arXiv:2606. 07253v1 Announce Type: new Abstract: Traditional TOPSIS derives its reference points -- the Positive Ideal Solution ($PIS$) and Negative Ideal Solution ($NIS$) -- from the observed alternative set, making rankings susceptible to misalignment with decision-maker (DM) requirements, sensitivity to outlier performances, and rank reversal.
arXiv:2608. 11318v1 Announce Type: cross Abstract: Many sequential construction tasks exhibit exact symmetry at completion while their execution remains directed and history-dependent.
arXiv:2608. 05958v1 Announce Type: new Abstract: The paper addresses several ranking-dependent decision support methods.
arXiv:2607. 24063v1 Announce Type: new Abstract: On standard factuality tasks, frontier models now cluster near the top of the scale.
arXiv:2606. 05104v1 Announce Type: new Abstract: Knowledge benchmarks for LLMs face three issues: scaling-driven designs that do not operationalize disciplinary representativeness; flat-payment annotation that permits lazy consensus; and unaudited ranking instability under bounded test budgets.
arXiv:2607. 27083v1 Announce Type: new Abstract: As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure.
On standard factuality tasks, frontier models now cluster near the top of the scale. The question is therefore shifting from how factual a system is toward how much compute that factuality costs.
arXiv:2508. 00129v2 Announce Type: replace Abstract: Rank Reversal, where the relative order of alternatives changes in ways that violate axioms of rational decision-making, is a well-documented threat to the reliability of Multi-Criteria Decision Analysis (MCDA) methods.
arXiv:2605. 04495v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on evidence ranking to determine what information is exposed to the generator, yet existing retrieval and reranking methods primarily estimate query--document relevance.
arXiv:2607. 02104v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as cheap, scalable judges that compare candidate outputs pairwise.
arXiv:2607. 16240v1 Announce Type: cross Abstract: Direct Alignment Algorithms (DAAs) such as DPO have become a common way to post-train and align LLMs with human preferences.
arXiv:2607. 10038v1 Announce Type: cross Abstract: For many years, the pairwise comparison method has been widely used for decision-making involving experts.
arXiv:2606. 17756v1 Announce Type: new Abstract: Fairness has become a central concern in ranking problems involving individuals or social groups, particularly under the Responsible Artificial Intelligence agenda.