Metraipolis › archive › dc_f901ff5953bb

Cross-Primitive Consistency in Jev — arXiv:2610.38623

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.38623
License (as archived): CC0-1.0
Archived: 2026-10-31T06:17:17.600Z
SHA-256 of the archived text: 81efce8ddd9ca3ef71bb51e5fe3b203b4ac58c6749ff1a49acdf5920a8df3eff
Stable URL of this page: https://metraipolis.com/documento/dc_f901ff5953bb

How to cite

Cross-Primitive Consistency in Jev — arXiv:2610.38623 — arXiv (en), https://arxiv.org/abs/2610.38623 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f901ff5953bb (Metraipolis archive dc_f901ff5953bb, sha256 81efce8ddd9ca3ef71bb51e5fe3b203b4ac58c6749ff1a49acdf5920a8df3eff)

BibTeX

@misc{dcf901ff5953bb,
  title = {Cross-Primitive Consistency in Jev — arXiv:2610.38623},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.38623},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f901ff5953bb; sha256 81efce8ddd9ca3ef71bb51e5fe3b203b4ac58c6749ff1a49acdf5920a8df3eff},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.38623. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Jev enables efficient decision-making with large language models (LLMs) through typed querying primitives, allowing the same semantic state to be expressed in different formats, such as binary and multiple-choice queries. However, the consistency of decisions across primitives has not been systematically evaluated. This work introduces PrimaCons, a four-axis benchmark for assessing cross-primitive consistency while preserving the semantics of the query. Evaluations across three Jev sizes reveal consistency violations across all four axes. Our analysis shows that these violations follow systematic patterns and that cross-primitive discrepancies predict decision failure with a ROC-AUC of 0.91. To mitigate these inconsistencies, we propose the Cross-Primitive Residual Calibrator (CPRC), which attaches a lightweight residual network to a frozen backbone and jointly optimizes predictive accuracy and cross-primitive consistency. Under a leave-one-domain-out protocol, CPRC reduces cross-primitive inconsistency by 58.57% and improves accuracy by +2.97%., demonstrating its effectiveness in both in-domain and out-of-domain settings. Our findings establish cross-primitive consistency as a key dimension for evaluating Jev and provide a practical approach to improving the reliability of its decisions. Code and benchmark is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.38623 — 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 2026-10-31T06:07:23.397Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).