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CaRe-KT: Continuity-Aware Retrieval-Augmented Reasoning for Knowledge Tracing — arXiv:2611.03744

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03744
License (as archived): CC0-1.0
Archived: 2026-11-04T12:39:18.503Z
SHA-256 of the archived text: acabb27a430566499fa091476d123589bd34fced5a6705cd8216f089b4567c08
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How to cite

CaRe-KT: Continuity-Aware Retrieval-Augmented Reasoning for Knowledge Tracing — arXiv:2611.03744 — arXiv (en), https://arxiv.org/abs/2611.03744 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dea00a1b2bdd (Metraipolis archive dc_dea00a1b2bdd, sha256 acabb27a430566499fa091476d123589bd34fced5a6705cd8216f089b4567c08)

BibTeX

@misc{dcdea00a1b2bdd,
  title = {CaRe-KT: Continuity-Aware Retrieval-Augmented Reasoning for Knowledge Tracing — arXiv:2611.03744},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03744},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dea00a1b2bdd; sha256 acabb27a430566499fa091476d123589bd34fced5a6705cd8216f089b4567c08},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03744. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Knowledge tracing (KT) estimates learners' evolving knowledge states from interaction histories to support adaptive learning. Retrieval-augmented KT improves these estimates using cross-learner behavioral evidence, but existing approaches typically separate evidence retrieval from downstream prediction. This creates a retrieval-inference alignment gap because evidence relevance varies with the learner's evolving state and prediction context. We propose CaRe-KT, a query-adaptive framework that maintains relevance continuity between evidence acquisition and inference without equating retrieval relevance with predictive utility. CaRe-KT introduces an Evidence-Graph Cache (EGC) that combines persistent question-level relational compatibility with interaction-level temporal validity to construct query-specific evidence subgraphs. Historical learning trajectories retrieved through these subgraphs are encoded alongside the target learner's history in a shared representation space using outcome-supervised contrastive learning. An attention mechanism combines acquisition relevance with learned query-evidence compatibility, allowing relevance to inform, rather than determine, each trajectory's predictive influence. Experiments across five benchmarks show that CaRe-KT outperforms strong KT baselines and remains robust under sparse-history and cold-start conditions. These findings support relevance continuity as a design principle for retrieval-augmented knowledge tracing.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.03744 — 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 2026-11-04T12:29:47.241Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).