AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
[Submitted on 16 Jul 2026] Title:WaveCommitments: Private and Verifiable Speech-Agent Marketplaces View a PDF of the paper titled WaveCommitments: Private and Verifiable Speech-Agent Marketplaces, by Oguzhan Baser and 3 other authors View PDF HTML (experimental) Abstract:Modern speech systems are evolving into ecosystems of autonomous speech agents that speak together, negotiate, and trade assets such as embeddings, spectrograms, or raw data. In such markets, sellers cannot reveal assets for evaluation due to privacy constraints, and buyers cannot price what they cannot test. Thus, we open-source WaveCommitments, an end-to-end design that pairs confidential asset valuation via Data Shapley inside a trusted execution environment with multivariate polynomial commitments. It enables selective openings and tamper-evident provenance to prove that a delivered asset matches the valuation while hiding everything else. Our extensive experiments across three known datasets and various tasks with state-of-the-art encoders, including Whisper, reveal that we speed up the best benchmarks by up to 20x while reducing their proof sizes by up to 39x. We also systematically study how a speaker's value shifts across tasks and encoders, revealing agent-level market prices. Comments: 5 pages, 3 figures, under review for a conference Subjects: Computation and Language (cs.CL); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA); Sound (cs.SD) Cite as: arXiv:2610.26867 [cs.CL] (or arXiv:2610.26867v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2610.26867 arXiv-issued DOI via DataCite Submission history From: Oguzhan Baser [view email] [v1] Thu, 16 Jul 2026 03:46:22 UTC (2,426 KB) Full-text links: Access Paper: View a PDF of the paper titled WaveCommitments: Private and Verifiable Speech-Agent Marketplaces, by Oguzhan Baser and 3 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CL new | recent | 2026-10 Change to browse by: cs cs.CR cs.MA cs.SD References & Citations SciX Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)