A T-estimation-based procedure for adaptive density estimation and optimal control in offline contextual MDPs without stationarity, providing oracle risk bounds under two loss functions and finite-sample cost guarantees.
Title resolution pending
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 7verdicts
UNVERDICTED 7roles
background 2representative citing papers
A systematic review of 50 studies identifies 69 LLM-assisted tasks in empirical software engineering, concentrated in data processing and analysis with gaps in human-centered integration and reproducibility reporting.
AI use in science has grown exponentially since 2015 but stays confined to computer science and statistics topics, shows higher retraction rates and citations, and follows distinct global adoption patterns.
A scoping review and empirical analysis produce a six-category taxonomy of factors driving AI non-development and abandonment, showing that practical issues like resource limits and organizational dynamics often outweigh ethical concerns in real decisions.
A think-aloud study reveals that AI tools in early research misrepresent uncertainty, obscure provenance, and create fragile trust, leading researchers to develop compensatory strategies to preserve scholarly judgment.
RedVLA is a red-teaming method that constructs feasible risk scenes and iteratively optimizes risk factors to achieve up to 95.5% attack success rate on unsafe physical behaviors across six VLA models.
LLMs exhibit substantial heterogeneity and non-determinism in SLR evidence screening, abstracts are decisive for performance, and they show no reliable superiority over classical classifiers on two real SLRs.
citing papers explorer
-
Adaptive Estimation and Optimal Control in Offline Contextual MDPs without Stationarity
A T-estimation-based procedure for adaptive density estimation and optimal control in offline contextual MDPs without stationarity, providing oracle risk bounds under two loss functions and finite-sample cost guarantees.
-
LLM-Assisted Empirical Software Engineering: Systematic Literature Review and Research Agenda
A systematic review of 50 studies identifies 69 LLM-assisted tasks in empirical software engineering, concentrated in data processing and analysis with gaps in human-centered integration and reproducibility reporting.
-
When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge
AI use in science has grown exponentially since 2015 but stays confined to computer science and statistics topics, shows higher retraction rates and citations, and follows distinct global adoption patterns.
-
To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems
A scoping review and empirical analysis produce a six-category taxonomy of factors driving AI non-development and abandonment, showing that practical issues like resource limits and organizational dynamics often outweigh ethical concerns in real decisions.
-
How Researchers Navigate Accountability, Transparency, and Trust When Using AI Tools in Early-Stage Research: A Think-Aloud Study
A think-aloud study reveals that AI tools in early research misrepresent uncertainty, obscure provenance, and create fragile trust, leading researchers to develop compensatory strategies to preserve scholarly judgment.
-
RedVLA: Physical Red Teaming for Vision-Language-Action Models
RedVLA is a red-teaming method that constructs feasible risk scenes and iteratively optimizes risk factors to achieve up to 95.5% attack success rate on unsafe physical behaviors across six VLA models.
-
Beyond Accuracy: LLM Variability in Evidence Screening for Software Engineering SLRs
LLMs exhibit substantial heterogeneity and non-determinism in SLR evidence screening, abstracts are decisive for performance, and they show no reliable superiority over classical classifiers on two real SLRs.