While Multimodal Large Language Models (MLLMs) show promising performance in automated electrocardiogram interpretation, it remains unclear whether they perform actual step-by-step reasoning or just rely on superficial visual cues. To investigate this, we introduce ECG-Reasoning-Benchmark, a novel multi-turn evaluation...
Jungwoo Oh, Hyunseung Chung, Junhee Lee et al.· 0 citations
Large language models routinely produce factually incorrect or misleading outputs, yet conventional automated benchmarks overlook how users perceive reliability during interaction. We introduce the System Hallucination Scale (SHS), a lightweight instrument modeling the famous System Usability Scale (SUS) and System Cau...
Heimo M\"uller, Dominik Steiger, Markus Plass et al.· 0 citations
State-of-the-art deep reinforcement learning (RL) methods have achieved remarkable performance in continuous control tasks, yet their computational complexity is often incompatible with the constraints of resource-limited hardware, due to their reliance on replay buffers, batch updates, and target networks. The emergin...
Riccardo De Monte, Matteo Cederle, Gian Antonio Susto· 0 citations
A Brouwer-inspired assertibility constraint for responsible AI is proposed: in high-stakes domains, systems may assert or deny claims only if they can provide a publicly inspectable and contestable certificate of entitlement; otherwise they must return Undetermined.
M. Julich· 1 citation
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Distributed denial of service (DDoS) attacks threaten the availability of Internet of Things (IoT) infrastructures, particularly under resource-constrained deployment conditions. Although transfer learning models have shown promising detection accuracy, their reliability, computational feasibility, and interpretability...
Scientific discovery increasingly depends on finding meaningful patterns within volumes of sensor data too large for manual inspection. Existing time-frequency event extractors are often engineered or trained on manually labeled examples for each instrument, which can be laborious and difficult to adapt to new environm...
Nathaniel Chen, Kouroche Bouchiat, Peter Steiner et al.· 0 citations
The Control Barrier Function (CBF) method has been widely adopted in solving safety-critical control problems. However, the existence of a CBF is only a sufficient condition for system safety. The recently proposed Taylor-Lagrange Control (TLC) method addresses this limitation by providing a necessary and sufficient co...
Wei Xiao, Christos Cassandras, Anni Li· 0 citations
Anagent, a multi-agent framework for enhanced scientific table \&figure analysis through four specialized agents that leverage supervised finetuning and specialized reinforcement learning to optimize individual capabilities while maintaining effective collaboration, is proposed.
Xue-Hang Guo, Zhiyong Lu, Tom Hope et al.· arXiv.org· 0 citations
Fine-tuning an LLM to maximize performance on a downstream task requires finding the optimal data and model configuration. This is a black-box optimization problem that Bayesian optimization (BO) addresses by sequentially evaluating training configurations and using observed performance feedback to adaptively guide the...
Zhiliang Chen, Alfred Wei Lun Leong, Shao Yong Ong et al.· 0 citations
The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents findings from the first wave of a longitudinal expert perception survey (N=21) involving AI researchers, policymakers, and disinformation s...
Alexander Loth, Martin Kappes, Marc-Oliver Pahl· 0 citations
Large multimodal reasoning models solve challenging visual problems via explicit long-chain inference: they gather visual clues from images and decode clues into textual tokens. Yet this capability also increases hallucinations, where the model generates content that is not supported by the input image or the question....
Gongli Xi, Kun Wang, Zeming Gao et al.· 0 citations
Generative AI and misinformation research have evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical countermeasures. We report on changes in the threat landscape, including improved AI-generated content through Large Language Models (LLMs) and mult...
Alexander Loth, Martin Kappes, Marc-Oliver Pahl· 0 citations
