OpenRaiser/Intern-Atlas huggingface dataset:
Preprint: Poster: Did I Just Browse A Website Written by LLMs?
Venue: arXiv cs.NI 2025
Year: 2025

Increasingly, web content is automatically generated by large language models (LLMs) with little human input. We call this "LLM-dominant" content. Since LLMs plagiarize and hallucinate, LLM-dominant content can be unreliable and unethical. Yet, websites rarely disclose such content, and human readers struggle to distinguish it. Thus, we must develop reliable detectors for LLM-dominant content. However, state-of-the-art LLM detectors are inaccurate on web content, because web content has low positive rates, complex markup, and diverse genres, instead of clean, prose-like benchmark data SoTA detectors are optimized for. We propose a highly reliable, scalable pipeline that classifies entire websites. Instead of naively classifying text extracted from each page, we classify each site based on an LLM text detector's outputs of multiple prose-like pages to boost accuracies. We train and evaluate our detector by collecting 2 distinct ground truth datasets totaling 120 sites, and obtain 100% accuracies testing across them. In the wild, we detect a sizable portion of sites as LLM-dominant among 10k sites in search engine results and 10k in Common Crawl archives. We find LLM-dominant sites are growing in prevalence and rank highly in search results, raising questions about their impact on end users and the overall Web ecosystem.
NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning
Venue: arXiv cs.RO 2025
Year: 2025

Safe Multi-Agent Motion Planning (MAMP) is a significant challenge in robotics. Despite substantial advancements, existing methods often face a dilemma. Decentralized algorithms typically rely on predicting the behavior of other agents, sharing contracts, or maintaining communication for safety, while centralized approaches struggle with scalability and real-time decision-making. To address these challenges, we introduce Neural Hamilton-Jacobi Reachability Learning (HJR) for Decentralized Multi-Agent Motion Planning. Our method provides scalable neural HJR modeling to tackle high-dimensional configuration spaces and capture worst-case collision and safety constraints between agents. We further propose a decentralized trajectory optimization framework that incorporates the learned HJR solutions to solve MAMP tasks in real-time. We demonstrate that our method is both scalable and data-efficient, enabling the solution of MAMP problems in higher-dimensional scenarios with complex collision constraints. Our approach generalizes across various dynamical systems, including a 12-dimensional dual-arm setup, and outperforms a range of state-of-the-art techniques in successfully addressing challenging MAMP tasks. Video demonstrations are available at https://youtu.be/IZiePX0p1Mc.
Exploiting Primacy Effect To Improve Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have become essential in many Natural Language Processing (NLP) tasks, leveraging extensive pre-training and fine-tuning to achieve high accuracy. However, like humans, LLMs exhibit biases, particularly positional biases such as primacy and recency effects, which can influence the accuracy of the answers. The primacy effect-where items presented first are more likely to be remembered or selected-plays a key role in Multiple Choice Question Answering (MCQA), where the order of answer options can affect prediction outcomes. This study focuses on primacy bias in fine-tuned LLMs: We first show that fine-tuning amplifies this bias, probably due to exposure to human-like patterns. Hence, we strategically leverage this effect by reordering response options based on semantic similarity to the query, without requiring knowledge of the correct answer. Our experimental results show that this approach significantly improves performance in MCQA. More generally, our findings underscore the dual nature of biases as both challenges and opportunities, offering insights for bias-aware model design and NLP applications.
DUALRec: A Hybrid Sequential and Language Model Framework for Context-Aware Movie Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

The modern recommender systems are facing an increasing challenge of modelling and predicting the dynamic and context-rich user preferences. Traditional collaborative filtering and content-based methods often struggle to capture the temporal patternings and evolving user intentions. While Large Language Models (LLMs) have gained gradual attention in recent years, by their strong semantic understanding and reasoning abilities, they are not inherently designed to model chronologically evolving user preference and intentions. On the other hand, for sequential models like LSTM (Long-Short-Term-Memory) which is good at capturing the temporal dynamics of user behaviour and evolving user preference over time, but still lacks a rich semantic understanding for comprehensive recommendation generation. In this study, we propose DUALRec (Dynamic User-Aware Language-based Recommender), a novel recommender that leverages the complementary strength of both models, which combines the temporal modelling abilities of LSTM networks with semantic reasoning power of the fine-tuned Large Language Models. The LSTM component will capture users evolving preference through their viewing history, while the fine-tuned LLM variants will leverage these temporal user insights to generate next movies that users might enjoy. Experimental results on MovieLens-1M dataset shows that the DUALRec model outperforms a wide range of baseline models, with comprehensive evaluation matrices of Hit Rate (HR@k), Normalized Discounted Cumulative Gain (NDCG@k), and genre similarity metrics. This research proposes a novel architecture that bridges the gap between temporal sequence modeling and semantic reasoning, and offers a promising direction for developing more intelligent and context-aware recommenders.
Towards Constraint Temporal Answer Set Programming
Venue: arXiv cs.AI 2025
Year: 2025

Reasoning about dynamic systems with a fine-grained temporal and numeric resolution presents significant challenges for logic-based approaches like Answer Set Programming (ASP). To address this, we introduce and elaborate upon a novel temporal and constraint-based extension of the logic of Here-and-There and its nonmonotonic equilibrium extension, representing, to the best of our knowledge, the first approach to nonmonotonic temporal reasoning with constraints specifically tailored for ASP. This expressive system is achieved by a synergistic combination of two foundational ASP extensions: the linear-time logic of Here-and-There, providing robust nonmonotonic temporal reasoning capabilities, and the logic of Here-and-There with constraints, enabling the direct integration and manipulation of numeric constraints, among others. This work establishes the foundational logical framework for tackling complex dynamic systems with high resolution within the ASP paradigm.
A Segmented Robot Grasping Perception Neural Network for Edge AI
Venue: arXiv cs.RO 2025
Year: 2025

Robotic grasping, the ability of robots to reliably secure and manipulate objects of varying shapes, sizes and orientations, is a complex task that requires precise perception and control. Deep neural networks have shown remarkable success in grasp synthesis by learning rich and abstract representations of objects. When deployed at the edge, these models can enable low-latency, low-power inference, making real-time grasping feasible in resource-constrained environments. This work implements Heatmap-Guided Grasp Detection, an end-to-end framework for the detection of 6-Dof grasp poses, on the GAP9 RISC-V System-on-Chip. The model is optimised using hardware-aware techniques, including input dimensionality reduction, model partitioning, and quantisation. Experimental evaluation on the GraspNet-1Billion benchmark validates the feasibility of fully on-chip inference, highlighting the potential of low-power MCUs for real-time, autonomous manipulation.
Structural Connectome Harmonization Using Deep Learning: The Strength of Graph Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Small sample sizes in neuroimaging in general, and in structural connectome (SC) studies in particular limit the development of reliable biomarkers for neurological and psychiatric disorders - such as Alzheimer's disease and schizophrenia - by reducing statistical power, reliability, and generalizability. Large-scale multi-site studies have exist, but they have acquisition-related biases due to scanner heterogeneity, compromising imaging consistency and downstream analyses. While existing SC harmonization methods - such as linear regression (LR), ComBat, and deep learning techniques - mitigate these biases, they often rely on detailed metadata, traveling subjects (TS), or overlook the graph-topology of SCs. To address these limitations, we propose a site-conditioned deep harmonization framework that harmonizes SCs across diverse acquisition sites without requiring metadata or TS that we test in a simulated scenario based on the Human Connectome Dataset. Within this framework, we benchmark three deep architectures - a fully connected autoencoder (AE), a convolutional AE, and a graph convolutional AE - against a top-performing LR baseline. While non-graph models excel in edge-weight prediction and edge existence detection, the graph AE demonstrates superior preservation of topological structure and subject-level individuality, as reflected by graph metrics and fingerprinting accuracy, respectively. Although the LR baseline achieves the highest numerical performance by explicitly modeling acquisition parameters, it lacks applicability to real-world multi-site use cases as detailed acquisition metadata is often unavailable. Our results highlight the critical role of model architecture in SC harmonization performance and demonstrate that graph-based approaches are particularly well-suited for structure-aware, domain-generalizable SC harmonization in large-scale multi-site SC studies.
Optimal antimatroid sorting
Venue: arXiv cs.DS 2025
Year: 2025

The classical comparison-based sorting problem asks us to find the underlying total order of a given set of elements, where we can only access the elements via comparisons. In this paper, we study a restricted version, where, as a hint, a set $T$ of possible total orders is given, usually in some compressed form. Recently, an algorithm called topological heapsort with optimal running time was found for the case where $T$ is the set of topological orderings of a given directed acyclic graph, or, equivalently, $T$ is the set of linear extensions of a given partial order [Haeupler et al. 2024]. We show that a simple generalization of topological heapsort is applicable to a much broader class of restricted sorting problems, where $T$ corresponds to a given antimatroid. As a consequence, we obtain optimal algorithms for the following restricted sorting problems, where the allowed total orders are restricted by: a given set of monotone precedence formulas; the perfect elimination orders of a given chordal graph; or the possible vertex search orders of a given connected rooted graph.
QuantEIT: Ultra-Lightweight Quantum-Assisted Inference for Chest Electrical Impedance Tomography
Venue: arXiv cs.CV 2025
Year: 2025

Electrical Impedance Tomography (EIT) is a non-invasive, low-cost bedside imaging modality with high temporal resolution, making it suitable for bedside monitoring. However, its inherently ill-posed inverse problem poses significant challenges for accurate image reconstruction. Deep learning (DL)-based approaches have shown promise but often rely on complex network architectures with a large number of parameters, limiting efficiency and scalability. Here, we propose an Ultra-Lightweight Quantum-Assisted Inference (QuantEIT) framework for EIT image reconstruction. QuantEIT leverages a Quantum-Assisted Network (QA-Net), combining parallel 2-qubit quantum circuits to generate expressive latent representations that serve as implicit nonlinear priors, followed by a single linear layer for conductivity reconstruction. This design drastically reduces model complexity and parameter number. Uniquely, QuantEIT operates in an unsupervised, training-data-free manner and represents the first integration of quantum circuits into EIT image reconstruction. Extensive experiments on simulated and real-world 2D and 3D EIT lung imaging data demonstrate that QuantEIT outperforms conventional methods, achieving comparable or superior reconstruction accuracy using only 0.2% of the parameters, with enhanced robustness to noise.
Foundation Models as Class-Incremental Learners for Dermatological Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

Class-Incremental Learning (CIL) aims to learn new classes over time without forgetting previously acquired knowledge. The emergence of foundation models (FM) pretrained on large datasets presents new opportunities for CIL by offering rich, transferable representations. However, their potential for enabling incremental learning in dermatology remains largely unexplored. In this paper, we systematically evaluate frozen FMs pretrained on large-scale skin lesion datasets for CIL in dermatological disease classification. We propose a simple yet effective approach where the backbone remains frozen, and a lightweight MLP is trained incrementally for each task. This setup achieves state-of-the-art performance without forgetting, outperforming regularization, replay, and architecture based methods. To further explore the capabilities of frozen FMs, we examine zero training scenarios using nearest mean classifiers with prototypes derived from their embeddings. Through extensive ablation studies, we demonstrate that this prototype based variant can also achieve competitive results. Our findings highlight the strength of frozen FMs for continual learning in dermatology and support their broader adoption in real world medical applications. Our code and datasets are available here.
Shipwright: Proving liveness of distributed systems with Byzantine participants
Venue: arXiv cs.DC 2025
Year: 2025

Ensuring liveness in a decentralized system, such as PBFT, is critical, because there may not be any single administrator that can restart the system if it encounters a liveness bug. At the same time, liveness is challenging to achieve because any single participant could be malicious, and yet the overall system must make forward progress. While verification is a promising approach for ensuring the absence of bugs, no prior work has been able to verify liveness for an executable implementation of PBFT. Shipwright is a verification framework for proving correctness and liveness of distributed systems where some participants might be malicious. Shipwright introduces three techniques that enable formal reasoning about decentralized settings with malicious participants, allow developers to decompose their system and proof in a modular fashion into sub-protocols and sub-proofs, and support sound reasoning about cryptographic signatures that may be embedded in messages. We used Shipwright to implement and verify an initial prototype of agreement on a single log entry in PBFT (with a few limitations) and translate it to an executable implementation in Go. We experimentally demonstrate its operation and liveness both in the common case and in several failure scenarios.
From Bias to Behavior: Learning Bull-Bear Market Dynamics with Contrastive Modeling
Venue: arXiv cs.LG 2025
Year: 2025

Financial markets exhibit highly dynamic and complex behaviors shaped by both historical price trajectories and exogenous narratives, such as news, policy interpretations, and social media sentiment. The heterogeneity in these data and the diverse insight of investors introduce biases that complicate the modeling of market dynamics. Unlike prior work, this paper explores the potential of bull and bear regimes in investor-driven market dynamics. Through empirical analysis on real-world financial datasets, we uncover a dynamic relationship between bias variation and behavioral adaptation, which enhances trend prediction under evolving market conditions. To model this mechanism, we propose the Bias to Behavior from Bull-Bear Dynamics model (B4), a unified framework that jointly embeds temporal price sequences and external contextual signals into a shared latent space where opposing bull and bear forces naturally emerge, forming the foundation for bias representation. Within this space, an inertial pairing module pairs temporally adjacent samples to preserve momentum, while the dual competition mechanism contrasts bullish and bearish embeddings to capture behavioral divergence. Together, these components allow B4 to model bias-driven asymmetry, behavioral inertia, and market heterogeneity. Experimental results on real-world financial datasets demonstrate that our model not only achieves superior performance in predicting market trends but also provides interpretable insights into the interplay of biases, investor behaviors, and market dynamics.
From Cell Towers to Satellites: A 2040 Blueprint for Urban-Grade Direct-to-Device Mobile Networks
Venue: arXiv cs.NI 2025
Year: 2025

In 2023, satellite and mobile networks crossed a historic threshold: standard smartphones, using unmodified 3GPP protocols, connected directly to low Earth orbit (LEO) satellites. This first wave of direct-to-device (D2D) demonstrations validated the physical feasibility of satellite-based mobile access. However, these systems remain fallback-grade--rural-only, bandwidth-limited, and fully dependent on Earth-based mobile cores for identity, session, and policy control. This paper asks a more ambitious question: Can a complete mobile network, including radio access, core functions, traffic routing, and content delivery, operate entirely from orbit? And can it deliver sustained, urban-grade service in the world's densest cities? We present the first end-to-end system architecture for a fully orbital telco, integrating electronically steered phased arrays with 1000-beam capacity, space-based deployment of 5G core functions (UPF, AMF), and inter-satellite laser mesh backhaul. We analyze spectral efficiency, beam capacity, and link budgets under dense urban conditions, accounting for path loss, Doppler, and multipath. Simulations show that rooftop and line-of-sight users can sustain 64-QAM throughput, while street-level access is feasible with relay or assisted beam modes. The paper outlines the remaining constraints, power, thermal dissipation, compute radiation hardening, and regulatory models, and demonstrates that these are engineering bottlenecks, not physical limits. Finally, we propose a staged 15-year roadmap from today's fallback D2D systems to autonomous orbital overlays delivering 50-100 Mbps to handhelds in megacities, with zero reliance on terrestrial infrastructure.
DM-RSA: An Extension of RSA with Dual Modulus
Venue: arXiv cs.CR 2025
Year: 2025

We introduce DM-RSA (Dual Modulus RSA), a variant of the RSA cryptosystem that employs two distinct moduli symmetrically to enhance security. By leveraging the Chinese Remainder Theorem (CRT) for decryption, DM-RSA provides increased robustness against side-channel attacks while preserving the efficiency of classical RSA. This approach improves resistance to partial compromise of a modulus and integrates easily into existing infrastructures.
White paper: Towards Human-centric and Sustainable 6G Services -- the fortiss Research Perspective
Venue: arXiv cs.NI 2025
Year: 2025

As a leading research institute in software-intensive systems, fortiss is actively shaping the vision of Sixth Generation Mobile Communication (6G). Our mission is to ensure that 6G technologies go beyond technical advancements and are aligned with societal needs. fortiss plays a key role in 6G initiatives worldwide, including contributions to standardization bodies and collaborative Research and Development programs. We focus on software-defined, AI-enabled, and sustainable communication services that prioritize human values and long-term impact. 6G will redefine digital connectivity through cognitive intelligence, decentralized orchestration, and sustainability-oriented architectures. As expectations rise for ultra-reliable low-latency communication (URLLC) and personalized digital services, 6G must outperform prior generations. It will rely on AI-native networking, Edge-Cloud resource orchestration, and energy-aware data frameworks, ensuring both technical performance and societal relevance. This white paper presents the fortiss vision for a human-centric, sustainable, and AI-integrated 6G network. It outlines key research domains such as semantic communication, green orchestration, and distributed AI, all linked to societal and technological challenges. The white paper is aimed at researchers, industry experts, policymakers, and developers. It articulates the strategic direction and contributions of fortiss to 6G, emphasizing responsible innovation and interdisciplinary collaboration toward a meaningful 2030 vision.
Geometry-Aware Active Learning of Pattern Rankings via Choquet-Based Aggregation
Venue: arXiv cs.LG 2025
Year: 2025

We address the pattern explosion problem in pattern mining by proposing an interactive learning framework that combines nonlinear utility aggregation with geometry-aware query selection. Our method models user preferences through a Choquet integral over multiple interestingness measures and exploits the geometric structure of the version space to guide the selection of informative comparisons. A branch-and-bound strategy with tight distance bounds enables efficient identification of queries near the decision boundary. Experiments on UCI datasets show that our approach outperforms existing methods such as ChoquetRank, achieving better ranking accuracy with fewer user interactions.
Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix, an automatic prompt optimization framework that transforms natural language task descriptions into high-quality prompts without requiring manual tuning or domain expertise. Promptomatix supports both a lightweight meta-prompt-based optimizer and a DSPy-powered compiler, with modular design enabling future extension to more advanced frameworks. The system analyzes user intent, generates synthetic training data, selects prompting strategies, and refines prompts using cost-aware objectives. Evaluated across 5 task categories, Promptomatix achieves competitive or superior performance compared to existing libraries, while reducing prompt length and computational overhead making prompt optimization scalable and efficient.