The results show that benchmark reliability in educational AI is constrained less by algorithm choice than by data structure, group heterogeneity, and evaluation design, and increasing model complexity did not remove this pattern: ensemble models improved structurally sound datasets but amplified instability or failed...
Financial QA systems are typically improved before deployment through better retrieval, prompting, or agent coordination, leaving their reliability behavior fixed thereafter. In practice, new SEC-filing questions repeatedly expose heterogeneous errors in period, entity, evidence use, and calculation. Existing self-impr...
Yanzhang Ma, Zhenghan Tai, Han-Wei Wu et al.· 0 citations
FinSAgent is proposed, an evidence-grounded multi-agent framework that reframes SEC filing QA as corpus-aligned retrieval planning and corrects both ends with a single principle: inject corpus-side conditioning wherever model priors would otherwise dominate.
Jijun Chi, Zhenghan Tai, Han-Wei Wu et al.· arXiv.org· 0 citations
AnchorScore provides a low-cost ranking signal that directs expensive MLLM evaluation to the classes where it is most informative, and three practical applications follow: a deployable hybrid CLIP/MLLM routing strategy, prompt disambiguation on hard classes (exploratory), and review-priority prediction for human verifi...
Yanzhang Ma, Li-Zhuo Zhang· 2 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
