Anatomical Review: Optimizing and Harnessing Workflows — A Section-by-Section Reading of Noguer i Alonso (2026), Task-Specific Combinations of Humans, Software, and AI Agents
Oct 2026· Zenodo (CERN European Organization for Nuclear Research)· 1 citation
Ethics and Social Impacts of AI
Abstract
This anatomical review systematically dissects Noguer i Alonso's (2026) framework for hybrid workflows integrating human decision makers (H), specified computational procedures (D), and adaptive artificial delegates (A). The paper advances a principled account of agentification, deagentification, automation, and harnessing, grounded in a partial order on delegation profiles rather than scalar autonomy scores, and supported by formal results on permission restriction, invariant preservation, retry exposure, advisory isolation, and the proceduralization gap. While the contribution offers valuable conceptual clarity for task-specific authority allocation in agentic systems, the review identifies limitations in empirical scope (synthetic examples only), the open problem of semantic promotion, and the transferability of formal guarantees to production environments. Implications for AI governance, hybrid intelligence, and institutional design in Global South contexts are discussed.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
