OpenRaiser/Intern-Atlas huggingface dataset:
Exploring the Perspectives of Social VR-Aware Non-Parent Adults and Parents on Children's Use of Social Virtual Reality
Venue: arXiv cs.HC 2025
Year: 2025

Social Virtual Reality (VR), where people meet in virtual spaces via 3D avatars, is used by children and adults alike. Children experience new forms of harassment in social VR where it is often inaccessible to parental oversight. To date, there is limited understanding of how parents and non-parent adults within the child social VR ecosystem perceive the appropriateness of social VR for different age groups and the measures in place to safeguard children. We present results of a mixed-methods questionnaire (N=149 adults, including 79 parents) focusing on encounters with children in social VR and perspectives towards children's use of social VR. We draw novel insights on the frequency of social VR use by children under 13 and current use of, and future aspirations for, child protection interventions. Compared to non-parent adults, parents familiar with social VR propose lower minimum ages and are more likely to allow social VR without supervision. Adult users experience immaturity from children in social VR, while children face abuse, encounter age-inappropriate behaviours and self-disclose to adults. We present directions to enhance the safety of social VR through pre-planned controls, real-time oversight, post-event insight and the need for evidence-based guidelines to support parents and platforms around age-appropriate interventions.
6GStarLab -- A CubeSat Mission to support the development and standardization of Non-Terrestrial Networks towards 6G
Venue: arXiv cs.NI 2025
Year: 2025

The emergence of the Non-Terrestrial Network (NTN) concept in the last years has revolutionized the space industry. This novel network architecture composed of aircraft and spacecraft is currently being standardized by the 3GPP. This standardization process follows dedicated phases in which experimentation of the technology is needed. Although some missions have been conducted to demonstrate specific and service-centric technologies, a open flexible in-orbit infrastructure is demanded to support this standardization process. This work presents the 6GStarLab mission, which aims to address this gap. Specifically, this mission envisions to provide a 6U CubeSat as the main in-orbit infrastructure in which multiple technology validations can be uploaded. The concept of this mission is depicted. Additionally, this work presents the details of the satellite platform and the payload. This last one is designed to enable the experimentation in multiple radio-frequency bands (i.e. UHF, S-, X-, and Ka-bands) and an optical terminal. The launch of the satellite is scheduled for Q2 2025, and it will contribute to the standardization of future NTN architectures.
VIPER: Visual Perception and Explainable Reasoning for Sequential Decision-Making
Venue: arXiv cs.LG 2025
Year: 2025

While Large Language Models (LLMs) excel at reasoning on text and Vision-Language Models (VLMs) are highly effective for visual perception, applying those models for visual instruction-based planning remains a widely open problem. In this paper, we introduce VIPER, a novel framework for multimodal instruction-based planning that integrates VLM-based perception with LLM-based reasoning. Our approach uses a modular pipeline where a frozen VLM generates textual descriptions of image observations, which are then processed by an LLM policy to predict actions based on the task goal. We fine-tune the reasoning module using behavioral cloning and reinforcement learning, improving our agent's decision-making capabilities. Experiments on the ALFWorld benchmark show that VIPER significantly outperforms state-of-the-art visual instruction-based planners while narrowing the gap with purely text-based oracles. By leveraging text as an intermediate representation, VIPER also enhances explainability, paving the way for a fine-grained analysis of perception and reasoning components.
A Comparative Study of Human Motion Models in Reinforcement Learning Algorithms for Social Robot Navigation
Venue: arXiv cs.HC 2025
Year: 2025

Social robot navigation is an evolving research field that aims to find efficient strategies to safely navigate dynamic environments populated by humans. A critical challenge in this domain is the accurate modeling of human motion, which directly impacts the design and evaluation of navigation algorithms. This paper presents a comparative study of two popular categories of human motion models used in social robot navigation, namely velocity-based models and force-based models. A system-theoretic representation of both model types is presented, which highlights their common feedback structure, although with different state variables. Several navigation policies based on reinforcement learning are trained and tested in various simulated environments involving pedestrian crowds modeled with these approaches. A comparative study is conducted to assess performance across multiple factors, including human motion model, navigation policy, scenario complexity and crowd density. The results highlight advantages and challenges of different approaches to modeling human behavior, as well as their role during training and testing of learning-based navigation policies. The findings offer valuable insights and guidelines for selecting appropriate human motion models when designing socially-aware robot navigation systems.
Preference Construction: A Bayesian Interactive Preference Elicitation Framework Based on Monte Carlo Tree Search
Venue: arXiv cs.LG 2025
Year: 2025

We present a novel preference learning framework to capture participant preferences efficiently within limited interaction rounds. It involves three main contributions. First, we develop a variational Bayesian approach to infer the participant's preference model by estimating posterior distributions and managing uncertainty from limited information. Second, we propose an adaptive questioning policy that maximizes cumulative uncertainty reduction, formulating questioning as a finite Markov decision process and using Monte Carlo Tree Search to prioritize promising question trajectories. By considering long-term effects and leveraging the efficiency of the Bayesian approach, the policy avoids shortsightedness. Third, we apply the framework to Multiple Criteria Decision Aiding, with pairwise comparison as the preference information and an additive value function as the preference model. We integrate the reparameterization trick to address high-variance issues, enhancing robustness and efficiency. Computational studies on real-world and synthetic datasets demonstrate the framework's practical usability, outperforming baselines in capturing preferences and achieving superior uncertainty reduction within limited interactions.
Radon: a Programming Model and Platform for Computing Continuum Systems
Venue: arXiv cs.DC 2025
Year: 2025

Emerging compute continuum environments pose new challenges that traditional cloud-centric architectures struggle to address. Latency, bandwidth constraints, and the heterogeneity of edge environments hinder the efficiency of centralized cloud solutions. While major cloud providers extend their platforms to the edge, these approaches often overlook its unique characteristics, limiting its potential. To tackle these challenges, we introduce Radon, a flexible programming model and platform designed for the edge-to-cloud continuum. Radon applications are structured as atoms, isolated stateful entities that communicate through messaging and can be composed into complex systems. The Radon runtime, based on WebAssembly (WASM), enables language- and deployment-independent execution, ensuring portability and adaptability across heterogeneous environments. This decoupling allows developers to focus on application logic while the runtime optimizes for diverse infrastructure conditions. We present a prototype implementation of Radon and evaluate its effectiveness through a distributed key-value store case study. We analyze the implementation in terms of code complexity and performance. Our results demonstrate that Radon facilitates the development and operation of scalable applications across the edge-to-cloud continuum advancing the current state-of-the-art.
When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection
Venue: arXiv cs.HC 2025
Year: 2025

Swine disease surveillance is critical to the sustainability of global agriculture, yet its effectiveness is frequently undermined by limited veterinary resources, delayed identification of cases, and variability in diagnostic accuracy. To overcome these barriers, we introduce a novel AI-powered, multi-agent diagnostic system that leverages Retrieval-Augmented Generation (RAG) to deliver timely, evidence-based disease detection and clinical guidance. By automatically classifying user inputs into either Knowledge Retrieval Queries or Symptom-Based Diagnostic Queries, the system ensures targeted information retrieval and facilitates precise diagnostic reasoning. An adaptive questioning protocol systematically collects relevant clinical signs, while a confidence-weighted decision fusion mechanism integrates multiple diagnostic hypotheses to generate robust disease predictions and treatment recommendations. Comprehensive evaluations encompassing query classification, disease diagnosis, and knowledge retrieval demonstrate that the system achieves high accuracy, rapid response times, and consistent reliability. By providing a scalable, AI-driven diagnostic framework, this approach enhances veterinary decision-making, advances sustainable livestock management practices, and contributes substantively to the realization of global food security.
Model Hubs and Beyond: Analyzing Model Popularity, Performance, and Documentation
Venue: arXiv cs.CL 2025
Year: 2025

With the massive surge in ML models on platforms like Hugging Face, users often lose track and struggle to choose the best model for their downstream tasks, frequently relying on model popularity indicated by download counts, likes, or recency. We investigate whether this popularity aligns with actual model performance and how the comprehensiveness of model documentation correlates with both popularity and performance. In our study, we evaluated a comprehensive set of 500 Sentiment Analysis models on Hugging Face. This evaluation involved massive annotation efforts, with human annotators completing nearly 80,000 annotations, alongside extensive model training and evaluation. Our findings reveal that model popularity does not necessarily correlate with performance. Additionally, we identify critical inconsistencies in model card reporting: approximately 80% of the models analyzed lack detailed information about the model, training, and evaluation processes. Furthermore, about 88% of model authors overstate their models' performance in the model cards. Based on our findings, we provide a checklist of guidelines for users to choose good models for downstream tasks.
Are "Solved Issues" in SWE-bench Really Solved Correctly? An Empirical Study
Venue: arXiv cs.SE 2025
Year: 2025

Automated issue solving aims to resolve real-world issues in software repositories. The most popular benchmarks for automated issue solving are SWE-bench and its human-filtered subset SWE-bench Verified. These benchmarks leverage testing to validate generated patches. However, because testing is rarely exhaustive, a patch may pass the tests but nevertheless fail to match the developers' expectations. Unfortunately, it is currently unclear to what extent evaluations performed with SWE-bench suffer from such plausible but incorrect patches. This paper presents an in-depth empirical study of the correctness of plausible patches generated by three state-of-the-art issue-solving tools evaluated on SWE-bench Verified. We extensively test and inspect generated patches, and compare them against human-written ground truth patches. The core of our methodology is a novel technique PatchDiff for differential patch testing, which automatically exposes behavioral discrepancies between two patches. Our findings reveal critical weaknesses in SWE-bench's patch validation mechanism, which causes 7.8% of all patches to count as correct while failing the developer-written test suite. Moreover, our novel automated technique reveals that even more (29.6%) plausible patches induce different behavior than the ground truth patches. These behavioral differences are often due to similar, but divergent implementations (46.8%) and due to generated patches that adapt more behavior than the ground truth patches (27.3%). Our manual inspection shows that 28.6% of behaviorally divergent patches are certainly incorrect. Combined, the different weaknesses lead to an inflation of reported resolution rates by 6.2 absolute percent points. Our findings are a call to arms for more robust and reliable evaluation of issue-solving tools. We envision our automated differential patch testing technique to be useful for this purpose.
Your Signal, Their Data: An Empirical Privacy Analysis of Wireless-scanning SDKs in Android
Venue: arXiv cs.CR 2025
Year: 2025

Mobile apps frequently use Bluetooth Low Energy (BLE) and WiFi scanning permissions to discover nearby devices like peripherals and connect to WiFi Access Points (APs). However, wireless interfaces also serve as a covert proxy for geolocation data, enabling continuous user tracking and profiling. This includes technologies like BLE beacons, which are BLE devices broadcasting unique identifiers to determine devices' indoor physical locations; such beacons are easily found in shopping centres. Despite the widespread use of wireless scanning APIs and their potential for privacy abuse, the interplay between commercial mobile SDKs with wireless sensing and beaconing technologies remains largely unexplored. In this work, we conduct the first systematic analysis of 52 wireless-scanning SDKs, revealing their data collection practices and privacy risks. We develop a comprehensive analysis pipeline that enables us to detect beacon scanning capabilities, inject wireless events to trigger app behaviors, and monitor runtime execution on instrumented devices. Our findings show that 86% of apps integrating these SDKs collect at least one sensitive data type, including device and user identifiers such as AAID, email, along with GPS coordinates, WiFi and Bluetooth scan results. We uncover widespread SDK-to-SDK data sharing and evidence of ID bridging, where persistent and resettable identifiers are shared and synchronized within SDKs embedded in applications to potentially construct detailed mobility profiles, compromising user anonymity and enabling long-term tracking. We provide evidence of key actors engaging in these practices and conclude by proposing mitigation strategies such as stronger SDK sandboxing, stricter enforcement of platform policies, and improved transparency mechanisms to limit unauthorized tracking.
Do Chains-of-Thoughts of Large Language Models Suffer from Hallucinations, Cognitive Biases, or Phobias in Bayesian Reasoning?
Venue: arXiv cs.AI 2025
Year: 2025

Learning to reason and carefully explain arguments is central to students' cognitive, mathematical, and computational thinking development. This is particularly challenging in problems under uncertainty and in Bayesian reasoning. With the new generation of large language models (LLMs) capable of reasoning using Chain-of-Thought (CoT), there is an excellent opportunity to learn with them as they explain their reasoning through a dialogue with their artificial internal voice. It is an engaging and excellent opportunity to learn Bayesian reasoning. Furthermore, given that different LLMs sometimes arrive at opposite solutions, CoT generates opportunities for deep learning by detailed comparisons of reasonings. However, unlike humans, we found that they do not autonomously explain using ecologically valid strategies like natural frequencies, whole objects, and embodied heuristics. This is unfortunate, as these strategies help humans avoid critical mistakes and have proven pedagogical value in Bayesian reasoning. In order to overcome these biases and aid understanding and learning, we included prompts that induce LLMs to use these strategies. We found that LLMs with CoT incorporate them but not consistently. They show persistent biases towards symbolic reasoning and avoidance or phobia of ecologically valid strategies.
Mapping AI Avant-Gardes in Time: Posthumanism, Transhumanism, Genhumanism
Venue: arXiv cs.CY 2025
Year: 2025

Three directions for the AI avant-garde are sketched against the background of time. Posthumanism changes what we are, and belongs to the radical future. Transhumanism changes how we are, and corresponds with the radical past. Genhumanism changes who we are, and exists in the radical present. While developing the concepts, this essay intersects in two ways with theoretical debates about humanism in the face of technological advance. First, it describes how temporal divisions may cleanly differentiate post- and transhumanism. Second, the essay introduces generative humanism, which contributes to discussions about AI and society by delineating a novel humanistic response to contemporary technology. Finally, grounds are provided for a practical project, one where philosophers work with AI engineers in the area of genhumanism. Contemporary AI research into serendipity in recommendation engines provides natural support for the shared research.
Optimum Network Slicing for Ultra-reliable Low Latency Communication (URLLC) Services in Campus Networks
Venue: arXiv cs.NI 2025
Year: 2025

Within 3GPP, the campus network architecture has evolved as a deployment option for industries and can be provisioned using network slicing over already installed 5G public network infrastructure. In campus networks, the ultra-reliable low latency communication (URLLC) service category is of major interest for applications with strict latency and high-reliability requirements. One way to achieve high reliability in a shared infrastructure is through resource isolation, whereby network slicing can be optimized to adequately reserve computation and transmission capacity. This paper proposes an approach for vertical slicing the radio access network (RAN) to enable the deployment of multiple and isolated campus networks to accommodate URLLC services. To this end, we model RAN function placement as a mixed integer linear programming problem with URLLC-related constraints. We demonstrate that our approach can find optimal solutions in real-world scenarios. Furthermore, unlike existing solutions, our model considers the user traffic flow from a known source node on the network's edge to an unknown \textit{a priori} destination node. This flexibility could be explored in industrial campus networks by allowing dynamic placement of user plane functions (UPFs) to serve the URLLC.
VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning
Venue: arXiv cs.CL 2025
Year: 2025

Natural language processing (NLP) has significantly influenced scientific domains beyond human language, including protein engineering, where pre-trained protein language models (PLMs) have demonstrated remarkable success. However, interdisciplinary adoption remains limited due to challenges in data collection, task benchmarking, and application. This work presents VenusFactory, a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs. VenusFactory supports both computer science and biology communities with choices of both a command-line execution and a Gradio-based no-code interface, integrating $40+$ protein-related datasets and $40+$ popular PLMs. All implementations are open-sourced on https://github.com/tyang816/VenusFactory.
A Space-Efficient Algorithm for Longest Common Almost Increasing Subsequence of Two Sequences
Venue: arXiv cs.DS 2025
Year: 2025

Let $A$ and $B$ be two number sequences of length $n$ and $m$, respectively, where $m\le n$. Given a positive number $δ$, a common almost increasing sequence $s_1\ldots s_k$ is a common subsequence for both $A$ and $B$ such that for all $2\le i\le k$, $s_i+δ> \max_{1\le j < i} s_j$. The LCaIS problem seeks to find the longest common almost increasing subsequence (LCaIS) of $A$ and $B$. An LCaIS can be computed in $O(nm\ell)$ time and $O(nm)$ space [Ta, Shieh, Lu (TCS 2021)], where $\ell$ is the length of the LCaIS of $A$ and $B$. In this paper we first give an $O(nm\ell)$-time and $O(n+m\ell)$-space algorithm to find LCaIS, which improves the space complexity. We then design an $O((n+m)\log n +\mathcal{M}\log \mathcal{M} + \mathcal{C}\ell)$-time and $O(\mathcal{M}(\ell+\log \mathcal{M}))$-space algorithm, which is faster when the number of matching pairs $\mathcal{M}$ and the number of compatible matching pairs $\mathcal{C}$ are in $o(nm/\log m)$.
FP4DiT: Towards Effective Floating Point Quantization for Diffusion Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion Models (DM) have revolutionized the text-to-image visual generation process. However, the large computational cost and model footprint of DMs hinders practical deployment, especially on edge devices. Post-training quantization (PTQ) is a lightweight method to alleviate these burdens without the need for training or fine-tuning. While recent DM PTQ methods achieve W4A8 \blue{(i.e., 4-bit weights and 8-bit activations)} on integer-based PTQ, two key limitations remain: First, while most existing DM PTQ methods evaluate on classical DMs like Stable Diffusion XL, 1.5 or earlier, which use convolutional U-Nets, newer Diffusion Transformer (DiT) models like the PixArt series, Hunyuan and others adopt fundamentally different transformer backbones to achieve superior image synthesis. Second, integer (INT) quantization is prevailing in DM PTQ but does not align well with the network weight and activation distribution, while Floating-Point Quantization (FPQ) is still under-investigated, yet it holds the potential to better align the weight and activation distributions in low-bit settings for DiT. In this paper, we introduce FP4DiT, a PTQ method that leverages FPQ to achieve W4A6 quantization. Specifically, we extend and generalize the Adaptive Rounding PTQ technique to adequately calibrate weight quantization for FPQ and demonstrate that DiT activations depend on input patch data, necessitating robust online activation quantization techniques. Experimental results demonstrate that FP4DiT achieves higher CLIP, ImageReward and HPSv2 performance compared to integer-based PTQ at the W4A6 and W4A8 precision levels while generating convincing visual content on PixArt-$α$, PixArt-$Σ$ and Hunyuan.
SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks
Venue: arXiv cs.LG 2025
Year: 2025

Large language model (LLM) agents need to perform multi-turn interactions in real-world tasks. However, existing multi-turn RL algorithms for optimizing LLM agents fail to perform effective credit assignment over multiple turns while leveraging the generalization capabilities of LLMs and it remains unclear how to develop such algorithms. To study this, we first introduce a new benchmark, ColBench, where an LLM agent interacts with a human collaborator over multiple turns to solve realistic tasks in backend programming and frontend design. Building on this benchmark, we propose a novel RL algorithm, SWEET-RL (RL with Step-WisE Evaluation from Training-time information), that uses a carefully designed optimization objective to train a critic model with access to additional training-time information. The critic provides step-level rewards for improving the policy model. Our experiments demonstrate that SWEET-RL achieves a 6% absolute improvement in success and win rates on ColBench compared to other state-of-the-art multi-turn RL algorithms, enabling Llama-3.1-8B to match or exceed the performance of GPT4-o in realistic collaborative content creation.