OpenRaiser/Intern-Atlas huggingface dataset:
SYMBIOSIS: Systems Thinking and Machine Intelligence for Better Outcomes in Society
Venue: arXiv cs.CY 2025
Year: 2025

This paper presents SYMBIOSIS, an AI-powered framework and platform designed to make Systems Thinking accessible for addressing societal challenges and unlock paths for leveraging systems thinking frameworks to improve AI systems. The platform establishes a centralized, open-source repository of systems thinking/system dynamics models categorized by Sustainable Development Goals (SDGs) and societal topics using topic modeling and classification techniques. Systems Thinking resources, though critical for articulating causal theories in complex problem spaces, are often locked behind specialized tools and intricate notations, creating high barriers to entry. To address this, we developed a generative co-pilot that translates complex systems representations - such as causal loop and stock-flow diagrams - into natural language (and vice-versa), allowing users to explore and build models without extensive technical training. Rooted in community-based system dynamics (CBSD) and informed by community-driven insights on societal context, we aim to bridge the problem understanding chasm. This gap, driven by epistemic uncertainty, often limits ML developers who lack the community-specific knowledge essential for problem understanding and formulation, often leading to ill informed causal assumptions, reduced intervention effectiveness and harmful biases. Recent research identifies causal and abductive reasoning as crucial frontiers for AI, and Systems Thinking provides a naturally compatible framework for both. By making Systems Thinking frameworks more accessible and user-friendly, SYMBIOSIS aims to serve as a foundational step to unlock future research into responsible and society-centered AI. Our work underscores the need for ongoing research into AI's capacity to understand essential characteristics of complex adaptive systems paving the way for more socially attuned, effective AI systems.
Minimum cost flow decomposition on arc-coloured networks
Venue: arXiv cs.CC 2025
Year: 2025

A network $\mathcal{N}$ is formed by a (multi)digraph $D$ together with a \emph{capacity function} $u : A(D) \to R_+$, and it is denoted by $\mathcal{N} = (D,u)$. A flow on $\mathcal{N}$ is a function $x: A(D) \to R_+$ such that $x(a) \leq u(a)$ for all $a \in A(D)$, and it is said to be $k$-splittable if it can be decomposed into up to $k$ paths. We say that a flow is $λ$-uniform if its value on each arc of the network with positive flow value is exactly $λ$, for some $λ\in R_+^*$. Arc-coloured networks are used to model qualitative differences among different regions through which the flow will be sent. They have applications in several areas such as communication networks, multimodal transportation, molecular biology, packing etc. We consider the problem of decomposing a flow over an arc-coloured network with minimum cost, that is, with minimum sum of the cost of its paths, where the cost of each path is given by its number of colours. We show that this problem is NP-Hard for general flows. When we restrict the problem to $λ$-uniform flows, we show that it can be solved in polynomial time for networks with at most two colours, and it is NP-Hard for general networks with three colours and for acyclic networks with at least five colours.
ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games
Venue: arXiv cs.LG 2025
Year: 2025

Behavioral game theory models serve two purposes: yielding insights into how human decision-making works, and predicting how people would behave in novel strategic settings. A system called GameNet represents the state of the art for predicting human behavior in the setting of unrepeated simultaneous-move games, combining a simple "level-k" model of strategic reasoning with a complex neural network model of non-strategic "level-0" behavior. Although this reliance on well-established ideas from cognitive science ought to make GameNet interpretable, the flexibility of its level-0 model raises the possibility that it is able to emulate strategic reasoning. In this work, we prove that GameNet's level-0 model is indeed too general. We then introduce ElementaryNet, a novel neural network that is provably incapable of expressing strategic behavior. We show that these additional restrictions are empirically harmless, with ElementaryNet and GameNet having statistically indistinguishable performance. We then show how it is possible to derive insights about human behavior by varying ElementaryNet's features and interpreting its parameters, finding evidence of iterative reasoning, learning about the depth of this reasoning process, and showing the value of a rich level-0 specification.
Evaluation Framework for Sensor Configuration Impact on Deep Learning-Based Perception
Venue: arXiv cs.RO 2025
Year: 2025

Current research on automotive perception systems predominantly focusses on either improving the performance of sensor technology or enhancing the perception functions in isolation. High-level perception functions are increasingly based on deep learning (DL) models due to their improved performance and generalisability compared to traditional algorithms. Despite the vital need to evaluate the performance of DL-based perception functions under real-world conditions using onboard sensor inputs, there is a lack of frameworks to implement such systematic evaluations. This paper presents a versatile framework to evaluate the impact of perception sensor modalities and parameter settings on DL-based perception functions. Using a simulation environment, the framework facilitates sensor modality selection and parameter tuning under different operational design domain conditions. Its effectiveness is demonstrated through a case study involving a state-of-the-art surround trajectory prediction model, highlighting performance differences across the sensor modalities radar and camera. Different settings for the parameter, horizontal field of view (HFOV) were evaluated to identify the optimal configuration. The results indicate that a radar sensor with a narrow HFOV is the most suitable configuration for the evaluated perception algorithm. The proposed framework offers a holistic approach to the design of the perception sensor suite, significantly contributing to the development of robust perception systems for automated driving systems.
Bayesian Fields: Task-driven Open-Set Semantic Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

Open-set semantic mapping requires (i) determining the correct granularity to represent the scene (e.g., how should objects be defined), and (ii) fusing semantic knowledge across multiple 2D observations into an overall 3D reconstruction -ideally with a high-fidelity yet low-memory footprint. While most related works bypass the first issue by grouping together primitives with similar semantics (according to some manually tuned threshold), we recognize that the object granularity is task-dependent, and develop a task-driven semantic mapping approach. To address the second issue, current practice is to average visual embedding vectors over multiple views. Instead, we show the benefits of using a probabilistic approach based on the properties of the underlying visual-language foundation model, and leveraging Bayesian updating to aggregate multiple observations of the scene. The result is Bayesian Fields, a task-driven and probabilistic approach for open-set semantic mapping. To enable high-fidelity objects and a dense scene representation, Bayesian Fields uses 3D Gaussians which we cluster into task-relevant objects, allowing for both easy 3D object extraction and reduced memory usage. We release Bayesian Fields open-source at https: //github.com/MIT-SPARK/Bayesian-Fields.
Bayesian Graph Traversal
Venue: arXiv cs.AI 2025
Year: 2025

This research considers Bayesian decision-analytic approaches toward the traversal of an uncertain graph. Namely, a traveler progresses over a graph in which rewards are gained upon a node's first visit and costs are incurred for every edge traversal. The traveler knows the graph's adjacency matrix and his starting position but does not know the rewards and costs. The traveler is a Bayesian who encodes his beliefs about these values using a Gaussian process prior and who seeks to maximize his expected utility over these beliefs. Adopting a decision-analytic perspective, we develop sequential decision-making solution strategies for this coupled information-collection and network-routing problem. We show that the problem is NP-Hard and derive properties of the optimal walk. These properties provide heuristics for the traveler's problem that balance exploration and exploitation. We provide a practical case study focused on the use of unmanned aerial systems for public safety and empirically study policy performance in myriad Erdos-Renyi settings.
Psycholinguistic Analyses in Software Engineering Text: A Systematic Literature Review
Venue: arXiv cs.SE 2025
Year: 2025

Context: A deeper understanding of human factors in software engineering (SE) is essential for improving team collaboration, decision-making, and productivity. Communication channels like code reviews and chats provide insights into developers' psychological and emotional states. While large language models excel at text analysis, they often lack transparency and precision. Psycholinguistic tools like Linguistic Inquiry and Word Count (LIWC) offer clearer, interpretable insights into cognitive and emotional processes exhibited in text. Despite its wide use in SE research, no comprehensive review of LIWC's use has been conducted. Objective: We examine the importance of psycholinguistic tools, particularly LIWC, and provide a thorough analysis of its current and potential future applications in SE research. Methods: We conducted a systematic review of six prominent databases, identifying 43 SE-related papers using LIWC. Our analysis focuses on five research questions. Results: Our findings reveal a wide range of applications, including analyzing team communication to detect developer emotions and personality, developing ML models to predict deleted Stack Overflow posts, and more recently comparing AI-generated and human-written text. LIWC has been primarily used with data from project management platforms (e.g., GitHub) and Q&A forums (e.g., Stack Overflow). Key BSE concepts include Communication, Organizational Climate, and Positive Psychology. 26 of 43 papers did not formally evaluate LIWC. Concerns were raised about some limitations, including difficulty handling SE-specific vocabulary. Conclusion: We highlight the potential of psycholinguistic tools and their limitations, and present new use cases for advancing the research of human factors in SE (e.g., bias in human-LLM conversations).
Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning agents are fundamentally limited by the quality of the reward functions they learn from, yet reward design is often overlooked under the assumption that a well-defined reward is readily available. However, in practice, designing rewards is difficult, and even when specified, evaluating their correctness is equally problematic: how do we know if a reward function is correctly specified? In our work, we address these challenges by focusing on reward alignment -- assessing whether a reward function accurately encodes the preferences of a human stakeholder. As a concrete measure of reward alignment, we introduce the Trajectory Alignment Coefficient to quantify the similarity between a human stakeholder's ranking of trajectory distributions and those induced by a given reward function. We show that the Trajectory Alignment Coefficient exhibits desirable properties, such as not requiring access to a ground truth reward, invariance to potential-based reward shaping, and applicability to online RL. Additionally, in an 11 -- person user study of RL practitioners, we found that access to the Trajectory Alignment Coefficient during reward selection led to statistically significant improvements. Compared to relying only on reward functions, our metric reduced cognitive workload by 1.5x, was preferred by 82% of users and increased the success rate of selecting reward functions that produced performant policies by 41%.
Knowledge Workers' Perspectives on AI Training for Responsible AI Use
Venue: arXiv cs.HC 2025
Year: 2025

AI expansion has accelerated workplace adoption of new technologies. Yet, it is unclear whether and how knowledge workers are supported and trained to safely use AI. Inadequate training may lead to unrealized benefits if workers abandon tools, or perpetuate biases if workers misinterpret AI-based outcomes. In a workshop with 39 workers from 26 countries specializing in human resources, labor law, standards creation, and worker training, we explored questions and ideas they had about safely adopting AI. We held 17 follow-up interviews to further investigate what skills and training knowledge workers need to achieve safe and effective AI in practice. We synthesize nine training topics participants surfaced for knowledge workers related to challenges around understanding what AI is, misinterpreting outcomes, exacerbating biases, and worker rights. We reflect how these training topics might be addressed under different contexts, imagine HCI research prototypes as potential training tools, and consider ways to ensure training does not perpetuate harmful values.
The Liabilities of Robots.txt
Venue: arXiv cs.CY 2025
Year: 2025

This paper explores the legal implications of violating "robots.txt", a technical standard widely used by webmasters to communicate restrictions on automated access to website content. Although historically regarded as a voluntary guideline, the rise of generative AI and large-scale web scraping has amplified the consequences of disregarding "robots.txt" directives. While previous legal discourse has largely focused on criminal or copyright-based remedies, we argue that civil doctrines, particularly in contract and tort law, offer a more balanced and sustainable framework for regulating web robot behavior in common law jurisdictions. Under certain conditions, "robots.txt" can give rise to a unilateral contract or serve as a form of notice sufficient to establish tortious liability, including trespass to chattels and negligence. Ultimately, we argue that clarifying liability for "robots.txt" violations is essential to addressing the growing fragmentation of the internet. By restoring balance and accountability in the digital ecosystem, our proposed framework helps preserve the internet's open and cooperative foundations. Through this lens, "robots.txt" can remain an equitable and effective tool for digital governance in the age of AI.
A Label-Free High-Precision Residual Moveout Picking Method for Travel Time Tomography based on Deep Learning
Venue: arXiv cs.CV 2025
Year: 2025

Residual moveout (RMO) provides critical information for travel time tomography. The current industry-standard method for fitting RMO involves scanning high-order polynomial equations. However, this analytical approach does not accurately capture local saltation, leading to low iteration efficiency in tomographic inversion. Supervised learning-based image segmentation methods for picking can effectively capture local variations; however, they encounter challenges such as a scarcity of reliable training samples and the high complexity of post-processing. To address these issues, this study proposes a deep learning-based cascade picking method. It distinguishes accurate and robust RMOs using a segmentation network and a post-processing technique based on trend regression. Additionally, a data synthesis method is introduced, enabling the segmentation network to be trained on synthetic datasets for effective picking in field data. Furthermore, a set of metrics is proposed to quantify the quality of automatically picked RMOs. Experimental results based on both model and real data demonstrate that, compared to semblance-based methods, our approach achieves greater picking density and accuracy.
TransParking: A Dual-Decoder Transformer Framework with Soft Localization for End-to-End Automatic Parking
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, fully differentiable end-to-end autonomous driving systems have become a research hotspot in the field of intelligent transportation. Among various research directions, automatic parking is particularly critical as it aims to enable precise vehicle parking in complex environments. In this paper, we present a purely vision-based transformer model for end-to-end automatic parking, trained using expert trajectories. Given camera-captured data as input, the proposed model directly outputs future trajectory coordinates. Experimental results demonstrate that the various errors of our model have decreased by approximately 50% in comparison with the current state-of-the-art end-to-end trajectory prediction algorithm of the same type. Our approach thus provides an effective solution for fully differentiable automatic parking.
Biased Federated Learning under Wireless Heterogeneity
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning (FL) has emerged as a promising framework for distributed learning, enabling collaborative model training without sharing private data. Existing wireless FL works primarily adopt two communication strategies: (1) over-the-air (OTA) computation, which exploits wireless signal superposition for simultaneous gradient aggregation, and (2) digital communication, which allocates orthogonal resources for gradient uploads. Prior works on both schemes typically assume \emph{homogeneous} wireless conditions (equal path loss across devices) to enforce zero-bias updates or permit uncontrolled bias, resulting in suboptimal performance and high-variance model updates in \emph{heterogeneous} environments, where devices with poor channel conditions slow down convergence. This paper addresses FL over heterogeneous wireless networks by proposing novel OTA and digital FL updates that allow a structured, time-invariant model bias, thereby reducing variance in FL updates. We analyze their convergence under a unified framework and derive an upper bound on the model ``optimality error", which explicitly quantifies the effect of bias and variance in terms of design parameters. Next, to optimize this trade-off, we study a non-convex optimization problem and develop a successive convex approximation (SCA)-based framework to jointly optimize the design parameters. We perform extensive numerical evaluations with several related design variants and state-of-the-art OTA and digital FL schemes. Our results confirm that minimizing the bias-variance trade-off while allowing a structured bias provides better FL convergence performance than existing schemes.
ClueCart: Supporting Game Story Interpretation and Narrative Inference from Fragmented Clues
Venue: arXiv cs.HC 2025
Year: 2025

Indexical storytelling is gaining popularity in video games, where the narrative unfolds through fragmented clues. This approach fosters player-generated content and discussion, as story interpreters piece together the overarching narrative from these scattered elements. However, the fragmented and non-linear nature of the clues makes systematic categorization and interpretation challenging, potentially hindering efficient story reconstruction and creative engagement. To address these challenges, we first proposed a hierarchical taxonomy to categorize narrative clues, informed by a formative study. Using this taxonomy, we designed ClueCart, a creativity support tool aimed at enhancing creators' ability to organize story clues and facilitate intricate story interpretation. We evaluated ClueCart through a between-subjects study (N=40), using Miro as a baseline. The results showed that ClueCart significantly improved creators' efficiency in organizing and retrieving clues, thereby better supporting their creative processes. Additionally, we offer design insights for future studies focused on player-centric narrative analysis.
Prefer2SD: A Human-in-the-Loop Approach to Balancing Similarity and Diversity in In-Game Friend Recommendations
Venue: arXiv cs.HC 2025
Year: 2025

In-game friend recommendations significantly impact player retention and sustained engagement in online games. Balancing similarity and diversity in recommendations is crucial for fostering stronger social bonds across diverse player groups. However, automated recommendation systems struggle to achieve this balance, especially as player preferences evolve over time. To tackle this challenge, we introduce Prefer2SD (derived from Preference to Similarity and Diversity), an iterative, human-in-the-loop approach designed to optimize the similarity-diversity (SD) ratio in friend recommendations. Developed in collaboration with a local game company, Prefer2D leverages a visual analytics system to help experts explore, analyze, and adjust friend recommendations dynamically, incorporating players' shifting preferences. The system employs interactive visualizations that enable experts to fine-tune the balance between similarity and diversity for distinct player groups. We demonstrate the efficacy of Prefer2SD through a within-subjects study (N=12), a case study, and expert interviews, showcasing its ability to enhance in-game friend recommendations and offering insights for the broader field of personalized recommendation systems.
Feature Fusion Attention Network with CycleGAN for Image Dehazing, De-Snowing and De-Raining
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents a novel approach to image dehazing by combining Feature Fusion Attention (FFA) networks with CycleGAN architecture. Our method leverages both supervised and unsupervised learning techniques to effectively remove haze from images while preserving crucial image details. The proposed hybrid architecture demonstrates significant improvements in image quality metrics, achieving superior PSNR and SSIM scores compared to traditional dehazing methods. Through extensive experimentation on the RESIDE and DenseHaze CVPR 2019 dataset, we show that our approach effectively handles both synthetic and real-world hazy images. CycleGAN handles the unpaired nature of hazy and clean images effectively, enabling the model to learn mappings even without paired data.
AF-KAN: Activation Function-Based Kolmogorov-Arnold Networks for Efficient Representation Learning
Venue: arXiv cs.LG 2025
Year: 2025

Kolmogorov-Arnold Networks (KANs) have inspired numerous works exploring their applications across a wide range of scientific problems, with the potential to replace Multilayer Perceptrons (MLPs). While many KANs are designed using basis and polynomial functions, such as B-splines, ReLU-KAN utilizes a combination of ReLU functions to mimic the structure of B-splines and take advantage of ReLU's speed. However, ReLU-KAN is not built for multiple inputs, and its limitations stem from ReLU's handling of negative values, which can restrict feature extraction. To address these issues, we introduce Activation Function-Based Kolmogorov-Arnold Networks (AF-KAN), expanding ReLU-KAN with various activations and their function combinations. This novel KAN also incorporates parameter reduction methods, primarily attention mechanisms and data normalization, to enhance performance on image classification datasets. We explore different activation functions, function combinations, grid sizes, and spline orders to validate the effectiveness of AF-KAN and determine its optimal configuration. In the experiments, AF-KAN significantly outperforms MLP, ReLU-KAN, and other KANs with the same parameter count. It also remains competitive even when using fewer than 6 to 10 times the parameters while maintaining the same network structure. However, AF-KAN requires a longer training time and consumes more FLOPs. The repository for this work is available at https://github.com/hoangthangta/All-KAN.