The paper evaluates the use of large language models (LLMs) as judges for assessing conversational voice agents, comparing human judgments with GPT‑4.1 and GPT‑5 across telecom and retail interactions. It examines agreement, metric‑level correlations, and consistency across three evaluation configurations (p0, p1, p2) to determine how reliably LLMs can judge conversational quality and safety. The study finds that LLM‑based evaluation can be effective but its reliability varies by metric and configuration, suggesting a hybrid approach where LLMs handle scalable assessment while humans focus on metrics requiring contextual interpretation.
arXiv:2607. 12085v1 Announce Type: new Abstract: Evaluating retail conversational agents requires methods beyond lexical-overlap metrics to assess intent alignment, factuality, helpfulness, clarity, tone, and overall response quality.
The paper introduces Inquesto Score (IS), a protocol that measures voice‑agent reliability by calculating the percentage of calls that reach the caller’s goal without functional failure. IS defines explicit failure events and severity levels, evaluates timing, semantic, and state‑dependent failures using audio, scenario predicates, tool traces, and a pinned open‑model judge, and provides diagnostic views on behavior, acoustic robustness, identity handling, and speaker groups. The authors evaluate IS v0.1 on 30 scenarios, three acoustic conditions, four speaker groups, and 306 calls per agent across 13 configurations, demonstrating that reliable measurement requires evidence beyond transcripts and explicit treatment of deployment conditions.
arXiv:2609.38867v1 Announce Type: new Abstract: Large language model (LLM) computer-use agents are typically evaluated with clean written instructions, despite speech being an increasingly popular in...
The paper introduces UPHELD, a large benchmark of human-to-human dialogues written by professional script writers, featuring realistic turn densities and over 36,000 per-turn human annotations. It evaluates existing automatic metrics and LLM-as-a-judge methods, finding them unreliable against expert human judgment. Using UPHELD, the authors develop a Mixture-of-Judges framework that improves correlation with human assessments by about 30%.
The paper reviews the fragmented literature on real‑time voice agents, noting that architecture, turn‑taking, and agentic evaluation communities rarely cite each other. It presents three evidence‑based claims: (1) architecture choice is a deployment constraint rather than a definitive solution, (2) evaluation has shifted from component quality to grounded outcomes, and (3) the dyadic assumption is breaking down as multiparty turn‑taking and reasoning become essential. The authors propose the TRG reporting standard to characterize agents by timing, recovery, and state‑verified outcomes, with an optional fourth axis for multiparty contexts.
arXiv:2608. 10716v1 Announce Type: cross Abstract: Speech-to-speech (S2S) voice agents are increasingly being incorporated into enterprise for customer care and as daily companions for consumers owing to the ease of the conversational modality over text.
arXiv:2609.24812v1 Announce Type: new Abstract: Voice provides a natural and immediate interface for AI agents. Many settings in which voice agents could be useful, including meetings, households, an...
MTVA-Bench is a new benchmark designed to evaluate the language model component of cascaded voice agents under realistic conditions. It simulates callers with an LLM, mocks backend tool responses, and scores both tool‑call correctness and conversational quality using two LLM judges, covering 49 agents, 490 scenarios, and 7 languages. The benchmark reveals that while models perform similarly on tool selection, they differ widely in argument handling, rule compliance, and dialogue quality, highlighting the nuanced challenges of real‑world voice interactions.
The paper introduces a validation‑gated audit framework for voice AI customer‑care systems, treating them as stateful, multi‑turn, tool‑mediated interactions where bias and safety can manifest as added burdens before a final decision. The framework distinguishes between native speech‑to‑speech, cascaded ASR‑to‑LM‑to‑TTS, and hybrid architectures, and applies matched service facts across controlled caller presentation conditions to validate fact invariance, presentation cues, artifacts, and acoustic measurements. It outlines seven validation gates, a six‑family metric set, and demonstrates the approach with a synthetic refund‑dispute audit example, while noting that production results are withheld until the protocol is satisfied.
arXiv:2605.13841v3 Announce Type: replace-cross Abstract: Voice agents are increasingly deployed across enterprise applications. However, no existing benchmark jointly addresses realistic conversatio...
arXiv:2609.24516v1 Announce Type: new Abstract: In recent years, large language models (LLMs) have emerged as a popular alternative for evaluation. Often referred to as LLMs as judges (LLJs), these s...
arXiv:2608. 11236v1 Announce Type: cross Abstract: Roleplay evaluation should do more than assign a single score: it should reveal which role requirements were tested, which failed, and which dialogue evidence supports the judgment.