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Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generation — arXiv:2609.34419

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.34419
License (as archived): CC0-1.0
Archived: 2026-10-07T20:57:46.294Z
SHA-256 of the archived text: febeff76bb2aed2db4762483c28118f496e50030b41cd50a4e412767da94781f
Stable URL of this page: https://metraipolis.com/documento/dc_fa4fac76d7e4

How to cite

Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generation — arXiv:2609.34419 — arXiv (en), https://arxiv.org/abs/2609.34419 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fa4fac76d7e4 (Metraipolis archive dc_fa4fac76d7e4, sha256 febeff76bb2aed2db4762483c28118f496e50030b41cd50a4e412767da94781f)

BibTeX

@misc{dcfa4fac76d7e4,
  title = {Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generation — arXiv:2609.34419},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.34419},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fa4fac76d7e4; sha256 febeff76bb2aed2db4762483c28118f496e50030b41cd50a4e412767da94781f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.34419. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automatic human-like facial reaction generation (FRG) is essential for building intelligent systems that can engage in human-computer interaction (HCI). While diverse and context-appropriate facial reactions can reflect latent appraisal and affective processes in human interaction, most existing FRG methods rely on end-to-end architectures that directly map speaker behaviours to listener expressions without an explicit intermediate internal state. We reformulate FRG as generation mediated by a structured internal-state process and propose the \textbf{Intentional Agent}, which shifts FRG from direct stimulus-response mapping to stimulus-grounded generation through explicit intermediate states. To represent temporal internal-state evolution, we propose an internal dynamics model that integrates emotional drives with an iterative Inner Thought Flow (ITF) within a structured intermediate state used for subsequent generation. This state can continue to update during conversational silences. Furthermore, to bridge abstract internal states with physiological actions, we formulate FRG as a downstream affective mapping from this latent thought flow to facial expressions. Experiments on the REACT 2025 dataset show an FRDist of 72.39 and an FRDiv of 0.5057; perceptual plausibility is evaluated separately through blinded human ratings. A blinded human evaluation of 96 reactions found no significant difference in mean score between Full and ground truth ($5.527$ vs.\ $5.195$, $p_{\mathrm{Holm}}=.076$), while Full significantly outperformed Event-Triggered and Heuristic-Only (both $p_{\mathrm{Holm}}<.001$). The Reaction Quality Scorer (RQS) correlated strongly with human judgements (Pearson $r=.855$; Spearman $\rho=.821$, both $p<.05$), supporting its use as an automatic metric. These results underscore the immense potential of endogenous dynamics in building highly autonomous, human-like agents.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.34419 — 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 2026-10-07T20:29:01.549Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).