OpenRaiser/Intern-Atlas huggingface dataset:
Causal Inference Isn't Special: Why It's Just Another Prediction Problem
Venue: arXiv cs.LG 2025
Year: 2025

Causal inference is often portrayed as fundamentally distinct from predictive modeling, with its own terminology, goals, and intellectual challenges. But at its core, causal inference is simply a structured instance of prediction under distribution shift. In both cases, we begin with labeled data from a source domain and seek to generalize to a target domain where outcomes are not observed. The key difference is that in causal inference, the labels -- potential outcomes -- are selectively observed based on treatment assignment, introducing bias that must be addressed through assumptions. This perspective reframes causal estimation as a familiar generalization problem and highlights how techniques from predictive modeling, such as reweighting and domain adaptation, apply directly to causal tasks. It also clarifies that causal assumptions are not uniquely strong -- they are simply more explicit. By viewing causal inference through the lens of prediction, we demystify its logic, connect it to familiar tools, and make it more accessible to practitioners and educators alike.
IMPersona: Evaluating Individual Level LM Impersonation
Venue: arXiv cs.CL 2025
Year: 2025

As language models achieve increasingly human-like capabilities in conversational text generation, a critical question emerges: to what extent can these systems simulate the characteristics of specific individuals? To evaluate this, we introduce IMPersona, a framework for evaluating LMs at impersonating specific individuals' writing style and personal knowledge. Using supervised fine-tuning and a hierarchical memory-inspired retrieval system, we demonstrate that even modestly sized open-source models, such as Llama-3.1-8B-Instruct, can achieve impersonation abilities at concerning levels. In blind conversation experiments, participants (mis)identified our fine-tuned models with memory integration as human in 44.44% of interactions, compared to just 25.00% for the best prompting-based approach. We analyze these results to propose detection methods and defense strategies against such impersonation attempts. Our findings raise important questions about both the potential applications and risks of personalized language models, particularly regarding privacy, security, and the ethical deployment of such technologies in real-world contexts.
Future-Proof Yourself: An AI Era Survival Guide
Venue: arXiv cs.LG 2025
Year: 2025

Future-Proof Yourself is a practical guide that helps readers navigate the fast-changing world of artificial intelligence in everyday life. The book begins by explaining how computers learn from data in simple, relatable terms, and gradually introduces the methods used in modern AI. It shows how basic ideas in machine learning evolve into advanced systems that can recognize images, understand language, and even make decisions. The guide also reviews the history of AI and highlights the major breakthroughs that have shaped its growth. Looking ahead, the book explores emerging trends such as the integration of AI with digital twins, wearable devices, and virtual environments. Designed for a general audience, the text avoids heavy technical jargon and presents complex ideas in clear, straightforward language so that anyone can gain a solid understanding of the technology that is set to transform our future.
Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications
Venue: arXiv cs.RO 2025
Year: 2025

Driving scenario data play an increasingly vital role in the development of intelligent vehicles and autonomous driving. Accurate and efficient scenario data search is critical for both online vehicle decision-making and planning, and offline scenario generation and simulations, as it allows for leveraging the scenario experiences to improve the overall performance. Especially with the application of large language models (LLMs) and Retrieval-Augmented-Generation (RAG) systems in autonomous driving, urgent requirements are put forward. In this paper, we introduce the Driving-RAG framework to address the challenges of efficient scenario data embedding, search, and applications for RAG systems. Our embedding model aligns fundamental scenario information and scenario distance metrics in the vector space. The typical scenario sampling method combined with hierarchical navigable small world can perform efficient scenario vector search to achieve high efficiency without sacrificing accuracy. In addition, the reorganization mechanism by graph knowledge enhances the relevance to the prompt scenarios and augment LLM generation. We demonstrate the effectiveness of the proposed framework on typical trajectory planning task for complex interactive scenarios such as ramps and intersections, showcasing its advantages for RAG applications.
UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding
Venue: arXiv cs.CV 2025
Year: 2025

We introduce UniToken, an auto-regressive generation model that encodes visual inputs through a combination of discrete and continuous representations, enabling seamless integration of unified visual understanding and image generation tasks. Unlike previous approaches that rely on unilateral visual representations, our unified visual encoding framework captures both high-level semantics and low-level details, delivering multidimensional information that empowers heterogeneous tasks to selectively assimilate domain-specific knowledge based on their inherent characteristics. Through in-depth experiments, we uncover key principles for developing a unified model capable of both visual understanding and image generation. Extensive evaluations across a diverse range of prominent benchmarks demonstrate that UniToken achieves state-of-the-art performance, surpassing existing approaches. These results establish UniToken as a robust foundation for future research in this domain. The code and models are available at https://github.com/SxJyJay/UniToken.
Evaluation framework for Image Segmentation Algorithms
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a comprehensive evaluation framework for image segmentation algorithms, encompassing naive methods, machine learning approaches, and deep learning techniques. We begin by introducing the fundamental concepts and importance of image segmentation, and the role of interactive segmentation in enhancing accuracy. A detailed background theory section explores various segmentation methods, including thresholding, edge detection, region growing, feature extraction, random forests, support vector machines, convolutional neural networks, U-Net, and Mask R-CNN. The implementation and experimental setup are thoroughly described, highlighting three primary approaches: algorithm assisting user, user assisting algorithm, and hybrid methods. Evaluation metrics such as Intersection over Union (IoU), computation time, and user interaction time are employed to measure performance. A comparative analysis presents detailed results, emphasizing the strengths, limitations, and trade-offs of each method. The paper concludes with insights into the practical applicability of these approaches across various scenarios and outlines future work, focusing on expanding datasets, developing more representative approaches, integrating real-time feedback, and exploring weakly supervised and self-supervised learning paradigms to enhance segmentation accuracy and efficiency. Keywords: Image Segmentation, Interactive Segmentation, Machine Learning, Deep Learning, Computer Vision
DRAMA: A Dynamic Packet Routing Algorithm using Multi-Agent Reinforcement Learning with Emergent Communication
Venue: arXiv cs.NI 2025
Year: 2025

The continuous expansion of network data presents a pressing challenge for conventional routing algorithms. As the demand escalates, these algorithms are struggling to cope. In this context, reinforcement learning (RL) and multi-agent reinforcement learning (MARL) algorithms emerge as promising solutions. However, the urgency and importance of the problem are clear, as existing RL/MARL-based routing approaches lack effective communication in run time among routers, making it challenging for individual routers to adapt to complex and dynamic changing networks. More importantly, they lack the ability to deal with dynamically changing network topology, especially the addition of the router, due to the non-scalability of their neural networks. This paper proposes a novel dynamic routing algorithm, DRAMA, incorporating emergent communication in multi-agent reinforcement learning. Through emergent communication, routers could learn how to communicate effectively to maximize the optimization objectives. Meanwhile, a new Q-network and graph-based emergent communication are introduced to dynamically adapt to the changing network topology without retraining while ensuring robust performance. Experimental results showcase DRAMA's superior performance over the traditional routing algorithm and other RL/MARL-based algorithms, achieving a higher delivery rate and lower latency in diverse network scenarios, including dynamic network load and topology. Moreover, an ablation experiment validates the prospect of emergent communication in facilitating packet routing.
Do We Need Responsible XR? Drawing on Responsible AI to Inform Ethical Research and Practice into XRAI / the Metaverse
Venue: arXiv cs.HC 2025
Year: 2025

This position paper for the CHI 2025 workshop "Everyday AR through AI-in-the-Loop" reflects on whether as a field HCI needs to define Responsible XR as a parallel to, and in conjunction with, Responsible AI, addressing the unique vulnerabilities posed by mass adoption of wearable AI-enabled AR glasses and XR devices that could enact AI-driven human perceptual augmentation.
Robust and scalable nonlinear solvers for finite element discretizations of biological transportation networks
Venue: arXiv cs.CE 2025
Year: 2025

We develop robust and scalable fully implicit nonlinear finite element solvers for the simulations of biological transportation networks driven by the gradient flow minimization of a non-convex energy cost functional. Our approach employs a discontinuous space for the conductivity tensor that allows us to guarantee the preservation of its positive semi-definiteness throughout the entire minimization procedure arising from the time integration of the gradient flow dynamics using a backward Euler scheme. Extensive tests in two and three dimensions demonstrate the robustness and performance of the solver, highlight the sensitivity of the emergent network structures to mesh resolution and topology, and validate the resilience of the linear preconditioner to the ill-conditioning of the model. The implementation achieves near-optimal parallel scaling on large-scale, high-performance computing platforms. To the best of our knowledge, the network formation system has never been simulated in three dimensions before. Consequently, our three-dimensional results are the first of their kind.
LoopGen: Training-Free Loopable Music Generation
Venue: arXiv cs.SD 2025
Year: 2025

Loops--short audio segments designed for seamless repetition--are central to many music genres, particularly those rooted in dance and electronic styles. However, current generative music models struggle to produce truly loopable audio, as generating a short waveform alone does not guarantee a smooth transition from its endpoint back to its start, often resulting in audible discontinuities. We address this gap by modifying a non-autoregressive model (MAGNeT) to generate tokens in a circular pattern, letting the model attend to the beginning of the audio when creating its ending. This inference-only approach results in generations that are aware of future context and loop naturally, without the need for any additional training or data. We evaluate the consistency of loop transitions by computing token perplexity around the seam of the loop, observing a 55% improvement. Blind listening tests further confirm significant perceptual gains over baseline methods, improving mean ratings by 70%. Taken together, these results highlight the effectiveness of inference-only approaches in improving generative models and underscore the advantages of non-autoregressive methods for context-aware music generation.
Fast Maximization of Current Flow Group Closeness Centrality
Venue: arXiv cs.SI 2025
Year: 2025

Derived from effective resistances, the current flow closeness centrality (CFCC) for a group of nodes measures the importance of node groups in an undirected graph with $n$ nodes. Given the widespread applications of identifying crucial nodes, we investigate the problem of maximizing CFCC for a node group $S$ subject to the cardinality constraint $|S|=k\ll n$. Despite the proven NP-hardness of this problem, we propose two novel greedy algorithms for its solution. Our algorithms are based on spanning forest sampling and Schur complement, which exhibit nearly linear time complexities and achieve an approximation factor of $1-\frac{k}{k-1}\frac{1}{\mathrm{e}}-ε$ for any $0<ε<1$. Extensive experiments on real-world graphs illustrate that our algorithms outperform the state-of-the-art method in terms of efficiency and effectiveness, scaling to graphs with millions of nodes.
Skin Color Measurement from Dermatoscopic Images: An Evaluation on a Synthetic Dataset
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a comprehensive evaluation of skin color measurement methods from dermatoscopic images using a synthetic dataset (S-SYNTH) with controlled ground-truth melanin content, lesion shapes, hair models, and 18 distinct lighting conditions. This allows for rigorous assessment of the robustness and invariance to lighting conditions. We assess four classes of image colorimetry approaches: segmentation-based, patch-based, color quantization, and neural networks. We use these methods to estimate the Individual Typology Angle (ITA) and Fitzpatrick types from dermatoscopic images. Our results show that segmentation-based and color quantization methods yield robust, lighting-invariant estimates, whereas patch-based approaches exhibit significant lighting-dependent biases that require calibration. Furthermore, neural network models, particularly when combined with heavy blurring to reduce overfitting, can provide light-invariant Fitzpatrick predictions, although their generalization to real-world images remains unverified. We conclude with practical recommendations for designing fair and reliable skin color estimation methods.
Active Learning with a Noisy Annotator
Venue: arXiv cs.LG 2025
Year: 2025

Active Learning (AL) aims to reduce annotation costs by strategically selecting the most informative samples for labeling. However, most active learning methods struggle in the low-budget regime where only a few labeled examples are available. This issue becomes even more pronounced when annotators provide noisy labels. A common AL approach for the low- and mid-budget regimes focuses on maximizing the coverage of the labeled set across the entire dataset. We propose a novel framework called Noise-Aware Active Sampling (NAS) that extends existing greedy, coverage-based active learning strategies to handle noisy annotations. NAS identifies regions that remain uncovered due to the selection of noisy representatives and enables resampling from these areas. We introduce a simple yet effective noise filtering approach suitable for the low-budget regime, which leverages the inner mechanism of NAS and can be applied for noise filtering before model training. On multiple computer vision benchmarks, including CIFAR100 and ImageNet subsets, NAS significantly improves performance for standard active learning methods across different noise types and rates.
SAM2MOT: A Novel Paradigm of Multi-Object Tracking by Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Inspired by Segment Anything 2, which generalizes segmentation from images to videos, we propose SAM2MOT--a novel segmentation-driven paradigm for multi-object tracking that breaks away from the conventional detection-association framework. In contrast to previous approaches that treat segmentation as auxiliary information, SAM2MOT places it at the heart of the tracking process, systematically tackling challenges like false positives and occlusions. Its effectiveness has been thoroughly validated on major MOT benchmarks. Furthermore, SAM2MOT integrates pre-trained detector, pre-trained segmentor with tracking logic into a zero-shot MOT system that requires no fine-tuning. This significantly reduces dependence on labeled data and paves the way for transitioning MOT research from task-specific solutions to general-purpose systems. Experiments on DanceTrack, UAVDT, and BDD100K show state-of-the-art results. Notably, SAM2MOT outperforms existing methods on DanceTrack by +2.1 HOTA and +4.5 IDF1, highlighting its effectiveness in MOT. Code is available at https://github.com/TripleJoy/SAM2MOT.
The Point, the Vision and the Text: Does Point Cloud Boost Spatial Reasoning of Large Language Models?
Venue: arXiv cs.CV 2025
Year: 2025

3D Large Language Models (LLMs) leveraging spatial information in point clouds for 3D spatial reasoning attract great attention. Despite some promising results, the role of point clouds in 3D spatial reasoning remains under-explored. In this work, we comprehensively evaluate and analyze these models to answer the research question: \textit{Does point cloud truly boost the spatial reasoning capacities of 3D LLMs?} We first evaluate the spatial reasoning capacity of LLMs with different input modalities by replacing the point cloud with the visual and text counterparts. We then propose a novel 3D QA (Question-answering) benchmark, ScanReQA, that comprehensively evaluates models' understanding of binary spatial relationships. Our findings reveal several critical insights: 1) LLMs without point input could even achieve competitive performance even in a zero-shot manner; 2) existing 3D LLMs struggle to comprehend the binary spatial relationships; 3) 3D LLMs exhibit limitations in exploiting the structural coordinates in point clouds for fine-grained spatial reasoning. We think these conclusions can help the next step of 3D LLMs and also offer insights for foundation models in other modalities. We release datasets and reproducible codes in the anonymous project page: https://3d-llm.xyz.
A model agnostic eXplainable AI based fuzzy framework for sensor constrained Aerospace maintenance applications
Venue: arXiv cs.CE 2025
Year: 2025

Machine Learning methods have extensively evolved to support industrial big data methods and their corresponding need in gas turbine maintenance and prognostics. However, most unsupervised methods need extensively labeled data to perform predictions across many dimensions. The cutting edge of small and medium applications do not necessarily maintain operational sensors and data acquisition with rising costs and diminishing profits. We propose a framework to make sensor maintenance priority decisions using a combination of SHAP, UMAP, Fuzzy C-means clustering. An aerospace jet engine dataset is used as a case study.
Automated Verification of Soundness of DNN Certifiers
Venue: arXiv cs.PL 2025
Year: 2025

The uninterpretability of Deep Neural Networks (DNNs) hinders their use in safety-critical applications. Abstract Interpretation-based DNN certifiers provide promising avenues for building trust in DNNs. Unsoundness in the mathematical logic of these certifiers can lead to incorrect results. However, current approaches to ensure their soundness rely on manual, expert-driven proofs that are tedious to develop, limiting the speed of developing new certifiers. Automating the verification process is challenging due to the complexity of verifying certifiers for arbitrary DNN architectures and handling diverse abstract analyses. We introduce ProveSound, a novel verification procedure that automates the soundness verification of DNN certifiers for arbitrary DNN architectures. Our core contribution is the novel concept of a symbolic DNN, using which, ProveSound reduces the soundness property, a universal quantification over arbitrary DNNs, to a tractable symbolic representation, enabling verification with standard SMT solvers. By formalizing the syntax and operational semantics of ConstraintFlow, a DSL for specifying certifiers, ProveSound efficiently verifies both existing and new certifiers, handling arbitrary DNN architectures. Our code is available at https://github.com/uiuc-focal-lab/constraintflow.git
Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language Models
Venue: arXiv cs.RO 2025
Year: 2025

Foundation models have demonstrated strong reasoning and generalization capabilities in driving-related tasks, including scene understanding, planning, and control. However, they still face challenges in hallucinations, uncertainty, and long inference latency. While existing foundation models have general knowledge of avoiding collisions, they often lack transportation-specific safety knowledge. To overcome these limitations, we introduce LetsPi, a physics-informed, dual-phase, knowledge-driven framework for safe, human-like trajectory planning. To prevent hallucinations and minimize uncertainty, this hybrid framework integrates Large Language Model (LLM) reasoning with physics-informed social force dynamics. LetsPi leverages the LLM to analyze driving scenes and historical information, providing appropriate parameters and target destinations (goals) for the social force model, which then generates the future trajectory. Moreover, the dual-phase architecture balances reasoning and computational efficiency through its Memory Collection phase and Fast Inference phase. The Memory Collection phase leverages the physics-informed LLM to process and refine planning results through reasoning, reflection, and memory modules, storing safe, high-quality driving experiences in a memory bank. Surrogate safety measures and physics-informed prompt techniques are introduced to enhance the LLM's knowledge of transportation safety and physical force, respectively. The Fast Inference phase extracts similar driving experiences as few-shot examples for new scenarios, while simplifying input-output requirements to enable rapid trajectory planning without compromising safety. Extensive experiments using the HighD dataset demonstrate that LetsPi outperforms baseline models across five safety metrics.See PDF for project Github link.