OpenRaiser/Intern-Atlas huggingface dataset:
A framework for distributed discrete evacuation strategies
Venue: arXiv cs.DM 2025
Year: 2025

Consider the following discrete evacuation model. The evacuation terrain is modeled by a simple graph $G=(V,E)$ whose certain vertices $X\subseteq V$ are called \emph{exits}. Initially, each vertex is either \emph{empty} or \emph{occupied} by an agent. We assume that each vertex has a unique \emph{id} (and therefore the agents do have unique ids), each agent has finite but arbitrarily large memory, and the graph is initially stored in the memory of each agent. In other words, the agents do know the topology of the network along with the locations of the exits, but they do not know the initial positions nor the quantity of other agents. The time is divided into \emph{steps}; in each step any pair of agents present at vertices at a distance of at most two can exchange an arbitrary number of messages, and then each agent can either make a move or stay put. The agents should make moves in a collision-free manner, i.e., no two agents can be located at the same vertex in the same step. At the end of each step, any agent located at an exit \emph{evacuates}, i.e., it is removed from the graph. The goal is to provide an algorithm to the agents (referred to as an evacuation strategy) that ensures the evacuation of all agents and minimizes the number of steps. This work provides an algorithmic framework that allows constructing valid evacuation strategies for arbitrary input graphs. Specifically, we focus on the properties of the input graphs that lead to evacuation strategies with constant competitive ratios. In particular, we describe an application of the above framework that gives an asymptotically optimal evacuation for grids (and by extension hexagonal or triangular grids as well).
Sentiment Analysis of Airbnb Reviews: Exploring Their Impact on Acceptance Rates and Pricing Across Multiple U.S. Regions
Venue: arXiv cs.CY 2025
Year: 2025

This research examines whether Airbnb guests' positive and negative comments influence acceptance rates and rental prices across six U.S. regions: Rhode Island, Broward County, Chicago, Dallas, San Diego, and Boston. Thousands of reviews were collected and analyzed using Natural Language Processing (NLP) to classify sentiments as positive or negative, followed by statistical testing (t-tests and basic correlations) on the average scores. The findings reveal that over 90 percent of reviews in each region are positive, indicating that having additional reviews does not significantly enhance prices. However, listings with predominantly positive feedback exhibit slightly higher acceptance rates, suggesting that sentiment polarity, rather than the sheer volume of reviews, is a more critical factor for host success. Additionally, budget listings often gather extensive reviews while maintaining competitive pricing, whereas premium listings sustain higher prices with fewer but highly positive reviews. These results underscore the importance of sentiment quality over quantity in shaping guest behavior and pricing strategies in an overwhelmingly positive review environment.
Calliope: An Online Generative Music System for Symbolic Multi-Track Composition
Venue: arXiv cs.HC 2025
Year: 2025

With the rise of artificial intelligence in recent years, there has been a rapid increase in its application towards creative domains, including music. There exist many systems built that apply machine learning approaches to the problem of computer-assisted music composition (CAC). Calliope is a web application that assists users in performing a variety of multi-track composition tasks in the symbolic domain. The user can upload (Musical Instrument Digital Interface) MIDI files, visualize and edit MIDI tracks, and generate partial (via bar in-filling) or complete multi-track content using the Multi-Track Music Machine (MMM). Generation of new MIDI excerpts can be done in batch and can be combined with active playback listening for an enhanced assisted-composition workflow. The user can export generated MIDI materials or directly stream MIDI playback from the system to their favorite Digital Audio Workstation (DAW). We present a demonstration of the system, its features, generative parameters and describe the co-creative workflows that it affords.
DoomArena: A framework for Testing AI Agents Against Evolving Security Threats
Venue: arXiv cs.CR 2025
Year: 2025

We present DoomArena, a security evaluation framework for AI agents. DoomArena is designed on three principles: 1) It is a plug-in framework and integrates easily into realistic agentic frameworks like BrowserGym (for web agents) and $τ$-bench (for tool calling agents); 2) It is configurable and allows for detailed threat modeling, allowing configuration of specific components of the agentic framework being attackable, and specifying targets for the attacker; and 3) It is modular and decouples the development of attacks from details of the environment in which the agent is deployed, allowing for the same attacks to be applied across multiple environments. We illustrate several advantages of our framework, including the ability to adapt to new threat models and environments easily, the ability to easily combine several previously published attacks to enable comprehensive and fine-grained security testing, and the ability to analyze trade-offs between various vulnerabilities and performance. We apply DoomArena to state-of-the-art (SOTA) web and tool-calling agents and find a number of surprising results: 1) SOTA agents have varying levels of vulnerability to different threat models (malicious user vs malicious environment), and there is no Pareto dominant agent across all threat models; 2) When multiple attacks are applied to an agent, they often combine constructively; 3) Guardrail model-based defenses seem to fail, while defenses based on powerful SOTA LLMs work better. DoomArena is available at https://github.com/ServiceNow/DoomArena.
5Guard: Isolation-aware End-to-End Slicing of 5G Networks
Venue: arXiv cs.NI 2025
Year: 2025

Network slicing logically partitions the 5G infrastructure to cater to diverse verticals with varying requirements. However, resource sharing exposes the slices to threats and performance degradation, making slice isolation essential. Fully isolating slices is resource-prohibitive, prompting the need for isolation-aware network slicing, where each slice is assigned a tailored isolation level to balance security, usability, and overhead. This paper investigates end-to-end 5G network slicing with resource isolation from the perspective of the infrastructure provider, ensuring compliance with the customers' service-level agreements. We formulate the online 5G isolation-aware network slicing (5G-INS) as a mixed-integer programming problem, modeling realistic slice isolation levels and integrating slice prioritization. To solve 5G-INS, we propose 5Guard, a novel adaptive framework that leverages an ensemble of custom optimization algorithms to achieve the best solution within resource budget and time constraints. Our results show that 5Guard increases profit by up to 10.1% in resource-constrained environments and up to 25.4% in a real-world large-scale network compared to the best-performing individual algorithm. Furthermore, we analyze the trade-offs between isolation levels, their impact on resource utilization, and the effects of slice placement, demonstrating significant advantages over baseline approaches that enforce uniform isolation policies.
Enhancing Math Learning in an LMS Using AI-Driven Question Recommendations
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents an AI-driven approach to enhance math learning in a modern Learning Management System (LMS) by recommending similar math questions. Deep embeddings for math questions are generated using Meta's Llama-3.2-11B-Vision-Instruct model, and three recommendation methods-cosine similarity, Self-Organizing Maps (SOM), and Gaussian Mixture Models (GMM)-are applied to identify similar questions. User interaction data, including session durations, response times, and correctness, are used to evaluate the methods. Our findings suggest that while cosine similarity produces nearly identical question matches, SOM yields higher user satisfaction whereas GMM generally underperforms, indicating that introducing variety to a certain degree may enhance engagement and thereby potential learning outcomes until variety is no longer balanced reasonably, which our data about the implementations of all three methods demonstrate.
Exploring Language Patterns of Prompts in Text-to-Image Generation and Their Impact on Visual Diversity
Venue: arXiv cs.HC 2025
Year: 2025

Following the initial excitement, Text-to-Image (TTI) models are now being examined more critically. While much of the discourse has focused on biases and stereotypes embedded in large-scale training datasets, the sociotechnical dynamics of user interactions with these models remain underexplored. This study examines the linguistic and semantic choices users make when crafting prompts and how these choices influence the diversity of generated outputs. Analyzing over six million prompts from the Civiverse dataset on the CivitAI platform across seven months, we categorize users into three groups based on their levels of linguistic experimentation: consistent repeaters, occasional repeaters, and non-repeaters. Our findings reveal that as user participation grows over time, prompt language becomes increasingly homogenized through the adoption of popular community tags and descriptors, with repeated prompts comprising 40-50% of submissions. At the same time, semantic similarity and topic preferences remain relatively stable, emphasizing common subjects and surface aesthetics. Using Vendi scores to quantify visual diversity, we demonstrate a clear correlation between lexical similarity in prompts and the visual similarity of generated images, showing that linguistic repetition reinforces less diverse representations. These findings highlight the significant role of user-driven factors in shaping AI-generated imagery, beyond inherent model biases, and underscore the need for tools and practices that encourage greater linguistic and thematic experimentation within TTI systems to foster more inclusive and diverse AI-generated content.
Transforming Hyperspectral Images Into Chemical Maps: A Novel End-to-End Deep Learning Approach
Venue: arXiv cs.CV 2025
Year: 2025

Current approaches to chemical map generation from hyperspectral images are based on models such as partial least squares (PLS) regression, generating pixel-wise predictions that do not consider spatial context and suffer from a high degree of noise. This study proposes an end-to-end deep learning approach using a modified version of U-Net and a custom loss function to directly obtain chemical maps from hyperspectral images, skipping all intermediate steps required for traditional pixel-wise analysis. This study compares the U-Net with the traditional PLS regression on a real dataset of pork belly samples with associated mean fat reference values. The U-Net obtains a test set root mean squared error that is 7% lower than that of PLS regression on the task of mean fat prediction. At the same time, U-Net generates fine detail chemical maps where 99.91% of the variance is spatially correlated. Conversely, only 2.37% of the variance in the PLS-generated chemical maps is spatially correlated, indicating that each pixel-wise prediction is largely independent of neighboring pixels. Additionally, while the PLS-generated chemical maps contain predictions far beyond the physically possible range of 0-100%, U-Net learns to stay inside this range. Thus, the findings of this study indicate that U-Net is superior to PLS for chemical map generation.
Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach
Venue: arXiv cs.CV 2025
Year: 2025

Compared to single-target adversarial attacks, multi-target attacks have garnered significant attention due to their ability to generate adversarial images for multiple target classes simultaneously. However, existing generative approaches for multi-target attacks primarily encode target labels into one-dimensional tensors, leading to a loss of fine-grained visual information and overfitting to model-specific features during noise generation. To address this gap, we first identify and validate that the semantic feature quality and quantity are critical factors affecting the transferability of targeted attacks: 1) Feature quality refers to the structural and detailed completeness of the implanted target features, as deficiencies may result in the loss of key discriminative information; 2) Feature quantity refers to the spatial sufficiency of the implanted target features, as inadequacy limits the victim model's attention to this feature. Based on these findings, we propose the 2D Tensor-Guided Adversarial Fusion (TGAF) framework, which leverages the powerful generative capabilities of diffusion models to encode target labels into two-dimensional semantic tensors for guiding adversarial noise generation. Additionally, we design a novel masking strategy tailored for the training process, ensuring that parts of the generated noise retain complete semantic information about the target class. Extensive experiments demonstrate that TGAF consistently surpasses state-of-the-art methods across various settings.
Self-Correction Makes LLMs Better Parsers
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have achieved remarkable success across various natural language processing (NLP) tasks. However, recent studies suggest that they still face challenges in performing fundamental NLP tasks essential for deep language understanding, particularly syntactic parsing. In this paper, we conduct an in-depth analysis of LLM parsing capabilities, delving into the specific shortcomings of their parsing results. We find that LLMs may stem from limitations to fully leverage grammar rules in existing treebanks, which restricts their capability to generate valid syntactic structures. To help LLMs acquire knowledge without additional training, we propose a self-correction method that leverages grammar rules from existing treebanks to guide LLMs in correcting previous errors. Specifically, we automatically detect potential errors and dynamically search for relevant rules, offering hints and examples to guide LLMs in making corrections themselves. Experimental results on three datasets with various LLMs, demonstrate that our method significantly improves performance in both in-domain and cross-domain settings on the English and Chinese datasets.
Hypothetical Documents or Knowledge Leakage? Rethinking LLM-based Query Expansion
Venue: arXiv cs.CL 2025
Year: 2025

Query expansion methods powered by large language models (LLMs) have demonstrated effectiveness in zero-shot retrieval tasks. These methods assume that LLMs can generate hypothetical documents that, when incorporated into a query vector, enhance the retrieval of real evidence. However, we challenge this assumption by investigating whether knowledge leakage in benchmarks contributes to the observed performance gains. Using fact verification as a testbed, we analyze whether the generated documents contain information entailed by ground-truth evidence and assess their impact on performance. Our findings indicate that, on average, performance improvements consistently occurred for claims whose generated documents included sentences entailed by gold evidence. This suggests that knowledge leakage may be present in fact-verification benchmarks, potentially inflating the perceived performance of LLM-based query expansion methods.
FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Recommendation algorithms rely on user historical interactions to deliver personalized suggestions, which raises significant privacy concerns. Federated recommendation algorithms tackle this issue by combining local model training with server-side model aggregation, where most existing algorithms use a uniform weighted summation to aggregate item embeddings from different client models. This approach has three major limitations: 1) information loss during aggregation, 2) failure to retain personalized local features, and 3) incompatibility with parameter-free recommendation algorithms. To address these limitations, we first review the development of recommendation algorithms and recognize that their core function is to share collaborative information, specifically the global relationship between users and items. With this understanding, we propose a novel aggregation paradigm named collaborative information aggregation, which focuses on sharing collaborative information rather than item parameters. Based on this new paradigm, we introduce the federated collaborative information aggregation (FedCIA) method for privacy-preserving recommendation. This method requires each client to upload item similarity matrices for aggregation, which allows clients to align their local models without constraining embeddings to a unified vector space. As a result, it mitigates information loss caused by direct summation, preserves the personalized embedding distributions of individual clients, and supports the aggregation of parameter-free models. Theoretical analysis and experimental results on real-world datasets demonstrate the superior performance of FedCIA compared with the state-of-the-art federated recommendation algorithms. Code is available at https://github.com/Mingzhe-Han/FedCIA.
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Vision Foundation Models (VFMs) have become a de facto choice for many downstream vision tasks, like image classification, image segmentation, and object localization. However, they can also provide significant utility for downstream 3D tasks that can leverage the cross-modal information (e.g., from paired image data). In our work, we further explore the utility of VFMs for adapting from a labeled source to unlabeled target data for the task of LiDAR-based 3D semantic segmentation. Our method consumes paired 2D-3D (image and point cloud) data and relies on the robust (cross-domain) features from a VFM to train a 3D backbone on a mix of labeled source and unlabeled target data. At the heart of our method lies a fusion network that is guided by both the image and point cloud streams, with their relative contributions adjusted based on the target domain. We extensively compare our proposed methodology with different state-of-the-art methods in several settings and achieve strong performance gains. For example, achieving an average improvement of 6.5 mIoU (over all tasks), when compared with the previous state-of-the-art.
Assessing AI-Generated Questions' Alignment with Cognitive Frameworks in Educational Assessment
Venue: arXiv cs.AI 2025
Year: 2025

This study evaluates the integration of Bloom's Taxonomy into OneClickQuiz, an Artificial Intelligence (AI) driven plugin for automating Multiple-Choice Question (MCQ) generation in Moodle. Bloom's Taxonomy provides a structured framework for categorizing educational objectives into hierarchical cognitive levels. Our research investigates whether incorporating this taxonomy can improve the alignment of AI-generated questions with specific cognitive objectives. We developed a dataset of 3691 questions categorized according to Bloom's levels and employed various classification models-Multinomial Logistic Regression, Naive Bayes, Linear Support Vector Classification (SVC), and a Transformer-based model (DistilBERT)-to evaluate their effectiveness in categorizing questions. Our results indicate that higher Bloom's levels generally correlate with increased question length, Flesch-Kincaid Grade Level (FKGL), and Lexical Density (LD), reflecting the increased complexity of higher cognitive demands. Multinomial Logistic Regression showed varying accuracy across Bloom's levels, performing best for "Knowledge" and less accurately for higher-order levels. Merging higher-level categories improved accuracy for complex cognitive tasks. Naive Bayes and Linear SVC also demonstrated effective classification for lower levels but struggled with higher-order tasks. DistilBERT achieved the highest performance, significantly improving classification of both lower and higher-order cognitive levels, achieving an overall validation accuracy of 91%. This study highlights the potential of integrating Bloom's Taxonomy into AI-driven assessment tools and underscores the advantages of advanced models like DistilBERT for enhancing educational content generation.
Ordered Completion for Non-Locally Tight mini-gringo Programs
Venue: arXiv cs.LO 2025
Year: 2025

Completion is a well-known transformation that captures the stable model semantics of logic programs by turning a program into a set of first-order definitions. Stable models are models of the completion, but not all models of the completion are stable models. For tight programs (programs without positive recursion) the two semantics coincide. Recently this correspondence was extended to locally tight programs, which avoid non-terminating recursion. However, unlike tightness, local tightness cannot be checked with simple syntactic methods. Completion is crucial for verifying answer set programs, especially for external equivalence: a form of equivalence based on selected output predicates under certain inputs. Standard equivalence and adherence to a first-order specification are special cases of external equivalence. The anthem verification tool has two limitations for checking external equivalence: (1) there is no way to check local tightness automatically, and (2) it is not possible to verify programs that are not locally tight. Therefore, alternatives to completion are of interest. This thesis investigates ordered completion, introduced in [Asuncion et al., 2012], which captures stable models of arbitrary logic programs, but only for finite models. This work extends ordered completion to the mini-gringo language (a subset of the language used by the clingo solver). Additionally, it introduces a modification of ordered completion to handle infinite stable models. This extended ordered completion is implemented in anthem as a translation, and initial experiments demonstrate its use for verifying simple logic programs.
Maker-Maker games of rank 4 are PSPACE-complete
Venue: arXiv cs.DM 2025
Year: 2025

The Maker-Maker convention of positional games is played on a hypergraph whose edges are interpreted as winning sets. Two players take turns picking a previously unpicked vertex, aiming at being first to pick all the vertices of some edge. Optimal play can only lead to a first player win or a draw, and deciding between the two is known to be PSPACE-complete even for 6-uniform hypergraphs. We establish PSPACE-completeness for hypergraphs of rank 4. As an intermediary, we use the recently introduced achievement positional games, a more general convention in which each player has their own winning sets (blue and red). We show that deciding whether the blue player has a winning strategy as the first player is PSPACE-complete even with blue edges of size 2 or 3 and pairwise disjoint red edges of size 2. The result for hypergraphs of rank 4 in the Maker-Maker convention follows as a simple corollary.