OpenRaiser/Intern-Atlas huggingface dataset:
Tatami Printer: Physical ZKPs for Tatami Puzzles
Venue: arXiv cs.CR 2025
Year: 2025

Tatami puzzles are pencil puzzles with an objective to partition a rectangular grid into rectangular regions such that no four regions share a corner point, as well as satisfying other constraints. In this paper, we develop a physical card-based protocol called Tatami printer that can help verify solutions of Tatami puzzles. We then use the Tatami printer to construct zero-knowledge proof protocols for two such puzzles: Tatamibari and Square Jam. These protocols enable a prover to show a verifier the existence of the puzzles' solutions without revealing them.
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text Generation
Venue: arXiv cs.CL 2025
Year: 2025

Sampling-based decoding strategies have been widely adopted for Large Language Models (LLMs) in numerous applications, targeting a balance between diversity and quality via temperature tuning and tail truncation. Considering the strong dependency of the candidate next tokens on different prefixes, recent studies propose to adaptively truncate the tail of LLMs' predicted distribution. Although improved results have been reported with these methods on open-ended text generation tasks, the results are highly dependent on the curated parameters and the limited exemplar text. In this paper, we propose a systematic way to estimate the capacity of a truncation sampling method by considering the trade-off between diversity and risk at each decoding step, based on our collected prefix tree which preserves the context of a full sentence. Our work offers a comprehensive comparison of existing truncation sampling methods and serves as a practical user guideline for their parameter selection.
APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching
Venue: arXiv cs.CR 2025
Year: 2025

Timely and effective vulnerability patching is essential for cybersecurity defense, for which various approaches have been proposed yet still struggle to generate valid and correct patches for real-world vulnerabilities. In this paper, we leverage the power and merits of pre-trained language language models (LLMs) to enable automated vulnerability patching using no test input/exploit evidence and without model training/fine-tuning. To elicit LLMs to effectively reason about vulnerable code behaviors, which is essential for quality patch generation, we introduce vulnerability semantics reasoning and adaptive prompting on LLMs and instantiate the methodology as APPATCH, an automated LLM-based patching system. Our evaluation of APPATCH on 97 zero-day vulnerabilities and 20 existing vulnerabilities demonstrates its superior performance to both existing prompting methods and state-of-the-art non-LLM-based techniques (by up to 28.33% in F1 and 182.26% in recall over the best baseline). Through APPATCH, we demonstrate what helps for LLM-based patching and how, as well as discussing what still lacks and why.
Real-Time Rendering of Glints in the Presence of Area Lights
Venue: arXiv cs.GR 2025
Year: 2025

Many real-world materials are characterized by a glittery appearance. Reproducing this effect in physically based renderings is a challenging problem due to its discrete nature, especially in real-time applications which require a consistently low runtime. Recent work focuses on glittery appearance illuminated by infinitesimally small light sources only. For light sources like the sun this approximation is a reasonable choice. In the real world however, all light sources are fundamentally area light sources. In this paper, we derive an efficient method for rendering glints illuminated by spatially constant diffuse area lights in real time. To this end, we require an adequate estimate for the probability of a single microfacet to be correctly oriented for reflection from the source to the observer. A good estimate is achieved either using linearly transformed cosines (LTC) for large light sources, or a locally constant approximation of the normal distribution for small spherical caps of light directions. To compute the resulting number of reflecting microfacets, we employ a counting model based on the binomial distribution. In the evaluation, we demonstrate the visual accuracy of our approach, which is easily integrated into existing real-time rendering frameworks, especially if they already implement shading for area lights using LTCs and a counting model for glint shading under point and directional illumination. Besides the overhead of the preexisting constituents, our method adds little to no additional overhead.
Sample-Independent Federated Learning Backdoor Attack in Speaker Recognition
Venue: arXiv cs.CR 2025
Year: 2025

In federated learning, backdoor attacks embed triggers in the adversarial client's data to inject a backdoor into the model. In order to enhance the stealth, an attack method based on the dropout layer has been proposed, which can implant the backdoor without modifying the sample. However, these methods struggle to covertly utilize dropout in evaluation mode, thus hindering their deployment in real-world scenarios. To address these, this paper introduces GhostB, a novel approach to federated learning backdoor attacks in speaker recognition that neither alters samples nor relies on dropout. This method employs the behavior of neurons producing specific values as triggers. By mapping these neuronal values to categories specified by the adversary, the backdoor is implanted and activated when particular feature values are detected at designated neurons. Our experiments conducted on TIMIT, LibriSpeech, and VoxCeleb2 databases in both Closed Set Identification (CSI) and Open Set Identification (OSI) scenarios demonstrate that GhostB achieves a 100% success rate upon activation in speaker recognition, with this rate maintained across experiments involving 1 to 50 ghost neurons. This paper investigates how the dispersion of neurons and their depth within hidden layers affect the success rate, revealing that increased dispersion and positioning of neurons can significantly decrease effectiveness, potentially rendering the attack unsuccessful.
Game Design Prototype with GIMs: Fostering Neurodiverse Connections through Storytelling
Venue: arXiv cs.HC 2025
Year: 2025

This ongoing experimental project investigates the use of Generative Image Models (GIMs) in crafting a picture book creation game designed to nurture social connections among autistic children and their neurotypical peers within a neuro-affirming environment. Moving away from traditional methods that often seek to condition neurodivergent children to socialize in prescribed ways, this project strives to cultivate a space where children can engage with one another naturally and creatively through art and storytelling, free from the pressure to adhere to standard social norms. Beyond merely "story-choosing," the research highlights the potential of GIMs to facilitate "story-creating," fostering peer social connections in a creative and structured collaborative learning experience.
Avatar Concept Slider: Controllable Editing of Concepts in 3D Human Avatars
Venue: arXiv cs.CV 2025
Year: 2025

Text-based editing of 3D human avatars to precisely match user requirements is challenging due to the inherent ambiguity and limited expressiveness of natural language. To overcome this, we propose the Avatar Concept Slider (ACS), a 3D avatar editing method that allows precise editing of semantic concepts in human avatars towards a specified intermediate point between two extremes of concepts, akin to moving a knob along a slider track. To achieve this, our ACS has three designs: Firstly, a Concept Sliding Loss based on linear discriminant analysis to pinpoint the concept-specific axes for precise editing. Secondly, an Attribute Preserving Loss based on principal component analysis for improved preservation of avatar identity during editing. We further propose a 3D Gaussian Splatting primitive selection mechanism based on concept-sensitivity, which updates only the primitives that are the most sensitive to our target concept, to improve efficiency. Results demonstrate that our ACS enables controllable 3D avatar editing, without compromising the avatar quality or its identifying attributes.
Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

Efficient navigation in dynamic environments is crucial for autonomous robots interacting with moving agents and static obstacles. We present a novel deep reinforcement learning approach that improves robot navigation and interaction with different types of agents and obstacles based on specific safety requirements. Our approach uses information about the entity types, improving collision avoidance and ensuring safer navigation. We introduce a new reward function that penalizes the robot for being close to or colliding with different entities such as adults, bicyclists, children, and static obstacles, while also encouraging the robot's progress toward the goal. We propose an optimized algorithm that significantly accelerates the training, validation, and testing phases, enabling efficient learning in complex environments. Comprehensive experiments demonstrate that our approach consistently outperforms state-of-the-art navigation and collision avoidance methods.
Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark
Venue: arXiv cs.CV 2025
Year: 2025

Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors that deviate from normal. At its core, human-centric VAD faces substantial challenges, such as the complexity of diverse human behaviors, the rarity of anomalies, and ethical constraints. These challenges limit access to high-quality datasets and highlight the need for a dataset and framework supporting continual learning. Moving towards adaptive human-centric VAD, we introduce the HuVAD (Human-centric privacy-enhanced Video Anomaly Detection) dataset and a novel Unsupervised Continual Anomaly Learning (UCAL) framework. UCAL enables incremental learning, allowing models to adapt over time, bridging traditional training and real-world deployment. HuVAD prioritizes privacy by providing de-identified annotations and includes seven indoor/outdoor scenes, offering over 5x more pose-annotated frames than previous datasets. Our standard and continual benchmarks, utilize a comprehensive set of metrics, demonstrating that UCAL-enhanced models achieve superior performance in 82.14% of cases, setting a new state-of-the-art (SOTA). The dataset can be accessed at https://github.com/TeCSAR-UNCC/HuVAD.
Guard Analysis and Safe Erasure Gradual Typing: a Type System for Elixir
Venue: arXiv cs.PL 2025
Year: 2025

We formalize a new type system for Elixir, a dynamically typed functional programming language of growing popularity that runs on the Erlang virtual machine. Our system combines gradual typing with semantic subtyping to enable precise, sound, and practical static type analysis, without requiring any changes to Elixir's compilation pipeline or runtime. Type soundness is ensured by leveraging runtime checks -- both implicit, from the Erlang VM, and explicit, via developer-written guards. Central to our approach are two key innovations: the notion of "strong functions", which can be assigned precise types even when applied to inputs that may fall outside their intended domain; and a fine-grained analysis of guards that enables accurate type refinement for case expressions and guarded function definitions. While type information is erased before execution and not used by the compiler, our "safe erasure" gradual typing strategy maintains soundness and expressiveness without compromising compatibility or performance. This work lays the theoretical foundation for Elixir's new type system, outlines its integration into recent versions of the language, and demonstrates its effectiveness on large-scale industrial codebases.
LogProber: Disentangling confidence from contamination in LLM responses
Venue: arXiv cs.CL 2025
Year: 2025

In machine learning, contamination refers to situations where testing data leak into the training set. The issue is particularly relevant for the evaluation of the performance of Large Language Models (LLMs), which are generally trained on gargantuan, and generally opaque, corpora of text scraped from the world wide web. Developing tools to detect contamination is therefore crucial to be able to fairly and properly track the evolution of the performance of LLMs. To date, only a few recent studies have attempted to address the issue of quantifying and detecting contamination in short text sequences, such as those commonly found in benchmarks. However, these methods have limitations that can sometimes render them impractical. In the present paper, we introduce LogProber, a novel, efficient algorithm that we show to be able to detect contamination in a black box setting that tries to tackle some of these drawbacks by focusing on the familiarity with the question rather than the answer. Here, we explore the properties of the proposed method in comparison with concurrent approaches, identify its advantages and limitations, and illustrate how different forms of contamination can go undetected depending on the design of the detection algorithm.
On the Limitations of Language Targeted Pruning: Investigating the Calibration Language Impact in Multilingual LLM Pruning
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in large language model (LLM) pruning have shown state-of-the-art (SotA) compression results in post-training and retraining-free settings while maintaining high predictive performance. However, previous research mainly considered calibrating based on English text, despite the multilingual nature of modern LLMs and their frequent use in non-English languages. This analysis paper conducts an in-depth investigation of the performance and internal representation changes associated with pruning multilingual language models for monolingual applications. We present the first comprehensive empirical study, comparing different calibration languages for pruning multilingual models across diverse languages, tasks, models, and SotA pruning techniques. We further analyze the latent subspaces, pruning masks, and individual neurons within pruned models. Our results reveal that while calibration on the target language effectively retains perplexity and yields high signal-to-noise ratios, it does not consistently improve downstream task performance. Further analysis of internal representations at three different levels highlights broader limitations of current pruning approaches: While they effectively preserve dominant information like language-specific features, this is insufficient to counteract the loss of nuanced, language-agnostic features that are crucial for knowledge retention and reasoning.
Evaluating Large Language Models on Spatial Tasks: A Multi-Task Benchmarking Study
Venue: arXiv cs.CL 2025
Year: 2025

The emergence of large language models such as ChatGPT, Gemini, and others highlights the importance of evaluating their diverse capabilities, ranging from natural language understanding to code generation. However, their performance on spatial tasks has not been thoroughly assessed. This study addresses this gap by introducing a new multi-task spatial evaluation dataset designed to systematically explore and compare the performance of several advanced models on spatial tasks. The dataset includes twelve distinct task types, such as spatial understanding and simple route planning, each with verified and accurate answers. We evaluated multiple models, including OpenAI's gpt-3.5-turbo, gpt-4-turbo, gpt-4o, ZhipuAI's glm-4, Anthropic's claude-3-sonnet-20240229, and MoonShot's moonshot-v1-8k, using a two-phase testing approach. First, we conducted zero-shot testing. Then, we categorized the dataset by difficulty and performed prompt-tuning tests. Results show that gpt-4o achieved the highest overall accuracy in the first phase, with an average of 71.3%. Although moonshot-v1-8k slightly underperformed overall, it outperformed gpt-4o in place name recognition tasks. The study also highlights the impact of prompt strategies on model performance in specific tasks. For instance, the Chain-of-Thought (CoT) strategy increased gpt-4o's accuracy in simple route planning from 12.4% to 87.5%, while a one-shot strategy improved moonshot-v1-8k's accuracy in mapping tasks from 10.1% to 76.3%.
Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review
Venue: arXiv cs.LG 2025
Year: 2025

Recent technological advancements in multimodal machine learning--including the rise of large language models (LLMs)--have improved our ability to collect, process, and analyze diverse multimodal data such as speech, video, and eye gaze in learning and training contexts. While prior reviews have addressed individual components of the multimodal pipeline (e.g., conceptual models, data fusion), a comprehensive review of empirical methods in applied multimodal environments remains notably absent. This review addresses that, introducing a taxonomy and framework that capture both established practices and recent innovations driven by LLMs and generative AI. We identify five modality groups: Natural Language, Vision, Physiological Signals, Human-Centered Evidence, and Environment Logs. Our analysis reveals that integrating modalities enables richer insights into learner and trainee behaviors, revealing latent patterns often overlooked by unimodal approaches. However, persistent challenges in multimodal data collection and integration continue to hinder the adoption of these systems in real-time classroom settings.
EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory
Venue: arXiv cs.AI 2025
Year: 2025

EVINCE (Entropy and Variation IN Conditional Exchanges) is a novel framework for optimizing multi-LLM dialogues using conditional statistics and information theory. It addresses limitations in multi-agent debate (MAS) frameworks, where multiple LLMs ``chat'' without behavior modulation or mutual information quality assessment. Using dual entropy optimization to balance perspective diversity and prior knowledge, $\EVINCE$ provides quantitative tools to dynamically regulate LLM linguistic behaviors. When mutual information is low and both cross-entropy and Wasserstein distance are high, EVINCE promotes contentious dialogues to expose diverse perspectives and uncover inconsistencies. Conversely, as cross-entropy decreases and mutual information stabilizes, it transitions discussions into a conciliatory phase, encouraging compromise and acknowledgment of valid points. Using information-theoretic metrics and optimizing mutual information, $\EVINCE$ emerges as a structured and highly effective framework for multi-LLM collaboration.
WIP: Identifying Tutorial Affordances for Interdisciplinary Learning Environments
Venue: arXiv cs.CY 2025
Year: 2025

This work-in-progress research paper explores the effectiveness of tutorials in interdisciplinary learning environments, specifically focusing on bioinformatics. Tutorials are typically designed for a single audience, but our study aims to uncover how they function in contexts where learners have diverse backgrounds. With the rise of interdisciplinary learning, the importance of learning materials that accommodate diverse learner needs has become evident. We chose bioinformatics as our context because it involves at least two distinct user groups: those with computational backgrounds and those with biological backgrounds. The goal of our research is to better understand current bioinformatics software tutorial designs and assess them in the conceptual framework of interdisciplinarity. We conducted a content analysis of 22 representative bioinformatics software tutorials to identify design patterns and understand their strengths and limitations. We found common codes in the representative tutorials and synthesized them into ten themes. Our assessment shows degrees to which current bioinformatics software tutorials fulfill interdisciplinarity.
DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning
Venue: arXiv cs.CV 2025
Year: 2025

Simple data augmentation techniques, such as rotations and flips, are widely used to enhance the generalization power of computer vision models. However, these techniques often fail to modify high-level semantic attributes of a class. To address this limitation, researchers have explored generative augmentation methods like the recently proposed DA-Fusion. Despite some progress, the variations are still largely limited to textural changes, thus falling short on aspects like varied viewpoints, environment, weather conditions, or even class-level semantic attributes (eg, variations in a dog's breed). To overcome this challenge, we propose DIAGen, building upon DA-Fusion. First, we apply Gaussian noise to the embeddings of an object learned with Textual Inversion to diversify generations using a pre-trained diffusion model's knowledge. Second, we exploit the general knowledge of a text-to-text generative model to guide the image generation of the diffusion model with varied class-specific prompts. Finally, we introduce a weighting mechanism to mitigate the impact of poorly generated samples. Experimental results across various datasets show that DIAGen not only enhances semantic diversity but also improves the performance of subsequent classifiers. The advantages of DIAGen over standard augmentations and the DA-Fusion baseline are particularly pronounced with out-of-distribution samples.
Surprisingly Fragile: Assessing and Addressing Prompt Instability in Multimodal Foundation Models
Venue: arXiv cs.CL 2025
Year: 2025

Multimodal foundation models (MFMs) such as OFASys show the potential to unlock analysis of complex data such as images, videos, and audio data via text prompts alone. However, their performance may suffer in the face of text input that differs even slightly from their training distribution, which is surprising considering the use of modality-specific data to "ground" the text input. This study demonstrates that prompt instability is a major concern for MFMs, leading to a consistent drop in performance across all modalities, but that instability can be mitigated with additional training with augmented data. We evaluate several methods for grounded prompt perturbation, where we generate perturbations and filter based on similarity to text and/or modality data. After re-training the models on the augmented data, we find improved accuracy and more stable performance on the perturbed test data regardless of perturbation condition, suggesting that the data augmentation strategy helps the models handle domain shifts more effectively. In error analysis, we find consistent patterns of performance improvement across domains, suggesting that retraining on prompt perturbations tends to help general reasoning capabilities in MFMs.