OpenRaiser/Intern-Atlas huggingface dataset:
Scaffolding Creativity: Integrating Generative AI Tools and Real-world Experiences in Business Education
Venue: arXiv cs.AI 2025
Year: 2025

This exploratory study investigates the intersection of Generative AI tools and experiential learning in business education. Through a case study of an innovative undergraduate course, we examine how students interact with and adapt to various AI modalities-from text-based tools to image generation-alongside real-world experiences. Our findings reveal how this integrated approach enables novice users to overcome creative barriers, accelerates skill acquisition, and creates a dynamic interplay between AI-generated insights and real-world validation. We identify critical interaction challenges, including prompt engineering patterns and the need for more intuitive AI interfaces in educational contexts. These insights inform the design of future AI tools for creative learning and contribute to broader HCI discussions about human-AI collaboration in educational settings.
Dispersion Measures as Predictors of Lexical Decision Time, Word Familiarity, and Lexical Complexity
Venue: arXiv cs.CL 2025
Year: 2025

Various measures of dispersion have been proposed to paint a fuller picture of a word's distribution in a corpus, but only little has been done to validate them externally. We evaluate a wide range of dispersion measures as predictors of lexical decision time, word familiarity, and lexical complexity in five diverse languages. We find that the logarithm of range is not only a better predictor than log-frequency across all tasks and languages, but that it is also the most powerful additional variable to log-frequency, consistently outperforming the more complex dispersion measures. We discuss the effects of corpus part granularity and logarithmic transformation, shedding light on contradictory results of previous studies.
Aster: Enhancing LSM-structures for Scalable Graph Database
Venue: arXiv cs.DB 2025
Year: 2025

There is a proliferation of applications requiring the management of large-scale, evolving graphs under workloads with intensive graph updates and lookups. Driven by this challenge, we introduce Poly-LSM, a high-performance key-value storage engine for graphs with the following novel techniques: (1) Poly-LSM is embedded with a new design of graph-oriented LSM-tree structure that features a hybrid storage model for concisely and effectively storing graph data. (2) Poly-LSM utilizes an adaptive mechanism to handle edge insertions and deletions on graphs with optimized I/O efficiency. (3) Poly-LSM exploits the skewness of graph data to encode the key-value entries. Building upon this foundation, we further implement Aster, a robust and versatile graph database that supports Gremlin query language facilitating various graph applications. In our experiments, we compared Aster against several mainstream real-world graph databases. The results demonstrate that Aster outperforms all baseline graph databases, especially on large-scale graphs. Notably, on the billion-scale Twitter graph dataset, Aster achieves up to 17x throughput improvement compared to the best-performing baseline graph system.
EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses
Venue: arXiv cs.LG 2025
Year: 2025

The widespread adoption of generative AI has generated diverse opinions, with individuals expressing both support and criticism of its applications. This study investigates the emotional dynamics surrounding generative AI by analyzing human tweets referencing terms such as ChatGPT, OpenAI, Copilot, and LLMs. To further understand the emotional intelligence of ChatGPT, we examine its responses to selected tweets, highlighting differences in sentiment between human comments and LLM-generated responses. We introduce EmoXpt, a sentiment analysis framework designed to assess both human perspectives on generative AI and the sentiment embedded in ChatGPT's responses. Unlike prior studies that focus exclusively on human sentiment, EmoXpt uniquely evaluates the emotional expression of ChatGPT. Experimental results demonstrate that LLM-generated responses are notably more efficient, cohesive, and consistently positive than human responses.
SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)
Venue: arXiv cs.CR 2025
Year: 2025

Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without requiring clients to share their private local data. The DNN is partitioned in SL, with most layers residing on the server and a few initial layers and inputs on the client side. This configuration allows resource-constrained clients to participate in training and inference. However, the distributed architecture exposes SL to backdoor attacks, where malicious clients can manipulate local datasets to alter the DNN's behavior. Existing defenses from other distributed frameworks like Federated Learning are not applicable, and there is a lack of effective backdoor defenses specifically designed for SL. We present SafeSplit, the first defense against client-side backdoor attacks in Split Learning (SL). SafeSplit enables the server to detect and filter out malicious client behavior by employing circular backward analysis after a client's training is completed, iteratively reverting to a trained checkpoint where the model under examination is found to be benign. It uses a two-fold analysis to identify client-induced changes and detect poisoned models. First, a static analysis in the frequency domain measures the differences in the layer's parameters at the server. Second, a dynamic analysis introduces a novel rotational distance metric that assesses the orientation shifts of the server's layer parameters during training. Our comprehensive evaluation across various data distributions, client counts, and attack scenarios demonstrates the high efficacy of this dual analysis in mitigating backdoor attacks while preserving model utility.
Comparing Few-Shot Prompting of GPT-4 LLMs with BERT Classifiers for Open-Response Assessment in Tutor Equity Training
Venue: arXiv cs.HC 2025
Year: 2025

Assessing learners in ill-defined domains, such as scenario-based human tutoring training, is an area of limited research. Equity training requires a nuanced understanding of context, but do contemporary large language models (LLMs) have a knowledge base that can navigate these nuances? Legacy transformer models like BERT, in contrast, have less real-world knowledge but can be more easily fine-tuned than commercial LLMs. Here, we study whether fine-tuning BERT on human annotations outperforms state-of-the-art LLMs (GPT-4o and GPT-4-Turbo) with few-shot prompting and instruction. We evaluate performance on four prediction tasks involving generating and explaining open-ended responses in advocacy-focused training lessons in a higher education student population learning to become middle school tutors. Leveraging a dataset of 243 human-annotated open responses from tutor training lessons, we find that BERT demonstrates superior performance using an offline fine-tuning approach, which is more resource-efficient than commercial GPT models. We conclude that contemporary GPT models may not adequately capture nuanced response patterns, especially in complex tasks requiring explanation. This work advances the understanding of AI-driven learner evaluation under the lens of fine-tuning versus few-shot prompting on the nuanced task of equity training, contributing to more effective training solutions and assisting practitioners in choosing adequate assessment methods.
TWIX: Automatically Reconstructing Structured Data from Templatized Documents
Venue: arXiv cs.DB 2025
Year: 2025

Many documents, that we call templatized documents, are programmatically generated by populating fields in a visual template. Effective data extraction from these documents is crucial to supporting downstream analytical tasks. Current data extraction tools often struggle with complex document layouts, incur high latency and/or cost on large datasets, and often require significant human effort, when extracting tables or values given user-specified fields from documents. The key insight of our tool, TWIX, is to predict the underlying template used to create such documents, modeling the visual and structural commonalities across documents. Data extraction based on this predicted template provides a more principled, accurate, and efficient solution at a low cost. Comprehensive evaluations on 34 diverse real-world datasets show that uncovering the template is crucial for data extraction from templatized documents. TWIX achieves over 90% precision and recall on average, outperforming tools from industry: Textract and Azure Document Intelligence, and vision-based LLMs like GPT-4-Vision, by over 25% in precision and recall. TWIX scales easily to large datasets and is 734X faster and 5836X cheaper than vision-based LLMs for extracting data from a large document collection with 817 pages.
Learning dynamical systems with hit-and-run random feature maps
Venue: arXiv cs.LG 2025
Year: 2025

We show how random feature maps can be used to forecast dynamical systems with excellent forecasting skill. We consider the tanh activation function and judiciously choose the internal weights in a data-driven manner such that the resulting features explore the nonlinear, non-saturated regions of the activation function. We introduce skip connections and construct a deep variant of random feature maps by combining several units. To mitigate the curse of dimensionality, we introduce localization where we learn local maps, employing conditional independence. Our modified random feature maps provide excellent forecasting skill for both single trajectory forecasts as well as long-time estimates of statistical properties, for a range of chaotic dynamical systems with dimensions up to 512. In contrast to other methods such as reservoir computers which require extensive hyperparameter tuning, we effectively need to tune only a single hyperparameter, and are able to achieve state-of-the-art forecast skill with much smaller networks.
Generative AI in Education: From Foundational Insights to the Socratic Playground for Learning
Venue: arXiv cs.AI 2025
Year: 2025

This paper explores the synergy between human cognition and Large Language Models (LLMs), highlighting how generative AI can drive personalized learning at scale. We discuss parallels between LLMs and human cognition, emphasizing both the promise and new perspectives on integrating AI systems into education. After examining challenges in aligning technology with pedagogy, we review AutoTutor-one of the earliest Intelligent Tutoring Systems (ITS)-and detail its successes, limitations, and unfulfilled aspirations. We then introduce the Socratic Playground, a next-generation ITS that uses advanced transformer-based models to overcome AutoTutor's constraints and provide personalized, adaptive tutoring. To illustrate its evolving capabilities, we present a JSON-based tutoring prompt that systematically guides learner reflection while tracking misconceptions. Throughout, we underscore the importance of placing pedagogy at the forefront, ensuring that technology's power is harnessed to enhance teaching and learning rather than overshadow it.
Fine-tuning ChatGPT for Automatic Scoring of Written Scientific Explanations in Chinese
Venue: arXiv cs.AI 2025
Year: 2025

The development of explanations for scientific phenomena is essential in science assessment, but scoring student-written explanations remains challenging and resource-intensive. Large language models (LLMs) have shown promise in addressing this issue, particularly in alphabetic languages like English. However, their applicability to logographic languages is less explored. This study investigates the potential of fine-tuning ChatGPT, a leading LLM, to automatically score scientific explanations written in Chinese. Student responses to seven scientific explanation tasks were collected and automatically scored, with scoring accuracy examined in relation to reasoning complexity using the Kendall correlation. A qualitative analysis explored how linguistic features influenced scoring accuracy. The results show that domain-specific adaptation enables ChatGPT to score Chinese scientific explanations with accuracy. However, scoring accuracy correlates with reasoning complexity: a negative correlation for lower-level responses and a positive one for higher-level responses. The model overrates complex reasoning in low-level responses with intricate sentence structures and underrates high-level responses using concise causal reasoning. These correlations stem from linguistic features--simplicity and clarity enhance accuracy for lower-level responses, while comprehensiveness improves accuracy for higher-level ones. Simpler, shorter responses tend to score more accurately at lower levels, whereas longer, information-rich responses yield better accuracy at higher levels. These findings demonstrate the effectiveness of LLMs in automatic scoring within a Chinese context and emphasize the importance of linguistic features and reasoning complexity in fine-tuning scoring models for educational assessments.
ELIZA Reanimated: The world's first chatbot restored on the world's first time sharing system
Venue: arXiv cs.AI 2025
Year: 2025

ELIZA, created by Joseph Weizenbaum at MIT in the early 1960s, is usually considered the world's first chatbot. It was developed in MAD-SLIP on MIT's CTSS, the world's first time-sharing system, on an IBM 7094. We discovered an original ELIZA printout in Prof. Weizenbaum's archives at MIT, including an early version of the famous DOCTOR script, a nearly complete version of the MAD-SLIP code, and various support functions in MAD and FAP. Here we describe the reanimation of this original ELIZA on a restored CTSS, itself running on an emulated IBM 7094. The entire stack is open source, so that any user of a unix-like OS can run the world's first chatbot on the world's first time-sharing system.
Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution
Venue: arXiv cs.CV 2025
Year: 2025

Satellite imagery is a cornerstone for numerous Remote Sensing (RS) applications; however, limited spatial resolution frequently hinders the precision of such systems, especially in multi-label scene classification tasks as it requires a higher level of detail and feature differentiation. In this study, we explore the efficacy of image Super-Resolution (SR) as a pre-processing step to enhance the quality of satellite images and thus improve downstream classification performance. We investigate four SR models - SRResNet, HAT, SeeSR, and RealESRGAN - and evaluate their impact on multi-label scene classification across various CNN architectures, including ResNet-50, ResNet-101, ResNet-152, and Inception-v4. Our results show that applying SR significantly improves downstream classification performance across various metrics, demonstrating its ability to preserve spatial details critical for multi-label tasks. Overall, this work offers valuable insights into the selection of SR techniques for multi-label prediction in remote sensing and presents an easy-to-integrate framework to improve existing RS systems.
OptiCarVis: Improving Automated Vehicle Functionality Visualizations Using Bayesian Optimization to Enhance User Experience
Venue: arXiv cs.HC 2025
Year: 2025

Automated vehicle (AV) acceptance relies on their understanding via feedback. While visualizations aim to enhance user understanding of AV's detection, prediction, and planning functionalities, establishing an optimal design is challenging. Traditional "one-size-fits-all" designs might be unsuitable, stemming from resource-intensive empirical evaluations. This paper introduces OptiCarVis, a set of Human-in-the-Loop (HITL) approaches using Multi-Objective Bayesian Optimization (MOBO) to optimize AV feedback visualizations. We compare conditions using eight expert and user-customized designs for a Warm-Start HITL MOBO. An online study (N=117) demonstrates OptiCarVis's efficacy in significantly improving trust, acceptance, perceived safety, and predictability without increasing cognitive load. OptiCarVis facilitates a comprehensive design space exploration, enhancing in-vehicle interfaces for optimal passenger experiences and broader applicability.
SuperNeRF-GAN: A Universal 3D-Consistent Super-Resolution Framework for Efficient and Enhanced 3D-Aware Image Synthesis
Venue: arXiv cs.CV 2025
Year: 2025

Neural volume rendering techniques, such as NeRF, have revolutionized 3D-aware image synthesis by enabling the generation of images of a single scene or object from various camera poses. However, the high computational cost of NeRF presents challenges for synthesizing high-resolution (HR) images. Most existing methods address this issue by leveraging 2D super-resolution, which compromise 3D-consistency. Other methods propose radiance manifolds or two-stage generation to achieve 3D-consistent HR synthesis, yet they are limited to specific synthesis tasks, reducing their universality. To tackle these challenges, we propose SuperNeRF-GAN, a universal framework for 3D-consistent super-resolution. A key highlight of SuperNeRF-GAN is its seamless integration with NeRF-based 3D-aware image synthesis methods and it can simultaneously enhance the resolution of generated images while preserving 3D-consistency and reducing computational cost. Specifically, given a pre-trained generator capable of producing a NeRF representation such as tri-plane, we first perform volume rendering to obtain a low-resolution image with corresponding depth and normal map. Then, we employ a NeRF Super-Resolution module which learns a network to obtain a high-resolution NeRF. Next, we propose a novel Depth-Guided Rendering process which contains three simple yet effective steps, including the construction of a boundary-correct multi-depth map through depth aggregation, a normal-guided depth super-resolution and a depth-guided NeRF rendering. Experimental results demonstrate the superior efficiency, 3D-consistency, and quality of our approach. Additionally, ablation studies confirm the effectiveness of our proposed components.
Equitable Allocations of Mixtures of Goods and Chores
Venue: arXiv cs.GT 2025
Year: 2025

Equitable allocation of indivisible items involves partitioning the items among agents such that everyone derives (almost) equal utility. We consider the approximate notion of \textit{equitability up to one item} (EQ1) and focus on the settings containing mixtures of items (goods and chores), where an agent may derive positive, negative, or zero utility from an item. We first show that -- in stark contrast to the goods-only and chores-only settings -- an EQ1 allocation may not exist even for additive $\{-1,1\}$ bivalued instances, and its corresponding decision problem is computationally intractable. We focus on a natural domain of normalized valuations where the value of the entire set of items is constant for all agents. On the algorithmic side, we show that an EQ1 allocation can be computed efficiently for (i) $\{-1, 0, 1\}$ normalized valuations, (ii) objective but non-normalized valuations, (iii) two agents with type-normalized valuations. Previously, EQX allocations were known to exist only for 2 agents and objective valuations, while the case of subjective valuations remained computationally intractable even with two agents. We make progress by presenting an efficient algorithm that outputs an EQX allocation for $\{-1,1\}$ normalized subjective valuations for any number of agents. We complement our study by providing a comprehensive picture of achieving EQ1 allocations in conjunction with economic efficiency notions such as Pareto optimality and social welfare.
Optimizing Sequencing Coverage Depth in DNA Storage: Insights From DNA Storage Data
Venue: arXiv cs.IT 2025
Year: 2025

DNA storage is now being considered as a new archival storage method for its durability and high information density, but still facing some challenges like high costs and low throughput. By reducing sequencing sample size for decoding digital data, minimizing DNA coverage depth helps lower both costs and system latency. Previous studies have mainly focused on minimizing coverage depth in uniform distribution channels under theoretical assumptions. In contrast, our work uses real DNA storage experimental data to extend this problem to log-normal distribution channels, a conclusion derived from our PCR and sequencing data analysis. In this framework, we investigate both noiseless and noisy channels. We first demonstrate a detailed positive correlation between MDS code rate and the expected minimum sequencing coverage depth. Moreover, we observe that the probability of successfully decoding all information in a single sequencing run decreases and then increases as code rate rises, when the sample size is optimized for complete decoding. Then we extend the lower bounds of the DNA coverage depth from uniform to log-normal noisy channels. The findings of this study provide valuable insights for the efficient execution of DNA storage experiments.
Unifying Two Types of Scaling Laws from the Perspective of Conditional Kolmogorov Complexity
Venue: arXiv cs.AI 2025
Year: 2025

In 2020, OpenAI proposed the first type of Scaling Laws, describing the relationships between model loss and the scale of parameters, data, and training computation. In 2024, OpenAI proposed the second type of Scaling Laws, describing the relationship between model inference performance and inference computation. In this paper, we analyze LLMs training and inference processes from the perspective of lossless compression using conditional Kolmogorov complexity, and unify these two types of Scaling Laws. We find that both types of Scaling Laws improve approximation of conditional Kolmogorov complexity by increasing execution steps of Turing machine. The first type of Scaling Laws increases execution steps by increasing number of model parameters. The second type of Scaling Laws increases execution steps by increasing the number of intermediate tokens.
RSRefSeg: Referring Remote Sensing Image Segmentation with Foundation Models
Venue: arXiv cs.CV 2025
Year: 2025

Referring remote sensing image segmentation is crucial for achieving fine-grained visual understanding through free-format textual input, enabling enhanced scene and object extraction in remote sensing applications. Current research primarily utilizes pre-trained language models to encode textual descriptions and align them with visual modalities, thereby facilitating the expression of relevant visual features. However, these approaches often struggle to establish robust alignments between fine-grained semantic concepts, leading to inconsistent representations across textual and visual information. To address these limitations, we introduce a referring remote sensing image segmentation foundational model, RSRefSeg. RSRefSeg leverages CLIP for visual and textual encoding, employing both global and local textual semantics as filters to generate referring-related visual activation features in the latent space. These activated features then serve as input prompts for SAM, which refines the segmentation masks through its robust visual generalization capabilities. Experimental results on the RRSIS-D dataset demonstrate that RSRefSeg outperforms existing methods, underscoring the effectiveness of foundational models in enhancing multimodal task comprehension. The code is available at \url{https://github.com/KyanChen/RSRefSeg}.