DuplexLedger: Benchmarking Transactional Memory in Full-Duplex Speech Agents

Key points

• Full-duplex speech agents must update persistent memory while listening and speaking, yet it remains unclear which events should be stored, revised, or excluded.1
• Existing benchmarks isolate interaction timing from memory outcomes, obscuring the state transitions behind a correct answer.1
• Evaluating five full-duplex models reveals an interaction-memory gap: no system combines strong boundary control with coherent final-state memory, and the best final-state fact coverage is 2.01%.1
• We propose ACT-Mem, an 8.92M-parameter sidecar comprising an event controller, a versioned transactional ledger, and a typed retriever coupled with a frozen MiniCPM-o 4.5 backbone.1

Sources (1)

• [1]DuplexLedger: Benchmarking Transactional Memory in Full-Duplex Speech Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 03:47 AM

  We introduce DuplexLedger, a bilingual benchmark of 1,094 persistent sessions aligning four full-duplex scenarios with event-level Write/Update/Ignore/Retain supervision, explicit pre/post memory states, and 6,564 same-session questions and checks.

  Full-duplex speech agents must update persistent memory while listening and speaking, yet it remains unclear which events should be stored, revised, or excluded.

Extractive summary: sentences quoted from the sources.