OpenRaiser/Intern-Atlas huggingface dataset:
CodeEdu: A Multi-Agent Collaborative Platform for Personalized Coding Education
Venue: arXiv cs.MA 2025
Year: 2025

Large Language Models (LLMs) have demonstrated considerable potential in improving coding education by providing support for code writing, explanation, and debugging. However, existing LLM-based approaches generally fail to assess students' abilities, design learning plans, provide personalized material aligned with individual learning goals, and enable interactive learning. Current work mostly uses single LLM agents, which limits their ability to understand complex code repositories and schedule step-by-step tutoring. Recent research has shown that multi-agent LLMs can collaborate to solve complicated problems in various domains like software engineering, but their potential in the field of education remains unexplored. In this work, we introduce CodeEdu, an innovative multi-agent collaborative platform that combines LLMs with tool use to provide proactive and personalized education in coding. Unlike static pipelines, CodeEdu dynamically allocates agents and tasks to meet student needs. Various agents in CodeEdu undertake certain functions specifically, including task planning, personalized material generation, real-time QA, step-by-step tutoring, code execution, debugging, and learning report generation, facilitated with extensive external tools to improve task efficiency. Automated evaluations reveal that CodeEdu substantially enhances students' coding performance.
RAG-based Architectures for Drug Side Effect Retrieval in LLMs
Venue: arXiv cs.IR 2025
Year: 2025

Drug side effects are a major global health concern, necessitating advanced methods for their accurate detection and analysis. While Large Language Models (LLMs) offer promising conversational interfaces, their inherent limitations, including reliance on black-box training data, susceptibility to hallucinations, and lack of domain-specific knowledge, hinder their reliability in specialized fields like pharmacovigilance. To address this gap, we propose two architectures: Retrieval-Augmented Generation (RAG) and GraphRAG, which integrate comprehensive drug side effect knowledge into a Llama 3 8B language model. Through extensive evaluations on 19,520 drug side effect associations (covering 976 drugs and 3,851 side effect terms), our results demonstrate that GraphRAG achieves near-perfect accuracy in drug side effect retrieval. This framework offers a highly accurate and scalable solution, signifying a significant advancement in leveraging LLMs for critical pharmacovigilance applications.
DistFlow: A Fully Distributed RL Framework for Scalable and Efficient LLM Post-Training
Venue: arXiv cs.DC 2025
Year: 2025

Reinforcement learning (RL) has become the pivotal post-training technique for large language model (LLM). Effectively scaling reinforcement learning is now the key to unlocking advanced reasoning capabilities and ensuring safe, goal-aligned behavior in the most powerful LLMs. Mainstream frameworks usually employ a hybrid-controller architecture where a single-controller dispatches the overall execution logic and manages overall data transfer and the multi-controller executes distributed computation. For large-scale reinforcement learning, minor load imbalances can introduce significant bottlenecks, ultimately constraining the scalability of the system. To address this limitation, we introduce DistFlow, a novel, fully distributed RL framework designed to break scaling barrier. We adopt a multi-controller paradigm that dispatches data transfer and execution tasks to all workers, which eliminates the centralized node. This allows each worker to operate independently, leading to near-linear scalability up to 1024 GPUs and dramatic efficiency gains. Furthermore, our architecture decouples resource configuration from execution logic, allowing each worker to have a unique execution flow, offering significant flexibility for rapid and cost-effective algorithmic experimentation. Extensive experiments show that DistFlow achieves excellent linear scalability and up to a 7x end-to-end throughput improvement in specific scenarios over state-of-the-art (SOTA) frameworks.
Principles and Reasons Behind Automated Vehicle Decisions in Ethically Ambiguous Everyday Scenarios
Venue: arXiv cs.CY 2025
Year: 2025

Automated vehicles (AVs) increasingly encounter ethically ambiguous situations in everyday driving--scenarios involving conflicting human interests and lacking clearly optimal courses of action. While existing ethical models often focus on rare, high-stakes dilemmas (e.g., crash avoidance or trolley problems), routine decisions such as overtaking cyclists or navigating social interactions remain underexplored. This study addresses that gap by applying the tracking condition of Meaningful Human Control (MHC), which holds that AV behaviour should align with human reasons--defined as the values, intentions, and expectations that justify actions. We conducted qualitative interviews with 18 AV experts to identify the types of reasons that should inform AV manoeuvre planning. Thirteen categories of reasons emerged, organised across normative, strategic, tactical, and operational levels, and linked to the roles of relevant human agents. A case study on cyclist overtaking illustrates how these reasons interact in context, revealing a consistent prioritisation of safety, contextual flexibility regarding regulatory compliance, and nuanced trade-offs involving efficiency, comfort, and public acceptance. Based on these insights, we propose a principled conceptual framework for AV decision-making in routine, ethically ambiguous scenarios. The framework supports dynamic, human-aligned behaviour by prioritising safety, allowing pragmatic actions when strict legal adherence would undermine key values, and enabling constrained deviations when appropriately justified. This empirically grounded approach advances current guidance by offering actionable, context-sensitive design principles for ethically aligned AV systems.
When Seeing Overrides Knowing: Disentangling Knowledge Conflicts in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-language models (VLMs) increasingly leverage diverse knowledge sources to address complex tasks, often encountering conflicts between their internal parametric knowledge and external information. Knowledge conflicts can result in hallucinations and unreliable responses, but the mechanisms governing such interactions remain unknown. To address this gap, we analyze the mechanisms that VLMs use to resolve cross-modal conflicts by introducing a dataset of multimodal counterfactual queries that deliberately contradict internal commonsense knowledge. We localize with logit inspection a small set of heads that control the conflict. Moreover, by modifying these heads, we can steer the model towards its internal knowledge or the visual inputs. Finally, we show that attention from such heads pinpoints localized image regions driving visual overrides, outperforming gradient-based attribution in precision.
Effects of Cognitive Distraction and Driving Environment Complexity on Adaptive Cruise Control Use and Its Impact on Driving Performance: A Simulator Study
Venue: arXiv cs.HC 2025
Year: 2025

In this simulator study, we adopt a human-centered approach to explore whether and how drivers' cognitive state and driving environment complexity influence reliance on driving automation features. Besides, we examine whether such reliance affects driving performance. Participants operated a vehicle equipped with adaptive cruise control (ACC) in a simulator across six predefined driving scenarios varying in traffic conditions while either performing a cognitively demanding task (i.e., responding to mental calculations) or not. Throughout the experiment, participants had to respect speed limits and were free to activate or deactivate ACC. In complex driving environments, we found that the overall ACC engagement time was lower compared to less complex driving environments. We observed no significant effect of cognitive load on ACC use. Furthermore, while ACC use had no effect on the number of lane changes, it impacted the speed limits compliance and improved lateral control.
Application Placement with Constraint Relaxation
Venue: arXiv cs.LO 2025
Year: 2025

Novel utility computing paradigms rely upon the deployment of multi-service applications to pervasive and highly distributed cloud-edge infrastructure resources. Deciding onto which computational nodes to place services in cloud-edge networks, as per their functional and non-functional constraints, can be formulated as a combinatorial optimisation problem. Most existing solutions in this space are not able to deal with \emph{unsatisfiable} problem instances, nor preferences, i.e. requirements that DevOps may agree to relax to obtain a solution. In this article, we exploit Answer Set Programming optimisation capabilities to tackle this problem. Experimental results in simulated settings show that our approach is effective on lifelike networks and applications.
Enhancing LiDAR Point Features with Foundation Model Priors for 3D Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in foundation models have opened up new possibilities for enhancing 3D perception. In particular, DepthAnything offers dense and reliable geometric priors from monocular RGB images, which can complement sparse LiDAR data in autonomous driving scenarios. However, such priors remain underutilized in LiDAR-based 3D object detection. In this paper, we address the limited expressiveness of raw LiDAR point features, especially the weak discriminative capability of the reflectance attribute, by introducing depth priors predicted by DepthAnything. These priors are fused with the original LiDAR attributes to enrich each point's representation. To leverage the enhanced point features, we propose a point-wise feature extraction module. Then, a Dual-Path RoI feature extraction framework is employed, comprising a voxel-based branch for global semantic context and a point-based branch for fine-grained structural details. To effectively integrate the complementary RoI features, we introduce a bidirectional gated RoI feature fusion module that balances global and local cues. Extensive experiments on the KITTI benchmark show that our method consistently improves detection accuracy, demonstrating the value of incorporating visual foundation model priors into LiDAR-based 3D object detection.
Self-supervised learning on gene expression data
Venue: arXiv cs.LG 2025
Year: 2025

Predicting phenotypes from gene expression data is a crucial task in biomedical research, enabling insights into disease mechanisms, drug responses, and personalized medicine. Traditional machine learning and deep learning rely on supervised learning, which requires large quantities of labeled data that are costly and time-consuming to obtain in the case of gene expression data. Self-supervised learning has recently emerged as a promising approach to overcome these limitations by extracting information directly from the structure of unlabeled data. In this study, we investigate the application of state-of-the-art self-supervised learning methods to bulk gene expression data for phenotype prediction. We selected three self-supervised methods, based on different approaches, to assess their ability to exploit the inherent structure of the data and to generate qualitative representations which can be used for downstream predictive tasks. By using several publicly available gene expression datasets, we demonstrate how the selected methods can effectively capture complex information and improve phenotype prediction accuracy. The results obtained show that self-supervised learning methods can outperform traditional supervised models besides offering significant advantage by reducing the dependency on annotated data. We provide a comprehensive analysis of the performance of each method by highlighting their strengths and limitations. We also provide recommendations for using these methods depending on the case under study. Finally, we outline future research directions to enhance the application of self-supervised learning in the field of gene expression data analysis. This study is the first work that deals with bulk RNA-Seq data and self-supervised learning.
Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support
Venue: arXiv cs.CL 2025
Year: 2025

We present Marcel, a lightweight and open-source conversational agent designed to support prospective students with admission-related inquiries. The system aims to provide fast and personalized responses, while reducing workload of university staff. We employ retrieval-augmented generation to ground answers in university resources and to provide users with verifiable, contextually relevant information. We introduce a Frequently Asked Question (FAQ) retriever that maps user questions to knowledge-base entries, which allows administrators to steer retrieval, and improves over standard dense/hybrid retrieval strategies. The system is engineered for easy deployment in resource-constrained academic settings. We detail the system architecture, provide a technical evaluation of its components, and report insights from a real-world deployment.
Democratizing Game Modding with GenAI: A Case Study of StarCharM, a Stardew Valley Character Maker
Venue: arXiv cs.HC 2025
Year: 2025

Game modding offers unique and personalized gaming experiences, but the technical complexity of creating mods often limits participation to skilled users. We envision a future where every player can create personalized mods for their games. To explore this space, we designed StarCharM, a GenAI-based non-player character (NPC) creator for Stardew Valley. Our tool enables players to iteratively create new NPC mods, requiring minimal user input while allowing for fine-grained adjustments through user control. We conducted a user study with ten Stardew Valley players who had varied mod usage experiences to understand the impacts of StarCharM and provide insights into how GenAI tools may reshape modding, particularly in NPC creation. Participants expressed excitement in bringing their character ideas to life, although they noted challenges in generating rich content to fulfill complex visions. While they believed GenAI tools like StarCharM can foster a more diverse modding community, some voiced concerns about diminished originality and community engagement that may come with such technology. Our findings provided implications and guidelines for the future of GenAI-powered modding tools and co-creative modding practices.
Robust Anomaly Detection with Graph Neural Networks using Controllability
Venue: arXiv cs.LG 2025
Year: 2025

Anomaly detection in complex domains poses significant challenges due to the need for extensive labeled data and the inherently imbalanced nature of anomalous versus benign samples. Graph-based machine learning models have emerged as a promising solution that combines attribute and relational data to uncover intricate patterns. However, the scarcity of anomalous data exacerbates the challenge, which requires innovative strategies to enhance model learning with limited information. In this paper, we hypothesize that the incorporation of the influence of the nodes, quantified through average controllability, can significantly improve the performance of anomaly detection. We propose two novel approaches to integrate average controllability into graph-based frameworks: (1) using average controllability as an edge weight and (2) encoding it as a one-hot edge attribute vector. Through rigorous evaluation on real-world and synthetic networks with six state-of-the-art baselines, our proposed methods demonstrate improved performance in identifying anomalies, highlighting the critical role of controllability measures in enhancing the performance of graph machine learning models. This work underscores the potential of integrating average controllability as additional metrics to address the challenges of anomaly detection in sparse and imbalanced datasets.
Secretive Hotplug Coded Caching
Venue: arXiv cs.IT 2025
Year: 2025

In this work, we consider a coded caching model called \textit{hotplug coded caching}, in which some users are offline during the delivery phase. The concept of Hotplug Placement Delivery Arrays (HpPDAs) for hotplug coded caching systems has been introduced in the literature, and two classes of HpPDAs are known. In this paper, we consider a secrecy constraint in hotplug coded caching setup, where users should not learn anything about any file from their cache content, and active users should not gain any information about files other than their demanded file from either their cache content or the server transmissions. We propose two secretive schemes for the two classes of HpPDAs and compare them with a baseline scheme, which is a secretive scheme using PDAs for the classical coded caching setup and can be trivially adapted for the hotplug coded caching setup. We numerically show that our schemes outperform the baseline scheme in certain memory regions.
Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
Venue: arXiv cs.CL 2025
Year: 2025

Language models traditionally used for cross-domain generalization have recently demonstrated task-specific reasoning. However, their top-down training approach on general corpora is insufficient for acquiring abstractions needed for deep domain expertise. This may require a bottom-up approach that acquires expertise by learning to compose simple domain concepts into more complex ones. A knowledge graph (KG) provides this compositional structure, where domain primitives are represented as head-relation-tail edges and their paths encode higher-level concepts. We present a task generation pipeline that synthesizes tasks directly from KG primitives, enabling models to acquire and compose them for reasoning. We fine-tune language models on the resultant KG-grounded curriculum to demonstrate domain-specific superintelligence. While broadly applicable, we validate our approach in medicine, where reliable KGs exist. Using a medical KG, we curate 24,000 reasoning tasks paired with thinking traces derived from diverse medical primitives. We fine-tune the QwQ-32B model on this curriculum to obtain QwQ-Med-3 that takes a step towards medical superintelligence. We also introduce ICD-Bench, an evaluation suite to quantify reasoning abilities across 15 medical domains. Our experiments demonstrate that QwQ-Med-3 significantly outperforms state-of-the-art reasoning models on ICD-Bench categories. Further analysis reveals that QwQ-Med-3 utilizes acquired primitives to widen the performance gap on the hardest tasks of ICD-Bench. Finally, evaluation on medical question-answer benchmarks shows that QwQ-Med-3 transfers acquired expertise to enhance the base model's performance. While the industry's approach to artificial general intelligence (AGI) emphasizes broad expertise, we envision a future in which AGI emerges from the composable interaction of efficient domain-specific superintelligent agents.
DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation
Venue: arXiv cs.CV 2025
Year: 2025

Generating 3D scenes from natural language holds great promise for applications in gaming, film, and design. However, existing methods struggle with automation, 3D consistency, and fine-grained control. We present DreamScene, an end-to-end framework for high-quality and editable 3D scene generation from text or dialogue. DreamScene begins with a scene planning module, where a GPT-4 agent infers object semantics and spatial constraints to construct a hybrid graph. A graph-based placement algorithm then produces a structured, collision-free layout. Based on this layout, Formation Pattern Sampling (FPS) generates object geometry using multi-timestep sampling and reconstructive optimization, enabling fast and realistic synthesis. To ensure global consistent, DreamScene employs a progressive camera sampling strategy tailored to both indoor and outdoor settings. Finally, the system supports fine-grained scene editing, including object movement, appearance changes, and 4D dynamic motion. Experiments demonstrate that DreamScene surpasses prior methods in quality, consistency, and flexibility, offering a practical solution for open-domain 3D content creation. Code and demos are available at https://jahnsonblack.github.io/DreamScene-Full/.
ChemLog: Making MSOL Viable for Ontological Classification and Learning
Venue: arXiv cs.LO 2025
Year: 2025

Despite its prevalence, in many domains, OWL is not expressive enough to define ontology classes. In this paper, we present an approach that allows to use monadic second-order formalisations for ontology classification. As a case study, we have applied our approach to 14 peptide-related classes from the chemistry ontology ChEBI. For these classes, a monadic second-order logic formalisation has been developed and applied both to ChEBI as well as to 119 million molecules from the chemistry database PubChem. While this logical approach alone is limited to classification for the specified classes (in our case, (sub)classes of peptides), transformer deep learning models scale classification to the whole of the ChEBI ontology. We show that when using the classifications obtained by the logical approach as training data, the performance of the deep learning models can be significantly enhanced.
Photonic Fabric Platform for AI Accelerators
Venue: arXiv cs.PF 2025
Year: 2025

This paper presents the Photonic FabricTM and the Photonic Fabric ApplianceTM (PFA), a photonic-enabled switch and memory subsystem that delivers low latency, high bandwidth, and low per-bit energy. By integrating high-bandwidth HBM3E memory, an on-module photonic switch, and external DDR5 in a 2.5D electro-optical system-in-package, the PFA offers up to 32 TB of shared memory alongside 115 Tbps of all-to-all digital switching. The Photonic FabricTM enables distributed AI training and inference to execute parallelism strategies more efficiently. The Photonic Fabric removes the silicon beachfront constraint that limits the fixed memory-to-compute ratio observed in virtually all current XPU accelerator designs. Replacing a local HBM stack on an XPU with a chiplet that connects to the Photonic Fabric increases its memory capacity and correspondingly its memory bandwidth by offering a flexible path to scaling well beyond the limitations of on-package HBM alone. We introduce CelestiSim, a lightweight analytical simulator validated on NVIDIA H100 and H200 systems. It is used to evaluate the performance of LLM reference and energy savings on PFA, without any significant change to the GPU core design. With the PFA, the simulation results show that up to 3.66x throughput and 1.40x latency improvements in LLM inference at 405B parameters, up to 7.04x throughput and 1.41x latency improvements at 1T parameters, and 60-90% energy savings in data movement for heavy collective operations in all LLM training scenarios. While these results are shown for NVIDIA GPUs, they can be applied similarly to other AI accelerator designs (XPUs) that share the same fundamental limitation of fixed memory to compute.
Bounds and Constructions of High-Memory Spatially-Coupled Codes
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we apply the Clique Lovász Local Lemma to provide sufficient conditions on memory and lifting degree for removing certain harmful combinatorial structures in spatially-coupled (SC) codes that negatively impact decoding performance. Additionally, we present, for the first time, a constructive algorithm based on the Moser-Tardos algorithm that ensures predictable performance. Furthermore, leveraging the properties of LLL-distribution and M-T-distribution, we establish the dependencies among the harmful structures during the construction process. We provide upper bounds on the probability change of remaining harmful structures after eliminating some of them. In particular, the elimination of 4-cycles increases the probability of 6-cycles becoming active by at most a factor of $e^{8/3}$.