimises SimCLR-style NT-Xent contrastive learning and bidirectional BYOL-style exponential-moving-average target prediction within a shared online projection space. Using a ViT-L/16 backbone and 10% of the downstream labels, we report single-seed point estimates on four primary agricultural test benchmarks plus an exploratory 33-image New Plant Diseases evaluation. CBRL records the highest macro-F1 point estimate on the four primary benchmarks. On DeepWeeds, its 79.77% test macro-F1 is 32.29 points above the random-initialisation, label-only baseline and 15.78 points above the next-highest comparison point estimate; on PlantVillage, its 98.14% macro-F1 is 10.43 and 0.43 points higher, respectively. On PlantDoc, however, all methods fail to reach practically useful performance. These results identify the evaluated joint formulation as a promising candidate for label-limited transfer, but they do not establish objective synergy, statistical superiority, or robustness across stochastic runs.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03776

Proceedings of the CSCW 2026 Workshop on Women's Health as Cooperative Work: Participatory AI Across Care, Community, and Policy

Sherilyn Francis

原文摘要

This submission indexes contributions to the CSCW 2026 workshop, "Women's Health as Cooperative Work: Participatory AI Across Care, Community, and Policy," held on October 11, 2026. The workshop examines women's health as cooperative work spanning patients, caregivers, clinicians, communities, institutions, and digital technologies. Contributions explore participatory AI, coordination breakdowns, the distribution of care labor, governance, decision-making authority, and accountability across care, community, and policy contexts. The index provides paper titles, authors, affiliations, thematic areas, and unique report numbers to support discovery of the corresponding individual arXiv submissions. The workshop proposal is separately published under DOI 10.1145/3785651.3818821.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15609

GLCR: Global-Local Collaborative Routing for Multi-Task Electromagnetic Signal Processing

Fang Lu, Yao Lu, Zhuangzhi Chen, Sicheng Zhang, Shanqing Yu, Yun Lin, Qi Xuan, Guan Gui

原文摘要

Deep neural networks for electromagnetic signal processing are typically developed as single-task experts with fixed inputs and objectives, limiting cross-task transferability. Electromagnetic foundation models offer broader task coverage but incur substantial pretraining and inference costs. To balance accuracy and efficiency, we adopt model routing to dynamically dispatch unknown signals to matched experts within a heterogeneous pool. We construct HeteroSignal-7, a multi-task routing benchmark with 140,000 samples from seven datasets covering six task types, paired with seven classification and waveform-recovery experts and a unified evaluation protocol. We then propose GLCR, a two-stage routing method combining global dispatch with local discrimination of similar tasks. First, a lightweight one-dimensional convolutional neural network (CNN) with an additive-margin head predicts one of seven routing classes. Second, a gradient-boosted-tree specialist uses four physical statistics to re-evaluate samples whose top two candidates match a confusion pair automatically identified from validation errors. The pipeline is trained across the full signal-to-noise ratio (SNR) range and requires neither SNR labels nor noise estimation at inference time. GLCR outperforms all 19 baselines, achieving 96.16% routing accuracy and a 76.30% system score, exceeding the respective second-best baselines by 1.16% and 1.14%. It achieves the lowest routing latency among the compared methods at 1.43 ms per sample and reduces expected overall system FLOPs by 5.9 times compared with executing all experts. This work provides a public evaluation platform and an efficient, reproducible baseline for heterogeneous expert routing in electromagnetic signal processing.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15607

FADO: Learning Causal Explanations with Prior-Fitted Networks

Seth Flaxman, Jess Carr, Annabel Jakob, Marek Masiak, Dino Sejdinovic

原文摘要

In regression and classification tasks with tabular datasets, most of the literature on explanation in ML (e.g. SHAP, permutation importance) has focused on predictive attribution: does feature $X_i$ help predict outcome $Y$? At its heart, predictive attribution captures correlation, not causation. We instead ask a causal question: would manipulating feature $X_i$ change outcome $Y$? We define a natural quantity to answer this question, the feature intervention effect: $Δ_i = E[Y\mid do(X_i=+1)]-E[Y\mid do(X_i=-1)]$. In general, estimating this quantity from observational data alone would entail strong causal assumptions. We propose FADO (Feature-wise Amortized Do-Operators), an amortized inference method using prior-fitted networks trained on synthetic tabular datasets generated by structural causal models. During training, the synthetic data-generating process is known, so we can compute ground-truth intervention effects $Δ_i$, and train a model to predict them at test time from a purely observational tabular dataset. As we demonstrate through extensive experiments, our new causal explainability method is easy to train and provides better explanations under a range of settings for which standard predictive attribution fails, including proxy variables, confounders, colliders, and placebos. We evaluate it on real, synthetic and semi-synthetic datasets, with and without hidden confounders, and compare it to existing predictive attribution methods and state-of-the-art causal inference methods.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v3arXiv: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:2610.11482

Evaluating Local Language Model Agents for Data Engineering: An Empirical Software Engineering Study of Mobility Workflows

Jorge García-Carrasco, Javier Sanchis, Alejandro Reina-Reina, Alejandro Maté, Juan Trujillo

原文摘要

Context: Large language model (LLM) agents are increasingly used as software and data-engineering assistants, yet evidence about locally deployable open-weight agents remains limited. Existing evaluations often emphasise textual responses or isolated code rather than complete engineering artifacts. Objectives: We evaluate whether local LLM agents can produce correct and reliable data-engineering artifacts, isolate the contribution of a closed-loop workspace and of input-structure knowledge, and examine trade-offs in runtime, cost, and failure behaviour. Methods: We introduce a benchmark of fifteen mobility data-engineering tasks with deterministic checkers for the scripts, tables, structured files, figures, and reports they produce. Sixteen configurations from six model families are evaluated in three conditions (chat-only, chat-schema, agent-with-tools) with five repetitions per model, condition, and task, yielding 3,600 scored attempts on a consumer-grade GPU. Results: A 4-billion-parameter model at 4-bit reaches 77.3%, a 9-billion-parameter model 80.0% within about 6.5 GB, and a 12-billion-parameter model from another family 81.3%, against 98.7% for the highest-scoring configuration, which solves 14 of 15 tasks in all five repetitions. Above two billion parameters the workspace loop raises pass rates by 24.0 to 65.3 points over uninformed one-shot generation, and its contribution exceeds that of the input-structure summary in eleven of fifteen tasks. Conclusion: Open-weight models that fit on one consumer GPU support a meaningful share of data-engineering work once given a workspace loop, although capability still separates usable from unusable configurations and high-stakes outputs need validation. Being mobility-specific and checker-based, the benchmark characterizes the evaluated configurations, not all data-engineering practice.

cs.SE · cs.AI · cs.DB · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.03217

An Asymptotic Theory of Chain-of-Thought in In-Context Learning

Kaito Takanami, Cengiz Pehlevan

原文摘要

Chain-of-thought (CoT) reasoning has become a widely used mechanism for eliciting multi-step reasoning in large language models by generating intermediate reasoning steps at inference time. Yet the scaling behavior of generalization with CoT depth remains poorly understood. To address this question, we study a theoretically solvable model of CoT for in-context weight prediction in linear regression, where test-time reasoning is represented as an iterative refinement of the weight-parameter estimate. Using tools from random matrix theory under high-dimensional asymptotics, we derive an exact formula for the generalization error as a function of reasoning depth, pretraining data amount, and context length. Our analysis reveals a sharp phase transition separating exponential and polynomial improvement, saturation, and overthinking, and characterizes how the optimal reasoning depth scales. We further show that deeper reasoning is most effective with sufficiently rich pretraining and in-context information, whereas limited pretraining or context makes longer reasoning prone to error amplification or saturation. We also validate these predictions through experiments on fully learned linear attention and softmax attention models. Our results provide a unified theoretical account of how test-time CoT depth affects generalization.

stat.ML · cond-mat.dis-nn · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15603

Are Scaling Laws of Universal MLIPs Universal?

Theo Keane, Anthony Onwuli, Richard Tomsett, Robert M. Forrest, Jonathan J. Bean

原文摘要

Neural scaling laws enable the prediction of returns from larger training datasets, but machine-learned interatomic potential (MLIP) datasets are heterogeneous and expensive to generate, and equal numbers of structures need not contain equal amounts of useful information. We study data scaling under fixed model configurations for M3GNet, MACE, and PET trained on subsets of a combined MatPES-MP-ALOE training pool. Across training structure subsets from 10,000 to 640,000, together with the full 1.16-million-structure training set, we compare uniform random sampling, three chemistry- and structure-informed stratification policies, iterative DIRECT, and furthest-point sampling using embeddings from three pretrained MLIPs. Models are evaluated using cohesive-energy, force, stress, and aggregate losses on both a held-out test set from the parent distribution and the distribution shifted MP-r$^2$SCAN test set. We observe power-law scaling improvements with increasing data, but the fitted scaling exponent, apparent error floor, and relative sample efficiency depend on the model architecture, target quantity, data sampling policy, and test set distribution. Sampling-policy rankings can reverse across test distributions: chemical stratification can improve cross-dataset performance while degrading in-distribution performance, whereas the embedding-space diversity policies frequently under-perform random sampling. Relative loss is strongly associated with test-to-train chemical-space divergence. These results show that structure count is a necessary but insufficient scaling variable for MLIPs. For training MLIPs, the return on additional first-principles calculations depends not only on how many additional structures are included, but also on how their chemical and configurational content aligns with the model's intended use.

cs.LG · cond-mat.mtrl-sci · cs.AI · physics.chem-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15602

CoCode-World: Shared 3D Proxy Representation for Multi-Player World Models

Qirui Li, Xin Zhang, Yabo Chen, Jinkun Hao, Paul L Rosin, Yu-Kun Lai, Haibin Huang, Yin Chen 等 10 位作者

原文摘要

Multiplayer generative world models must produce observations that agree on shared entities despite different camera motions and occlusions. Explicit world representations provide spatial control, but precise modeling of individual game maps limits scene diversity, while shared geometry alone cannot ensure consistent appearance across views. We present CoCode-World, a shared 3D proxy framework that separates structural control from visual synthesis for two-player video generation. Programmable proxies specify scene layout, motion, and occlusion, while RGB references guide character and scene appearance. To coordinate visual details across players, we concatenate paired proxy videos, appearance references, and target views in a consistent spatial layout and generate both views jointly with a pretrained video model. This formulation uses the model's native attention for cross-view communication within a single generation branch, with LoRA adaptation learning proxy adherence and appearance correspondence from paired observations. We construct a two-player GTA~V dataset that pairs synchronized RGB observations with proxy videos derived from shared map geometry and game-engine state. Experiments demonstrate appearance transfer to characters and scene styles unseen during training, with improvements in video quality and cross-view player identity over the evaluated baseline. Ablations further examine how proxy construction and cross-view communication affect generation. Project page: https://iapple233.github.io/CCW/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15601

Cat Royale Comprehensive Dataset: A Dataset for Pose Detection, Segmentation, Animal Action Recognition and Scene Understanding

Zane K. J. Hartley, Joseph Ho, Alan Chamberlain, Michael P. Pound, Steve Benford

原文摘要

Animal focused computer vision datasets are of a growing interest in the fields of both Computer Vision and Animal behaviour. The ability to make sense of complex scenes via a variety of Computer Vision tasks has substantial implications for behavioural understanding and welfare. The perfection of these tasks will help lead to better health and emotional outcomes for wild and domestic animals. In this work we present the Cat Royale Comprehensive Dataset (CRCD), a large, comprehensive, multi-modal dataset for feline understanding, based on a huge database of over 500 hours of video captured during the Cat Royale artwork and research project. Our dataset contains annotations for a range of computer vision problems including 20,000 images with instance segmentation masks, 5000 images with multi-subject pose coordinates, over 800 video clips with action classes, and over 800 clips with detailed text description. This is the first such dataset to link this wide range of annotation types together for multi-modal use. We release this entire dataset, and our custom annotation tools will also be made available, in order to allow for ongoing expansion. We also provide a series of benchmarks for the tasks of Instance Segmentation, Pose detection, Temporal Action Localisation, and finally video-text retrieval. The totality of this multi-modal dataset is the data needed to solve both these individual computer vision tasks as well as enabling more complex multi-modal tasks making use of a combination of annotations.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15600

MemCredit: From Relevance to Utility for Decision-Time Memory in Long-Horizon Web Agents

Quanjiang Guo, Sijie Wang, Qianyang Zhu, Ben Zhang, Jinchuan Zhang, Ke Yan, Zhao Kang

原文摘要

Large language model (LLM) agents increasingly operate over long Web interaction histories, making memory management critical for reliable and efficient decision making. Existing methods primarily optimize storage, retrieval, or compression, but the memories most semantically relevant to the current state are not necessarily those that most improve the agent's action. We introduce MemCredit, a decision-centric memory framework that shifts memory selection from semantic relevance to decision utility. MemCredit estimates the state-, reference-context-, and policy-conditional marginal contribution of individual memories through controlled counterfactual interventions, and distills these intervention-derived credits into a lightweight utility estimator. At inference time, semantic relevance is used only for high-recall candidate generation, while predicted utility arbitrates which memories are retained under a fixed token budget, without modifying the underlying agent policy or requiring additional environment interaction. We evaluate MemCredit on ALFWorld and WebShop against representative memory baselines. On ALFWorld, MemCredit achieves 86.57 SR@50 under a 500-token historical-memory budget, compared with 65.67% for BM25 retrieval. Counterfactual analysis further shows that memory utility is sparse, strongly state dependent, and poorly captured by semantic relevance. These results suggest that decision utility provides a more effective principle than relevance alone for long-horizon agent memory management.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15598

Imputing Missing Near-Surface Air Temperature using Graph Attention Networks with Distance-Aware Edge Features

Emma C. Weber, Dominique Guillot, Daniyar Abykhanov, Julien Emile-Geay

原文摘要

We introduce a graph attention network for reconstructing near-surface air temperature fields from incomplete observations. The model represents each grid cell as a node connected to its $k$ nearest neighbors, with attention weights that depend on node features, distance, and land-ocean transitions. This graph representation avoids the polar distortions associated with treating the globe as a flat latitude-longitude image, as most deep learning reconstruction methods do. This is particularly relevant for historical temperature records, which extend back to the mid-nineteenth century but remain incomplete, with the largest gaps early in the record and at high latitudes. We train our model on CMIP6 CESM2 historical simulations masked with the time-varying HadCRUT5 observational coverage from 1850 to 2014. We then compare our approach with ordinary kriging, GraphEM, and the LaMa inpainting network on a held-out realization. The graph model achieves the lowest spatial RMSE (0.997 K), 10.1% lower than LaMa and approximately 31% lower than kriging and GraphEM. The largest gains occur in the Arctic and Antarctic, while LaMa remains slightly more accurate in the densely observed tropics. The graph model's error also falls faster as coverage increases, so its advantage over LaMa grows by more than a third from the 1850s to the 2000s. An ablation study shows that the model's skill comes mainly from the geometric graph construction rather than from auxiliary physical covariates.

cs.LG · physics.geo-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15597

When Local Definitions Are Missing: Stable Errors in Language Models

Zengqing Wu, Chuan Xiao

原文摘要

Contracts, technical manuals, and online communities often give familiar words special meanings and define them in one place. A language model that reads a passage without that definition may use the usual meaning and give an answer that sounds right but is wrong. We study this with multiple-choice questions whose correct answers depend on a local definition. The questions come from a synthetic vocabulary, commercial contracts, Reddit community glossaries, and the documentation of a decentralized exchange. We ask three language models each question with and without the definition. Without using the answer key, we then test whether their repeated answers and stated confidence reveal their own errors. Separately, we give a small open model the answer key and test whether it can predict which questions larger models get wrong. We report three findings. (1) Familiar words that a source redefines cause the most errors. Models often repeat the same wrong answer, and neither repeated answers nor stated confidence reliably flag it. (2) The source definition fixes most errors on synthetic questions, but a definition drawn from the model's own prior knowledge does not. In real contracts, the source definition fixes only about half of the errors. (3) Given an answer key, a small model can rank questions by how likely larger models are to get them wrong. These results call for tests that check answers against the full source, including its local definitions.

cs.CL · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15596

Bug Finding With Agentic Symbolic Execution

George Granberry, Wolfgang Ahrendt, Andy Gordon, Moa Johansson

原文摘要

Symbolic execution, in tools such as CrossHair for Python, is a powerful technique for finding bugs, but two barriers hinder its adoption: the human effort of annotating code with assertions, and the need to model the execution environment outside Python, such as an external database. To overcome both, we design an agentic harness that orchestrates the symbolic execution tool CrossHair together with an AI coding agent. Without human intervention, it instruments a repository with assertions, synthesizes a constraint-aware symbolic environment for the database, runs symbolic execution, and constructs a concrete test as evidence for each bug it reports. We evaluate our harness on 1,156,044 lines of Python code over 11 popular open-source Python web applications (Zulip, NetBox, Apache Superset, and others). With the synthesized environment, CrossHair executes 81% of the 5,406 contracted functions, against 4.5% without it. Our pipeline finds 1,050 bugs, each reproduced by a test against the unmodified application, 221 of them by CrossHair, and 312 of them are missed by a baseline in which the same LLM inspects the code. The two find different kinds of bugs: CrossHair's are mostly crashes on input-domain corner cases that validation admits, such as empty strings, missing fields and unusual characters, while inspection's are mostly bugs that produce wrong output. Our framework thus lets symbolic execution supplement inspection-based bug finding.

cs.SE · cs.AI · cs.LO · cs.SC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15595

From Isolated Protocols to Integrated Pipelines: Benchmarking IoT Communication in the MASA Living Lab

Antonio Solida, Enrico Rossini, Marcello Pietri, Marco Picone, Carlo Augusto Grazia

原文摘要

The performance of real-time IoT communication systems depends not only on individual transport protocols, serialization formats, and data-processing stages, but also on how these components interact within the complete communication pipeline. Selecting an appropriate communication stack is therefore application-dependent and requires considering processing capabilities, network characteristics, communication patterns, and reliability requirements. This paper provides an experimental, evidence-based analysis of these interactions through a controlled benchmark of integrated communication pipelines deployed in the MASA living lab. We jointly evaluate local processing overhead, end-to-end latency, jitter, throughput, and delivery reliability across different combinations of transport, messaging, serialization, and compression mechanisms. Results show that component-level processing costs do not directly predict end-to-end performance. In particular, differences of only tens of microseconds in serialization or compression costs can coexist with several-millisecond differences in end-to-end latency, while middleware buffering and transport behavior substantially affect timing performance. The results demonstrate that communication-stack components should be evaluated as integrated pipelines and under representative operating loads rather than ranked independently.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.16666

Chronocooked: A Benchmark for Interval Timing in Reinforcement Learning Agents

Amrapali Pednekar, Alvaro Garrido-Perez, Yara Khaluf, Pieter Simoens

原文摘要

Interval timing is considered an important aspect of human cognition. As artificial agents increasingly function alongside humans, their interval timing abilities need to be studied. However, research in this area remains limited and scattered. This paper presents Chronocooked, a reinforcement learning (RL) benchmark environment that enables a systematic study of interval timing abilities in RL agents. The suite comprises cooking scenarios involving interval timing tasks inspired by psychology literature. Each task is accompanied by evaluation metrics to study different aspects of interval timing in RL agents, namely, task performance, human-like timing and scalability. We report baselines using a non-recurrent model (CNN), a recurrent model (LSTM), and a biologically inspired recurrent model (CTRNN). The CNN model showed limited ability to perform the basic timing tasks. In contrast, both recurrent models with sufficient memory successfully performed the timing tasks, exhibited temporal control in the Fixed Interval (FI) task, and qualitatively reproduced human-like psychometric curves in the Temporal Bisection task. However, no quantitative evidence of human-like timing behavior was found. A systematic study of interval timing in artificial agents may offer complementary perspectives for improving task performance in fields like human-robot interactions (HRI), while also contributing to broader research on time perception.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15592

Small Heads, Strong Decisions: A Controlled Study of Typed-Decision Models

Ritvik Rastogi

原文摘要

In September 2026, TypeSafe announced Jev as the first model in its System One category: unstructured state goes in, and typed, calibrated decisions that software can use directly come out. Jev's architecture and training data are unpublished, but many open typed-decision models have followed, including Nimble, the Decision 1.0--3.0 family and Clef. They answer structured questions about a piece of text, such as a yes/no check, a choice among labelled options or a graded score, by reading the text with a language model and scoring the options with a small readout. Each pairs a different readout with a different backbone, training mix and recipe, so it is unclear which design choices matter. We re-implement five readouts and train all of them under one protocol: the same backbone, the same 15 public English training sources, the same recipe with a per-head learning-rate sweep, and the same seed. Our experiments demonstrate that: The complexity of the head does not affect accuracy: on Qwen3-0.6B every readout reaches 81.3--82.0\% in-domain accuracy, and a 0.5M-parameter cosine head, which scores each option by its cosine similarity to the question, matches the 27M-parameter joint schema head adapted from Clef in-domain at half its inference time. Reading all of a text's questions from one prompt also matches one prompt per question in accuracy, with 1.6 times faster inference and 2.1--2.5 times faster training on one H100. The backbone's size matters more than either choice: moving from Qwen3-0.6B to Qwen3-4B adds 4 points in-domain and 11 points on held-out sources, while the base, instruct and thinking checkpoints of the same model tie. We recommend spending effort on the backbone and keeping the head minimal.

cs.AI

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新论文 · v1arXiv:2611.15589

EMS-Nav: Embodied Multi-Scale Navigation

Zenghui Yang, Xinan Wang, Danyang Li, Zhe Xu, Zheng Yang, Jingao Xu, Qiang Ma

原文摘要

Industrial inspection robots must reach work areas and specific equipment. These destinations span spatial scales from regions to objects, so robots must relate each target to its surroundings to interpret the task and determine where to go. Existing methods support multi-scale navigation through rich environment representations, but at substantial computational and storage cost. These costs limit deployment where wireless coverage is incomplete and robots must rely on onboard computation to keep navigating. We introduce EMS-Nav, a biologically inspired multi-scale navigation framework for online, on-device operation. Its core innovation is to organize perception, spatial understanding, and navigation around regions, making local spaces the basic units of both environmental understanding and computation. EMS-Nav groups objects and their relationships into functionally meaningful spaces and builds a hierarchy from local areas to the broader environment. This organization supports efficient map construction alongside the interpretation and execution of language goals at different scales. In multi-scale navigation experiments, EMS-Nav achieves the highest success rates across all four target categories: objects, rooms, regions, and specific instances. It outperforms state-of-the-art open-vocabulary mapping and navigation methods, improving overall first-attempt success by 9.39 percentage points over the strongest baseline while reducing mapping overhead and language-model input size. Local deployment in real indoor environments further demonstrates its applicability to on-device embodied navigation. By establishing regions as the organizing basis for perception, spatial understanding, and language-guided action, EMS-Nav provides a system that combines multi-scale understanding with computational efficiency to support diverse language goals on resource-constrained robots.

cs.RO · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.19757

What does it mean to understand language?

Colton Casto, Anna Ivanova, Evelina Fedorenko, Nancy Kanwisher

原文摘要

Language understanding entails not just extracting the surface-level meaning of the linguistic input, but constructing rich mental models of the situation it describes. Here we propose that because processing within the brain's core language system is fundamentally limited, deeply understanding language requires exporting information from the language system to other brain regions that construct mental models of minds, objects, and places, and that compute perceptual and motor representations. Building on earlier theorizing and empirical work, we argue that recent progress in cognitive neuroscience provides both the conceptual foundation and the methods to directly evaluate this hypothesis, thus opening up a new strategy to reveal what it means, cognitively and neurally, to understand language.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15587

VWEN: A Curated Small Language Model for Vietnamese Paraphrasing

Sang Quang Nguyen

原文摘要

We present VWEN, a 0.8B-parameter model for Vietnamese paraphrasing at a requested level of change and for five related tasks: generating several paraphrases, simplification, gist, register transfer and paraphrase judgement. VWEN is Qwen3.5-0.8B fine-tuned on about 7k samples chosen by a four-stage pipeline: rules and deduplication, a bidirectional entailment filter, a divergence score that assigns low and high paraphrase levels, and selection for coverage and instruction-following difficulty. We compare it with paraphrase-only recipes from the same pipeline and with training on the ViSP corpus under matched and larger step budgets. On high-divergence paraphrasing, VWEN reaches a success rate (requested level reached and meaning preserved) of 0.61 against 0.46 for the ViSP model at matched compute, a difference that holds on inputs resembling ViSP. Given four times more training examples, the ViSP model closes the gap on such inputs but not on questions, which ViSP lacks and which it often turns into statements. Across all comparisons the entailment filter is the most effective stage. Training on paraphrases alone erases abilities of the base model that the multi-task data preserves.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15585

Marginal-Preserving Coupled Diffusion Composition for Long-Horizon Planning

Hengrui Zhang, Qiyu Wang, Yuhu Cheng, Brooks Paige, Xuesong Wang

原文摘要

Compositional diffusion planners extend short-horizon trajectory models to long-horizon goal-conditioned planning by generating and stitching multiple local trajectory chunks. However, in multimodal environments, local plausibility is not sufficient for global consistency: independently initialized chunks may denoise toward incompatible overlap modes, producing plans that are individually feasible but incoherent after stitching. Existing remedies typically add search, guidance, pruning, or graph-based structure at inference time, increasing computational cost or relying on task-specific machinery. We instead investigate graph-structured dependence among chunk-wise initial diffusion noises as a lightweight coordination mechanism for compositional trajectory planning. We propose \textbf{M}arginal-Preserving \textbf{C}oupled \textbf{D}iffusion \textbf{C}omposition (MCDC), a training-free inference method for pretrained compositional diffusion planners. Instead of sampling chunk noises independently, MCDC constructs coupled Gaussian initializations whose per-chunk marginals remain exactly standard Gaussian, as required by the pretrained denoiser, while neighboring chunks receive positively correlated noise components. This preserves the standard-Gaussian initialization marginal of each chunk while biasing adjacent chunks toward compatible overlap-mode choices. We prove exact marginal preservation and show, under a simple multimodal overlap model, that positive coupling increases the probability of compatible mode selection. On OGBench long-horizon stitch tasks, MCDC consistently improves over independent compositional diffusion sampling while retaining nearly the same inference time, and achieves a favorable success--cost trade-off compared with search-based composition methods.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15584

AI4Plasma II: Geometry-Aware Surrogate Modeling for Variable Electrode Gap GEC Plasmas

Hao Yang, Zhenyu Zou, Wenshuai Jie, Luowei Yin, Yufeng Wei, Daniel Zhengyu Huang, Zhichao Peng, Chun Chen 等 14 位作者

原文摘要

Industrial plasma process development requires broad exploration of operating and geometric parameters. Geometric parameters, particularly electrode spacing, affect discharge structure and processing uniformity. Exploring these parameter spaces through repeated high-fidelity simulations can be computationally expensive. Surrogate models can offer rapid predictions, but geometric variation complicates their application because the computational domain and discretization may change across samples. In this work, six geometry-aware surrogate models based on prescribed and learned geometry mappings, extended-domain representation, continuous coordinates, and geometry-native discretizations are compared in terms of their data requirements for accurate predictions of full plasma fields and ion density profiles near the wafer. The simulation dataset spans pressure, voltage, frequency, and electrode gap, with each reference case requiring an average simulation cost of 5.32 CPU h. To achieve a 2% mean error across the four plasma fields, prescribed-mapping Geo-FNO and Transolver require 100 training cases, compared with 250-1000 for the other models. Predicting ion density profiles near the wafer is more demanding and reveals larger differences among models, with prescribed-mapping Geo-FNO requiring the fewest training cases for the profile accuracy targets considered. To further reduce the data required for accurate predictions near the wafer, we introduce charge-separation weighting into the training loss, which reduces the prescribed-mapping Geo-FNO's minimum tested training requirement for a 5% profile error from 1000 to 100 cases. These results support prescribed-mapping Geo-FNO as a practical starting point for the variable-gap problem studied here when a suitable geometry mapping is available, while Transolver achieves the highest prediction accuracy with sufficient training data.

cs.LG · physics.plasm-ph

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2412.16555

Divide and Conquer: A Hybrid Strategy Defeats Multimodal Large Language Models

Yanxu Mao, Peipei Liu, Tiehan Cui, Zhaoteng Yan, Congying Liu, Datao You

原文摘要

Large language models (LLMs) are widely applied in various fields of society due to their powerful reasoning, understanding, and generation capabilities. However, the security issues associated with these models are becoming increasingly severe. Jailbreaking attacks, as an important method for detecting vulnerabilities in LLMs, have been explored by researchers who attempt to induce these models to generate harmful content through various attack methods. Nevertheless, existing jailbreaking methods face numerous limitations, such as excessive query counts, limited coverage of jailbreak modalities, low attack success rates, and simplistic evaluation methods. To overcome these constraints, this paper proposes a multimodal jailbreaking method: JMLLM. This method integrates multiple strategies to perform comprehensive jailbreak attacks across text, visual, and auditory modalities. Additionally, we contribute a new and comprehensive dataset for multimodal jailbreaking research: TriJail, which includes jailbreak prompts for all three modalities. Experiments on the TriJail dataset and the benchmark dataset AdvBench, conducted on 13 popular LLMs, demonstrate advanced attack success rates and significant reduction in time overhead.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15583

Your Secrets Are Showing: Deep Image Hiding Models Are Surprisingly Easy to Attack

Hao Liu, Jing Yu, Weizhi Liu, Lingling Yuan, Fengyong Li, Keke Gai, Zhaoxia Yin, Xinpeng Zhang

原文摘要

In this work, we ask whether an attacker can go beyond detecting a hidden image and tell what it depicts, even without knowing which model hid it. We show that, for deep image hiding, this is surprisingly easy. To test this, we build an evaluation dataset of stego images generated by 11 hiding models. These models span two families (invertible neural networks and autoencoders), use different backbones (CNN, Transformer, and Mamba), and include both spatial- and frequency-domain hiding methods. We demonstrate that training HiNet [17] in the usual way yields a single decoder that can extract secret content from unseen models across these settings. Both humans and machines can recognize what it reveals. A frequency analysis shows that most models favor mid- and high-frequency embedding. Guided by this finding, we strengthen cross-model extraction by enhancing cover textures and suppressing secret textures during training. The attack extends to out-of-distribution secrets and to multi-image hiding. It even exposes private content hidden by AIRPNet [13], a recent image restoration method that uses steganography for privacy protection. What surprised us most is that decoders from every hiding model we test exhibit this ''universal'' extraction ability to varying degrees. Our code and datasets will be open-sourced.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15582

The Golden Ratio: EF1 and Ordinal-MMS for Additive Goods

Hadi Hosseini, Shraddha Pathak

原文摘要

Every complete allocation that is envy-free up to one good (EF1) guarantees each of the $n$ agents her $1$-out-of-$(2n-1)$ maximin share. Can we construct allocations that satisfy EF1 together with stronger ordinal maximin share guarantees? For additive valuations, we prove that there always exists a complete allocation that is simultaneously EF1 and ($1$-out-of-$\lfloor\varphi n\rfloor$)-MMS, where $\varphi=(1+\sqrt{5})/2$ is the golden ratio. For bivalued valuations, where each agent may have her own two non-negative values for the goods, we obtain a complete allocation that is simultaneously envy-free up to any good (EFX) and ($1$-out-of-$\lceil(7n-1)/6\rceil$)-MMS.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15580

Making Doula Coordination Breakdowns Visible: A Participatory Case Study

Emma Ricci-De Lucca, Saanvi Chilakapati, Jillian Berch, Ashley Kim, Sahithi Medikondla, Iha Rastogi, Margaret R. Emerson, Elizabeth L. Murnane 等 9 位作者

原文摘要

Doulas are trained, non-medical professionals who provide emotional, physical, and informational support to pregnant individuals before, during, and after childbirth. Doulas provide extensive relational and coordination labor for pregnant and postpartum patients, spending far more time with clients than traditional providers do, yet they operate with almost no doula-specific digital infrastructure. This gap is especially acute in rural communities, where postpartum depression affects about 25% of women, well above the national average, and where geographic isolation and provider shortages leave many without access to behavioral health care. DoulaConnect began as a 12-week evidence-based postpartum texting curriculum, piloted separately in a randomized controlled trial in rural New England. Building on that work, we present DoulaConnect's development into an AI-enabled platform co-designed with doulas and rural mothers through participatory human-centered design, as a case for surfacing where coordination breaks down in doula practice and who currently absorbs the resulting burden. Our community-based participatory study, conducted across the platform's Discover and Define phases, combines qualitative sessions with doulas and mothers to identify these breakdowns. We close with tensions this approach surfaces and open questions for the workshop about how these findings will inform future participatory AI infrastructure for doula care in rural and other under-resourced communities.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15579

FoveaDet: Software-Hardware Co-Design of Foveated CNN-Transformer for Object Detection

Deepak Mewada, Apporva Saraf, Ashwin Lele

原文摘要

Deploying real-time object detection on power-constrained edge platforms is challenging as processing full-resolution frames is often computationally redundant. Inspired by biological foveation which assigns variable resolution based on visual saliency, we propose a novel software-hardware co-design framework called FoveaDet. On the software side, FoveaDet dynamically identifies salient regions to predict fixation points, followed by aggressive patch pruning in Vision Transformers (ViT) to discard irrelevant background data. To mitigate slowdowns from sequential patch generation, we implement optimized interpolation and patch selection algorithms. Evaluated on standard GPUs, this foveated approach reduces floating-point operations by 65.4% to 92.2% and accelerates inference by 4.76x, while suffering a negligible mAP drop compared to dense baselines. To eliminate hardware bottlenecks, we complement the algorithm with a custom RISC-V core featuring specialized instructions to accelerate the foveated processing pipeline. FoveaDet provides a scalable, efficient architecture capable of unlocking advanced vision processing on severely constrained mobile platforms.

cs.CV

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arXiv ↗PDF ↗