Large Language Models (LLMs) demonstrate strong generation and reasoning abilities, but they still face challenges in long-term memory retention and multi-turn conversational consistency. Existing memory-augmented methods often incorporate full dialog histories without filtering, resulting in information redundancy and inference latency. Inspired by the episodic memory mechanism in human cognition, we abstract conversational context into Episodic Memory Units (EMUs). We then propose a comprehensive framework, Episodic Memory Agent (EMA), along with a filtering decision module called MemDecider. Specifically, EMA organizes and retrieves EMUs to support response generation, while MemDecider filters information to reduce noise and improve overall performance. Experiments on two widely-used benchmarks show that EMA maintains competitive performance, and integrating MemDecider into other methods reduces their token consumption by an average of 11.48% while effectively improving the overall performance. Code is available at https://github.com/Hongyi4221/EMA.

In this study, we aim to explore Multitask Speech Language Model (SpeechLM) efficient inference via token reduction. Unlike other modalities such as vision or text, speech has unique temporal dependencies, making previous efficient inference works on other modalities not directly applicable. Furthermore, methods for efficient SpeechLM inference on long sequence and sparse signals remain largely unexplored. In this work, we propose FastAdaSP, a weighted token merging framework specifically designed for various speech-related tasks to improve the trade-off between efficiency and performance. Experimental results on WavLLM and Qwen-Audio show that our method achieves the state-of-the-art (SOTA) efficiency-performance trade-off compared with other baseline methods. Specifically, FastAdaSP achieved 7x memory efficiency and 1.83x decoding throughput without any degradation on tasks like Emotion Recognition (ER) and Spoken Question Answering (SQA).