Why do dialogue systems need probabilistic reasoning?
Explores whether deterministic flowchart-based dialogue systems can handle realistic speech recognition error rates of 15-30 percent, and what alternative approaches might be necessary.
Can models learn to abstain when uncertain about predictions? Can dialogue planning balance fast responses with strategic depth? Can skipping transcription make voice assistants faster? Why do language models fail in gradually revealed conversations? Why do AI conversations reliably break down after multiple turns?
Calibrated models outperform larger ones by abstaining on uncertain predictions Dual-process dialogue planning balances efficiency with strategic depth through uncertainty switching Skipping transcription reduces voice assistant latency to 226 milliseconds Language models make unrecoverable premature assumptions in underspecified conversation. Multi-turn conversation failures stem from intent misalignment, not capability limits