# VoxMem

The authors propose a two-axis taxonomy of spoken conversational memory — acoustic evidence type (speech semantics, speaker identity, paralinguistic cues, environmental sound) crossed with memory operation (information extraction, multi-session reasoning, temporal evolution tracking, answer refusal) — and build VoxMem on it: 3,196 evaluation instances over 34,743 spoken sessions (177 hours) at four context budgets from 8K to 64K tokens; evaluating 15 large audio language models, no model exceeds 40% overall accuracy at 32K (best 38.5%), models remember what was said far better than who said it, how it was said, or what was audible, and replacing audio with exact transcripts drops speaker accuracy from 69.8% to 10.3% while speech-semantics accuracy barely moves (75.9% to 71.0%).

The authors propose a two-axis taxonomy of spoken conversational memory — acoustic evidence type (speech semantics, speaker identity, paralinguistic cues, environmental sound) crossed with memory operation (information extraction, multi-session reasoning, temporal evolution tracking, answer refusal) — and build VoxMem on it: 3,196 evaluation instances over 34,743 spoken sessions (177 hours) at four context budgets from 8K to 64K tokens; evaluating 15 large audio language models, no model exceeds 40% overall accuracy at 32K (best 38.5%), models remember what was said far better than who said it, how it was said, or what was audible, and replacing audio with exact transcripts drops speaker accuracy from 69.8% to 10.3% while speech-semantics accuracy barely moves (75.9% to 71.0%).