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NSF workshop urges a shift to clinic-aware algorithm–hardware co-design and sustained public investment in shared data, compute, and validation ecosystems to de-risk and speed medical AI commercialization. Without standardized benchmarks, continuous monitoring, and governance reforms, promising medical AI risks slow adoption, regulatory friction, and market concentration.
low descriptive arxiv
Report for NSF Workshop on Algorithm-Hardware Co-design for Medical Applications · Peipei Zhou, Zheng Dong, Insup Lee, Aidong Zhang, Robert Dick, Majid Sarrafzadeh... · Mar 11, 2026
Co-designed quantum–classical supercomputers are the missing link to scale useful hybrid algorithms: integrating QPUs with GPUs/CPUs and middleware can slash manual orchestration and speed materials and drug discovery, but the capital- and skill-intensity will favor well-resourced firms and cloud or national testbeds.
n/a descriptive arxiv
Reference Architecture of a Quantum-Centric Supercomputer · Seetharami Seelam, Jerry M. Chow, Antonio Córcoles, Sarah Sheldon, Tushar Mittal... · Mar 11, 2026
A dominance-based RLHF method cuts rare harmful outputs without sacrificing helpfulness. Using entropically regularized optimal transport to enforce stochastic dominance gives tunable, provable control over tail risk that can be aligned with firm or regulator risk preferences.
medium theoretical arxiv
Safe RLHF Beyond Expectation: Stochastic Dominance for Universal Spectral Risk Control · Yaswanth Chittepu, Ativ Joshi, Rajarshi Bhattacharjee, Scott Niekum · Mar 11, 2026
An AI trained on ~7,400 paired CBCT studies produces draft oral/maxillofacial reports at intermediate-radiologist quality and, as a co-authoring tool, systematically improves final report quality—helping novices reach intermediate standards, lifting intermediates toward senior quality, and cutting omission-related errors among seniors.
medium descriptive arxiv
Bridging the Skill Gap in Clinical CBCT Interpretation with CBCTRepD · Qinxin Wu, Fucheng Niu, Hengchuan Zhu, Yifan Sun, Ye Shen, Xu Li, Han Wu, Leqi L... · Mar 11, 2026
A KL-based shrinkage meta-analysis adaptively pools across heterogeneous studies, producing closed-form estimators that lower mean-squared error and deliver valid confidence intervals without assuming parameter homogeneity. The method offers a principled way to combine estimates across firms, regions or deployments—useful for evaluating AI impacts when settings differ.
n/a theoretical arxiv
Redefining shared information: a heterogeneity-adaptive framework for meta-analysis · Elizabeth M. Davis, Emily C. Hector · Mar 11, 2026
Luck gets you first through the door, but not the lock: rare fast discoveries driven by extreme-value first-passage times explain fragile early leads, while only one-sided interaction advantages (exclusive access, preferential treatment or asymmetric reinforcement) can convert those leads into stable monopolies; moreover, small spatial/network disadvantages demand exponentially more scale to match early-discovery probabilities.
n/a theoretical arxiv
Macroscopic Dominance from Microscopic Extremes: Symmetry Breaking in Spatial Competition · Stuti Guha, Shawn D. Ryan, Bhargav R. Karamched · Mar 11, 2026
Optimizing for average reward can hide persistent failure: when environment dynamics are non-ergodic a policy that looks optimal in expectation can lock a deployed agent into long-run low-reward regimes, so evaluators and regulators should prioritize time-average or distributional guarantees rather than ensemble means.
n/a theoretical arxiv
Ergodicity in reinforcement learning · Dominik Baumann, Erfaun Noorani, Arsenii Mustafin, Xinyi Sheng, Bert Verbruggen,... · Mar 11, 2026
A predictive prompt-selection method halves (or more) the costly rollouts needed for RL finetuning and improves reasoning accuracy across math, planning and visual-geometry benchmarks; by making iterative finetuning cheaper and faster, the technique could materially lower the marginal compute cost of model improvement for practitioners.
medium descriptive arxiv
Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models · Yixiu Mao, Yun Qu, Qi Wang, Heming Zou, Xiangyang Ji · Mar 11, 2026
Sentiment judgments of short, context-free team messages depend more on the message than the rater: individuals' labels are only moderately stable and ambiguous wording drives most disagreement, while mood traits produce only small positive biases. The result warns that automated sentiment signals—often trained on pooled human labels—can be noisy and systematically biased unless annotator and item heterogeneity are explicitly modeled.
medium correlational arxiv
Exploring Indicators of Developers' Sentiment Perceptions in Student Software Projects · Martin Obaidi, Marc Herrmann, Jendrik Martensen, Jil Klünder, Kurt Schneider · Mar 11, 2026
Growing agents through ongoing conversations with domain experts turns tacit know-how into reusable knowledge assets, lowering per-task effort and improving consistency while shifting costs from one-time engineering to continuous practitioner time; the approach scales through iterative crystallization but depends on sustained engagement and risks overfitting to individual practices.
low theoretical arxiv
Nurture-First Agent Development: Building Domain-Expert AI Agents Through Conversational Knowledge Crystallization · Linghao Zhang · Mar 11, 2026
Re-evaluating EVMbench finds AI agents fall short of replacing human auditors: expanded, contamination-free tests show unstable rankings, scaffold sensitivity and no end-to-end exploit success on post-release incidents, implying AI is best used for breadth and triage within human-in-the-loop workflows.
high descriptive arxiv
Re-Evaluating EVMBench: Are AI Agents Ready for Smart Contract Security? · Chaoyuan Peng, Lei Wu, Yajin Zhou · Mar 11, 2026
A new philosophical framework argues that neurotechnology and AI risk expanding capabilities while hollowing out lived experience unless designers prioritize 'temporal quality'; XChronos offers symbolic units to bridge first‑person temporality with neural readouts and to shape product, regulatory and valuation choices.
n/a theoretical openalex
XChronos and Conscious Transhumanism: A Philosophical Framework for Integrating Consciousness, Subjective Time, and Neurotechnology · JACONAAZAR SOUZA SILVA · Mar 11, 2026
Ibero‑American SMEs are likely to capture value from AI only when strategy, leadership, sensing and institutional links align; targeted managerial training, sensing subsidies and intermediary networks raise adoption and returns. The paper provides a structured framework—and eight testable propositions—for economists and policymakers to model diffusion, complementarities and heterogeneous returns among resource‑constrained firms.
n/a review_meta openalex
Beyond resource constraints: how Ibero-American SMEs leverage AI for competitive advantage through strategic capability development · Martin Sposato, Eduardo Carlos Dittmar · Mar 11, 2026
Academic ML intrusion-detection systems for IoT often report high detection rates in lab settings but rarely become production-ready. Heterogeneous devices, constrained compute/energy, dataset shortcomings and ML pipeline failures raise deployment costs and create commercial opportunities for firms that deliver lightweight, privacy-preserving, and operationally robust IDS solutions.
n/a review_meta openalex
International Journal on Cybernetics & Informatics · Vishal Karanam · Mar 11, 2026
AI-powered digital finance platforms expand access and cut costs in emerging markets, lifting efficiency and growth prospects; but without interoperable infrastructure and updated regulation they risk consumer harm, market concentration and systemic fragility.
n/a review_meta openalex
DIGITAL FINANCIAL ECOSYSTEMS AND FINANCIAL INCLUSION: AN INTEGRATED FRAMEWORK FOR SUSTAINABLE ECONOMIC GROWTH · Saydkhodjaeva Nigorakhon Ibaydullayevna · Mar 11, 2026
National‑security concerns yield three contrasting AI regimes: the EU enforces rights‑forward, risk‑based rules; Algeria pursues capacity and independence; Pakistan prioritizes economic governance while leaving military AI largely opaque. These regulatory gaps threaten market fragmentation, complicate export controls and procurement transparency, and heighten dual‑use risks.
low descriptive openalex
<b>Regulating AI in National Security: A Comparative Study of European, Algerian, and   Pakistani Approaches</b> · Ehsanullah Khan, Syed Rizwan Haider Bukhari, Tourkia Rebhi · Mar 11, 2026
The EU's Digital Omnibus could either deepen regulatory fragmentation or unlock harmonized rules for platform AI — much depends on design and institutional coordination; unclear boundaries with the DSA risk higher compliance costs and uneven enforcement that favor large incumbents.
low commentary openalex
The Digital Omnibus and the Future of EU Regulation: Implications for Policy Coherence and Enforcement · Alejandro Flores Moleon · Mar 11, 2026
Firms that invest in AI-skilled staff achieve measurably better tax planning and modest gains in after-tax returns; the benefits concentrate in complex organizations and where tax teams have strategic influence.
medium quasi_experimental openalex
The use of artificial intelligence in decision-making: evidence from the effectiveness of corporate tax strategies · Trent Krupa, Michele Mullaney · Mar 11, 2026
A reinforcement‑learning agent trained on the FRB/US macro model discovers fiscal strategies that raise simulated US GDP and lower unemployment across 2000–2024 versus standard FRB/US scenarios, but the improvements are earned inside the assumptions of the model and a growth‑focused objective.
low theoretical openalex
Fiscal Policy Towards Optimizing Macroeconomic Indicators by Integrating FRB/US with Reinforcement Learning · Quang Truong Dang, Huong Giang Hoang, Anh Son Ta · Mar 11, 2026
As AI takes over routine speed and pattern-matching, success should be redefined around distinctly human capacities—empathy, judgement and imagination—which firms should cultivate and reward rather than measured productivity alone.
n/a theoretical openalex
Deconstructing success: why being human still matters · Andrew Woods, Benton Jones · Mar 11, 2026
Higher education has ballooned while vocational and lifelong learning lag, creating a shortage of intermediate, work-based skills needed for effective AI adoption; without stronger vocational pathways and targeted adult training, older and lower‑skilled workers face unequal risks from automation.
low review_meta openalex
Balancing Higher Education, Vocational Training, and Lifelong Learning: Evidence on Skills Mismatch and Labor Market Outcomes · Ari Santoso, Arleiny Arleiny, Eka Wahyu Ardhi · Mar 11, 2026
Workers with clear career pathways, employer-sponsored reskilling and confidence in AI-related skills report markedly higher career optimism and stronger intentions to stay; by contrast, ambiguous progression and limited upskilling opportunities correlate with pessimism and turnover risk, suggesting investments in human‑centered learning infrastructures could ease AI-driven labor adjustments.
low correlational openalex
Leveraging Career Optimism to Enhance Employee Well-Being · Pamayla Darbyshire · Mar 11, 2026
GeoAI sharpens urban decision-making—improving risk maps, informal settlement detection and targeted policy evaluation—yet its benefits concentrate where data, technical capacity and governance are strongest. Without investment in open data, transparency standards and local capacity, GeoAI risks reinforcing rather than reducing urban inequalities.
n/a review_meta openalex
Advancing Urban Analytics: GeoAI Applications in Spatial Decision-Making and Sustainable Cities · Sorin Avram · Mar 11, 2026
Algorithmic controls are a double-edged sword for food-delivery riders: real-time tracking and punitive constraints heighten pressure, worsen mental health and raise risky riding, while standardized guidance reduces pressure and improves safety — effects depend on riders’ perceived autonomy.
low correlational openalex
Not all algorithmic controls are equal: the double-edged impact of algorithmic control dimensions on mental health and risky riding behavior among food delivery riders · Jinnan Wu, Wenqian Yang, Juan Qi, Wenpei Zhang · Mar 11, 2026
Enterprise risk management is linked to stronger finances, steadier revenues and easier credit for MSMEs, but only when risk practices are mature and genuinely integrated into decision‑making rather than applied as superficial compliance.
medium review_meta openalex
A Literature Review: Effect of Enterprise Risk Management (ERM) Implementation on Micro, Small and Medium Enterprises (MSMEs). · Ivan Malik · Mar 11, 2026