Report on the 9th Workshop on Search-Oriented Conversational Artificial Intelligence (SCAI 2025) at IJCAI 2025
Bibliographic record
Abstract
The goal of Search-oriented Conversational AI is to design systems that allow for a more convenient information access by means of a conversational user interface. Further development of Conversational Search systems requires closer integration and better information exchange between the diverse research communities. Over the past years, the Search-Oriented Conversational Artificial Intelligence (SCAI) workshop became an established venue that provides a discussion platform on Conversational AI for intelligent information access, bringing together researchers and practitioners across artificial intelligence, natural language processing, information retrieval, recommender systems, machine learning, dialogue systems and human-computer interaction subfields. This year, the full-day SCAI workshop at IJCAI 2025 once again brought together a group of researchers interested in informing the design of a new generation of systems for conversational information access. This paper, co-authored by both organizers and participants of the workshop, presents a summary of the insights gathered from the joint discussions that followed the invited talks. Date: 18 August 2025. Website: https://scai.info/scai-2025/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.127 | 0.056 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".