Voice Evaluation of Reasoning Ability: Diagnosing the Modality-Induced
  Performance Gap
Authors: name: Yueqian Lin, name: Zhengmian Hu, name: Qinsi Wang, name: Yudong Liu, name: Hengfan Zhang, name: Jayakumar Subramanian, name: Nikos Vlassis, name: Hai Helen Li, name: Yiran Chen
Organization: Adobe

We present Voice Evaluation of Reasoning Ability (VERA), a benchmark for
evaluating reasoning ability in voice-interactive systems under real-time
conversational constraints. VERA comprises 2,931 voice-native episodes derived
from established text benchmarks and organized into five tracks (Math, Web,
Science, Long-Context, Factual). Each item is adapted for speech interaction
while preserving reasoning difficulty. VERA enables direct text-voice
comparison within model families and supports analysis of how architectural
choices affect reliability. We assess 12 contemporary voice systems alongside
strong text baselines and observe large, consistent modality gaps: on
competition mathematics a leading text model attains 74.8% accuracy while its
voice counterpart reaches 6.1%; macro-averaged across tracks the best text
models achieve 54.0% versus 11.3% for voice. Latency-accuracy analyses reveal a
low-latency plateau, where fast voice systems cluster around ~10% accuracy,
while approaching text performance requires sacrificing real-time interaction.
Diagnostic experiments indicate that common mitigations are insufficient.
Increasing "thinking time" yields negligible gains; a decoupled cascade that
separates reasoning from narration improves accuracy but still falls well short
of text and introduces characteristic grounding/consistency errors. Failure
analyses further show distinct error signatures across native streaming,
end-to-end, and cascade designs. VERA provides a reproducible testbed and
targeted diagnostics for architectures that decouple thinking from speaking,
offering a principled way to measure progress toward real-time voice assistants
that are both fluent and reliably reasoned.