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Do Transaction Prices Reflect Visual Preference? A Diagnostic Study with Multimodal Language Models — arXiv:2610.41358

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41358
License (as archived): CC0-1.0
Archived: 2026-11-02T06:59:07.767Z
SHA-256 of the archived text: fd9658bb459a18f28bc08ac352c4acbcb4711dd01658b19dec9b740af8e227f8
Stable URL of this page: https://metraipolis.com/documento/dc_bedef736ed9f

How to cite

Do Transaction Prices Reflect Visual Preference? A Diagnostic Study with Multimodal Language Models — arXiv:2610.41358 — arXiv (en), https://arxiv.org/abs/2610.41358 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bedef736ed9f (Metraipolis archive dc_bedef736ed9f, sha256 fd9658bb459a18f28bc08ac352c4acbcb4711dd01658b19dec9b740af8e227f8)

BibTeX

@misc{dcbedef736ed9f,
  title = {Do Transaction Prices Reflect Visual Preference? A Diagnostic Study with Multimodal Language Models — arXiv:2610.41358},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41358},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bedef736ed9f; sha256 fd9658bb459a18f28bc08ac352c4acbcb4711dd01658b19dec9b740af8e227f8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41358. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Existing visual preference evaluation relies on costly human annotations or preference reward models, whereas online marketplace transaction prices are readily available and reflect real purchasing behavior, offering potential low-cost supervision. However, transaction prices conflate visual preferences with non-visual factors such as provenance, demand, and competition, making aggregate accuracy insufficient to distinguish model errors, unreliable price signals, and protocol confounds. To address this identifiability challenge, we introduce MarketImage, a diagnostic framework that identifies when transaction prices reflect visual preferences, where they fail, and whether multimodal language models can recover human-supported visual preference signals. MarketImage comprises three components: (i) MarketImageArt, 266 six-image comparison sets with transaction prices and price-blind human rankings; (ii) MarketImageLens, a tie-preserving protocol that partitions 3,990 within-set image pairs into price-human agreement, conflict, and price-tie regions while separately auditing presentation-order effects; and (iii) MarketValuator, six elicitation methods spanning direct comparison, independent scoring, budget constraints, and multi-agent aggregation to evaluate signal recovery by static visual scorers and three multimodal language models. Experiments show that only 24.2% of image pairs have a non-tied price direction, of which 34.7% agree with the human majority. Within this region, HPSv2 leads at 66.4% Recoverable Accuracy; Qwen's budgeted and aggregated variants trail baselines, while GPT interface gains remain statistically unresolved. Thus, transaction prices encode recoverable visual preferences only under limited conditions and should be treated as uncertain proxies rather than ground truth.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41358 — 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 2026-11-02T06:56:58.290Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).