OpenRaiser/Intern-Atlas huggingface dataset:
A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network
Venue: arXiv cs.LG 2025
Year: 2025

Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healthcare costs. Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC. However, a significant challenge with GNNs is their reliance on an existing graph structure, which is not readily available for MCC. To address this challenge, we propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data to enhance predictive analytics for MCC. Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data. This allows for a comprehensive understanding of individual health trajectories and facilitates the creation of diverse patient stochastic similarity graphs while preserving the original feature set. These variations of patient stochastic similarity graphs, generated from the GVAE decoder, are then processed by a GNN using a novel Laplacian regularization technique to refine the graph structure over time and improves the prediction accuracy of MCC. A contextual Bandit is designed to evaluate the stochastically generated graphs and identify the best-performing graph for the GNN model iteratively until model convergence. We validate the performance of the proposed contextual Bandit algorithm against $\varepsilon$-Greedy and multi-armed Bandit algorithms on a large cohort (n = 1,592) of patients with MCC. These advancements highlight the potential of the proposed approach to transform predictive healthcare analytics, enabling a more personalized and proactive approach to MCC management.
The FIX Benchmark: Extracting Features Interpretable to eXperts
Venue: arXiv cs.LG 2025
Year: 2025

Feature-based methods are commonly used to explain model predictions, but these methods often implicitly assume that interpretable features are readily available. However, this is often not the case for high-dimensional data, and it can be hard even for domain experts to mathematically specify which features are important. Can we instead automatically extract collections or groups of features that are aligned with expert knowledge? To address this gap, we present FIX (Features Interpretable to eXperts), a benchmark for measuring how well a collection of features aligns with expert knowledge. In collaboration with domain experts, we propose FIXScore, a unified expert alignment measure applicable to diverse real-world settings across cosmology, psychology, and medicine domains in vision, language, and time series data modalities. With FIXScore, we find that popular feature-based explanation methods have poor alignment with expert-specified knowledge, highlighting the need for new methods that can better identify features interpretable to experts.
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Venue: arXiv cs.LG 2025
Year: 2025

Continual learning (CL) refers to the ability of an algorithm to continuously and incrementally acquire new knowledge from its environment while retaining previously learned information. A model trained on one data modality often fails when tested with a different modality. A straightforward approach might be to fuse the two modalities by concatenating their features and training the model on the fused data. However, this requires retraining the model from scratch each time it encounters a new domain. In this paper, we introduce a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class-incremental and domain-incremental learning scenarios in an artificial environment where labeled data is scarce, yet non-iid (independent and identical distribution) unlabeled data from the environment is plentiful. The proposed algorithm is efficient and only requires storing prototypes for each class. We evaluate the algorithm's effectiveness on a challenging custom multimodal dataset comprising of tactile data from a soft pneumatic gripper, and visual data from non-stationary images of objects extracted from video sequences. Additionally, we conduct an ablation study on the custom dataset and the Core50 dataset to highlight the contributions of different components of the algorithm. To further demonstrate the robustness of the algorithm, we perform a real-time experiment for object classification using the soft gripper and an external independent camera setup, all synchronized with the Robot Operating System (ROS) framework.
Physics-Informed Variational State-Space Gaussian Processes
Venue: arXiv cs.LG 2025
Year: 2025

Differential equations are important mechanistic models that are integral to many scientific and engineering applications. With the abundance of available data there has been a growing interest in data-driven physics-informed models. Gaussian processes (GPs) are particularly suited to this task as they can model complex, non-linear phenomena whilst incorporating prior knowledge and quantifying uncertainty. Current approaches have found some success but are limited as they either achieve poor computational scalings or focus only on the temporal setting. This work addresses these issues by introducing a variational spatio-temporal state-space GP that handles linear and non-linear physical constraints while achieving efficient linear-in-time computation costs. We demonstrate our methods in a range of synthetic and real-world settings and outperform the current state-of-the-art in both predictive and computational performance.
Cross-Domain Knowledge Transfer for Underwater Acoustic Classification Using Pre-trained Models
Venue: arXiv cs.SD 2025
Year: 2025

Transfer learning is commonly employed to leverage large, pre-trained models and perform fine-tuning for downstream tasks. The most prevalent pre-trained models are initially trained using ImageNet. However, their ability to generalize can vary across different data modalities. This study compares pre-trained Audio Neural Networks (PANNs) and ImageNet pre-trained models within the context of underwater acoustic target recognition (UATR). It was observed that the ImageNet pre-trained models slightly out-perform pre-trained audio models in passive sonar classification. We also analyzed the impact of audio sampling rates for model pre-training and fine-tuning. This study contributes to transfer learning applications of UATR, illustrating the potential of pre-trained models to address limitations caused by scarce, labeled data in the UATR domain.
Misty: UI Prototyping Through Interactive Conceptual Blending
Venue: arXiv cs.HC 2025
Year: 2025

UI prototyping often involves iterating and blending elements from examples such as screenshots and sketches, but current tools offer limited support for incorporating these examples. Inspired by the cognitive process of conceptual blending, we introduce a novel UI workflow that allows developers to rapidly incorporate diverse aspects from design examples into work-in-progress UIs. We prototyped this workflow as Misty. Through an exploratory first-use study with 14 frontend developers, we assessed Misty's effectiveness and gathered feedback on this workflow. Our findings suggest that Misty's conceptual blending workflow helps developers kickstart creative explorations, flexibly specify intent in different stages of prototyping, and inspires developers through serendipitous UI blends. Misty demonstrates the potential for tools that blur the boundaries between developers and designers.
Role-Play Paradox in Large Language Models: Reasoning Performance Gains and Ethical Dilemmas
Venue: arXiv cs.CL 2025
Year: 2025

Role-play in large language models (LLMs) enhances their ability to generate contextually relevant and high-quality responses by simulating diverse cognitive perspectives. However, our study identifies significant risks associated with this technique. First, we demonstrate that autotuning, a method used to auto-select models' roles based on the question, can lead to the generation of harmful outputs, even when the model is tasked with adopting neutral roles. Second, we investigate how different roles affect the likelihood of generating biased or harmful content. Through testing on benchmarks containing stereotypical and harmful questions, we find that role-play consistently amplifies the risk of biased outputs. Our results underscore the need for careful consideration of both role simulation and tuning processes when deploying LLMs in sensitive or high-stakes contexts.
Point Cloud Structural Similarity-based Underwater Sonar Loop Detection
Venue: arXiv cs.RO 2025
Year: 2025

In this letter, we propose a point cloud structural similarity-based loop detection method for underwater Simultaneous Localization and Mapping using sonar sensors. Existing sonar-based loop detection approaches often rely on 2D projection and keypoint extraction, which can lead to data loss and poor performance in feature-scarce environments. Additionally, methods based on neural networks or Bag-of-Words require extensive preprocessing, such as model training or vocabulary creation, reducing adaptability to new environments. To address these challenges, our method directly utilizes 3D sonar point clouds without projection and computes point-wise structural feature maps based on geometry, normals, and curvature. By leveraging rotation-invariant similarity comparisons, the proposed approach eliminates the need for keypoint detection and ensures robust loop detection across diverse underwater terrains. We validate our method using two real-world datasets: the Antarctica dataset obtained from deep underwater and the Seaward dataset collected from rivers and lakes. Experimental results show that our method achieves the highest loop detection performance compared to existing keypointbased and learning-based approaches while requiring no additional training or preprocessing. Our code is available at https://github.com/donghwijung/point_cloud_structural_similarity_based_underwater_sonar_loop_detection.
Uncovering Latent Chain of Thought Vectors in Language Models
Venue: arXiv cs.CL 2025
Year: 2025

In this work, we examine how targeted perturbations in the activation space of Language Models (LMs) can encode complex reasoning patterns. We inject steering vectors, derived from LM activations, into LMs during inference time and study whether these vectors can induce Chain-of-Thought (CoT) reasoning in LMs without the need for natural language prompting. We demonstrate this approach on Llama3 8B Instruct and Mistral 7B v0.2 Instruct and show that activation-space interventions achieve competitive, if not superior, performance compared to traditional CoT prompting across multiple reasoning benchmarks, including GSM8k, MMLU, AGI Eval, and ARC AI2. These findings suggest that neural network activations can encode reasoning patterns, offering a new application of activation space manipulation as a tool for tuning model behavior.
An average case efficient algorithm for solving two-variable linear Diophantine equations
Venue: arXiv cs.CR 2025
Year: 2025

Solving two-variable linear Diophantine equations has applications in many cryptographic protocols such as RSA and Elliptic curve cryptography. The Extended Euclid's algorithm is the most widely used algorithm to solve these equations. We revisit two algorithms to solve two-variable linear Diophantine equations. We write the iterative version of one of the revisited algorithms. For another, we do a fine-grained analysis of the number of recursive calls and arrive at a periodic function that represents the number of recursive calls. We find the period and use it to derive an accurate closed-form expression for the average number of recursive calls incurred by that algorithm. We find multiple loose upper bounds on the average number of recursive calls in different cases based on whether a solution exists or not. We find that for a fixed value of $a,b$ and a varying $c$, such that the equation $ax+by=c$ (where $a > b$) is solvable, we can find the solution in $O\left(\frac{\log b}{gcd(a,b)}\right)$ average number of recursions or steps. We computationally evaluate this bound as well as one more upper bound and compare them with the average number of recursive calls in Extended Euclid's algorithm on a number of random $512$-bit inputs. We observe that the average number of iterations in the analyzed algorithm decreases with an increase in $gcd(a,b)$. We propose an iterative version of the algorithm. We implement this algorithm and find that the average number of iterations by our algorithm is less than that of two existing algorithms.
The Foundational Pose as a Selection Mechanism for the Design of Tool-Wielding Multi-Finger Robotic Hands
Venue: arXiv cs.RO 2025
Year: 2025

To wield an object means to hold and move it in a way that exploits its functions. When humans wield tools -- such as writing with a pen or cutting with scissors -- our hands would reach very specific poses, often drastically different from how we pick up the same objects just to transport them. In this work, we investigate the design of tool-wielding multi-finger robotic hand through a hypothesis: If a hand can kinematically reach a foundational pose (FP) with a tool, then it can wield the tool from that FP. We interpret FPs as snapshots that capture the workings of underlying parallel mechanisms formed by the tool and the hand, and one hand can form multiple mechanisms with the same tool. We tested our hypothesis in a hand design experiment, where we developed a sampling-based multi-objective design optimization framework that uses three FPs to computationally generate many different hand designs and evaluate them. The results show that 10,785 out of the 100,480 hand designs we sampled reached the FPs; more than 99\% of the 10,785 hands that reached the FPs successfully wielded tools, supporting our hypothesis. Meanwhile, our methods provide insights into the non-convex, multi-objective hand design optimization problem -- such as clustering and the Pareto front -- that could be hard to unveil with methods that return a single ``optimal" design. Lastly, we demonstrate our methods' real-world feasibility and potential with a hardware prototype equipped with rigid endoskeleton and soft skin.
QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling
Venue: arXiv cs.CL 2025
Year: 2025

Large Language models (LLMs) have brought about substantial advancements in the field of Question Answering (QA) systems. These models do remarkably well in addressing intricate inquiries in a variety of disciplines. However, because of domain-specific vocabulary, complex technological concepts, and the requirement for exact responses applying LLMs to specialized sectors like telecommunications presents additional obstacles. GPT-3.5 has been used in recent work, to obtain noteworthy accuracy for telecom-related questions in a Retrieval Augmented Generation (RAG) framework. Notwithstanding these developments, the practical use of models such as GPT-3.5 is restricted by their proprietary nature and high computing demands. This paper introduces QMOS, an innovative approach which uses a Question-Masked loss and Option Shuffling trick to enhance the performance of LLMs in answering Multiple-Choice Questions in the telecommunications domain. Our focus was on using opensource, smaller language models (Phi-2 and Falcon-7B) within an enhanced RAG framework. Our multi-faceted approach involves several enhancements to the whole LLM-RAG pipeline of finetuning, retrieval, prompt engineering and inference. Our approaches significantly outperform existing results, achieving accuracy improvements from baselines of 24.70% to 49.30% with Falcon-7B and from 42.07% to 84.65% with Phi-2.
Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review
Venue: arXiv cs.RO 2025
Year: 2025

Despite advancements in perception and planning for autonomous vehicles (AVs), validating their performance remains a significant challenge. The deployment of planning algorithms in real-world environments is often ineffective due to discrepancies between simulations and real traffic conditions. Evaluating AVs planning algorithms in simulation typically involves replaying driving logs from recorded real-world traffic. However, entities replayed from offline data are not reactive, lack the ability to respond to arbitrary AV behavior, and cannot behave in an adversarial manner to test certain properties of the driving policy. Therefore, simulation with realistic and potentially adversarial entities represents a critical task for AV planning software validation. In this work, we aim to review current research efforts in the field of traffic simulation, focusing on the application of advanced techniques for modeling realistic and adversarial behaviors of traffic entities. The objective of this work is to categorize existing approaches based on the proposed classes of traffic entity behavior and scenario behavior control. Moreover, we collect traffic datasets and examine existing traffic simulations with respect to their employed default traffic entities. Finally, we identify challenges and open questions that hold potential for future research.
Creative Writers' Attitudes on Writing as Training Data for Large Language Models
Venue: arXiv cs.HC 2025
Year: 2025

The use of creative writing as training data for large language models (LLMs) is highly contentious and many writers have expressed outrage at the use of their work without consent or compensation. In this paper, we seek to understand how creative writers reason about the real or hypothetical use of their writing as training data. We interviewed 33 writers with variation across genre, method of publishing, degree of professionalization, and attitudes toward and engagement with LLMs. We report on core principles that writers express (support of the creative chain, respect for writers and writing, and the human element of creativity) and how these principles can be at odds with their realistic expectations of the world (a lack of control, industry-scale impacts, and interpretation of scale). Collectively these findings demonstrate that writers have a nuanced understanding of LLMs and are more concerned with power imbalances than the technology itself.
Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis
Venue: arXiv cs.CL 2025
Year: 2025

Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of the world's languages. In this paper, we extend these probing methods to a multilingual context, investigating the behaviors of LLMs across diverse languages. We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across layers, and similarities between probing vectors for multiple languages. Our key findings reveal: (1) a consistent performance gap between high-resource and low-resource languages, with high-resource languages achieving significantly higher probing accuracy; (2) divergent layer-wise accuracy trends, where high-resource languages show substantial improvement in deeper layers similar to English; and (3) higher representational similarities among high-resource languages, with low-resource languages demonstrating lower similarities both among themselves and with high-resource languages. These results highlight significant disparities in LLMs' multilingual capabilities and emphasize the need for improved modeling of low-resource languages.
Visual-auditory Extrinsic Contact Estimation
Venue: arXiv cs.RO 2025
Year: 2025

Robust manipulation often hinges on a robot's ability to perceive extrinsic contacts-contacts between a grasped object and its surrounding environment. However, these contacts are difficult to observe through vision alone due to occlusions, limited resolution, and ambiguous near-contact states. In this paper, we propose a visual-auditory method for extrinsic contact estimation that integrates global scene information from vision with local contact cues obtained through active audio sensing. Our approach equips a robotic gripper with contact microphones and conduction speakers, enabling the system to emit and receive acoustic signals through the grasped object to detect external contacts. We train our perception pipeline entirely in simulation and zero-shot transfer to the real world. To bridge the sim-to-real gap, we introduce a real-to-sim audio hallucination technique, injecting real-world audio samples into simulated scenes with ground-truth contact labels. The resulting multimodal model accurately estimates both the location and size of extrinsic contacts across a range of cluttered and occluded scenarios. We further demonstrate that explicit contact prediction significantly improves policy learning for downstream contact-rich manipulation tasks.
Safe Expeditious Whole-Body Control of Mobile Manipulators for Collision Avoidance
Venue: arXiv cs.RO 2025
Year: 2025

Whole-body reactive obstacle avoidance for mobile manipulators (MM) remains an open research problem. Control Barrier Functions (CBF), combined with Quadratic Programming (QP), have become a popular approach for reactive control with safety guarantees. However, traditional CBF methods often face issues such as pseudo-equilibrium problems (PEP) and are ineffective in handling dynamic obstacles. To overcome these challenges, we introduce the Adaptive Cyclic Inequality (ACI) method. ACI takes into account both the obstacle's velocity and the robot's nominal control to define a directional safety constraint. When added to the CBF-QP, ACI helps avoid PEP and enables reliable collision avoidance in dynamic environments. We validate our approach on a MM that includes a low-dimensional mobile base and a high-dimensional manipulator, demonstrating the generality of the framework. In addition, we integrate a simple yet effective method for avoiding self-collisions, allowing the robot enabling comprehensive whole-body collision-free operation. Extensive benchmark comparisons and experiments demonstrate that our method performs well in unknown and dynamic scenarios, including difficult tasks like avoiding sticks swung by humans and rapidly thrown objects.
Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method
Venue: arXiv cs.CL 2025
Year: 2025

As the scale of training corpora for large language models (LLMs) grows, model developers become increasingly reluctant to disclose details on their data. This lack of transparency poses challenges to scientific evaluation and ethical deployment. Recently, pretraining data detection approaches, which infer whether a given text was part of an LLM's training data through black-box access, have been explored. The Min-K\% Prob method, which has achieved state-of-the-art results, assumes that a non-training example tends to contain a few outlier words with low token probabilities. However, the effectiveness may be limited as it tends to misclassify non-training texts that contain many common words with high probabilities predicted by LLMs. To address this issue, we introduce a divergence-based calibration method, inspired by the divergence-from-randomness concept, to calibrate token probabilities for pretraining data detection. We compute the cross-entropy (i.e., the divergence) between the token probability distribution and the token frequency distribution to derive a detection score. We have developed a Chinese-language benchmark, PatentMIA, to assess the performance of detection approaches for LLMs on Chinese text. Experimental results on English-language benchmarks and PatentMIA demonstrate that our proposed method significantly outperforms existing methods. Our code and PatentMIA benchmark are available at https://github.com/zhang-wei-chao/DC-PDD.