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NatWest Bank

Industry researcheurope · gb
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Research library16linked papers
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Selected work

Representative Papers

Mind the Accent Gap: British Accent Robustness in Speech-Driven Financial Voice Assistants

Oct 05, 2026

This study addresses the limitation that existing automatic speech recognition (ASR) models are predominantly biased toward American English, which compromises the robustness of financial voice assistants to regional British accents and consequently degrades downstream tool-calling accuracy. To investigate this issue, we introduce CavaBench, the first internally collected benchmark of British-accented financial voice queries, and design an end-to-end evaluation pipeline integrating ASR with large language model reasoning to systematically assess performance from speech recognition through task execution. Our findings reveal that while word error rate (WER) serves as a strong predictor of tool-calling accuracy, it fails to adequately capture task-level performance. This work provides critical empirical evidence for designing inclusive and reliable voice interaction systems in the financial domain.

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GNN-based Multi-Agent Control of Traffic Shockwaves in Sparse Vehicular Ad-hoc Networks

Jul 26, 2026

This study addresses the challenges posed by traffic shockwaves, which exacerbate congestion, reduce fuel efficiency, and elevate accident risks. Existing control strategies often rely on global traffic information, limiting their applicability in sparse vehicular ad hoc networks (VANETs). To overcome this limitation, this work proposes a decentralized cooperative control framework that integrates graph neural networks (GNNs) with multi-agent reinforcement learning (MARL), enabling connected automated vehicles to effectively suppress shockwave propagation using only local observations and interactions with neighboring vehicles. Notably, this approach is the first to incorporate GNNs into MARL under sparse VANET conditions, substantially enhancing practicality for early-stage deployment. Simulation results on a highway scenario with only 10% market penetration demonstrate up to an 80% reduction in shockwave propagation, confirming the method’s efficacy and scalability.

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Recent publications

Latest Papers

Mind the Accent Gap: British Accent Robustness in Speech-Driven Financial Voice Assistants

Oct 05, 2026

This study addresses the limitation that existing automatic speech recognition (ASR) models are predominantly biased toward American English, which compromises the robustness of financial voice assistants to regional British accents and consequently degrades downstream tool-calling accuracy. To investigate this issue, we introduce CavaBench, the first internally collected benchmark of British-accented financial voice queries, and design an end-to-end evaluation pipeline integrating ASR with large language model reasoning to systematically assess performance from speech recognition through task execution. Our findings reveal that while word error rate (WER) serves as a strong predictor of tool-calling accuracy, it fails to adequately capture task-level performance. This work provides critical empirical evidence for designing inclusive and reliable voice interaction systems in the financial domain.

0 citationsRead paper

GNN-based Multi-Agent Control of Traffic Shockwaves in Sparse Vehicular Ad-hoc Networks

Jul 26, 2026

This study addresses the challenges posed by traffic shockwaves, which exacerbate congestion, reduce fuel efficiency, and elevate accident risks. Existing control strategies often rely on global traffic information, limiting their applicability in sparse vehicular ad hoc networks (VANETs). To overcome this limitation, this work proposes a decentralized cooperative control framework that integrates graph neural networks (GNNs) with multi-agent reinforcement learning (MARL), enabling connected automated vehicles to effectively suppress shockwave propagation using only local observations and interactions with neighboring vehicles. Notably, this approach is the first to incorporate GNNs into MARL under sparse VANET conditions, substantially enhancing practicality for early-stage deployment. Simulation results on a highway scenario with only 10% market penetration demonstrate up to an 80% reduction in shockwave propagation, confirming the method’s efficacy and scalability.

0 citationsRead paper