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RadarLLM: Adapting Pretrained Large Language Models for Marine Radar Target Detection with Preference-aware Loss
Qiying Hu
https://arxiv.org/abs/2509.12089 https://

RadarLLM: Adapting Pretrained Large Language Models for Marine Radar Target Detection with Preference-aware Loss
Recent advances in pre-trained large language models (LLMs) have demonstrated their capacities to capture universal knowledge, making them promising general-purpose optimization solvers for wireless signal processing. Motivated by these findings, we take the first step towards fine-tuning pre-trained LLMs for the effective analysis of radar signal features in marine target detection tasks. Nevertheless, directly fine-tuning pre-trained LLMs on marine target detection tasks tends to suffer from …

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Full Integer Arithmetic Online Training for Spiking Neural Networks
Ismael Gomez, Guangzhi Tang
https://arxiv.org/abs/2509.06636 https://arxiv.org/pdf/2509…

Full Integer Arithmetic Online Training for Spiking Neural Networks
Spiking Neural Networks (SNNs) are promising for neuromorphic computing due to their biological plausibility and energy efficiency. However, training methods like Backpropagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL) remain computationally intensive. This work introduces an integer-only, online training algorithm using a mixed-precision approach to improve efficiency and reduce memory usage by over 60%. The method replaces floating-point operations with integer arithmetic …

Modernist Translation and Readerly Difficulty (ACLA 2026 in Montreal)
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updated: Thursday, August 28, 2025 - 8:04amfull name / name of organization: ACLA 2026contact…
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