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$S^3$-Bench: Evaluating Speech Interaction Models as Scientific Voice Assistants — arXiv:2609.09852

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.09852
License (as archived): CC0-1.0
Archived: 2026-10-16T07:53:21.978Z
SHA-256 of the archived text: cd39cf3c065d3f93b0df4d977a884d37f44544bb87bcb9f7b32f120a73e4c2bd
Stable URL of this page: https://metraipolis.com/documento/dc_366c617dd931

How to cite

$S^3$-Bench: Evaluating Speech Interaction Models as Scientific Voice Assistants — arXiv:2609.09852 — arXiv (en), https://arxiv.org/abs/2609.09852 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_366c617dd931 (Metraipolis archive dc_366c617dd931, sha256 cd39cf3c065d3f93b0df4d977a884d37f44544bb87bcb9f7b32f120a73e4c2bd)

BibTeX

@misc{dc366c617dd931,
  title = {$S^3$-Bench: Evaluating Speech Interaction Models as Scientific Voice Assistants — arXiv:2609.09852},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.09852},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_366c617dd931; sha256 cd39cf3c065d3f93b0df4d977a884d37f44544bb87bcb9f7b32f120a73e4c2bd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.09852. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The advance of multimodal large language models (MLLMs) has fundamentally reshaped the paradigm of human-computer interaction, especially speech interaction models capable of seamless conversations. Despite remarkable performance as general voice assistants, their performance in specialized domains remains underexplored, particularly in scientific areas. Scientific interactions introduce formidable challenges, involving rare technical terminology, spoken norms of abbreviations, and the natural verbalization of symbolic special expressions. In this paper, we introduce S$^3$-Bench, a systematic evaluation framework covering 10 major disciplines, consisting of a Knowledge set for speech question-answering and a Dialogue set for multi-turn progressive interactions with simulated user agents. By decomposing a complete atomic turn into stages of speech recognition, perception, knowledge utilization with reasoning, and response pronunciation, we systematically characterize the common challenges and performance tradeoffs of existing approaches. Furthermore, experiments on multi-turn interactions reveal persistent limitations in user adaptation and the generation of accurate, comprehensive, and efficient responses.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-10 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.09852 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T07:24:00.903Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-10?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).