OpenRaiser/Intern-Atlas huggingface dataset:
Bottom-Up Reputation Promotes Cooperation with Multi-Agent Reinforcement Learning
Venue: arXiv cs.MA 2025
Year: 2025

Reputation serves as a powerful mechanism for promoting cooperation in multi-agent systems, as agents are more inclined to cooperate with those of good social standing. While existing multi-agent reinforcement learning methods typically rely on predefined social norms to assign reputations, the question of how a population reaches a consensus on judgement when agents hold private, independent views remains unresolved. In this paper, we propose a novel bottom-up reputation learning method, Learning with Reputation Reward (LR2), designed to promote cooperative behaviour through rewards shaping based on assigned reputation. Our agent architecture includes a dilemma policy that determines cooperation by considering the impact on neighbours, and an evaluation policy that assigns reputations to affect the actions of neighbours while optimizing self-objectives. It operates using local observations and interaction-based rewards, without relying on centralized modules or predefined norms. Our findings demonstrate the effectiveness and adaptability of LR2 across various spatial social dilemma scenarios. Interestingly, we find that LR2 stabilizes and enhances cooperation not only with reward reshaping from bottom-up reputation but also by fostering strategy clustering in structured populations, thereby creating environments conducive to sustained cooperation.
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing
Venue: arXiv cs.CL 2025
Year: 2025

Large language models have achieved remarkable success in various tasks but suffer from high computational costs during inference, limiting their deployment in resource-constrained applications. To address this issue, we propose a novel Collaborative Inference with Token-lEvel Routing (CITER) framework that enables efficient collaboration between small and large language models (SLMs \& LLMs) through a token-level routing strategy. Specifically, CITER routes non-critical tokens to an SLM for efficiency and routes critical tokens to an LLM for generalization quality. We formulate router training as a policy optimization, where the router receives rewards based on both the quality of predictions and the inference costs of generation. This allows the router to learn to predict token-level routing scores and make routing decisions based on both the current token and the future impact of its decisions. To further accelerate the reward evaluation process, we introduce a shortcut which significantly reduces the costs of the reward estimation and improving the practicality of our approach. Extensive experiments on five benchmark datasets demonstrate that CITER reduces the inference costs while preserving high-quality generation, offering a promising solution for real-time and resource-constrained applications. Our data and code are available at https://github.com/aiming-lab/CITER.
Evaluating Fault Tolerance and Scalability in Distributed File Systems: A Case Study of GFS, HDFS, and MinIO
Venue: arXiv cs.DC 2025
Year: 2025

Distributed File Systems (DFS) are essential for managing vast datasets across multiple servers, offering benefits in scalability, fault tolerance, and data accessibility. This paper presents a comprehensive evaluation of three prominent DFSs - Google File System (GFS), Hadoop Distributed File System (HDFS), and MinIO - focusing on their fault tolerance mechanisms and scalability under varying data loads and client demands. Through detailed analysis, how these systems handle data redundancy, server failures, and client access protocols, ensuring reliability in dynamic, large-scale environments is assessed. In addition, the impact of system design on performance, particularly in distributed cloud and computing architectures is assessed. By comparing the strengths and limitations of each DFS, the paper provides practical insights for selecting the most appropriate system for different enterprise needs, from high availability storage to big data analytics.
Licensing Open Government Data
Venue: arXiv cs.CY 2025
Year: 2025

This article focuses on the legal issues associated with open government data licenses. This study compares current open data licenses and argues that licensing terms reflect policy considerations, which are quite different from those contemplated in business transactions or shared in typical commons communities. This article investigates the ambiguous legal status of data together with the new wave of open government data, which concerns some fundamental intellectual property (IP) questions not covered by, or analyzed in depth in, the current literature. Moreover, this study suggests that government should choose or adapt open data licenses according to their own IP regimes. In the end, this article argues that the design or choice of open government data license forms an important element of information policy; government, therefore, should make this decision in accordance with their policy goals and in compliance with their own jurisdictions' IP laws.
The Wisdom of Intellectually Humble Networks
Venue: arXiv cs.SI 2025
Year: 2025

People's collectively shared beliefs can have significant social implications, including on democratic processes and policies. Unfortunately, as people interact with peers to form and update their beliefs, various cognitive and social biases can hinder their collective wisdom. In this paper, we probe whether and how the psychological construct of intellectual humility can modulate collective wisdom in a networked interaction setting. Through agent-based modeling and data-calibrated simulations, we provide a proof of concept demonstrating that intellectual humility can foster more accurate estimations while mitigating polarization in social networks. We investigate the mechanisms behind the performance improvements and confirm robustness across task settings and network structures. Our work can guide intervention designs to capitalize on the promises of intellectual humility in boosting collective wisdom in social networks.
Sound Judgment: Properties of Consequential Sounds Affecting Human-Perception of Robots
Venue: arXiv cs.RO 2025
Year: 2025

Positive human-perception of robots is critical to achieving sustained use of robots in shared environments. One key factor affecting human-perception of robots are their sounds, especially the consequential sounds which robots (as machines) must produce as they operate. This paper explores qualitative responses from 182 participants to gain insight into human-perception of robot consequential sounds. Participants viewed videos of different robots performing their typical movements, and responded to an online survey regarding their perceptions of robots and the sounds they produce. Topic analysis was used to identify common properties of robot consequential sounds that participants expressed liking, disliking, wanting or wanting to avoid being produced by robots. Alongside expected reports of disliking high pitched and loud sounds, many participants preferred informative and audible sounds (over no sound) to provide predictability of purpose and trajectory of the robot. Rhythmic sounds were preferred over acute or continuous sounds, and many participants wanted more natural sounds (such as wind or cat purrs) in-place of machine-like noise. The results presented in this paper support future research on methods to improve consequential sounds produced by robots by highlighting features of sounds that cause negative perceptions, and providing insights into sound profile changes for improvement of human-perception of robots, thus enhancing human robot interaction.
Optimal Routing in the Presence of Hooks: Three Case Studies
Venue: arXiv cs.GT 2025
Year: 2025

We consider the problem of optimally executing a user trade over networks of constant function market makers (CFMMs) in the presence of hooks. Hooks, introduced in an upcoming version of Uniswap, are auxiliary smart contracts that allow for extra information to be added to liquidity pools. This allows liquidity providers to enable constraints on trades, allowing CFMMs to read external data, such as volatility information, and implement additional features, such as onchain limit orders. We consider three important case studies for how to optimally route trades in the presence of hooks: 1) routing through limit orders, 2) optimal liquidations and time-weighted average market makers (TWAMMs), and 3) noncomposable hooks, which provide additional output in exchange for fill risk. Leveraging tools from convex optimization and dynamic programming, we propose simple methods for formulating and solving these problems that can be useful for practitioners.
Hardware and software build flow with SoCMake
Venue: arXiv cs.AR 2025
Year: 2025

The increasing demand for electronics is driving shorter development cycles for application-specific integrated circuits (ASICs). To meet these constraints, hardware designers emphasize reusability and modularity of IP blocks, leveraging standard system-on-chip (SoC) architectures with integrated processors and common interconnects. While these architectures reduce design and verification efforts, they also introduce complexity, as verification must encompass both hardware and software execution. To enhance reusability, hardware IP blocks are often described in higher-abstraction-level languages such as Chisel and SystemRDL, relying on compilers to generate Verilog for RTL simulation and implementation. At the system level, SoC modeling and verification leverage C++ and SystemC, underscoring the need for software compilation. Consequently, an effective build system must support both hardware design flows and software compilation, including cross-compilation for C++, C, and assembly. Existing hardware build systems lack sufficient support for software compilation, necessitating the development of a new solution. In response, the Microelectronics section of CERN initiated SoCMake, initially as part of the System-on-Chip Radiation Tolerant Ecosystem (SOCRATES). Designed to automate the generation of fault-tolerant RISC-V SoCs for high-energy physics environments, SoCMake has since evolved into a generic open-source build tool for SoC generation.
Position Paper: Building Trust in Synthetic Data for Clinical AI
Venue: arXiv cs.LG 2025
Year: 2025

Deep generative models and synthetic medical data have shown significant promise in addressing key challenges in healthcare, such as privacy concerns, data bias, and the scarcity of realistic datasets. While research in this area has grown rapidly and demonstrated substantial theoretical potential, its practical adoption in clinical settings remains limited. Despite the benefits synthetic data offers, questions surrounding its reliability and credibility persist, leading to a lack of trust among clinicians. This position paper argues that fostering trust in synthetic medical data is crucial for its clinical adoption. It aims to spark a discussion on the viability of synthetic medical data in clinical practice, particularly in the context of current advancements in AI. We present empirical evidence from brain tumor segmentation to demonstrate that the quality, diversity, and proportion of synthetic data directly impact trust in clinical AI models. Our findings provide insights to improve the deployment and acceptance of synthetic data-driven AI systems in real-world clinical workflows.
Efficient Laser Frequency Allocation in Packet-Optical Nodes with Coherent Transceivers
Venue: arXiv cs.NI 2025
Year: 2025

The introduction of silicon chipsets with the capability of processing incoming optical packet traffic, creates a new generation of packet-optical nodes, the whiteboxes. Their inherent functionality of carrying pluggable coherent transceiver modules, extends their scope in the field of transport optical networks. Also, their hybrid nature improves the overall efficiency since higher layer functionality is now performed at wire speed. This is feasible by embedding in the computational logic, apart from the typical packet inspection routines and security features, Machine Learning-based traffic engineering as well. In this work, a topology based on multiple packet-optical nodes residing at the edges (ingress and egress) of a ROADM network, is evaluated according to the average laser frequency configuration time. This is achieved by exploiting telemetry analytics which are collected by the CMIS driver of the pluggable transceivers, for allocating efficient frequency slots to the source and destination of connectivity requests traversing through the ROADM network. This pair of nodes is supervised by the packet SDN controller which is part of the control plane of the optical transport network. This controller receives telemetry feedback from the whiteboxes and uses it to execute efficient ML techniques locally, for finding efficient frequency slots for the incoming transport requests. Next, it applies them to request's edge nodes. The decrease of the average laser configuration time is achieved in the evaluated topology, improving the overall efficiency of the control plane.
DeepForest: Sensing Into Self-Occluding Volumes of Vegetation With Aerial Imaging
Venue: arXiv cs.CV 2025
Year: 2025

Access to below-canopy volumetric vegetation data is crucial for understanding ecosystem dynamics. We address the long-standing limitation of remote sensing to penetrate deep into dense canopy layers. LiDAR and radar are currently considered the primary options for measuring 3D vegetation structures, while cameras can only extract the reflectance and depth of top layers. Using conventional, high-resolution aerial images, our approach allows sensing deep into self-occluding vegetation volumes, such as forests. It is similar in spirit to the imaging process of wide-field microscopy, but can handle much larger scales and strong occlusion. We scan focal stacks by synthetic-aperture imaging with drones and reduce out-of-focus signal contributions using pre-trained 3D convolutional neural networks with mean squared error (MSE) as the loss function. The resulting volumetric reflectance stacks contain low-frequency representations of the vegetation volume. Combining multiple reflectance stacks from various spectral channels provides insights into plant health, growth, and environmental conditions throughout the entire vegetation volume. Compared with simulated ground truth, our correction leads to ~x7 average improvements (min: ~x2, max: ~x12) for forest densities of 220 trees/ha - 1680 trees/ha. In our field experiment, we achieved an MSE of 0.05 when comparing with the top-vegetation layer that was measured with classical multispectral aerial imaging.
Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge
Venue: arXiv cs.CL 2025
Year: 2025

Recent research has explored methods for updating and modifying factual knowledge in large language models, often focusing on specific multi-layer perceptron blocks. This study expands on this work by examining the effectiveness of existing knowledge editing methods across languages and delving into the role of attention mechanisms in this process. Drawing from the insights gained, we propose Mass-Editing Memory with Attention in Transformers (MEMAT), a method that achieves significant improvements in all metrics while requiring minimal parameter modifications. MEMAT delivers a remarkable 10% increase in magnitude metrics, benefits languages not included in the training data and also demonstrates a high degree of portability. Our code and data are at https://github.com/dtamayo-nlp/MEMAT.
The TechDebt Game -- Enabling Discussions about Technical Debt
Venue: arXiv cs.SE 2025
Year: 2025

Context. Technical Debt (TD), defined as software constructs that are beneficial in the short term but may hinder future change, is a frequently used term in software development practice. Nevertheless, practitioners do not always fully understand its definition and, in particular, conceptual model. Previous research highlights that communication about TD is challenging, especially with non-technical stakeholders. Discussions on this topic often cause conflicts due to misunderstandings related to other stakeholders' perspectives. Goal. We designed a board game to emulate TD concepts to make them tangible to all stakeholders, including non-technical ones. The game aims to encourage discussions about TD in an emulated and safe environment, thereby avoiding real-life conflicts. Method. To evaluate the game's effectiveness, we surveyed 46 practitioners from diverse domains, positions, and experience levels who played the game in 13 sessions following extensive testing during its development. In addition to the players' general feedback, we examined situations where players recognized new insights about TD or connected game scenarios to real-life experiences. Results. Overall, the feedback on the game and its enjoyment factor were highly positive. While developers and software architects often connected game situations to their real-world experiences, non-technical stakeholders, such as scrum masters, product owners, and less experienced developers, encountered multiple new insights on TD. Numerous players have shifted their attitudes toward TD and have outlined a plan to modify their behavior regarding TD management. Conclusions. Although the game may not lead to long-term behavior change among stakeholders, participants' feedback provides evidence that it might serve as a valuable starting point for team discussions on technical debt management.
Multi-level Supervised Contrastive Learning
Venue: arXiv cs.LG 2025
Year: 2025

Contrastive learning is a well-established paradigm in representation learning. The standard framework of contrastive learning minimizes the distance between "similar" instances and maximizes the distance between dissimilar ones in the projection space, disregarding the various aspects of similarity that can exist between two samples. Current methods rely on a single projection head, which fails to capture the full complexity of different aspects of a sample, leading to suboptimal performance, especially in scenarios with limited training data. In this paper, we present a novel supervised contrastive learning method in a unified framework called multilevel contrastive learning (MLCL), that can be applied to both multi-label and hierarchical classification tasks. The key strength of the proposed method is the ability to capture similarities between samples across different labels and/or hierarchies using multiple projection heads. Extensive experiments on text and image datasets demonstrate that the proposed approach outperforms state-of-the-art contrastive learning methods
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have shown impressive capabilities in natural language processing tasks, including dialogue generation. This research aims to conduct a novel comparative analysis of two prominent techniques, fine-tuning with LoRA (Low-Rank Adaptation) and the Retrieval-Augmented Generation (RAG) framework, in the context of doctor-patient chat conversations with multiple datasets of mixed medical domains. The analysis involves three state-of-the-art models: Llama-2, GPT, and the LSTM model. Employing real-world doctor-patient dialogues, we comprehensively evaluate the performance of models, assessing key metrics such as language quality (perplexity, BLEU score), factual accuracy (fact-checking against medical knowledge bases), adherence to medical guidelines, and overall human judgments (coherence, empathy, safety). The findings provide insights into the strengths and limitations of each approach, shedding light on their suitability for healthcare applications. Furthermore, the research investigates the robustness of the models in handling diverse patient queries, ranging from general health inquiries to specific medical conditions. The impact of domain-specific knowledge integration is also explored, highlighting the potential for enhancing LLM performance through targeted data augmentation and retrieval strategies.
A User's Guide to Sampling Strategies for Sliced Optimal Transport
Venue: arXiv cs.LG 2025
Year: 2025

This paper serves as a user's guide to sampling strategies for sliced optimal transport. We provide reminders and additional regularity results on the Sliced Wasserstein distance. We detail the construction methods, generation time complexity, theoretical guarantees, and conditions for each strategy. Additionally, we provide insights into their suitability for sliced optimal transport in theory. Extensive experiments on both simulated and real-world data offer a representative comparison of the strategies, culminating in practical recommendations for their best usage.
FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection
Venue: arXiv cs.LG 2025
Year: 2025

Adversarial attacks pose a significant threat to data-driven systems, and researchers have spent considerable resources studying them. Despite its economic relevance, this trend largely overlooked the issue of credit card fraud detection. To address this gap, we propose a new threat model that demonstrates the limitations of existing attacks and highlights the necessity to investigate new approaches. We then design a new adversarial attack for credit card fraud detection, employing reinforcement learning to bypass classifiers. This attack, called FRAUD-RLA, is designed to maximize the attacker's reward by optimizing the exploration-exploitation tradeoff and working with significantly less required knowledge than competitors. Our experiments, conducted on three different heterogeneous datasets and against two fraud detection systems, indicate that FRAUD-RLA is effective, even considering the severe limitations imposed by our threat model.
UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training
Venue: arXiv cs.CV 2025
Year: 2025

Despite decades of research on data collection and model architectures, current gaze estimation models encounter significant challenges in generalizing across diverse data domains. Recent advances in self-supervised pre-training have shown remarkable performances in generalization across various vision tasks. However, their effectiveness in gaze estimation remains unexplored. We propose UniGaze, for the first time, leveraging large-scale in-the-wild facial datasets for gaze estimation through self-supervised pre-training. Through systematic investigation, we clarify critical factors that are essential for effective pretraining in gaze estimation. Our experiments reveal that self-supervised approaches designed for semantic tasks fail when applied to gaze estimation, while our carefully designed pre-training pipeline consistently improves cross-domain performance. Through comprehensive experiments of challenging cross-dataset evaluation and novel protocols including leave-one-dataset-out and joint-dataset settings, we demonstrate that UniGaze significantly improves generalization across multiple data domains while minimizing reliance on costly labeled data. source code and model are available at https://github.com/ut-vision/UniGaze.