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From Ranked Documents to Reliable Contexts: An Answer-Oriented Context Construct Framework for AI Search — arXiv:2609.23354

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.23354
License (as archived): CC0-1.0
Archived: 2026-10-09T21:58:48.074Z
SHA-256 of the archived text: 54995d9069278d5e91820f7d17b9a917f93a5af450d71389608a160985c2f14e
Stable URL of this page: https://metraipolis.com/documento/dc_301bdebee80d

How to cite

From Ranked Documents to Reliable Contexts: An Answer-Oriented Context Construct Framework for AI Search — arXiv:2609.23354 — arXiv (en), https://arxiv.org/abs/2609.23354 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_301bdebee80d (Metraipolis archive dc_301bdebee80d, sha256 54995d9069278d5e91820f7d17b9a917f93a5af450d71389608a160985c2f14e)

BibTeX

@misc{dc301bdebee80d,
  title = {From Ranked Documents to Reliable Contexts: An Answer-Oriented Context Construct Framework for AI Search — arXiv:2609.23354},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.23354},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_301bdebee80d; sha256 54995d9069278d5e91820f7d17b9a917f93a5af450d71389608a160985c2f14e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.23354. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Traditional Web search follows a human-facing paradigm in which users inspect ranked documents and synthesize information themselves. In AI Search, retrieved documents instead serve as inputs to a generation model, shifting the retrieval objective from ranking documents by Search Satisfaction to constructing reliable context for correct answer generation. We formulate this shift as answer-oriented context construction through a three-stage framework: (1) Answer Support identifies candidate documents that contribute information to answer generation; (2) Content Trustworthiness assesses whether this information provides a reliable basis for correct answers from source, temporal, and factual perspectives; and (3) Context Organization selects, consolidates, and structures retained information under a finite context budget for consistent and robust generation. We further develop an industrial workflow spanning prior and posterior optimization and establish a systematic evaluation protocol covering both retrieval-side context and final answers. Experiments show consistent improvements at both Retrieval and Answer levels, demonstrating the effectiveness of the framework and its industrial implementation.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-22 in cs.IR).
Categorie arXiv: Information Retrieval (cs.IR).
Pagina del lavoro: https://arxiv.org/abs/2609.23354 — 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 2026-10-09T21:45:02.214Z dalla pagina https://arxiv.org/catchup/cs.IR/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).