7 papers
MemoryDocDataSet: A Benchmark for Joint Conversational Memory and Long Document Reasoning
Qiyang Xie, Jialun Wu, Xinjie He +4
AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents. Y…
When Does Persona Prompting Actually Help? A Retrieval and Metric Analysis of Expert Role Injection in LLMs
Shuai Xiao, Su Liu, Weikai Zhou +4
Persona prompting is widely used to steer large language models, yet its practical value remains unclear. Prior work often evaluates persona prompting using aggregate scores, makin…
What Training Data Teaches RL Memory Agents: An Empirical Study of Curriculum Effects in Memory-Augmented QA
Xinjie He, Zhiyuan Lin, Su Liu +4
Reinforcement learning (RL) has emerged as a viable recipe for training LLM agents to reason over external memory banks in multi-session dialogue. Existing work trains exclusively…
Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
Zhiyuan Jerry Lin, Benjamin Letham, Samuel Dooley +2
System prompts are a central control mechanism in modern AI systems, shaping behavior across conversations, tasks, and user populations. Yet they are difficult to tune when feedbac…
S2S-Arena: Evaluating Paralinguistic Instruction Following in Speech-to-Speech Models
Feng Jiang, Zhiyu Lin, Yiyang Liu +6
Recent advances in large language models (LLMs) have fundamentally reshaped speech-to-speech (S2S) systems, enabling increasingly natural spoken interaction. However, existing benc…
Decoding the Ear: A Framework for Objectifying Expressiveness from Human Preference Through Efficient Alignment
Zhiyu Lin, Jingwen Yang, Jiale Zhao +3
Recent speech-to-speech (S2S) models generate intelligible speech but still lack natural expressiveness, largely due to the absence of a reliable evaluation metric. Existing approa…