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Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis — arXiv:2609.12403

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12403
License (as archived): CC0-1.0
Archived: 2026-10-16T03:02:43.034Z
SHA-256 of the archived text: 64b1bde2b21f16ca044dbf45296d805013c79924d370b326d2d8e88961d941e7
Stable URL of this page: https://metraipolis.com/documento/dc_98eaaa4582d4

How to cite

Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis — arXiv:2609.12403 — arXiv (en), https://arxiv.org/abs/2609.12403 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_98eaaa4582d4 (Metraipolis archive dc_98eaaa4582d4, sha256 64b1bde2b21f16ca044dbf45296d805013c79924d370b326d2d8e88961d941e7)

BibTeX

@misc{dc98eaaa4582d4,
  title = {Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis — arXiv:2609.12403},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12403},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_98eaaa4582d4; sha256 64b1bde2b21f16ca044dbf45296d805013c79924d370b326d2d8e88961d941e7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12403. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Cognitive Diagnosis Models (CDMs) play a pivotal role in personalized online learning. Traditional CDMs rely on discrete, ID-based embeddings to represent students, exercises, and concepts. This paradigm diverges from the nature of learner cognition, where knowledge is not stored and retrieved as isolated symbols. As a result, CDMs suffer from semantic limitations when new exercises or concepts appear. In this paper, we propose a Process-aware Language Cognitive Diagnosis (PLCD) framework that uses language-derived structures as cognitive priors and response records to calibrate student posterior states. PLCD leverages large language models (LLMs) to construct concept schemas and cognitive process graphs, and uses target-conditioned semantic memory to retrieve historical responses that are relevant to each target exercise. A process-grounded Language-to-Cognition Mapper with DA-MoE experts and process-level contrastive learning then maps the textual evidence into a unified cognitive space. Experimental results show that PLCD not only outperforms traditional baselines in predicting student performance but also exhibits strong cognitive transfer capabilities. These results connect the computational power of LLMs with the psychometric goal of measuring latent knowledge states, suggesting that structured language priors calibrated by response records can improve cold-start robustness and cognitive grounding.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-14 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.12403 — 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 2026-10-16T02:51:27.787Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).