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arXiv:2603.03379v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly used for long-duration tasks, maintaining effective long-term memory has become a critical challenge. Current methods often face a trade-off between cost and accuracy. Simple storage methods often fail to retrieve relevant information, while complex indexing methods (such as memory graphs) require heavy computation and can cause information loss. Furthermore, relying on the working LLM to process all memories is computationally expensive and slow. To address these […]

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arXiv:2603.03376v1 Announce Type: new Abstract: As V2X (Vehicle-to-Everything) technology becomes increasingly prevalent, the security of V2X networks has garnered growing attention worldwide. In North America, the IEEE 1609 series standards are primarily used, while Europe adopts the ETSI series standards, and China has also established its industry standard, YD/T 3957-2021, among others. Although these standards share some commonalities, they also exhibit differences. To achieve compatibility across these standards, analyzing their similarities and differences is a crucial issue. Therefore, […]

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arXiv:2603.03371v1 Announce Type: new Abstract: The proliferation of open-weight Large Language Models (LLMs) has democratized agentic AI, yet fine-tuned weights are frequently shared and adopted with limited scrutiny beyond leaderboard performance. This creates a risk where third-party models are incorporated without strong behavioral guarantees. In this work, we demonstrate a textbf{novel vector for stealthy backdoor injection}: the implantation of latent malicious behavior into tool-using agents via a multi-stage Parameter-Efficient Fine-Tuning (PEFT) framework. Our method, textbf{SFT-then-GRPO}, decouples capability injection […]

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arXiv:2603.03369v1 Announce Type: new Abstract: Hidden communication systems (HCS) embed covert messages within ordinary network activity to hide the presence of communication. In practice, the undetectability of an HCS is typically evaluated using ad hoc traffic statistics or specific detectors, making security claims tightly coupled to experimental setups and implicit adversarial assumptions. In this work, we formalize undetectability as the statistical indistinguishability of observable execution traces under two deployments: a baseline system without hidden communication and an HCS […]

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arXiv:2603.03367v1 Announce Type: new Abstract: Our analysis of recent AI4H publications reveals that, despite a trend toward utilizing open datasets and sharing modeling code, 74% of AI4H papers still rely on private datasets or do not share their code. This is especially concerning in healthcare applications, where trust is essential. Furthermore, inconsistent and poorly documented data preprocessing pipelines result in variable model performance reports, even for identical tasks and datasets, making it challenging to evaluate the true effectiveness […]

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arXiv:2603.03359v1 Announce Type: new Abstract: ASR systems exhibit persistent performance disparities across accents, yet the internal mechanisms underlying these gaps remain poorly understood. We introduce ACES, a representation-centric audit that extracts accent-discriminative subspaces and uses them to probe model fragility and disparity. Analyzing Wav2Vec2-base with five English accents, we find that accent information concentrates in a low-dimensional early-layer subspace (layer 3, k=8). Projection magnitude correlates with per-utterance WER (r=0.26), and crucially, subspace-constrained perturbations yield stronger coupling between representation […]

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arXiv:2603.03341v1 Announce Type: new Abstract: This paper introduces a unified machine learning operations (MLOps) framework that brings ethical artificial intelligence principles into practical use by enforcing fairness, explainability, and governance throughout the machine learning lifecycle. The proposed method reduces bias by lowering the demographic parity difference (DPD) from 0.31 to 0.04 without model retuning, and cross-dataset validation achieves an area under the curve (AUC) of 0.89 on the Statlog Heart dataset. The framework maintains fairness metrics within operational […]

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arXiv:2603.03340v1 Announce Type: new Abstract: The emergence of autonomous, high-velocity Agentic AI systems is creating an internal assurance scalability crisis. Point-in-time, document-based audits cannot keep pace with non deterministic behaviour and distributed deployments of agents across rapidly evolving environments. The crisis is dual-scale: vertically, governance and control obligations change faster than frameworks can operationalise them; horizontally, assurance mechanisms fail to scale across complex, heterogeneous systems and evidence sources. Risk-based regulation now requires organisations to demonstrate ongoing control adequacy […]

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arXiv:2603.03339v1 Announce Type: new Abstract: The rapid global expansion of large language models (LLMs) has created new opportunities for personalised and inquiry-driven learning. However, most AI chatbot systems for education rely on continuous internet connectivity, cloud infrastructure, and modern hardware. These requirements reinforce digital inequalities and limit the practical deployment of AI-supported learning in bandwidth-constrained and resource-limited environments worldwide. This paper presents Arapai, an offline-first AI chatbot architecture designed to operate entirely without internet connectivity on low-specification, CPU-only […]

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arXiv:2603.03338v1 Announce Type: new Abstract: Many leading AI researchers expect AI development to exceed the transformative impact of all previous technological revolutions. This belief is based on the idea that AI will be able to automate the process of AI research itself, leading to a positive feedback loop. In August and September of 2025, we interviewed 25 leading researchers from frontier AI labs and academia, including participants from Google DeepMind, OpenAI, Anthropic, Meta, UC Berkeley, Princeton, and Stanford […]

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