arXiv cs.AI· Ramit Pahwa, Parivesh Priye, Apoorva Beedu·· 14 小时前AI 评分34
VoiceLongMemEval：AI 助手能否记住你说话的语气？
VoiceLongMemEval: Do Assistants Remember How You Sounded?
AI 导读
新基准 VoiceLongMemEval（VLME）要求每个答案都依赖对话轮次中的副语言元数据（情绪标签、韵律描述和声音事件），仅凭文字转录无法恢复。所有题目通过三阶段对抗性筛选，确保强语言模型在只给转录文本时失败。
正文 · 原文
Abstract:With the growing scale of multi-agent architectures and large language models, deployed AI assistants are increasingly tasked with reasoning over long, continuous, multi-session conversation histories. Current benchmarks evaluate this dialogue history as information retrieval over long horizon, temporal reasoning, or knowledge updates, while crucially ignoring the fundamental dynamics of human-agent interaction, i.e. how they said it. To address this gap, we present VoiceLongMemEval (VLME) benchmark, where every answer depends on paralinguistic metadata (emotion labels, prosody descriptors, and voice events) attached to conversational turns, which is otherwise unrecoverable from the words alone. Every item passes a three-stage adversarial gate, ensuring that a strong language model fails when given only the transcript. Evaluating leading frontier and open-weight models reveals a pervasive affect gap; providing text-track paralinguistic metadata yields a 0.09 to 0.38 accuracy boost (0.61 to 0.69 when prompted with evidence hints), while standard ASR pipelines systematically discard this signal. Additionally, audio-native models successfully extract these cues directly from speech (0.354 to 0.412 vs. 0.325 blind). Code and dataset will be made available upon acceptance.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.00570 [cs.AI] |
| (or arXiv:2609.00570v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.00570 arXiv-issued DOI via DataCite |
Submission history
From: Ramit Pahwa [view email]
[v1]
Tue, 1 Sep 2026 02:06:10 UTC (88 KB)
[v2]
Wed, 2 Sep 2026 03:32:07 UTC (80 KB)
[v3]
Thu, 29 Oct 2026 17:32:31 UTC (38 KB)
来源：arXiv cs.AI · arxiv.org