OpenRaiser/Intern-Atlas huggingface dataset:
Generative AI and News Consumption: Design Fictions and Critical Analysis
Venue: arXiv cs.HC 2025
Year: 2025

The emergence of Generative AI features in news applications may radically change news consumption and challenge journalistic practices. To explore the future potentials and risks of this understudied area, we created six design fictions depicting scenarios such as virtual companions delivering news summaries to the user, AI providing context to news topics, and content being transformed into other formats on demand. The fictions, discussed with a multi-disciplinary group of experts, enabled a critical examination of the diverse ethical, societal, and journalistic implications of AI shaping this everyday activity. The discussions raised several concerns, suggesting that such consumer-oriented AI applications can clash with journalistic values and processes. These include fears that neither consumers nor AI could successfully balance engagement, objectivity, and truth, leading to growing detachment from shared understanding. We offer critical insights into the potential long-term effects to guide design efforts in this emerging application area of GenAI.
Active Data Sampling and Generation for Bias Remediation
Venue: arXiv cs.LG 2025
Year: 2025

Adequate sampling space coverage is the keystone to effectively train trustworthy Machine Learning models. Unfortunately, real data do carry several inherent risks due to the many potential biases they exhibit when gathered without a proper random sampling over the reference population, and most of the times this is way too expensive or time consuming to be a viable option. Depending on how training data have been gathered, unmitigated biases can lead to harmful or discriminatory consequences that ultimately hinders large scale applicability of pre-trained models and undermine their truthfulness or fairness expectations. In this paper, a mixed active sampling and data generation strategy -- called samplation -- is proposed as a mean to compensate during fine-tuning of a pre-trained classifer the unfair classifications it produces, assuming that the training data come from a non-probabilistic sampling schema. Given a pre-trained classifier, first a fairness metric is evaluated on a test set, then new reservoirs of labeled data are generated and finally a number of reversely-biased artificial samples are generated for the fine-tuning of the model. Using as case study Deep Models for visual semantic role labeling, the proposed method has been able to fully cure a simulated gender bias starting from a 90/10 imbalance, with only a small percentage of new data and with a minor effect on accuracy.
An Algorithm for Illuminating $n$ Nonoverlapping Circular Discs' Boundaries on the Plane with Application to Tree Stem Illumination Problem
Venue: arXiv cs.CG 2025
Year: 2025

Given a set of $n$ nonoverlapping circular discs on a plane, we aim to determine possible positions of points (referred to as cameras) that could fully illuminate all the circular discs' boundaries. This work presents a geometric approach for determining feasible camera positions that would provide total illumination of all circular discs. The Laguerre Delaunay triangulation, coupled with the intersection of slabs formed by the boundaries of circular discs, is employed to form the region that satisfies the given conditions. The experiment is conducted using a set of randomly positioned circular discs on a plane. This study has the potential to address the issue of illumination in forests by utilizing a LiDAR camera to determine the possible number and placement of cameras that can effectively illuminate trees within a forest.
Lipschitz Constant Meets Condition Number: Learning Robust and Compact Deep Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Recent research has revealed that high compression of Deep Neural Networks (DNNs), e.g., massive pruning of the weight matrix of a DNN, leads to a severe drop in accuracy and susceptibility to adversarial attacks. Integration of network pruning into an adversarial training framework has been proposed to promote adversarial robustness. It has been observed that a highly pruned weight matrix tends to be ill-conditioned, i.e., increasing the condition number of the weight matrix. This phenomenon aggravates the vulnerability of a DNN to input noise. Although a highly pruned weight matrix is considered to be able to lower the upper bound of the local Lipschitz constant to tolerate large distortion, the ill-conditionedness of such a weight matrix results in a non-robust DNN model. To overcome this challenge, this work develops novel joint constraints to adjust the weight distribution of networks, namely, the Transformed Sparse Constraint joint with Condition Number Constraint (TSCNC), which copes with smoothing distribution and differentiable constraint functions to reduce condition number and thus avoid the ill-conditionedness of weight matrices. Furthermore, our theoretical analyses unveil the relevance between the condition number and the local Lipschitz constant of the weight matrix, namely, the sharply increasing condition number becomes the dominant factor that restricts the robustness of over-sparsified models. Extensive experiments are conducted on several public datasets, and the results show that the proposed constraints significantly improve the robustness of a DNN with high pruning rates.
Linear-Time Graph Programs without Preconditions
Venue: arXiv cs.PL 2025
Year: 2025

We report on a recent breakthrough in rule-based graph programming, which allows us to reach the time complexity of imperative linear-time algorithms. In general, achieving the complexity of graph algorithms in conventional languages using graph transformation rules is challenging due to the cost of graph matching. Previous work demonstrated that with rooted rules, certain algorithms can be executed in linear time using the graph programming language GP 2. However, for non-destructive algorithms that retain the structure of input graphs, achieving linear runtime required input graphs to be connected and of bounded node degree. In this paper, we overcome these preconditions by enhancing the graph data structure generated by the GP 2 compiler and exploiting the new structure in programs. We present three case studies, a cycle detection program, a program for numbering the connected components of a graph, and a breadth-first search program. Each of these programs runs in linear time on both connected and disconnected input graphs with arbitrary node degrees. We give empirical evidence for the linear time complexity by using timings for various classes of input graphs.
Explainable ICD Coding via Entity Linking
Venue: arXiv cs.CL 2025
Year: 2025

Clinical coding is a critical task in healthcare, although traditional methods for automating clinical coding may not provide sufficient explicit evidence for coders in production environments. This evidence is crucial, as medical coders have to make sure there exists at least one explicit passage in the input health record that justifies the attribution of a code. We therefore propose to reframe the task as an entity linking problem, in which each document is annotated with its set of codes and respective textual evidence, enabling better human-machine collaboration. By leveraging parameter-efficient fine-tuning of Large Language Models (LLMs), together with constrained decoding, we introduce three approaches to solve this problem that prove effective at disambiguating clinical mentions and that perform well in few-shot scenarios.
Combining Machine Learning and Sampling-Based Search for Multi-Goal Motion Planning with Dynamics
Venue: arXiv cs.RO 2025
Year: 2025

This paper considers multi-goal motion planning in unstructured, obstacle-rich environments where a robot is required to reach multiple regions while avoiding collisions. The planned motions must also satisfy the differential constraints imposed by the robot dynamics. To find solutions efficiently, this paper leverages machine learning, Traveling Salesman Problem (TSP), and sampling-based motion planning. The approach expands a motion tree by adding collision-free and dynamically-feasible trajectories as branches. A TSP solver is used to compute a tour for each node to determine the order in which to reach the remaining goals by utilizing a cost matrix. An important aspect of the approach is that it leverages machine learning to construct the cost matrix by combining runtime and distance predictions to single-goal motion-planning problems. During the motion-tree expansion, priority is given to nodes associated with low-cost tours. Experiments with a vehicle model operating in obstacle-rich environments demonstrate the computational efficiency and scalability of the approach.
Knowledge-Based Multi-Agent Framework for Automated Software Architecture Design
Venue: arXiv cs.SE 2025
Year: 2025

Architecture design is a critical step in software development. However, creating a high-quality architecture is often costly due to the significant need for human expertise and manual effort. Recently, agents built upon Large Language Models (LLMs) have achieved remarkable success in various software engineering tasks. Despite this progress, the use of agents to automate the architecture design process remains largely unexplored. To address this gap, we envision a Knowledge-based Multi-Agent Architecture Design (MAAD) framework. MAAD uses agents to simulate human roles in the traditional software architecture design process, thereby automating the design process. To empower these agents, MAAD incorporates knowledge extracted from three key sources: 1) existing system designs, 2) authoritative literature, and 3) architecture experts. By envisioning the MAAD framework, we aim to advance the full automation of application-level system development.
LACeS: An Open, Fast, Responsible, and Efficient Longitudinal Anycast Census System
Venue: arXiv cs.NI 2025
Year: 2025

IP anycast replicates an address at multiple locations to reduce latency and enhance resilience. Due to anycast's crucial role in the modern Internet, earlier research introduced tools to perform anycast censuses. The first, iGreedy, uses latency measurements from geographically dispersed locations to map anycast deployments. The second, MAnycast2, uses anycast to perform a census of other anycast networks. MAnycast2's advantage is speed and coverage but suffers from problems with accuracy, while iGreedy is highly accurate but slower using author-defined probing rates and costlier. In this paper we address the shortcomings of both systems and present LACeS (Longitudinal Anycast Census System). Taking MAnycast2 as a basis, we completely redesign its measurement pipeline, and add support for distributed probing, additional protocols (DNS over UDP, TCP SYN/ACK, and IPv6) and latency measurements similar to iGreedy. We validate LACeS on an anycast testbed with 32 globally distributed nodes, compare against an external anycast production deployment, extensive latency measurements with RIPE Atlas and cross-check over 60% of detected anycast using operator ground truth that shows LACeS achieves high accuracy. Finally, we provide a longitudinal analysis of anycast, covering 17+ months, showing LACeS achieves high precision. We make continual daily LACeS censuses available to the community and release the source code of the tool under a permissive open source license.
A Theoretical Framework for Prompt Engineering: Approximating Smooth Functions with Transformer Prompts
Venue: arXiv cs.LG 2025
Year: 2025

Prompt engineering has emerged as a powerful technique for guiding large language models (LLMs) toward desired responses, significantly enhancing their performance across diverse tasks. Beyond their role as static predictors, LLMs increasingly function as intelligent agents, capable of reasoning, decision-making, and adapting dynamically to complex environments. However, the theoretical underpinnings of prompt engineering remain largely unexplored. In this paper, we introduce a formal framework demonstrating that transformer models, when provided with carefully designed prompts, can act as a configurable computational system by emulating a ``virtual'' neural network during inference. Specifically, input prompts effectively translate into the corresponding network configuration, enabling LLMs to adjust their internal computations dynamically. Building on this construction, we establish an approximation theory for $β$-times differentiable functions, proving that transformers can approximate such functions with arbitrary precision when guided by appropriately structured prompts. Moreover, our framework provides theoretical justification for several empirically successful prompt engineering techniques, including the use of longer, structured prompts, filtering irrelevant information, enhancing prompt token diversity, and leveraging multi-agent interactions. By framing LLMs as adaptable agents rather than static models, our findings underscore their potential for autonomous reasoning and problem-solving, paving the way for more robust and theoretically grounded advancements in prompt engineering and AI agent design.
State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Recently, deep reinforcement learning (DRL) has emerged as a promising approach for robotic control. However, the deployment of DRL in real-world robots is hindered by its sensitivity to environmental perturbations. While existing whitebox adversarial attacks rely on local gradient information and apply uniform perturbations across all states to evaluate DRL robustness, they fail to account for temporal dynamics and state-specific vulnerabilities. To combat the above challenge, we first conduct a theoretical analysis of white-box attacks in DRL by establishing the adversarial victim-dynamics Markov decision process (AVD-MDP), to derive the necessary and sufficient conditions for a successful attack. Based on this, we propose a selective state-aware reinforcement adversarial attack method, named STAR, to optimize perturbation stealthiness and state visitation dispersion. STAR first employs a soft mask-based state-targeting mechanism to minimize redundant perturbations, enhancing stealthiness and attack effectiveness. Then, it incorporates an information-theoretic optimization objective to maximize mutual information between perturbations, environmental states, and victim actions, ensuring a dispersed state-visitation distribution that steers the victim agent into vulnerable states for maximum return reduction. Extensive experiments demonstrate that STAR outperforms state-of-the-art benchmarks.
Procedural Knowledge Ontology (PKO)
Venue: arXiv cs.AI 2025
Year: 2025

Processes, workflows and guidelines are core to ensure the correct functioning of industrial companies: for the successful operations of factory lines, machinery or services, often industry operators rely on their past experience and know-how. The effect is that this Procedural Knowledge (PK) remains tacit and, as such, difficult to exploit efficiently and effectively. This paper presents PKO, the Procedural Knowledge Ontology, which enables the explicit modeling of procedures and their executions, by reusing and extending existing ontologies. PKO is built on requirements collected from three heterogeneous industrial use cases and can be exploited by any AI and data-driven tools that rely on a shared and interoperable representation to support the governance of PK throughout its life cycle. We describe its structure and design methodology, and outline its relevance, quality, and impact by discussing applications leveraging PKO for PK elicitation and exploitation.
Immersive and Wearable Thermal Rendering for Augmented Reality
Venue: arXiv cs.HC 2025
Year: 2025

In augmented reality (AR), where digital content is overlaid onto the real world, realistic thermal feedback has been shown to enhance immersion. Yet current thermal feedback devices, heavily influenced by the needs of virtual reality, often hinder physical interactions and are ineffective for immersion in AR. To bridge this gap, we have identified three design considerations relevant for AR thermal feedback: indirect feedback to maintain dexterity, thermal passthrough to preserve real-world temperature perception, and spatiotemporal rendering for dynamic sensations. We then created a unique and innovative thermal feedback device that satisfies these criteria. Human subject experiments assessing perceptual sensitivity, object temperature matching, spatial pattern recognition, and moving thermal stimuli demonstrated the impact of our design, enabling realistic temperature discrimination, virtual object perception, and enhanced immersion. These findings demonstrate that carefully designed thermal feedback systems can bridge the sensory gap between physical and virtual interactions, enhancing AR realism and usability.
Semantic Communications via Features Identification
Venue: arXiv cs.IT 2025
Year: 2025

The development of the new generation of wireless technologies (6G) has led to an increased interest in semantic communication. Thanks also to recent developments in artificial intelligence and communication technologies, researchers in this field have defined new communication paradigms that go beyond those of syntactic communication to post-Shannon and semantic communication. However, there is still need to define a clear and practical framework for semantic communication, as well as an effective structure of semantic elements that can be used in it. The aim of this work is to bridge the gap between two post-Shannon communication paradigms, and to define a robust and effective semantic communication strategy that focuses on a dedicated semantic element that can be easily derived from any type of message. Our work will take form as an innovative communication method called identification via semantic features, which aims at exploiting the ambiguities present in semantic messages, allowing for their identification instead of reproducing them bit by bit. Our approach has been tested through numerical simulations using a combination of machine learning and data analysis. The proposed communication method showed promising results, demonstrating a clear and significant gain over traditional syntactic communication paradigms.
Quantum Neural Network Restatement of Markov Jump Process
Venue: arXiv cs.LG 2025
Year: 2025

Despite the many challenges in exploratory data analysis, artificial neural networks have motivated strong interests in scientists and researchers both in theoretical as well as practical applications. Among sources of such popularity of artificial neural networks the ability of modeling non-linear dynamical systems, generalization, and adaptation possibilities should be mentioned. Despite this, there is still significant debate about the role of various underlying stochastic processes in stabilizing a unique structure for data learning and prediction. One of such obstacles to the theoretical and numerical study of machine intelligent systems is the curse of dimensionality and the sampling from high-dimensional probability distributions. In general, this curse prevents efficient description of states, providing a significant complexity barrier for the system to be efficiently described and studied. In this strand of research, direct treatment and description of such abstract notions of learning theory in terms of quantum information be one of the most favorable candidates. Hence, the subject matter of these articles is devoted to problems of design, adaptation and the formulations of computationally hard problems in terms of quantum mechanical systems. In order to characterize the microscopic description of such dynamics in the language of inferential statistics, covariance matrix estimation of d-dimensional Gaussian densities and Bayesian interpretation of eigenvalue problem for dynamical systems is assessed.
Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data
Venue: arXiv cs.CL 2025
Year: 2025

Recent research shows that LLM Agents can generate ``believable'' human behaviors via prompt-only methods, and such agents have been increasingly adopted in downstream applications. However, existing evaluation of these agents only focuses on qualitative believability (whether human raters think they are accurate), leaving open questions of whether LLM agents can accurately generate step-by-step actions mimicking a particular human's behavior in a multi-turn interaction task. In this work, we take shopping as a case study and present the first large-scale quantitative evaluation of state-of-the-art LLMs' ability to accurately simulate human behavior. Using real-world data from 31,865 online shopping sessions containing 230,965 user actions, our evaluation reveals that prompt-based LLMs (DeepSeek-R1, Llama, Claude) achieve only 11.86% accuracy in generating human actions, highlighting a substantial gap in actual behavioral accuracy. Through experiments, we also showcase that strategies as simple as fine-tuning LLMs on real human click-through data augmented with synthesized reasoning traces can greatly enhance models' performance. The fine-tuned Qwen2.5-7B achieves 17.26% action generation accuracy and 33.86% F1 score on final purchase prediction, representing substantial improvements of 5.4% and 13.85% over prompt-only baselines. This work establishes the first rigorous benchmark for human behavior simulation and provides actionable insights for developing more accurate LLM agents for future downstream applications.
Zero-Shot Audio-Visual Editing via Cross-Modal Delta Denoising
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we introduce zero-shot audio-video editing, a novel task that requires transforming original audio-visual content to align with a specified textual prompt without additional model training. To evaluate this task, we curate a benchmark dataset, AvED-Bench, designed explicitly for zero-shot audio-video editing. AvED-Bench includes 110 videos, each with a 10-second duration, spanning 11 categories from VGGSound. It offers diverse prompts and scenarios that require precise alignment between auditory and visual elements, enabling robust evaluation. We identify limitations in existing zero-shot audio and video editing methods, particularly in synchronization and coherence between modalities, which often result in inconsistent outcomes. To address these challenges, we propose AvED, a zero-shot cross-modal delta denoising framework that leverages audio-video interactions to achieve synchronized and coherent edits. AvED demonstrates superior results on both AvED-Bench and the recent OAVE dataset to validate its generalization capabilities. Results are available at https://genjib.github.io/project_page/AVED/index.html