# Research Papers — 2025-11 · bundle page 150 of 197

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### Sensor Calibration Model Balancing Accuracy, Real-time, and Efficiency

Authors: Jinyong Yun, Hyungjin Kim, Seokho Ahn, Euijong Lee, Young-Duk Seo
Published: 2025-11-10
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2511.06715v1

Most on-device sensor calibration studies benchmark models only against three macroscopic requirements (i.e., accuracy, real-time, and resource efficiency), thereby hiding deployment bottlenecks such as instantaneous error and worst-case latency. We therefore decompose this triad into eight microscopic requirements and introduce Scare (Sensor Calibration model balancing Accuracy, Real-time, and Efficiency), an ultra-compressed transformer that fulfills them all. SCARE comprises three core components: (1) Sequence Lens Projector (SLP) that logarithmically compresses time-series data while preserving boundary information across bins, (2) Efficient Bitwise Attention (EBA) module that replaces costly multiplications with bitwise operations via binary hash codes, and (3) Hash optimization strategy that ensures stable training without auxiliary loss terms. Together, these components minimize computational overhead while maintaining high accuracy and compatibility with microcontroller units (MCUs). Extensive experiments on large-scale air-quality datasets and real microcontroller deployments demonstrate that Scare outperforms existing linear, hybrid, and deep-learning baselines, making Scare, to the best of our knowledge, the first model to meet all eight microscopic requirements simultaneously.

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### The Wisdom of the Crowd: High-Fidelity Classification of Cyber-Attacks and Faults in Power Systems Using Ensemble and Machine Learning

Authors: Emad Abukhousa, Syed Sohail Feroz Syed Afroz, Fahad Alsaeed, Abdulaziz Qwbaiban, Saman Zonouz, A. P. Sakis Meliopoulos
Published: 2025-11-10
Categories: eess.SY, cs.LG
arXiv: https://arxiv.org/abs/2511.06714v1

This paper presents a high-fidelity evaluation framework for machine learning (ML)-based classification of cyber-attacks and physical faults using electromagnetic transient simulations with digital substation emulation at 4.8 kHz. Twelve ML models, including ensemble algorithms and a multi-layer perceptron (MLP), were trained on labeled time-domain measurements and evaluated in a real-time streaming environment designed for sub-cycle responsiveness. The architecture incorporates a cycle-length smoothing filter and confidence threshold to stabilize decisions. Results show that while several models achieved near-perfect offline accuracies (up to 99.9%), only the MLP sustained robust coverage (98-99%) under streaming, whereas ensembles preserved perfect anomaly precision but abstained frequently (10-49% coverage). These findings demonstrate that offline accuracy alone is an unreliable indicator of field readiness and underscore the need for realistic testing and inference pipelines to ensure dependable classification in inverter-based resources (IBR)-rich networks.

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### K-Stain: Keypoint-Driven Correspondence for H&E-to-IHC Virtual Staining

Authors: Sicheng Yang, Zhaohu Xing, Haipeng Zhou, Lei Zhu
Published: 2025-11-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06709v1

Virtual staining offers a promising method for converting Hematoxylin and Eosin (H&E) images into Immunohistochemical (IHC) images, eliminating the need for costly chemical processes. However, existing methods often struggle to utilize spatial information effectively due to misalignment in tissue slices. To overcome this challenge, we leverage keypoints as robust indicators of spatial correspondence, enabling more precise alignment and integration of structural details in synthesized IHC images. We introduce K-Stain, a novel framework that employs keypoint-based spatial and semantic relationships to enhance synthesized IHC image fidelity. K-Stain comprises three main components: (1) a Hierarchical Spatial Keypoint Detector (HSKD) for identifying keypoints in stain images, (2) a Keypoint-aware Enhancement Generator (KEG) that integrates these keypoints during image generation, and (3) a Keypoint Guided Discriminator (KGD) that improves the discriminator's sensitivity to spatial details. Our approach leverages contextual information from adjacent slices, resulting in more accurate and visually consistent IHC images. Extensive experiments show that K-Stain outperforms state-of-the-art methods in quantitative metrics and visual quality.

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### Sentiment Analysis On YouTube Comments Using Machine Learning Techniques Based On Video Games Content

Authors: Adi Danish Bin Muhammad Amin, Mohaiminul Islam Bhuiyan, Nur Shazwani Kamarudin, Zulfahmi Toh, Nur Syafiqah Nafis
Published: 2025-11-10
Categories: cs.CL
arXiv: https://arxiv.org/abs/2511.06708v1

The rapid evolution of the gaming industry, driven by technological advancements and a burgeoning community, necessitates a deeper understanding of user sentiments, especially as expressed on popular social media platforms like YouTube. This study presents a sentiment analysis on video games based on YouTube comments, aiming to understand user sentiments within the gaming community. Utilizing YouTube API, comments related to various video games were collected and analyzed using the TextBlob sentiment analysis tool. The pre-processed data underwent classification using machine learning algorithms, including Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). Among these, SVM demonstrated superior performance, achieving the highest classification accuracy across different datasets. The analysis spanned multiple popular gaming videos, revealing trends and insights into user preferences and critiques. The findings underscore the importance of advanced sentiment analysis in capturing the nuanced emotions expressed in user comments, providing valuable feedback for game developers to enhance game design and user experience. Future research will focus on integrating more sophisticated natural language processing techniques and exploring additional data sources to further refine sentiment analysis in the gaming domain.

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### SPAN: Spatial-Projection Alignment for Monocular 3D Object Detection

Authors: Yifan Wang, Yian Zhao, Fanqi Pu, Xiaochen Yang, Yang Tang, Xi Chen, Wenming Yang
Published: 2025-11-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06702v2

Existing monocular 3D detectors typically tame the pronounced nonlinear regression of 3D bounding box through decoupled prediction paradigm, which employs multiple branches to estimate geometric center, depth, dimensions, and rotation angle separately. Although this decoupling strategy simplifies the learning process, it inherently ignores the geometric collaborative constraints between different attributes, resulting in the lack of geometric consistency prior, thereby leading to suboptimal performance. To address this issue, we propose novel Spatial-Projection Alignment (SPAN) with two pivotal components: (i). Spatial Point Alignment enforces an explicit global spatial constraint between the predicted and ground-truth 3D bounding boxes, thereby rectifying spatial drift caused by decoupled attribute regression. (ii). 3D-2D Projection Alignment ensures that the projected 3D box is aligned tightly within its corresponding 2D detection bounding box on the image plane, mitigating projection misalignment overlooked in previous works. To ensure training stability, we further introduce a Hierarchical Task Learning strategy that progressively incorporates spatial-projection alignment as 3D attribute predictions refine, preventing early stage error propagation across attributes. Extensive experiments demonstrate that the proposed method can be easily integrated into any established monocular 3D detector and delivers significant performance improvements.

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### Structural Enforcement of Statistical Rigor in AI-Driven Discovery: A Functional Architecture

Authors: Karen Sargsyan
Published: 2025-11-10
Categories: cs.SE, cs.AI
arXiv: https://arxiv.org/abs/2511.06701v4

AI-Scientist systems risk manufacturing spurious discoveries through uncontrolled multiple testing. We present a functional architecture that enforces statistical rigor at two levels: a Haskell embedded domain-specific language (the Research monad) that makes it impossible to test a hypothesis without updating the error budget, and a declarative scaffold that fixes the data flow and the statistical test, together with an OS-level sandbox that makes validation data physically absent from the environment in which LLM-generated code runs. We treat FDR control as a formal requirement and trace it to the implementation. We ground the design in a machine-checked Lean~4 formalization of LORD online false-discovery-rate (FDR) control: we derive its error budget and prove marginal FDR control, and full FDR control when thresholds do not adapt to earlier rejections. We then verify in SPARK/Ada that the LORD thresholds, computed in IEEE~754 arithmetic, never exceed the available wealth, given a margin condition that our configurations meet by a factor of at least eight; without a margin the property fails. To our knowledge this is the first machine-checked proof of an online FDR control theorem. In simulation, the architecture holds the false discovery rate near 1\% against a 5\% target, where a naive approach reaches 41\%. In end-to-end case studies, a valid test avoids the false discoveries a flawed one produces, yet still finds real effects when the data allow. An adversarial evaluation confirms that, inside the sandbox, generated code cannot read the held-out data even when given its exact path.

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### Place Matters: Comparing LLM Hallucination Rates for Place-Based Legal Queries

Authors: Damian Curran, Vanessa Sporne, Lea Frermann, Jeannie Paterson
Published: 2025-11-10
Categories: cs.CY, cs.AI, cs.CL
arXiv: https://arxiv.org/abs/2511.06700v1

How do we make a meaningful comparison of a large language model's knowledge of the law in one place compared to another? Quantifying these differences is critical to understanding if the quality of the legal information obtained by users of LLM-based chatbots varies depending on their location. However, obtaining meaningful comparative metrics is challenging because legal institutions in different places are not themselves easily comparable. In this work we propose a methodology to obtain place-to-place metrics based on the comparative law concept of functionalism. We construct a dataset of factual scenarios drawn from Reddit posts by users seeking legal advice for family, housing, employment, crime and traffic issues. We use these to elicit a summary of a law from the LLM relevant to each scenario in Los Angeles, London and Sydney. These summaries, typically of a legislative provision, are manually evaluated for hallucinations. We show that the rate of hallucination of legal information by leading closed-source LLMs is significantly associated with place. This suggests that the quality of legal solutions provided by these models is not evenly distributed across geography. Additionally, we show a strong negative correlation between hallucination rate and the frequency of the majority response when the LLM is sampled multiple times, suggesting a measure of uncertainty of model predictions of legal facts.

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### Lassoed Forests: Random Forests with Adaptive Lasso Post-selection

Authors: Jing Shang, James Bannon, Benjamin Haibe-Kains, Robert Tibshirani
Published: 2025-11-10
Categories: stat.ML, cs.LG
arXiv: https://arxiv.org/abs/2511.06698v2

Random forests are a statistical learning technique that use bootstrap aggregation to average high-variance and low-bias trees. Improvements to random forests, such as applying Lasso regression to the tree predictions, have been proposed in order to reduce model bias. However, these changes can sometimes degrade performance (e.g., an increase in mean squared error). In this paper, we show in theory that the relative performance of these two methods, standard and Lasso-weighted random forests, depends on the signal-to-noise ratio. We further propose a unified framework to combine random forests and Lasso selection by applying adaptive weighting and show mathematically that it can strictly outperform the other two methods. We compare the three methods through simulation, including bias-variance decomposition, error estimates evaluation, and variable importance analysis. We also show the versatility of our method by applications to a variety of real-world datasets.

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### Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks

Authors: Dian Jin, Yancheng Yuan, Xiaoming Tao
Published: 2025-11-10
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2511.06696v2

Pretrained equivariant graph neural networks based on spherical harmonics offer efficient and accurate alternatives to computationally expensive ab-initio methods, yet adapting them to new tasks and chemical environments still requires fine-tuning. Conventional parameter-efficient fine-tuning (PEFT) techniques, such as Adapters and LoRA, typically break symmetry, making them incompatible with those equivariant architectures. ELoRA, recently proposed, is the first equivariant PEFT method. It achieves improved parameter efficiency and performance on many benchmarks. However, the relatively high degrees of freedom it retains within each tensor order can still perturb pretrained feature distributions and ultimately degrade performance. To address this, we present Magnitude-Modulated Equivariant Adapter (MMEA), a novel equivariant fine-tuning method which employs lightweight scalar gating to modulate feature magnitudes on a per-order and per-multiplicity basis. We demonstrate that MMEA preserves strict equivariance and, across multiple benchmarks, consistently improves energy and force predictions to state-of-the-art levels while training fewer parameters than competing approaches. These results suggest that, in many practical scenarios, modulating channel magnitudes is sufficient to adapt equivariant models to new chemical environments without breaking symmetry, pointing toward a new paradigm for equivariant PEFT design.

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### ML-EcoLyzer: Quantifying the Environmental Cost of Machine Learning Inference Across Frameworks and Hardware

Authors: Jose Marie Antonio Minoza, Rex Gregor Laylo, Christian F Villarin, Sebastian C. Ibanez
Published: 2025-11-10
Categories: cs.LG, cs.AI, cs.CV, cs.HC, cs.SE
arXiv: https://arxiv.org/abs/2511.06694v1

Machine learning inference occurs at a massive scale, yet its environmental impact remains poorly quantified, especially on low-resource hardware. We present ML-EcoLyzer, a cross-framework tool for measuring the carbon, energy, thermal, and water costs of inference across CPUs, consumer GPUs, and datacenter accelerators. The tool supports both classical and modern models, applying adaptive monitoring and hardware-aware evaluation. We introduce the Environmental Sustainability Score (ESS), which quantifies the number of effective parameters served per gram of CO$_2$ emitted. Our evaluation covers over 1,900 inference configurations, spanning diverse model architectures, task modalities (text, vision, audio, tabular), hardware types, and precision levels. These rigorous and reliable measurements demonstrate that quantization enhances ESS, huge accelerators can be inefficient for lightweight applications, and even small models may incur significant costs when implemented suboptimally. ML-EcoLyzer sets a standard for sustainability-conscious model selection and offers an extensive empirical evaluation of environmental costs during inference.

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### Peeling Context from Cause for Molecular Property Prediction

Authors: Tao Li, Kaiyuan Hou, Tuan Vinh, Monika Raj, Carl Yang
Published: 2025-11-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.06692v2

Deep models are used for molecular property prediction, yet they are often difficult to interpret and may rely on spurious context rather than causal structure, which reduces reliability under distribution shift and harms predictive performance. We introduce CLaP (Causal Layerwise Peeling), a framework that separates causal signal from context in a layerwise manner and integrates diverse graph representations of molecules. At each layer, a causal block performs a soft split into causal and non-causal branches, fuses causal evidence across modalities, and progressively removes batch-coupled context to focus on label-relevant structure, thereby limiting shortcut signals and stabilizing layerwise refinement. Across four molecular benchmarks, CLaP consistently improves MAE, MSE, and $R^2$ over competitive baselines. The model also produces atom-level causal saliency maps that highlight substructures responsible for predictions, providing actionable guidance for targeted molecular edits. Case studies confirm the accuracy of these maps and their alignment with chemical intuition. By peeling context from cause at every layer, the model yields predictors that are both accurate and interpretable for molecular design.

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### AnoStyler: Text-Driven Localized Anomaly Generation via Lightweight Style Transfer

Authors: Yulim So, Seokho Kang
Published: 2025-11-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06687v1

Anomaly generation has been widely explored to address the scarcity of anomaly images in real-world data. However, existing methods typically suffer from at least one of the following limitations, hindering their practical deployment: (1) lack of visual realism in generated anomalies; (2) dependence on large amounts of real images; and (3) use of memory-intensive, heavyweight model architectures. To overcome these limitations, we propose AnoStyler, a lightweight yet effective method that frames zero-shot anomaly generation as text-guided style transfer. Given a single normal image along with its category label and expected defect type, an anomaly mask indicating the localized anomaly regions and two-class text prompts representing the normal and anomaly states are generated using generalizable category-agnostic procedures. A lightweight U-Net model trained with CLIP-based loss functions is used to stylize the normal image into a visually realistic anomaly image, where anomalies are localized by the anomaly mask and semantically aligned with the text prompts. Extensive experiments on the MVTec-AD and VisA datasets show that AnoStyler outperforms existing anomaly generation methods in generating high-quality and diverse anomaly images. Furthermore, using these generated anomalies helps enhance anomaly detection performance.

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### Balancing Multi-modal Sensor Learning via Multi-objective Optimization

Authors: Heshan Fernando, Quan Xiao, Parikshit Ram, Yi Zhou, Horst Samulowitz, Nathalie Baracaldo, Tianyi Chen
Published: 2025-11-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.06686v2

Learning-enabled control systems increasingly rely on multiple sensing modalities (e.g., vision, audio, language, etc.) for perception and decision support. A key challenge is that multi-modal sensor training dynamics are often imbalanced: fast-to-learn sensing channels dominate optimization, while slower channels remain underutilized, degrading reliability under sensing perturbations. Existing balancing strategies are largely heuristic and can require computationally intensive subroutines. In this paper, we reformulate multi-modal sensor learning as a multi-objective optimization (MOO) problem that explicitly prioritizes the worst-performing modality while retaining the nominal multi-modal sensor fusion objective. We then propose a simple gradient-based method, MIMO (multi-modal sensor learning via MOO), for the resulting formulation. We provide convergence guarantees and evaluate the method on standard multi-modal benchmarks. Results show improved balanced performance over state-of-the-art balanced multi-modal learning and MOO baselines, together with up to ~20x reduction in subroutine computation time, highlighting the suitability of MIMO for resource-constrained control pipelines.

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### Textual Self-attention Network: Test-Time Preference Optimization through Textual Gradient-based Attention

Authors: Shibing Mo, Haoyang Ruan, Kai Wu, Jing Liu
Published: 2025-11-10
Categories: cs.CL, cs.AI
arXiv: https://arxiv.org/abs/2511.06682v2

Large Language Models (LLMs) have demonstrated remarkable generalization capabilities, but aligning their outputs with human preferences typically requires expensive supervised fine-tuning. Recent test-time methods leverage textual feedback to overcome this, but they often critique and revise a single candidate response, lacking a principled mechanism to systematically analyze, weigh, and synthesize the strengths of multiple promising candidates. Such a mechanism is crucial because different responses may excel in distinct aspects (e.g., clarity, factual accuracy, or tone), and combining their best elements may produce a far superior outcome. This paper proposes the Textual Self-Attention Network (TSAN), a new paradigm for test-time preference optimization that requires no parameter updates. TSAN emulates self-attention entirely in natural language to overcome this gap: it analyzes multiple candidates by formatting them into textual keys and values, weighs their relevance using an LLM-based attention module, and synthesizes their strengths into a new, preference-aligned response under the guidance of the learned textual attention. This entire process operates in a textual gradient space, enabling iterative and interpretable optimization. Empirical evaluations demonstrate that with just three test-time iterations on a base SFT model, TSAN outperforms supervised models like Llama-3.1-70B-Instruct and surpasses the current state-of-the-art test-time alignment method by effectively leveraging multiple candidate solutions.

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### An Adaptive Machine Learning Triage Framework for Predicting Alzheimer's Disease Progression

Authors: Richard Hou, Shengpu Tang, Wei Jin
Published: 2025-11-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.06681v1

Accurate predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) can enable effective personalized therapy. While cognitive tests and clinical data are routinely collected, they lack the predictive power of PET scans and CSF biomarker analysis, which are prohibitively expensive to obtain for every patient. To address this cost-accuracy dilemma, we design a two-stage machine learning framework that selectively obtains advanced, costly features based on their predicted "value of information". We apply our framework to predict AD progression for MCI patients using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our framework reduces the need for advanced testing by 20% while achieving a test AUROC of 0.929, comparable to the model that uses both basic and advanced features (AUROC=0.915, p=0.1010). We also provide an example interpretability analysis showing how one may explain the triage decision. Our work presents an interpretable, data-driven framework that optimizes AD diagnostic pathways and balances accuracy with cost, representing a step towards making early, reliable AD prediction more accessible in real-world practice. Future work should consider multiple categories of advanced features and larger-scale validation.

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### Steering LLMs toward Korean Local Speech: Iterative Refinement Framework for Faithful Dialect Translation

Authors: Keunhyeung Park, Seunguk Yu, Youngbin Kim
Published: 2025-11-10
Categories: cs.CL
arXiv: https://arxiv.org/abs/2511.06680v3

Standard-to-dialect machine translation remains challenging due to a persistent dialect gap in large language models and evaluation distortions inherent in n-gram metrics, which favor source copying over authentic dialect translation. In this paper, we propose the dialect refinement (DIA-REFINE) framework, which guides LLMs toward faithful target dialect outputs through an iterative loop of translation, verification, and feedback using external dialect classifiers. To address the limitations of n-gram-based metrics, we introduce the dialect fidelity score (DFS) to quantify linguistic shift and the target dialect ratio (TDR) to measure the success of dialect translation. Experiments on Korean dialects across zero-shot and in-context learning baselines demonstrate that DIA-REFINE consistently enhances dialect fidelity. The proposed metrics distinguish between False Success cases, where high n-gram scores obscure failures in dialectal translation, and True Attempt cases, where genuine attempts at dialectal translation yield low n-gram scores. We also observed that models exhibit varying degrees of responsiveness to the framework, and that integrating in-context examples further improves the translation of dialectal expressions. Our work establishes a robust framework for goal-directed, inclusive dialect translation, providing both rigorous evaluation and critical insights into model performance.

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### Flexible Concept Bottleneck Model

Authors: Xingbo Du, Qiantong Dou, Lei Fan, Rui Zhang
Published: 2025-11-10
Categories: cs.CV, cs.LG
arXiv: https://arxiv.org/abs/2511.06678v1

Concept bottleneck models (CBMs) improve neural network interpretability by introducing an intermediate layer that maps human-understandable concepts to predictions. Recent work has explored the use of vision-language models (VLMs) to automate concept selection and annotation. However, existing VLM-based CBMs typically require full model retraining when new concepts are involved, which limits their adaptability and flexibility in real-world scenarios, especially considering the rapid evolution of vision-language foundation models. To address these issues, we propose Flexible Concept Bottleneck Model (FCBM), which supports dynamic concept adaptation, including complete replacement of the original concept set. Specifically, we design a hypernetwork that generates prediction weights based on concept embeddings, allowing seamless integration of new concepts without retraining the entire model. In addition, we introduce a modified sparsemax module with a learnable temperature parameter that dynamically selects the most relevant concepts, enabling the model to focus on the most informative features. Extensive experiments on five public benchmarks demonstrate that our method achieves accuracy comparable to state-of-the-art baselines with a similar number of effective concepts. Moreover, the model generalizes well to unseen concepts with just a single epoch of fine-tuning, demonstrating its strong adaptability and flexibility.

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### How AI Fails: An Interactive Pedagogical Tool for Demonstrating Dialectal Bias in Automated Toxicity Models

Authors: Subhojit Ghimire
Published: 2025-11-10
Categories: cs.CL, cs.CY, cs.HC
arXiv: https://arxiv.org/abs/2511.06676v3

Now that AI-driven moderation has become pervasive in everyday life, we often hear claims that "the AI is biased". While this is often said jokingly, the light-hearted remark reflects a deeper concern. How can we be certain that an online post flagged as "inappropriate" was not simply the victim of a biased algorithm? This paper investigates this problem using a dual approach. First, I conduct a quantitative benchmark of a widely used toxicity model (unitary/toxic-bert) to measure performance disparity between text in African-American English (AAE) and Standard American English (SAE). The benchmark reveals a clear, systematic bias: on average, the model scores AAE text as 1.8 times more toxic and 8.8 times higher for "identity hate". Second, I introduce an interactive pedagogical tool that makes these abstract biases tangible. The tool's core mechanic, a user-controlled "sensitivity threshold," demonstrates that the biased score itself is not the only harm; instead, the more-concerning harm is the human-set, seemingly neutral policy that ultimately operationalises discrimination. This work provides both statistical evidence of disparate impact and a public-facing tool designed to foster critical AI literacy.

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### Adam symmetry theorem: characterization of the convergence of the stochastic Adam optimizer

Authors: Steffen Dereich, Thang Do, Arnulf Jentzen, Philippe von Wurstemberger
Published: 2025-11-10
Categories: math.OC, cs.LG
arXiv: https://arxiv.org/abs/2511.06675v2

Beside the standard stochastic gradient descent (SGD) method, the Adam optimizer due to Kingma & Ba (2014) is currently probably the best-known optimization method for the training of deep neural networks in artificial intelligence (AI) systems. Despite the popularity and the success of Adam it remains an \emph{open research problem} to provide a rigorous convergence analysis for Adam even for the class of strongly convex SOPs. In one of the main results of this work we establish convergence rates for Adam in terms of the number of gradient steps (convergence rate \nicefrac{1}{2} w.r.t. the size of the learning rate), the size of the mini-batches (convergence rate 1 w.r.t. the size of the mini-batches), and the size of the second moment parameter of Adam (convergence rate 1 w.r.t. the distance of the second moment parameter to 1) for the class of strongly convex SOPs. In a further main result of this work, which we refer to as \emph{Adam symmetry theorem}, we illustrate the optimality of the established convergence rates by proving for a special class of simple quadratic strongly convex SOPs that Adam converges as the number of gradient steps increases to infinity to the solution of the SOP (the unique minimizer of the strongly convex objective function) if and \emph{only} if the random variables in the SOP (the data in the SOP) are \emph{symmetrically distributed}. In particular, in the standard case where the random variables in the SOP are not symmetrically distributed we \emph{disprove} that Adam converges to the minimizer of the SOP as the number of Adam steps increases to infinity. We also complement the conclusions of our convergence analysis and the Adam symmetry theorem by several numerical simulations that indicate the sharpness of the established convergence rates and that illustrate the practical appearance of the phenomena revealed in the \emph{Adam symmetry theorem}.

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### Modeling and Topology Estimation of Low Rank Dynamical Networks

Authors: Wenqi Cao, Aming Li
Published: 2025-11-10
Categories: cs.GR, stat.ML
arXiv: https://arxiv.org/abs/2511.06674v1

Conventional topology learning methods for dynamical networks become inapplicable to processes exhibiting low-rank characteristics. To address this, we propose the low rank dynamical network model which ensures identifiability. By employing causal Wiener filtering, we establish a necessary and sufficient condition that links the sparsity pattern of the filter to conditional Granger causality. Building on this theoretical result, we develop a consistent method for estimating all network edges. Simulation results demonstrate the parsimony of the proposed framework and consistency of the topology estimation approach.

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### Contradictions in Context: Challenges for Retrieval-Augmented Generation in Healthcare

Authors: Saeedeh Javadi, Sara Mirabi, Manan Gangar, Bahadorreza Ofoghi
Published: 2025-11-10
Categories: cs.IR, cs.LG
arXiv: https://arxiv.org/abs/2511.06668v2

In high-stakes information domains such as healthcare, where large language models (LLMs) can produce hallucinations or misinformation, retrieval-augmented generation (RAG) has been proposed as a mitigation strategy, grounding model outputs in external, domain-specific documents. Yet, this approach can introduce errors when source documents contain outdated or contradictory information. This work investigates the performance of five LLMs in generating RAG-based responses to medicine-related queries. Our contributions are three-fold: i) the creation of a benchmark dataset using consumer medicine information documents from the Australian Therapeutic Goods Administration (TGA), where headings are repurposed as natural language questions, ii) the retrieval of PubMed abstracts using TGA headings, stratified across multiple publication years, to enable controlled temporal evaluation of outdated evidence, and iii) a comparative analysis of the frequency and impact of outdated or contradictory content on model-generated responses, assessing how LLMs integrate and reconcile temporally inconsistent information. Our findings show that contradictions between highly similar abstracts do, in fact, degrade performance, leading to inconsistencies and reduced factual accuracy in model answers. These results highlight that retrieval similarity alone is insufficient for reliable medical RAG and underscore the need for contradiction-aware filtering strategies to ensure trustworthy responses in high-stakes domains.

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### Rapidly Learning Soft Robot Control via Implicit Time-Stepping

Authors: Andrew Choi, Dezhong Tong, Xiaonan Huang
Published: 2025-11-10
Categories: cs.RO, cs.AI
arXiv: https://arxiv.org/abs/2511.06667v2

With the explosive growth of rigid-body simulators, policy learning in simulation has become the de facto standard for most rigid morphologies. In contrast, soft robotic simulation frameworks remain scarce and are seldom adopted by the soft robotics community. This gap stems partly from the lack of easy-to-use, general-purpose frameworks and partly from the high computational cost of accurately simulating continuum mechanics, which often renders policy learning infeasible. In this work, we demonstrate that rapid soft robot policy learning is indeed achievable via implicit time-stepping. Our simulator of choice, DisMech, is a general-purpose, fully implicit soft-body simulator capable of handling both soft dynamics and frictional contact. We further introduce delta natural curvature control, a method analogous to delta joint position control in rigid manipulators, providing an intuitive and effective means of enacting control for soft robot learning. To highlight the benefits of implicit time-stepping and delta curvature control, we conduct extensive comparisons across four diverse soft manipulator tasks against one of the most widely used soft-body frameworks, Elastica. With implicit time-stepping, parallel stepping of 500 environments achieves up to 6x faster speeds for non-contact cases and up to 40x faster for contact-rich scenarios. Finally, a comprehensive sim-to-sim gap evaluation--training policies in one simulator and evaluating them in another--demonstrates that implicit time-stepping provides a rare free lunch: dramatic speedups achieved without sacrificing accuracy.

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### REOcc: Camera-Radar Fusion with Radar Feature Enrichment for 3D Occupancy Prediction

Authors: Chaehee Song, Sanmin Kim, Hyeonjun Jeong, Juyeb Shin, Joonhee Lim, Dongsuk Kum
Published: 2025-11-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06666v1

Vision-based 3D occupancy prediction has made significant advancements, but its reliance on cameras alone struggles in challenging environments. This limitation has driven the adoption of sensor fusion, among which camera-radar fusion stands out as a promising solution due to their complementary strengths. However, the sparsity and noise of the radar data limits its effectiveness, leading to suboptimal fusion performance. In this paper, we propose REOcc, a novel camera-radar fusion network designed to enrich radar feature representations for 3D occupancy prediction. Our approach introduces two main components, a Radar Densifier and a Radar Amplifier, which refine radar features by integrating spatial and contextual information, effectively enhancing spatial density and quality. Extensive experiments on the Occ3D-nuScenes benchmark demonstrate that REOcc achieves significant performance gains over the camera-only baseline model, particularly in dynamic object classes. These results underscore REOcc's capability to mitigate the sparsity and noise of the radar data. Consequently, radar complements camera data more effectively, unlocking the full potential of camera-radar fusion for robust and reliable 3D occupancy prediction.

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### Sim4Seg: Boosting Multimodal Multi-disease Medical Diagnosis Segmentation with Region-Aware Vision-Language Similarity Masks

Authors: Lingran Song, Yucheng Zhou, Jianbing Shen
Published: 2025-11-10
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2511.06665v1

Despite significant progress in pixel-level medical image analysis, existing medical image segmentation models rarely explore medical segmentation and diagnosis tasks jointly. However, it is crucial for patients that models can provide explainable diagnoses along with medical segmentation results. In this paper, we introduce a medical vision-language task named Medical Diagnosis Segmentation (MDS), which aims to understand clinical queries for medical images and generate the corresponding segmentation masks as well as diagnostic results. To facilitate this task, we first present the Multimodal Multi-disease Medical Diagnosis Segmentation (M3DS) dataset, containing diverse multimodal multi-disease medical images paired with their corresponding segmentation masks and diagnosis chain-of-thought, created via an automated diagnosis chain-of-thought generation pipeline. Moreover, we propose Sim4Seg, a novel framework that improves the performance of diagnosis segmentation by taking advantage of the Region-Aware Vision-Language Similarity to Mask (RVLS2M) module. To improve overall performance, we investigate a test-time scaling strategy for MDS tasks. Experimental results demonstrate that our method outperforms the baselines in both segmentation and diagnosis.

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### GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising

Authors: Yuhang Li, Yang Lu, Bo Ai, Zhiguo Ding, Arumugam Nallanathan
Published: 2025-11-10
Categories: eess.SY, cs.LG
arXiv: https://arxiv.org/abs/2511.06663v2

Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks. However, obtaining high-resolution CSI remains challenging in practical wireless communication systems. To address this issue, we propose to utilize Graph Neural Networks (GNNs) and score-based generative models to enable robust HBF under imperfect CSI conditions. Firstly, we develop the Hybrid Message Graph Attention Network (HMGAT) which updates both node and edge features through node-level and edge-level message passing. Secondly, we design a Bidirectional Encoder Representations from Transformers (BERT)-based Noise Conditional Score Network (NCSN) to learn the distribution of high-resolution CSI, facilitating CSI generation and data augmentation to further improve HMGAT's performance. Finally, we present a Denoising Score Network (DSN) framework and its instantiation, termed DeBERT, which can denoise imperfect CSI under arbitrary channel error levels, thereby facilitating robust HBF. Experiments on DeepMIMO urban datasets demonstrate the proposed models' superior generalization, scalability, and robustness across various HBF tasks with perfect and imperfect CSI.

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### Dual-Pathway Fusion of EHRs and Knowledge Graphs for Predicting Unseen Drug-Drug Interactions

Authors: Franklin Lee, Tengfei Ma
Published: 2025-11-10
Categories: cs.LG, q-bio.QM
arXiv: https://arxiv.org/abs/2511.06662v1

Drug-drug interactions (DDIs) remain a major source of preventable harm, and many clinically important mechanisms are still unknown. Existing models either rely on pharmacologic knowledge graphs (KGs), which fail on unseen drugs, or on electronic health records (EHRs), which are noisy, temporal, and site-dependent. We introduce, to our knowledge, the first system that conditions KG relation scoring on patient-level EHR context and distills that reasoning into an EHR-only model for zero-shot inference. A fusion "Teacher" learns mechanism-specific relations for drug pairs represented in both sources, while a distilled "Student" generalizes to new or rarely used drugs without KG access at inference. Both operate under a shared ontology (set) of pharmacologic mechanisms (drug relations) to produce interpretable, auditable alerts rather than opaque risk scores. Trained on a multi-institution EHR corpus paired with a curated DrugBank DDI graph, and evaluated using a clinically aligned, decision-focused protocol with leakage-safe negatives that avoid artificially easy pairs, the system maintains precision across multi-institutuion test data, produces mechanism-specific, clinically consistent predictions, reduces false alerts (higher precision) at comparable overall detection performance (F1), and misses fewer true interactions compared to prior methods. Case studies further show zero-shot identification of clinically recognized CYP-mediated and pharmacodynamic mechanisms for drugs absent from the KG, supporting real-world use in clinical decision support and pharmacovigilance.

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### Active Learning for Animal Re-Identification with Ambiguity-Aware Sampling

Authors: Depanshu Sani, Mehar Khurana, Saket Anand
Published: 2025-11-10
Categories: cs.CV, cs.AI
arXiv: https://arxiv.org/abs/2511.06658v2

Animal Re-ID has recently gained substantial attention in the AI research community due to its high impact on biodiversity monitoring and unique research challenges arising from environmental factors. The subtle distinguishing patterns, handling new species and the inherent open-set nature make the problem even harder. To address these complexities, foundation models trained on labeled, large-scale and multi-species animal Re-ID datasets have recently been introduced to enable zero-shot Re-ID. However, our benchmarking reveals significant gaps in their zero-shot Re-ID performance for both known and unknown species. While this highlights the need for collecting labeled data in new domains, exhaustive annotation for Re-ID is laborious and requires domain expertise. Our analyses show that existing unsupervised (USL) and AL Re-ID methods underperform for animal Re-ID. To address these limitations, we introduce a novel AL Re-ID framework that leverages complementary clustering methods to uncover and target structurally ambiguous regions in the embedding space for mining pairs of samples that are both informative and broadly representative. Oracle feedback on these pairs, in the form of must-link and cannot-link constraints, facilitates a simple annotation interface, which naturally integrates with existing USL methods through our proposed constrained clustering refinement algorithm. Through extensive experiments, we demonstrate that, by utilizing only 0.033% of all annotations, our approach consistently outperforms existing foundational, USL and AL baselines. Specifically, we report an average improvement of 10.49%, 11.19% and 3.99% (mAP) on 13 wildlife datasets over foundational, USL and AL methods, respectively, while attaining state-of-the-art performance on each dataset. Furthermore, we also show an improvement of 11.09%, 8.2% and 2.06% for unknown individuals in an open-world setting.

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### HiMo-CLIP: Modeling Semantic Hierarchy and Monotonicity in Vision-Language Alignment

Authors: Ruijia Wu, Ping Chen, Fei Shen, Shaoan Zhao, Qiang Hui, Huanlin Gao, Ting Lu, Zhaoxiang Liu, Fang Zhao, Kai Wang, Shiguo Lian
Published: 2025-11-10
Categories: cs.CV, cs.CL
arXiv: https://arxiv.org/abs/2511.06653v1

Contrastive vision-language models like CLIP have achieved impressive results in image-text retrieval by aligning image and text representations in a shared embedding space. However, these models often treat text as flat sequences, limiting their ability to handle complex, compositional, and long-form descriptions. In particular, they fail to capture two essential properties of language: semantic hierarchy, which reflects the multi-level compositional structure of text, and semantic monotonicity, where richer descriptions should result in stronger alignment with visual content.To address these limitations, we propose HiMo-CLIP, a representation-level framework that enhances CLIP-style models without modifying the encoder architecture. HiMo-CLIP introduces two key components: a hierarchical decomposition (HiDe) module that extracts latent semantic components from long-form text via in-batch PCA, enabling flexible, batch-aware alignment across different semantic granularities, and a monotonicity-aware contrastive loss (MoLo) that jointly aligns global and component-level representations, encouraging the model to internalize semantic ordering and alignment strength as a function of textual completeness.These components work in concert to produce structured, cognitively-aligned cross-modal representations. Experiments on multiple image-text retrieval benchmarks show that HiMo-CLIP consistently outperforms strong baselines, particularly under long or compositional descriptions. The code is available at https://github.com/UnicomAI/HiMo-CLIP.

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### NOVO: Bridging LLaVA and SAM with Visual-only Prompts for Reasoning Segmentation

Authors: Kyung-Yoon Yoon, Yeong-Jun Cho
Published: 2025-11-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06651v1

In this study, we propose NOVO (NO text, Visual-Only prompts), a novel framework that bridges vision-language models (VLMs) and segmentation models through visual-only prompts. Unlike prior approaches that feed text-derived SEG token embeddings into segmentation models, NOVO instead generates a coarse mask and point prompts from the VLM output. These visual prompts are compatible with the Segment Anything Model (SAM), preserving alignment with its pretrained capabilities. To further enhance boundary quality and enable instance-level segmentation, we introduce a training-free refinement module that reduces visual artifacts and improves the quality of segmentation masks. We also present RISeg, a new benchmark comprising 918 images, 2,533 instance-level masks, and diverse reasoning queries to evaluate this task. Experiments demonstrate that NOVO achieves state-of-the-art performance across multiple metrics and model sizes, demonstrating its effectiveness and scalability in reasoning segmentation.

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### FreqGRL: Suppressing Low-Frequency Bias and Mining High-Frequency Knowledge for Cross-Domain Few-Shot Learning

Authors: Siqi Hui, Sanping Zhou, Ye deng, Wenli Huang, Jinjun Wang
Published: 2025-11-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06648v1

Cross-domain few-shot learning (CD-FSL) aims to recognize novel classes with only a few labeled examples under significant domain shifts. While recent approaches leverage a limited amount of labeled target-domain data to improve performance, the severe imbalance between abundant source data and scarce target data remains a critical challenge for effective representation learning. We present the first frequency-space perspective to analyze this issue and identify two key challenges: (1) models are easily biased toward source-specific knowledge encoded in the low-frequency components of source data, and (2) the sparsity of target data hinders the learning of high-frequency, domain-generalizable features. To address these challenges, we propose \textbf{FreqGRL}, a novel CD-FSL framework that mitigates the impact of data imbalance in the frequency space. Specifically, we introduce a Low-Frequency Replacement (LFR) module that substitutes the low-frequency components of source tasks with those from the target domain to create new source tasks that better align with target characteristics, thus reducing source-specific biases and promoting generalizable representation learning. We further design a High-Frequency Enhancement (HFE) module that filters out low-frequency components and performs learning directly on high-frequency features in the frequency space to improve cross-domain generalization. Additionally, a Global Frequency Filter (GFF) is incorporated to suppress noisy or irrelevant frequencies and emphasize informative ones, mitigating overfitting risks under limited target supervision. Extensive experiments on five standard CD-FSL benchmarks demonstrate that our frequency-guided framework achieves state-of-the-art performance.

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### Adaptive Testing for Segmenting Watermarked Texts From Language Models

Authors: Xingchi Li, Xiaochi Liu, Guanxun Li
Published: 2025-11-10
Categories: stat.ML, cs.CL, cs.LG
arXiv: https://arxiv.org/abs/2511.06645v1

The rapid adoption of large language models (LLMs), such as GPT-4 and Claude 3.5, underscores the need to distinguish LLM-generated text from human-written content to mitigate the spread of misinformation and misuse in education. One promising approach to address this issue is the watermark technique, which embeds subtle statistical signals into LLM-generated text to enable reliable identification. In this paper, we first generalize the likelihood-based LLM detection method of a previous study by introducing a flexible weighted formulation, and further adapt this approach to the inverse transform sampling method. Moving beyond watermark detection, we extend this adaptive detection strategy to tackle the more challenging problem of segmenting a given text into watermarked and non-watermarked substrings. In contrast to the approach in a previous study, which relies on accurate estimation of next-token probabilities that are highly sensitive to prompt estimation, our proposed framework removes the need for precise prompt estimation. Extensive numerical experiments demonstrate that the proposed methodology is both effective and robust in accurately segmenting texts containing a mixture of watermarked and non-watermarked content.

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### UniADC: A Unified Framework for Anomaly Detection and Classification

Authors: Ximiao Zhang, Min Xu, Zheng Zhang, Yap-Peng Tan, Xiuzhuang Zhou
Published: 2025-11-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06644v3

In this paper, we introduce a novel task termed unified anomaly detection and classification, which aims to simultaneously detect anomalous regions in images and identify their specific categories. Existing methods typically treat anomaly detection and classification as separate tasks, thereby neglecting their inherent correlations and limiting information sharing, which results in suboptimal performance. To address this, we propose UniADC, a model designed to effectively perform both tasks with only a few or even no anomaly images. Specifically, UniADC consists of two key components: a training-free Controllable Inpainting Network and an Implicit-Normal Discriminator. The inpainting network can synthesize anomaly images of specific categories by repainting normal regions guided by anomaly priors, and can also repaint few-shot anomaly samples to augment the available anomaly data. The implicit-normal discriminator addresses the severe challenge of the imbalance between normal and anomalous pixel distributions by implicitly modeling the normal state, achieving precise anomaly detection and classification by aligning fine-grained image features with anomaly-category embeddings. We conduct extensive experiments on four anomaly detection and classification datasets, including MVTec-FS, MTD, WFDD and Real-IAD, and the results demonstrate that UniADC consistently outperforms existing methods in anomaly detection, localization, and classification. The code is available at https://github.com/cnulab/UniADC.

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### Improving Asset Allocation in a Fast Moving Consumer Goods B2B Company: An Interpretable Machine Learning Framework for Commercial Cooler Assignment Based on Multi-Tier Growth Targets

Authors: Renato Castro, Rodrigo Paredes, Douglas Kahn
Published: 2025-11-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.06642v1

In the fast-moving consumer goods (FMCG) industry, deciding where to place physical assets, such as commercial beverage coolers, can directly impact revenue growth and execution efficiency. Although churn prediction and demand forecasting have been widely studied in B2B contexts, the use of machine learning to guide asset allocation remains relatively unexplored. This paper presents a framework focused on predicting which beverage clients are most likely to deliver strong returns in volume after receiving a cooler. Using a private dataset from a well-known Central American brewing and beverage company of 3,119 B2B traditional trade channel clients that received a cooler from 2022-01 to 2024-07, and tracking 12 months of sales transactions before and after cooler installation, three growth thresholds were defined: 10%, 30% and 50% growth in sales volume year over year. The analysis compares results of machine learning models such as XGBoost, LightGBM, and CatBoost combined with SHAP for interpretable feature analysis in order to have insights into improving business operations related to cooler allocation; the results show that the best model has AUC scores of 0.857, 0.877, and 0.898 across the thresholds on the validation set. Simulations suggest that this approach can improve ROI because it better selects potential clients to grow at the expected level and increases cost savings by not assigning clients that will not grow, compared to traditional volume-based approaches with substantial business management recommendations

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### Neyman-Pearson Classification under Both Null and Alternative Distributions Shift

Authors: Mohammadreza M. Kalan, Yuyang Deng, Eitan J. Neugut, Samory Kpotufe
Published: 2025-11-10
Categories: cs.LG, stat.ML
arXiv: https://arxiv.org/abs/2511.06641v1

We consider the problem of transfer learning in Neyman-Pearson classification, where the objective is to minimize the error w.r.t. a distribution $μ_1$, subject to the constraint that the error w.r.t. a distribution $μ_0$ remains below a prescribed threshold. While transfer learning has been extensively studied in traditional classification, transfer learning in imbalanced classification such as Neyman-Pearson classification has received much less attention. This setting poses unique challenges, as both types of errors must be simultaneously controlled. Existing works address only the case of distribution shift in $μ_1$, whereas in many practical scenarios shifts may occur in both $μ_0$ and $μ_1$. We derive an adaptive procedure that not only guarantees improved Type-I and Type-II errors when the source is informative, but also automatically adapt to situations where the source is uninformative, thereby avoiding negative transfer. In addition to such statistical guarantees, the procedures is efficient, as shown via complementary computational guarantees.

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### Bernstein-von Mises for Adaptively Collected Data

Authors: Kevin Du, Yash Nair, Lucas Janson
Published: 2025-11-10
Categories: math.ST, stat.ML
arXiv: https://arxiv.org/abs/2511.06639v1

Uncertainty quantification (UQ) for adaptively collected data, such as that coming from adaptive experiments, bandits, or reinforcement learning, is necessary for critical elements of data collection such as ensuring safety and conducting after-study inference. The data's adaptivity creates significant challenges for frequentist UQ, yet Bayesian UQ remains the same as if the data were independent and identically distributed (i.i.d.), making it an appealing and commonly used approach. Bayesian UQ requires the (correct) specification of a prior distribution while frequentist UQ does not, but for i.i.d. data the celebrated Bernstein-von Mises theorem shows that as the sample size grows, the prior 'washes out' and Bayesian UQ becomes frequentist-valid, implying that the choice of prior need not be a major impediment to Bayesian UQ as it makes no difference asymptotically. This paper for the first time extends the Bernstein-von Mises theorem to adaptively collected data, proving asymptotic equivalence between Bayesian UQ and Wald-type frequentist UQ in this challenging setting. Our result showing this asymptotic agreement does not require the standard stability condition required by works studying validity of Wald-type frequentist UQ; in cases where stability is satisfied, our results combined with these prior studies of frequentist UQ imply frequentist validity of Bayesian UQ. Counterintuitively however, they also provide a negative result that Bayesian UQ is not asymptotically frequentist valid when stability fails, despite the fact that the prior washes out and Bayesian UQ asymptotically matches standard Wald-type frequentist UQ. We empirically validate our theory (positive and negative) via a range of simulations.

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### CaberNet: Causal Representation Learning for Cross-Domain HVAC Energy Prediction

Authors: Kaiyuan Zhai, Jiacheng Cui, Zhehao Zhang, Junyu Xue, Yang Deng, Kui Wu, Guoming Tang
Published: 2025-11-10
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2511.06634v3

Cross-domain HVAC energy prediction is essential for scalable building energy management, particularly because collecting extensive labeled data for every new building is both costly and impractical. Yet, this task remains highly challenging due to the scarcity and heterogeneity of data across different buildings, climate zones, and seasonal patterns. In particular, buildings situated in distinct climatic regions introduce variability that often leads existing methods to overfit to spurious correlations, rely heavily on expert intervention, or compromise on data diversity. To address these limitations, we propose CaberNet, a causal and interpretable deep sequence model that learns invariant (Markov blanket) representations for robust cross-domain prediction. In a purely data-driven fashion and without requiring any prior knowledge, CaberNet integrates i) a global feature gate trained with a self-supervised Bernoulli regularization to distinguish superior causal features from inferior ones, and ii) a domain-wise training scheme that balances domain contributions, minimizes cross-domain loss variance, and promotes latent factor independence. We evaluate CaberNet on real-world datasets collected from three buildings located in three climatically diverse cities, and it consistently outperforms all baselines, achieving a 22.9% reduction in normalized mean squared error (NMSE) compared to the best benchmark. Our code is available at https://github.com/SusCom-Lab/CaberNet-CRL.

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### Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning

Authors: Qinghong Guo, Yu Wang, Ji Cao, Tongya Zheng, Junshu Dai, Bingde Hu, Shunyu Liu, Canghong Jin
Published: 2025-11-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.06633v2

Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced methods leverage Graph Neural Networks (GNNs) and contrastive learning to characterize the spatial structure of road segments in a self-supervised paradigm. However, spatial heterogeneity and temporal dynamics of road networks raise severe challenges to the neighborhood smoothing mechanism of self-supervised GNNs. To address these issues, we propose a $\textbf{D}$ual-branch $\textbf{S}$patial-$\textbf{T}$emporal self-supervised representation framework for enhanced road representations, termed as DST. On one hand, DST designs a mix-hop transition matrix for graph convolution to incorporate dynamic relations of roads from trajectories. Besides, DST contrasts road representations of the vanilla road network against that of the hypergraph in a spatial self-supervised way. The hypergraph is newly built based on three types of hyperedges to capture long-range relations. On the other hand, DST performs next token prediction as the temporal self-supervised task on the sequences of traffic dynamics based on a causal Transformer, which is further regularized by differentiating traffic modes of weekdays from those of weekends. Extensive experiments against state-of-the-art methods verify the superiority of our proposed framework. Moreover, the comprehensive spatiotemporal modeling facilitates DST to excel in zero-shot learning scenarios.

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### DIAL-GS: Dynamic Instance Aware Reconstruction for Label-free Street Scenes with 4D Gaussian Splatting

Authors: Chenpeng Su, Wenhua Wu, Chensheng Peng, Tianchen Deng, Zhe Liu, Hesheng Wang
Published: 2025-11-10
Categories: cs.CV
arXiv: https://arxiv.org/abs/2511.06632v2

Urban scene reconstruction is critical for autonomous driving, enabling structured 3D representations for data synthesis and closed-loop testing. Supervised approaches rely on costly human annotations and lack scalability, while current self-supervised methods often confuse static and dynamic elements and fail to distinguish individual dynamic objects, limiting fine-grained editing. We propose DIAL-GS, a novel dynamic instance-aware reconstruction method for label-free street scenes with 4D Gaussian Splatting. We first accurately identify dynamic instances by exploiting appearance-position inconsistency between warped rendering and actual observation. Guided by instance-level dynamic perception, we employ instance-aware 4D Gaussians as the unified volumetric representation, realizing dynamic-adaptive and instance-aware reconstruction. Furthermore, we introduce a reciprocal mechanism through which identity and dynamics reinforce each other, enhancing both integrity and consistency. Experiments on urban driving scenarios show that DIAL-GS surpasses existing self-supervised baselines in reconstruction quality and instance-level editing, offering a concise yet powerful solution for urban scene modeling.

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### Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives

Authors: Chloe Li, Mary Phuong, Daniel Tan
Published: 2025-11-10
Categories: cs.AI
arXiv: https://arxiv.org/abs/2511.06626v5

As AI systems become more capable of complex agentic tasks, they also become more capable of pursuing undesirable objectives and causing harm. Previous work has attempted to catch these unsafe instances by interrogating models directly about their objectives and behaviors. However, the main weakness of trusting interrogations is that models can lie. We propose self-report fine-tuning (SRFT), a simple supervised fine-tuning technique that trains models to occasionally make factual mistakes, then admit them when asked. We show that the admission of factual errors in simple question-answering settings generalizes out-of-distribution (OOD) to the admission of hidden misaligned objectives in adversarial agentic settings. We evaluate SRFT in OOD stealth tasks, where models are instructed to complete a hidden misaligned objective alongside a user-specified objective without being caught by monitoring. After SRFT, models are more likely to confess the details of their hidden objectives when interrogated, even under strong pressure not to disclose them. Interrogation on SRFT models can detect hidden objectives with near-ceiling performance (F1 score = 0.98), while the baseline model lies when interrogated under the same conditions (F1 score = 0). Interrogation on SRFT models can further elicit the content of the hidden objective, recovering 28-100% details, compared to 0% details recovered in the baseline model and by prefilled assistant turn attacks. This provides a promising technique for promoting honesty propensity and incriminating misaligned AIs.

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### Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography

Authors: Yifei Zhang, Jiashuo Zhang, Mojtaba Safari, Xiaofeng Yang, Liang Zhao
Published: 2025-11-10
Categories: cs.CV, cs.AI, cs.LG
arXiv: https://arxiv.org/abs/2511.06625v5

Low-dose chest computed tomography (LDCT) captures pulmonary and cardiac structures in a single scan, enabling joint assessment of lung and cardiovascular health. Existing approaches typically model these domains independently and do not explicitly represent their physiological interactions. We propose an Explainable Cross-Disease Reasoning Framework for cardiovascular risk assessment from LDCT. The framework follows a constrained clinical-information pathway: it extracts pulmonary findings, grounds cross-organ mechanisms in medical knowledge, and produces a cardiovascular prediction with a natural-language rationale. It combines four components: a frozen lung-risk prior, a pulmonary perception module, an agentic reasoning module, and a cardiac subvolume feature extractor. Their outputs are fused to integrate localized cardiac evidence with mechanism-level pulmonary context. On the National Lung Screening Trial cohort, the framework achieves an AUC of 0.919 for CVD screening and up to 0.838 for CVD mortality prediction, outperforming cardiac-specific, single-disease, and foundation-model baselines. Targeted controls indicate that the gains are not explained by additional thoracic visual features alone, fixed rule propagation, or a single reasoning backend. The proposed framework thus provides an auditable approach to cross-disease cardiovascular risk assessment from LDCT.

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### How Do VLAs Effectively Inherit from VLMs?

Authors: Chuheng Zhang, Rushuai Yang, Xiaoyu Chen, Kaixin Wang, Li Zhao, Yi Chen, Jiang Bian
Published: 2025-11-10
Categories: cs.RO, cs.AI
arXiv: https://arxiv.org/abs/2511.06619v1

Vision-language-action (VLA) models hold the promise to attain generalizable embodied control. To achieve this, a pervasive paradigm is to leverage the rich vision-semantic priors of large vision-language models (VLMs). However, the fundamental question persists: How do VLAs effectively inherit the prior knowledge from VLMs? To address this critical question, we introduce a diagnostic benchmark, GrinningFace, an emoji tabletop manipulation task where the robot arm is asked to place objects onto printed emojis corresponding to language instructions. This task design is particularly revealing -- knowledge associated with emojis is ubiquitous in Internet-scale datasets used for VLM pre-training, yet emojis themselves are largely absent from standard robotics datasets. Consequently, they provide a clean proxy: successful task completion indicates effective transfer of VLM priors to embodied control. We implement this diagnostic task in both simulated environment and a real robot, and compare various promising techniques for knowledge transfer. Specifically, we investigate the effects of parameter-efficient fine-tuning, VLM freezing, co-training, predicting discretized actions, and predicting latent actions. Through systematic evaluation, our work not only demonstrates the critical importance of preserving VLM priors for the generalization of VLA but also establishes guidelines for future research in developing truly generalizable embodied AI systems.

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### GRAPH-GRPO-LEX: Contract Graph Modeling and Reinforcement Learning with Group Relative Policy Optimization

Authors: Moriya Dechtiar, Daniel Martin Katz, Mari Sundaresan, Sylvain Jaume, Hongming Wang
Published: 2025-11-10
Categories: cs.AI, cs.CL, cs.LG, cs.SE
arXiv: https://arxiv.org/abs/2511.06618v1

Contracts are complex documents featuring detailed formal structures, explicit and implicit dependencies and rich semantic content. Given these document properties, contract drafting and manual examination of contracts have proven to be both arduous and susceptible to errors. This work aims to simplify and automate the task of contract review and analysis using a novel framework for transforming legal contracts into structured semantic graphs, enabling computational analysis and data-driven insights. We introduce a detailed ontology mapping core legal contract elements to their graph-theoretic equivalents of nodes and edges. We then present a reinforcement learning based Large Language Model (LLM) framework for segmentation and extraction of entities and relationships from contracts. Our method, GRAPH-GRPO-LEX, incorporates both LLMs and reinforcement learning with group relative policy optimization (GRPO). By applying a carefully drafted reward function of graph metrics, we demonstrate the ability to automatically identify direct relationships between clauses, and even uncover hidden dependencies. Our introduction of the gated GRPO approach shows a strong learning signal and can move contract analysis from a linear, manual reading process to an easily visualized graph. This allows for a more dynamic analysis, including building the groundwork for contract linting similar to what is now practiced in software engineering.

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### From LIF to QIF: Toward Differentiable Spiking Neurons for Scientific Machine Learning

Authors: Ruyin Wan, George Em Karniadakis, Panos Stinis
Published: 2025-11-10
Categories: cs.NE, math.NA
arXiv: https://arxiv.org/abs/2511.06614v1

Spiking neural networks (SNNs) offer biologically inspired computation but remain underexplored for continuous regression tasks in scientific machine learning. In this work, we introduce and systematically evaluate Quadratic Integrate-and-Fire (QIF) neurons as an alternative to the conventional Leaky Integrate-and-Fire (LIF) model in both directly trained SNNs and ANN-to-SNN conversion frameworks. The QIF neuron exhibits smooth and differentiable spiking dynamics, enabling gradient-based training and stable optimization within architectures such as multilayer perceptrons (MLPs), Deep Operator Networks (DeepONets), and Physics-Informed Neural Networks (PINNs). Across benchmarks on function approximation, operator learning, and partial differential equation (PDE) solving, QIF-based networks yield smoother, more accurate, and more stable predictions than their LIF counterparts, which suffer from discontinuous time-step responses and jagged activation surfaces. These results position the QIF neuron as a computational bridge between spiking and continuous-valued deep learning, advancing the integration of neuroscience-inspired dynamics into physics-informed and operator-learning frameworks.

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### Accurate Measurement of 3D and 2D Circular Centers With Application to LiDAR-Camera Extrinsic Calibration

Authors: Jiajun Jiang, Xiao Hu, Wancheng Liu, Wei Jiang
Published: 2025-11-10
Categories: cs.CV, cs.RO
arXiv: https://arxiv.org/abs/2511.06611v2

Accurate measurement of circular centers is a fun-damental geometric sensing problem in instrumentation and measurement tasks involving cameras, LiDARs, and other spa-tial sensors. In circular-target-based LiDAR-camera extrinsic calibration, a 3D circular center measured from LiDAR and its corresponding 2D projected center measured in the image serve as cross-modal geometric observations for estimating the rigid transformation between the two sensor frames. Conven-tional pipelines can bias both measurements: the 3D center is often obtained by sequential plane fitting, point projection, and 2D circle fitting, while the image ellipse center is frequently treated as the projected circle center even though the two generally differ under perspective projection. This paper focuses on accurate 3D and 2D circular-center measurement and uses LiDAR-camera extrinsic calibration as a representative applica-tion. A conformal-geometric-algebra estimator is integrated with RANSAC to jointly recover the 3D center, normal, and radius from noisy or partially observed LiDAR points. A chord-length-variance criterion then estimates the 2D projected center, with its twofold ambiguity resolved by homography validation or a quasi-RANSAC fallback. Synthetic and real-sensor experiments show that the proposed method improves circular-center measurement accuracy and reduces extrinsic calibration error across different target and sensor configurations.

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### Non-Rival Data as Rival Products: An Encapsulation-Forging Approach for Data Synthesis

Authors: Kaidong Wang, Jiale Li, Shao-Bo Lin, Yao Wang
Published: 2025-11-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.06610v1

The non-rival nature of data creates a dilemma for firms: sharing data unlocks value but risks eroding competitive advantage. Existing data synthesis methods often exacerbate this problem by creating data with symmetric utility, allowing any party to extract its value. This paper introduces the Encapsulation-Forging (EnFo) framework, a novel approach to generate rival synthetic data with asymmetric utility. EnFo operates in two stages: it first encapsulates predictive knowledge from the original data into a designated ``key'' model, and then forges a synthetic dataset by optimizing the data to intentionally overfit this key model. This process transforms non-rival data into a rival product, ensuring its value is accessible only to the intended model, thereby preventing unauthorized use and preserving the data owner's competitive edge. Our framework demonstrates remarkable sample efficiency, matching the original data's performance with a fraction of its size, while providing robust privacy protection and resistance to misuse. EnFo offers a practical solution for firms to collaborate strategically without compromising their core analytical advantage.

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### A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

Authors: Xuyang Li, John Harlim, Dibyajyoti Chakraborty, Romit Maulik
Published: 2025-11-10
Categories: cs.LG, math.DS
arXiv: https://arxiv.org/abs/2511.06609v5

The accurate forecasting of complex, high-dimensional dynamical systems from observational data is a fundamental task across numerous scientific and engineering disciplines. A significant challenge arises from noisy observations of deterministic dynamics, which severely degrade the performance of data-driven models. In chaotic dynamical systems, where small initial errors amplify exponentially, it is particularly difficult to develop a model from noisy data that achieves short-term accuracy while preserving long-term invariant properties. To overcome this, we consider the weak formulation as a complementary approach to the classical L2-loss function for training models of dynamical systems. We empirically verify that the weak formulation, with a proper choice of test function and integration domain, effectively filters noisy data. This insight explains why a weak form loss function is analogous to fitting a model to filtered data and provides a practical way to parameterize the weak form. Subsequently, we demonstrate how this approach overcomes the instability and inaccuracy of standard Neural ODE (NODE) in modeling chaotic systems. Through numerical examples, we show that our proposed training strategy, the Weak Penalty NODE, is computationally efficient, solver-agnostic, and yields accurate and robust forecasts across benchmark chaotic systems and a real-world climate dataset.

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### Beyond Fixed Depth: Adaptive Graph Neural Networks for Node Classification Under Varying Homophily

Authors: Asela Hevapathige, Asiri Wijesinghe, Ahad N. Zehmakan
Published: 2025-11-10
Categories: cs.LG, cs.AI
arXiv: https://arxiv.org/abs/2511.06608v1

Graph Neural Networks (GNNs) have achieved significant success in addressing node classification tasks. However, the effectiveness of traditional GNNs degrades on heterophilic graphs, where connected nodes often belong to different labels or properties. While recent work has introduced mechanisms to improve GNN performance under heterophily, certain key limitations still exist. Most existing models apply a fixed aggregation depth across all nodes, overlooking the fact that nodes may require different propagation depths based on their local homophily levels and neighborhood structures. Moreover, many methods are tailored to either homophilic or heterophilic settings, lacking the flexibility to generalize across both regimes. To address these challenges, we develop a theoretical framework that links local structural and label characteristics to information propagation dynamics at the node level. Our analysis shows that optimal aggregation depth varies across nodes and is critical for preserving class-discriminative information. Guided by this insight, we propose a novel adaptive-depth GNN architecture that dynamically selects node-specific aggregation depths using theoretically grounded metrics. Our method seamlessly adapts to both homophilic and heterophilic patterns within a unified model. Extensive experiments demonstrate that our approach consistently enhances the performance of standard GNN backbones across diverse benchmarks.

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### Explainable Probabilistic Machine Learning for Predicting Drilling Fluid Loss of Circulation in Marun Oil Field

Authors: Seshu Kumar Damarla, Xiuli Zhu
Published: 2025-11-10
Categories: cs.LG
arXiv: https://arxiv.org/abs/2511.06607v1

Lost circulation remains a major and costly challenge in drilling operations, often resulting in wellbore instability, stuck pipe, and extended non-productive time. Accurate prediction of fluid loss is therefore essential for improving drilling safety and efficiency. This study presents a probabilistic machine learning framework based on Gaussian Process Regression (GPR) for predicting drilling fluid loss in complex formations. The GPR model captures nonlinear dependencies among drilling parameters while quantifying predictive uncertainty, offering enhanced reliability for high-risk decision-making. Model hyperparameters are optimized using the Limited memory Broyden Fletcher Goldfarb Shanno (LBFGS) algorithm to ensure numerical stability and robust generalization. To improve interpretability, Local Interpretable Model agnostic Explanations (LIME) are employed to elucidate how individual features influence model predictions. The results highlight the potential of explainable probabilistic learning for proactive identification of lost-circulation risks, optimized design of lost circulation materials (LCM), and reduction of operational uncertainties in drilling applications.

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### SPUR: A Plug-and-Play Framework for Integrating Spatial Audio Understanding and Reasoning into Large Audio-Language Models

Authors: S Sakshi, Vaibhavi Lokegaonkar, Neil Zhang, Ramani Duraiswami, Sreyan Ghosh, Dinesh Manocha, Lie Lu
Published: 2025-11-10
Categories: eess.AS, cs.AI
arXiv: https://arxiv.org/abs/2511.06606v2

Spatial perception is central to auditory intelligence, enabling accurate understanding of real-world acoustic scenes and advancing human-level perception of the world around us. While recent large audio-language models (LALMs) show strong reasoning over complex audios, most operate on monaural inputs and lack the ability to capture spatial cues such as direction, elevation, and distance. We introduce SPUR, a lightweight, plug-in approach that equips LALMs with spatial perception through minimal architectural changes. SPUR consists of: (i) a First-Order Ambisonics (FOA) encoder that maps (W, X, Y, Z) channels to rotation-aware, listener-centric spatial features, integrated into target LALMs via a multimodal adapter; and (ii) SPUR-Set, a spatial QA dataset combining open-source FOA recordings with controlled simulations, emphasizing relative direction, elevation, distance, and overlap for supervised spatial reasoning. Fine-tuning our model on the SPUR-Set consistently improves spatial QA and multi-speaker attribution while preserving general audio understanding. SPUR provides a simple recipe that transforms monaural LALMs into spatially aware models. Extensive ablations validate the effectiveness of our approach.

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### Duality-based Mode Operations and Pyramid Multilayer Mapping for Rhetorical Modes

Authors: Zi-Niu Wu
Published: 2025-11-10
Categories: cs.CL, cs.FL, cs.PL
arXiv: https://arxiv.org/abs/2511.06601v1

Rhetorical modes are useful in both academic and non-academic writing, and can be subjects to be studied within linguistic research and computational modeling. Establishing a conceptual bridge among these domains could enable each to benefit from the others. This paper proposes duality-based mode operations (split-unite, forward-backward, expansion-reduction and orthogonal dualities) to expand the set of rhetorical modes, introducing generated modes like combination and generalization, thereby enhancing epistemic diversity across multiple applications. It further presents a pyramid multilayer mapping framework (e.g., three layers from the rhetorical model layer, to cognitive layer, and to epistemic layers) that reduces the resulting cognitive complexity. The degrees of expressive diversity and complexity reduction are quantified through binomial combinatorics and Shannon entropy analysis. A Marginal Rhetorical Bit (MRB) is identified, permitting the definition of a rhetorical-scalable parameter that measures expressive growth speed in bits per stage. A direct entropy measure shows that hierarchical selection over smaller subsets markedly reduces choice uncertainty compared with flat selection across all modes. These considerations appear to transform static and non-measurable rhetorical taxonomies into more dynamic and more measurable systems for discourse design. From this work, it would be possible to identify a pathway for future AI systems to operate not only on language tokens but on layered rhetorical reasoning structures, bridging linguistic, pedagogical, academic, and computational research

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