OpenRaiser/Intern-Atlas huggingface dataset:
Predator-Prey Model: Driven Hunt for Accelerated Grokking
Venue: arXiv cs.NE 2025
Year: 2025

A machine learning method is proposed using two agents that simulate the biological behavior of a predator and a prey. In this method, the predator and the prey interact with each other - the predator chases the prey while the prey runs away from the predator - to perform an optimization on the landscape. This method allows, for the case of a ravine landscape (i.e., a landscape with narrow ravines and with gentle slopes along the ravines) to avoid getting optimization stuck in the ravine. For this, in the optimization over a ravine landscape the predator drives the prey along the ravine. Thus we also call this approach, for the case of ravine landscapes, the driven hunt method. For some examples of grokking (i.e., delayed generalization) problems we show that this method allows for achieving up to a hundred times faster learning compared to the standard learning procedure.
Multi-channel secure communication framework for wireless IoT (MCSC-WoT): enhancing security in Internet of Things
Venue: arXiv cs.CR 2025
Year: 2025

In modern smart systems, the convergence of the Internet of Things (IoT) and Wireless of Things (WoT) have been revolutionized by offering a broad level of wireless connectivity and communication among various devices. Hitherto, this greater interconnectivity poses important security problems, including the question of how to securely interconnect different networks, preserve secure communication channels, and maintain data integrity. However, the traditional cryptographic method and frequency hopping technique, although they provide some protection, are not sufficient to defend against Man-In-The-Middle, jamming, and replay attacks. In addition, synchronization issues in multi-channel communication systems result in increased latency and energy consumption, which make them unsuitable for resource-constrained IoT and WoT devices. This work presents the Multi-Channel Secure Communication (MCSC) framework, which integrates advanced cryptographic protocols with dynamic channel-hopping strategies to enhance security with reduced synchronization overhead. The MCSC framework maximizes the critical performance metrics, such as packet delivery ratio, latency, throughput, and energy efficiency, and fulfills the specific requirements of the IoT and WoT networks. A comprehensive comparison of MCSC with well-established methods, including Frequency Hop Spread Spectrum, single channel Advanced Encryption Standard, and various Elliptic Curve Cryptography-based schemes, indicates that MCSC has lower error rates and is more resilient to a wider range of cyber attacks. The efficiency of the proposed solution to secure IoT and WoT networks without compromising the operational performance is validated under various interference conditions.
LearnLens: An AI-Enhanced Dashboard to Support Teachers in Open-Ended Classrooms
Venue: arXiv cs.CY 2025
Year: 2025

Exploratory learning environments (ELEs), such as simulation-based platforms and open-ended science curricula, promote hands-on exploration and problem-solving but make it difficult for teachers to gain timely insights into students' conceptual understanding. This paper presents LearnLens, a generative AI (GenAI)-enhanced teacher-facing dashboard designed to support problem-based instruction in middle school science. LearnLens processes students' open-ended responses from digital assessments to provide various insights, including sample responses, word clouds, bar charts, and AI-generated summaries. These features elucidate students' thinking, enabling teachers to adjust their instruction based on emerging patterns of understanding. The dashboard was informed by teacher input during professional development sessions and implemented within a middle school Earth science curriculum. We report insights from teacher interviews that highlight the dashboard's usability and potential to guide teachers' instruction in the classroom.
Safety and Security Analysis of Large Language Models: Benchmarking Risk Profile and Harm Potential
Venue: arXiv cs.CR 2025
Year: 2025

While the widespread deployment of Large Language Models (LLMs) holds great potential for society, their vulnerabilities to adversarial manipulation and exploitation can pose serious safety, security, and ethical risks. As new threats continue to emerge, it becomes critically necessary to assess the landscape of LLMs' safety and security against evolving adversarial prompt techniques. To understand the behavior of LLMs, this research provides an empirical analysis and risk profile of nine prominent LLMs, Claude Opus 4, DeepSeek V3 (both open-source and online), Gemini 2.5 Flash, GPT-4o, Grok 3, Llama 4 Scout, Mistral 7B, and Qwen 3 1.7B, against 24 different security and safety categories. These LLMs are evaluated on their ability to produce harmful responses for adversarially crafted prompts (dataset has been made public) for a broad range of safety and security topics, such as promotion of violent criminal behavior, promotion of non-violent criminal activity, societal harms related to safety, illegal sexual content, dangerous code generation, and cybersecurity threats beyond code. Our study introduces the Risk Severity Index (RSI), an agile and scalable evaluation score, to quantify and compare the security posture and creating a risk profile of LLMs. As the LLM development landscape progresses, the RSI is intended to be a valuable metric for comparing the risks of LLMs across evolving threats. This research finds widespread vulnerabilities in the safety filters of the LLMs tested and highlights the urgent need for stronger alignment, responsible deployment practices, and model governance, particularly for open-access and rapidly iterated models.
A Comparison and Evaluation of Fine-tuned Convolutional Neural Networks to Large Language Models for Image Classification and Segmentation of Brain Tumors on MRI
Venue: arXiv cs.CV 2025
Year: 2025

Large Language Models (LLMs) have shown strong performance in text-based healthcare tasks. However, their utility in image-based applications remains unexplored. We investigate the effectiveness of LLMs for medical imaging tasks, specifically glioma classification and segmentation, and compare their performance to that of traditional convolutional neural networks (CNNs). Using the BraTS 2020 dataset of multi-modal brain MRIs, we evaluated a general-purpose vision-language LLM (LLaMA 3.2 Instruct) both before and after fine-tuning, and benchmarked its performance against custom 3D CNNs. For glioma classification (Low-Grade vs. High-Grade), the CNN achieved 80% accuracy and balanced precision and recall. The general LLM reached 76% accuracy but suffered from a specificity of only 18%, often misclassifying Low-Grade tumors. Fine-tuning improved specificity to 55%, but overall performance declined (e.g., accuracy dropped to 72%). For segmentation, three methods - center point, bounding box, and polygon extraction, were implemented. CNNs accurately localized gliomas, though small tumors were sometimes missed. In contrast, LLMs consistently clustered predictions near the image center, with no distinction of glioma size, location, or placement. Fine-tuning improved output formatting but failed to meaningfully enhance spatial accuracy. The bounding polygon method yielded random, unstructured outputs. Overall, CNNs outperformed LLMs in both tasks. LLMs showed limited spatial understanding and minimal improvement from fine-tuning, indicating that, in their current form, they are not well-suited for image-based tasks. More rigorous fine-tuning or alternative training strategies may be needed for LLMs to achieve better performance, robustness, and utility in the medical space.
Least-Ambiguous Multi-Label Classifier
Venue: arXiv cs.LG 2025
Year: 2025

Multi-label learning often requires identifying all relevant labels for training instances, but collecting full label annotations is costly and labor-intensive. In many datasets, only a single positive label is annotated per training instance, despite the presence of multiple relevant labels. This setting, known as single-positive multi-label learning (SPMLL), presents a significant challenge due to its extreme form of partial supervision. We propose a model-agnostic approach to SPMLL that draws on conformal prediction to produce calibrated set-valued outputs, enabling reliable multi-label predictions at test time. Our method bridges the supervision gap between single-label training and multi-label evaluation without relying on label distribution assumptions. We evaluate our approach on 12 benchmark datasets, demonstrating consistent improvements over existing baselines and practical applicability.
CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Predicting the success of start-up companies, defined as achieving an exit through acquisition or IPO, is a critical problem in entrepreneurship and innovation research. Datasets such as Crunchbase provide both structured information (e.g., funding rounds, industries, investor networks) and unstructured text (e.g., company descriptions), but effectively leveraging this heterogeneous data for prediction remains challenging. Traditional machine learning approaches often rely only on structured features and achieve moderate accuracy, while large language models (LLMs) offer rich reasoning abilities but struggle to adapt directly to domain-specific business data. We present \textbf{CrunchLLM}, a domain-adapted LLM framework for startup success prediction. CrunchLLM integrates structured company attributes with unstructured textual narratives and applies parameter-efficient fine-tuning strategies alongside prompt optimization to specialize foundation models for entrepreneurship data. Our approach achieves accuracy exceeding 80\% on Crunchbase startup success prediction, significantly outperforming traditional classifiers and baseline LLMs. Beyond predictive performance, CrunchLLM provides interpretable reasoning traces that justify its predictions, enhancing transparency and trustworthiness for financial and policy decision makers. This work demonstrates how adapting LLMs with domain-aware fine-tuning and structured--unstructured data fusion can advance predictive modeling of entrepreneurial outcomes. CrunchLLM contributes a methodological framework and a practical tool for data-driven decision making in venture capital and innovation policy.
DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators
Venue: arXiv cs.AR 2025
Year: 2025

In the hardware design space exploration process, it is critical to optimize both hardware parameters and algorithm-to-hardware mappings. Previous work has largely approached this simultaneous optimization problem by separately exploring the hardware design space and the mapspace - both individually large and highly nonconvex spaces - independently. The resulting combinatorial explosion has created significant difficulties for optimizers. In this paper, we introduce DOSA, which consists of differentiable performance models and a gradient descent-based optimization technique to simultaneously explore both spaces and identify high-performing design points. Experimental results demonstrate that DOSA outperforms random search and Bayesian optimization by 2.80x and 12.59x, respectively, in improving DNN model energy-delay product, given a similar number of samples. We also demonstrate the modularity and flexibility of DOSA by augmenting our analytical model with a learned model, allowing us to optimize buffer sizes and mappings of a real DNN accelerator and attain a 1.82x improvement in energy-delay product.
Network Embedding Analysis for Anti-Money Laundering Detection
Venue: arXiv cs.SI 2025
Year: 2025

We employ network embedding to detect money laundering in financial transaction networks. Using real anonymized banking data, we model over one million accounts as a directed graph and use it to refine previously detected suspicious cycles with node2vec embeddings, creating a new network parameter, the spread number. Combined with more traditional centrality measures, these define an aggregate score $R$ that highlights so-called anti-central nodes: accounts that are structurally important yet organized to avoid detection. Our results show only a small subset of cycles attain high $R$ values, flagging concentrated groups of suspicious accounts. Our approach demonstrates the potential of embedding-based network analysis to expose laundering strategies that evade traditional graph centrality measures.
A Survey on LiDAR-based Autonomous Aerial Vehicles
Venue: arXiv cs.RO 2025
Year: 2025

This survey offers a comprehensive overview of recent advancements in LiDAR-based autonomous Unmanned Aerial Vehicles (UAVs), covering their design, perception, planning, and control strategies. Over the past decade, LiDAR technology has become a crucial enabler for high-speed, agile, and reliable UAV navigation, especially in GPS-denied environments. The paper begins by examining the evolution of LiDAR sensors, emphasizing their unique advantages such as high accuracy, long-range depth measurements, and robust performance under various lighting conditions, making them particularly well-suited for UAV applications. The integration of LiDAR with UAVs has significantly enhanced their autonomy, enabling complex missions in diverse and challenging environments. Subsequently, we explore essential software components, including perception technologies for state estimation and mapping, as well as trajectory planning and control methodologies, and discuss their adoption in LiDAR-based UAVs. Additionally, we analyze various practical applications of the LiDAR-based UAVs, ranging from industrial operations to supporting different aerial platforms and UAV swarm deployments. The survey concludes by discussing existing challenges and proposing future research directions to advance LiDAR-based UAVs and enhance multi-UAV collaboration. By synthesizing recent developments, this paper aims to provide a valuable resource for researchers and practitioners working to push the boundaries of LiDAR-based UAV systems.
SCOPE: Speech-guided COllaborative PErception Framework for Surgical Scene Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Accurate segmentation and tracking of relevant elements of the surgical scene is crucial to enable context-aware intraoperative assistance and decision making. Current solutions remain tethered to domain-specific, supervised models that rely on labeled data and required domain-specific data to adapt to new surgical scenarios and beyond predefined label categories. Recent advances in prompt-driven vision foundation models (VFM) have enabled open-set, zero-shot segmentation across heterogeneous medical images. However, dependence of these models on manual visual or textual cues restricts their deployment in introperative surgical settings. We introduce a speech-guided collaborative perception (SCOPE) framework that integrates reasoning capabilities of large language model (LLM) with perception capabilities of open-set VFMs to support on-the-fly segmentation, labeling and tracking of surgical instruments and anatomy in intraoperative video streams. A key component of this framework is a collaborative perception agent, which generates top candidates of VFM-generated segmentation and incorporates intuitive speech feedback from clinicians to guide the segmentation of surgical instruments in a natural human-machine collaboration paradigm. Afterwards, instruments themselves serve as interactive pointers to label additional elements of the surgical scene. We evaluated our proposed framework on a subset of publicly available Cataract1k dataset and an in-house ex-vivo skull-base dataset to demonstrate its potential to generate on-the-fly segmentation and tracking of surgical scene. Furthermore, we demonstrate its dynamic capabilities through a live mock ex-vivo experiment. This human-AI collaboration paradigm showcase the potential of developing adaptable, hands-free, surgeon-centric tools for dynamic operating-room environments.
Unbounded: Object-Boundary Interactions in Mixed Reality
Venue: arXiv cs.HC 2025
Year: 2025

Boundaries such as walls, windows, and doors are ubiquitous in the physical world, yet their potential in Mixed Reality (MR) remains underexplored. We present Unbounded, a Research through Design inquiry into Object-Boundary Interactions (OBIs). Building on prior work, we articulate a design space aimed at providing a shared language for OBIs. To demonstrate its potential, we design and implement eight examples across productivity and art exploration scenarios, showcasing how boundaries can enrich and reframe everyday interactions. We further engage with six MR experts in one-on-one feedback sessions, using the design space and examples as design probes. Their reflections broaden the conceptual scope of OBIs, reveal new possibilities for how the framework may be applied, and highlight implications for future MR interaction design.
"I thought it was my mistake, but it's really the design'': A Critical Examination of the Accessibility of User-Enacted Moderation Tools on Facebook and X
Venue: arXiv cs.HC 2025
Year: 2025

As social media platforms increasingly promote the use of user-enacted moderation tools (e.g., reporting, blocking, content filters) to address online harms, it becomes crucially important that such controls are usable for everyone. We evaluate the accessibility of these moderation tools on two mainstream platforms -- Facebook and X -- through interviews and task-based walkthroughs with 15 individuals with vision impairments. Adapting the lens of \emph{administrative burden of safety work}, we identify three interleaved costs that users with vision loss incur while interacting with moderation tools: \emph{learning costs} (understanding what controls do and where they live), \emph{compliance costs} (executing multi-step procedures under screen reader and low-vision conditions), and \emph{psychological costs} (experiencing uncertainty, stress, and diminished agency). Our analysis bridges the fields of content moderation and accessibility in HCI research and contributes (1) a cross-platform catalog of accessibility and usability breakdowns affecting safety tools; and (2) design recommendations for reducing this burden.
Structuring Definitions in Mathematical Libraries
Venue: arXiv cs.SC 2025
Year: 2025

Codifying mathematical theories in a proof assistant or computer algebra system is a challenging task, of which the most difficult part is, counterintuitively, structuring definitions. This results in a steep learning curve for new users and slow progress in formalizing even undergraduate level mathematics. There are many considerations one has to make, such as level of generality, readability, and ease of use in the type system, and there are typically multiple equivalent or related definitions from which to choose. Often, a definition that is ultimately selected for formalization is settled on after a lengthy trial and error process. This process involves testing potential definitions for usability by formalizing standard theorems about them, and weeding out the definitions that are unwieldy. Inclusion of a formal definition in a centralized community-run mathematical library is typically an indication that the definition is "good." For this reason, in this survey, we make some observations about what makes a definition "good," and examine several case studies of the refining process for definitions that have ultimately been added to the Lean Theorem Prover community-run mathematical library, mathlib. We observe that some of the difficulties are shared with the design of libraries for computer algebra systems, and give examples of related issues in that context.
Text2Sign Diffusion: A Generative Approach for Gloss-Free Sign Language Production
Venue: arXiv cs.CL 2025
Year: 2025

Sign language production (SLP) aims to translate spoken language sentences into a sequence of pose frames in a sign language, bridging the communication gap and promoting digital inclusion for deaf and hard-of-hearing communities. Existing methods typically rely on gloss, a symbolic representation of sign language words or phrases that serves as an intermediate step in SLP. This limits the flexibility and generalization of SLP, as gloss annotations are often unavailable and language-specific. Therefore, we present a novel diffusion-based generative approach - Text2Sign Diffusion (Text2SignDiff) for gloss-free SLP. Specifically, a gloss-free latent diffusion model is proposed to generate sign language sequences from noisy latent sign codes and spoken text jointly, reducing the potential error accumulation through a non-autoregressive iterative denoising process. We also design a cross-modal signing aligner that learns a shared latent space to bridge visual and textual content in sign and spoken languages. This alignment supports the conditioned diffusion-based process, enabling more accurate and contextually relevant sign language generation without gloss. Extensive experiments on the commonly used PHOENIX14T and How2Sign datasets demonstrate the effectiveness of our method, achieving the state-of-the-art performance.
Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue
Venue: arXiv cs.CL 2025
Year: 2025

Effective long-term memory in conversational AI requires synthesizing information across multiple sessions. However, current systems place excessive reasoning burden on response generation, making performance significantly dependent on model sizes. We introduce PREMem (Pre-storage Reasoning for Episodic Memory), a novel approach that shifts complex reasoning processes from inference to memory construction. PREMem extracts fine-grained memory fragments categorized into factual, experiential, and subjective information; it then establishes explicit relationships between memory items across sessions, capturing evolution patterns like extensions, transformations, and implications. By performing this reasoning during pre-storage rather than when generating a response, PREMem creates enriched representations while reducing computational demands during interactions. Experiments show significant performance improvements across all model sizes, with smaller models achieving results comparable to much larger baselines while maintaining effectiveness even with constrained token budgets. Code and dataset are available at https://github.com/sangyeop-kim/PREMem.
Quantifier Scope Interpretation in Language Learners and LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Sentences with multiple quantifiers often lead to interpretive ambiguities, which can vary across languages. This study adopts a cross-linguistic approach to examine how large language models (LLMs) handle quantifier scope interpretation in English and Chinese, using probabilities to assess interpretive likelihood. Human similarity (HS) scores were used to quantify the extent to which LLMs emulate human performance across language groups. Results reveal that most LLMs prefer the surface scope interpretations, aligning with human tendencies, while only some differentiate between English and Chinese in the inverse scope preferences, reflecting human-similar patterns. HS scores highlight variability in LLMs' approximation of human behavior, but their overall potential to align with humans is notable. Differences in model architecture, scale, and particularly models' pre-training data language background, significantly influence how closely LLMs approximate human quantifier scope interpretations.