mlsanityData to 7 Oct 2026

Does Your Voice Assistant Remember? Analyzing Conversational Context Recall and Utilization in Voice Interaction Models

arXiv 2502.19759 ↗First posted 27 Feb 2025cs.SD eess.AScited by 9

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Abstract (arXiv)

Recent advancements in multi-turn voice interaction models have improved user-model communication. However, while closed-source models effectively retain and recall past utterances, whether open-source models share this ability remains unexplored. To fill this gap, we systematically evaluate how well open-source interaction models utilize past utterances using ContextDialog, a benchmark we proposed for this purpose. Our findings show that speech-based models have more difficulty than text-based ones, especially when recalling information conveyed in speech, and even with retrieval-augmented generation, models still struggle with questions about past utterances. These insights highlight key limitations in open-source models and suggest ways to improve memory retention and retrieval robustness.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2025.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 6.

9 papers.

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  1. 2026VoiceLongMemEval: Do Assistants Remember How You Sounded?
  2. 2021Beyond Goldfish Memory: Long-Term Open-Domain Conversation
  3. 2026Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents
  4. 2026S3-Bench: Evaluating Speech Interaction Models as Scientific Voice Assistants
  5. 2025In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents
  6. 2026When Users Don't Ask: Benchmarking Context-Driven Memory Retrieval in Conversational Agents
  7. 2025Mem-PAL: Towards Memory-based Personalized Dialogue Assistants for Long-term User-Agent Interaction
  8. 2026PRAGMA: Evaluating Personalized Guidance with Memory Alignment in Lifelong Conversations