Proceedings of the First International Workshop on Trends in Knowledge Representation and Reasoning (TKR'25)
Bibliographic record
Abstract
The First International Workshop on Trends in Knowledge Representation and Reason ing (TKR’25) aimed at providing a forum for the general area of Knowledge Representation and Reasoning (KR), which is a well-established and active area of research within Artificial Intelligence. KR is about the declarative representation of knowledge and develops methods for automated reasoning under vagueness, uncertainty, incompleteness, and inconsistency. We welcomed contributions from all areas of KR and two types of submissions: full papers must be original and constitute significant contributions to the field and Extended abstracts of recently published works or teasers for ongoing work. All submissions have be evaluated through peer-reviewing based on originality, significance, technical soundness, and clarity of exposition. The reviewing process was single blind. We specifically welcomed extended abstracts of papers published at IJCAI’25, for which the workshop can serve as medium for extended presentations. TKR2025 was hosted as an IJCAI 2025 workshop and took place in August 2025 in Montreal, Canada. The workshop received 22 submissions (8 full papers and 14 extended abstracts) and 13 of them (4 full papers and 9 extended abstracts) had been accepted for this volume. We were also pleased to welcome Meghyn Bienvenu as a keynote speaker.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.077 | 0.043 |
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".