OpenRaiser/Intern-Atlas huggingface dataset:
Fair Diversity Maximization with Few Representatives
Venue: arXiv cs.DS 2025
Year: 2025

Diversity maximization problem is a well-studied problem where the goal is to find $k$ diverse items. Fair diversity maximization aims to select a diverse subset of $k$ items from a large dataset, while requiring that each group of items be well represented in the output. More formally, given a set of items with labels, our goal is to find $k$ items that maximize the minimum pairwise distance in the set, while maintaining that each label is represented within some budget. In many cases, one is only interested in selecting a handful (say a constant) number of items from each group. In such scenario we show that a randomized algorithm based on padded decompositions improves the state-of-the-art approximation ratio to $\sqrt{\log(m)}/(3m)$, where $m$ is the number of labels. The algorithms work in several stages: ($i$) a preprocessing pruning which ensures that points with the same label are far away from each other, ($ii$) a decomposition phase, where points are randomly placed in clusters such that there is a feasible solution with maximum one point per cluster and that any feasible solution will be diverse, $(iii)$ assignment phase, where clusters are assigned to labels, and a representative point with the corresponding label is selected from each cluster. We experimentally verify the effectiveness of our algorithm on large datasets.
Conservative Bias in Large Language Models: Measuring Relation Predictions
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) exhibit pronounced conservative bias in relation extraction tasks, frequently defaulting to No_Relation label when an appropriate option is unavailable. While this behavior helps prevent incorrect relation assignments, our analysis reveals that it also leads to significant information loss when reasoning is not explicitly included in the output. We systematically evaluate this trade-off across multiple prompts, datasets, and relation types, introducing the concept of Hobson's choice to capture scenarios where models opt for safe but uninformative labels over hallucinated ones. Our findings suggest that conservative bias occurs twice as often as hallucination. To quantify this effect, we use SBERT and LLM prompts to capture the semantic similarity between conservative bias behaviors in constrained prompts and labels generated from semi-constrained and open-ended prompts.
QA-LIGN: Aligning LLMs through Constitutionally Decomposed QA
Venue: arXiv cs.CL 2025
Year: 2025

Alignment of large language models (LLMs) with principles like helpfulness, honesty, and harmlessness typically relies on scalar rewards that obscure which objectives drive the training signal. We introduce QA-LIGN, which decomposes monolithic rewards into interpretable principle-specific evaluations through structured natural language programs. Models learn through a draft, critique, and revise pipeline, where symbolic evaluation against the rubrics provides transparent feedback for both initial and revised responses during GRPO training. Applied to uncensored Llama-3.1-8B-Instruct, QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate, achieving Pareto optimal safety-helpfulness performance and outperforming both DPO and GRPO with state-of-the-art reward models given equivalent training. These results demonstrate that making reward signals interpretable and modular improves alignment effectiveness, suggesting transparency enhances LLM safety.
Congestion-Aware Path Selection for Load Balancing in AI Clusters
Venue: arXiv cs.NI 2025
Year: 2025

Fast training of large machine learning models requires distributed training on AI clusters consisting of thousands of GPUs. The efficiency of distributed training crucially depends on the efficiency of the network interconnecting GPUs in the cluster. These networks are commonly built using RDMA following a Clos-like datacenter topology. To efficiently utilize the network bandwidth, load balancing is employed to distribute traffic across multiple redundant paths. While there exists numerous techniques for load-balancing in traditional datacenters, these are often either optimized for TCP traffic or require specialized network hardware, thus limiting their utility in AI clusters. This paper presents the design and evaluation of Hopper, a new load-balancing technique optimized for RDMA traffic in AI clusters. Operating entirely at the host level, Hopper requires no specialized hardware or modifications to network switches. It continuously monitors the current path for congestion and dynamically switches traffic to a less congested path when congestion is detected. Furthermore, it incorporates a lightweight mechanism to identify alternative paths and carefully controls the timing of path switching to prevent excessive out-of-order packets. We evaluated Hopper using ns-3 simulations and a testbed implementation. Our evaluations show that Hopper reduces the average and 99-percentile tail flow completion time by up to 20% and 14%, respectively, compared to state-of-the-art host-based load balancing techniques.
Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields
Venue: arXiv cs.LG 2025
Year: 2025

We propose a new framework for identifying mechanical properties of heterogeneous materials without a closed-form constitutive equation. Given a full-field measurement of the displacement field, for instance as obtained from digital image correlation (DIC), a continuous approximation of the strain field is obtained by training a neural network that incorporates Fourier features to effectively capture sharp gradients in the data. A physics-based data-driven method built upon ordinary neural differential equations (NODEs) is employed to discover constitutive equations. The NODE framework can represent arbitrary materials while satisfying constraints in the theory of constitutive equations by default. To account for heterogeneity, a hyper-network is defined, where the input is the material coordinate system, and the output is the NODE-based constitutive equation. The parameters of the hyper-network are optimized by minimizing a multi-objective loss function that includes penalty terms for violations of the strong form of the equilibrium equations of elasticity and the associated Neumann boundary conditions. We showcase the framework with several numerical examples, including heterogeneity arising from variations in material parameters, spatial transitions from isotropy to anisotropy, material identification in the presence of noise, and, ultimately, application to experimental data. As the numerical results suggest, the proposed approach is robust and general in identifying the mechanical properties of heterogeneous materials with very few assumptions, making it a suitable alternative to classical inverse methods.
Compiling Metric Temporal Answer Set Programming
Venue: arXiv cs.AI 2025
Year: 2025

We develop a computational approach to Metric Answer Set Programming (ASP) to allow for expressing quantitative temporal constrains, like durations and deadlines. A central challenge is to maintain scalability when dealing with fine-grained timing constraints, which can significantly exacerbate ASP's grounding bottleneck. To address this issue, we leverage extensions of ASP with difference constraints, a simplified form of linear constraints, to handle time-related aspects externally. Our approach effectively decouples metric ASP from the granularity of time, resulting in a solution that is unaffected by time precision.
Treewidth of Outer $k$-Planar Graphs
Venue: arXiv cs.DM 2025
Year: 2025

Treewidth is an important structural graph parameter that quantifies how closely a graph resembles a tree-like structure. It has applications in many algorithmic and combinatorial problems. In this paper, we study the treewidth of outer $k$-planar graphs, that is, graphs admitting a convex drawing (a straight-line drawing where all vertices lie on a circle) in which every edge crosses at most $k$ other edges. We also consider the more general class of outer min-$k$-planar graphs, which are graphs admitting a convex drawing where for every crossing of two edges at least one of these edges is crossed at most $k$ times. Firman, Gutowski, Kryven, Okada and Wolff [GD 2024] proved that every outer $k$-planar graph has treewidth at most $1.5k+2$ and provided a lower bound of $k+2$ for even $k$. We establish a lower bound of $1.5k+0.5$ for every odd $k$. Additionally, they showed that every outer min-$k$-planar graph has treewidth at most $3k+1$. We improve this upper bound to $3 \cdot \lfloor k/2 \rfloor+4$. Our approach also allows us to upper bound the separation number, a parameter closely related to treewidth, of outer min-$k$-planar graphs by $2 \cdot \lfloor k/2 \rfloor+4$. This improves upon the previous bound of $2k+1$ and achieves a bound with an optimal multiplicative constant.
Federated Learning on Stochastic Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning is a machine learning paradigm that leverages edge computing on client devices to optimize models while maintaining user privacy by ensuring that local data remains on the device. However, since all data is collected by clients, federated learning is susceptible to latent noise in local datasets. Factors such as limited measurement capabilities or human errors may introduce inaccuracies in client data. To address this challenge, we propose the use of a stochastic neural network as the local model within the federated learning framework. Stochastic neural networks not only facilitate the estimation of the true underlying states of the data but also enable the quantification of latent noise. We refer to our federated learning approach, which incorporates stochastic neural networks as local models, as Federated stochastic neural networks. We will present numerical experiments demonstrating the performance and effectiveness of our method, particularly in handling non-independent and identically distributed data.
No Stupid Questions: An Analysis of Question Query Generation for Citation Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Existing techniques for citation recommendation are constrained by their adherence to article contents and metadata. We leverage GPT-4o-mini's latent expertise as an inquisitive assistant by instructing it to ask questions which, when answered, could expose new insights about an excerpt from a scientific article. We evaluate the utility of these questions as retrieval queries, measuring their effectiveness in retrieving and ranking masked target documents. In some cases, generated questions ended up being better queries than extractive keyword queries generated by the same model. We additionally propose MMR-RBO, a variation of Maximal Marginal Relevance (MMR) using Rank-Biased Overlap (RBO) to identify which questions will perform competitively with the keyword baseline. As all question queries yield unique result sets, we contend that there are no stupid questions.
gh0stEdit: Exploiting Layer-Based Access Vulnerability Within Docker Container Images
Venue: arXiv cs.CR 2025
Year: 2025

Containerisation is a popular deployment process for application-level virtualisation using a layer-based approach. Docker is a leading provider of containerisation, and through the Docker Hub, users can supply Docker images for sharing and re-purposing popular software application containers. Using a combination of in-built inspection commands, publicly displayed image layer content, and static image scanning, Docker images are designed to ensure end users can clearly assess the content of the image before running them. In this paper we present gh0stEdit, an exploit that fundamentally undermines the integrity of Docker images and subverts the assumed trust and transparency they utilise. The use of gh0stEdit allows an attacker to maliciously edit Docker images, in a way that is not shown within the image history, hierarchy or commands. This attack can also be carried out against signed images (Docker Content Trust) without invalidating the image signature. We present a detailed case study for this exploit, and showcase how gh0stEdit is able to poison an image in a way that is not picked up through static or dynamic scanning tools. We highlight the issues in the current approach to Docker image security and trust, and expose an attack method which could potentially be exploited in the wild without being detected. To the best of our knowledge we are the first to provide detailed discussion on the exploit of this vulnerability.
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple components, compound AI systems have become increasingly adept at performing sophisticated tasks. However, as these systems grow in complexity, new challenges arise in optimizing not only individual components but also their interactions. While traditional optimization methods such as supervised fine-tuning (SFT) and reinforcement learning (RL) remain foundational, the rise of natural language feedback introduces promising new approaches, especially for optimizing non-differentiable systems. This paper provides a systematic review of recent progress in optimizing compound AI systems, encompassing both numerical and language-based techniques. We formalize the notion of compound AI system optimization, classify existing methods along several key dimensions, and highlight open research challenges and future directions in this rapidly evolving field. A list of surveyed papers is publicly available at https://github.com/MiuLab/AISysOpt-Survey.
PoSyn: Secure Power Side-Channel Aware Synthesis
Venue: arXiv cs.CR 2025
Year: 2025

Power Side-Channel (PSC) attacks exploit power consumption patterns to extract sensitive information, posing risks to cryptographic operations crucial for secure systems. Traditional countermeasures, such as masking, face challenges including complex integration during synthesis, substantial area overhead, and susceptibility to optimization removal during logic synthesis. To address these issues, we introduce PoSyn, a novel logic synthesis framework designed to enhance cryptographic hardware resistance against PSC attacks. Our method centers on optimal bipartite mapping of vulnerable RTL components to standard cells from the technology library, aiming to minimize PSC leakage. By utilizing a cost function integrating critical characteristics from both the RTL design and the standard cell library, we strategically modify mapping criteria during RTL-to-netlist conversion without altering design functionality. Furthermore, we theoretically establish that PoSyn minimizes mutual information leakage, strengthening its security against PSC vulnerabilities. We evaluate PoSyn across various cryptographic hardware implementations, including AES, RSA, PRESENT, and post-quantum cryptographic algorithms such as Saber and CRYSTALS-Kyber, at technology nodes of 65nm, 45nm, and 15nm. Experimental results demonstrate a substantial reduction in success rates for Differential Power Analysis (DPA) and Correlation Power Analysis (CPA) attacks, achieving lows of 3% and 6%, respectively. TVLA analysis further confirms that synthesized netlists exhibit negligible leakage. Additionally, compared to conventional countermeasures like masking and shuffling, PoSyn significantly lowers attack success rates, achieving reductions of up to 72%, while simultaneously enhancing area efficiency by as much as 3.79 times.
Seeing Voices: Generating A-Roll Video from Audio with Mirage
Venue: arXiv cs.CV 2025
Year: 2025

From professional filmmaking to user-generated content, creators and consumers have long recognized that the power of video depends on the harmonious integration of what we hear (the video's audio track) with what we see (the video's image sequence). Current approaches to video generation either ignore sound to focus on general-purpose but silent image sequence generation or address both visual and audio elements but focus on restricted application domains such as re-dubbing. We introduce Mirage, an audio-to-video foundation model that excels at generating realistic, expressive output imagery from scratch given an audio input. When integrated with existing methods for speech synthesis (text-to-speech, or TTS), Mirage results in compelling multimodal video. When trained on audio-video footage of people talking (A-roll) and conditioned on audio containing speech, Mirage generates video of people delivering a believable interpretation of the performance implicit in input audio. Our central technical contribution is a unified method for training self-attention-based audio-to-video generation models, either from scratch or given existing weights. This methodology allows Mirage to retain generality as an approach to audio-to-video generation while producing outputs of superior subjective quality to methods that incorporate audio-specific architectures or loss components specific to people, speech, or details of how images or audio are captured. We encourage readers to watch and listen to the results of Mirage for themselves (see paper and comments for links).
Z3Guide: A Scalable, Student-Centered, and Extensible Educational Environment for Logic Modeling
Venue: arXiv cs.HC 2025
Year: 2025

Constraint-satisfaction problems (CSPs) are ubiquitous, ranging from budgeting for grocery shopping to verifying software behavior. Logic modeling helps solve CSPs programmatically using SMT solvers. Despite its importance in many Computer Science disciplines, resources for teaching and learning logic modeling are scarce and scattered, and challenges remain in designing educational environments for logic modeling that are accessible and meet the needs of teachers and students. This paper explores how to design such an environment and probes the impact of the design on the learning experience. From a need-finding interview study and a design iteration with teachers of logic modeling, we curated 10 design guidelines spanning three main requirements: providing easy access, supporting various educational modalities, and allowing extensions for customized pedagogical needs. We implemented nine guidelines in Z3Guide, an open-source browser-based tool. Using Z3Guide in a logic modeling learning workshop with more than 100 students, we gathered positive feedback on its support for learning and identified opportunities for future improvements.
H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs
Venue: arXiv cs.LG 2025
Year: 2025

The growing interests and applications of graph learning in diverse domains have propelled the development of a unified model generalizing well across different graphs and tasks, known as the Graph Foundation Model (GFM). Existing research has leveraged text-attributed graphs (TAGs) to tackle the heterogeneity in node features among graphs. However, they primarily focus on homogeneous TAGs (HoTAGs), leaving heterogeneous TAGs (HeTAGs), where multiple types of nodes/edges reside, underexplored. To enhance the capabilities and applications of GFM, we introduce H$^2$GFM, a novel framework designed to generalize across both HoTAGs and HeTAGs. Our model projects diverse meta-relations among graphs under a unified textual space, and employs a context encoding to capture spatial and higher-order semantic relationships. To achieve robust node representations, we propose a novel context-adaptive graph transformer (CGT), effectively capturing information from both context neighbors and their relationships. Furthermore, we employ a mixture of CGT experts to capture the heterogeneity in structural patterns among graph types. Comprehensive experiments on a wide range of HoTAGs and HeTAGs as well as learning scenarios demonstrate the effectiveness of our model.
How Good LLM-Generated Password Policies Are?
Venue: arXiv cs.CR 2025
Year: 2025

Generative AI technologies, particularly Large Language Models (LLMs), are rapidly being adopted across industry, academia, and government sectors, owing to their remarkable capabilities in natural language processing. However, despite their strengths, the inconsistency and unpredictability of LLM outputs present substantial challenges, especially in security-critical domains such as access control. One critical issue that emerges prominently is the consistency of LLM-generated responses, which is paramount for ensuring secure and reliable operations. In this paper, we study the application of LLMs within the context of Cybersecurity Access Control Systems. Specifically, we investigate the consistency and accuracy of LLM-generated password policies, translating natural language prompts into executable pwquality$.$conf configuration files. Our experimental methodology adopts two distinct approaches: firstly, we utilize pre-trained LLMs to generate configuration files purely from natural language prompts without additional guidance. Secondly, we provide these models with official pwquality$.$conf documentation to serve as an informative baseline. We systematically assess the soundness, accuracy, and consistency of these AI-generated configurations. Our findings underscore significant challenges in the current generation of LLMs and contribute valuable insights into refining the deployment of LLMs in Access Control Systems.
Distortion Search, A Web Search Privacy Heuristic
Venue: arXiv cs.CR 2025
Year: 2025

Search engines have vast technical capabilities to retain Internet search logs for each user and thus present major privacy vulnerabilities to both individuals and organizations in revealing user intent. Additionally, many of the web search privacy enhancing tools available today require that the user trusts a third party, which make confidentiality of user intent even more challenging. The user is left at the mercy of the third party without the control over his or her own privacy. In this article, we suggest a user-centric heuristic, Distortion Search, a web search query privacy methodology that works by the formation of obfuscated search queries via the permutation of query keyword categories, and by strategically applying k-anonymised web navigational clicks on URLs and Ads to generate a distorted user profile and thus providing specific user intent and query confidentiality. We provide empirical results via the evaluation of distorted web search queries in terms of retrieved search results and the resulting web ads from search engines. Preliminary experimental results indicate that web search query and specific user intent privacy might be achievable from the user side without the involvement of the search engine or other third parties.