Papers (1699)

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AI partners match human partners on short virtual retail tasks, but whether they help depends on worker emotions; perceived service empathy drives how emotion translates into collaboration performance and interacts with partner type.

medium rct Summary semantic_scholar

Adoption of AI partners in temporary tasks: exploring the effects of emotion on collaboration proficiency · Xiaodong Li, Jingwen Duan, Shanshan Zhang, Ai Ren · Fetched Mar 18, 2026

AI’s employee dividend is finite: moderate AI adoption raises firms’ employee-focused CSR, but once AI is deeply embedded managerial attention shifts toward systems and employee-oriented CSR falls. Industry automation risk accelerates the decline, while diverse leadership and employee ownership delay and soften it.

medium correlational Summary semantic_scholar

Attention to Whom? AI Adoption and Corporate Social Responsibility Toward Human Employees · J. Lyu, Shi-Ye Wang, Chenhao Hu · Fetched Mar 18, 2026

AI can serve as a universal collaboration layer—using multilingual models, multimodal inputs and autonomous agents to mediate intent and execution—and thereby reduce coordination frictions, expand participation and raise productivity across distributed teams; however, the claim is currently theoretical and requires targeted empirical validation.

low theoretical Summary semantic_scholar

AI as a universal collaboration layer: Eliminating language barriers in global teams · Raju Bandaru · Fetched Mar 18, 2026

Trust in AI is not an individual attitude but a socially negotiated, situational process embedded in team routines, with transparency, role clarity and feedback determining whether AI is used as an augmenting complement or an overlooked substitute. Poorly calibrated trust reshapes oversight, erodes informal coordination and shifts organizations toward metric-driven evaluations—so design, training and governance matter as much as algorithmic accuracy.

low descriptive Full text semantic_scholar

AI in project teams: how trust calibration reconfigures team's collaboration and performance · Viraj Dawarka, M. Doargajudhur, Vincent Dutot · Fetched Mar 18, 2026

Treat AI as a partner, not an automaton: symbiarchic leadership sets out four practical leader behaviours and HR reforms that let firms realise AI productivity gains while maintaining oversight, trust and employee welfare; the framework is actionable but awaits empirical validation.

n/a theoretical Summary semantic_scholar

Symbiarchic leadership: leading integrated human and AI cyber teams · Jonathan Passmore · Fetched Mar 18, 2026

AI is reconfiguring jobs more than it is eliminating them: stronger AI adoption correlates with major skill upgrades and a surge in hybrid human–AI roles, while job losses concentrate in Manufacturing and Retail and employment expands in Healthcare and IT Services. Robust reskilling programs and governance frameworks materially tilt outcomes toward complementarity and employment growth.

low review_meta Full text semantic_scholar

AI-Driven Transformation of Labor Markets: Skill Shifts, Hybrid Employment, and Governance · Amit K Mogal, Trupti M Pagar, Arjun K Mahale · Fetched Mar 18, 2026

An AI-driven matching pilot in Nairobi improved job matches and raised reported short-term wages for participating youth by dynamically inferring skills and live demand. The findings are promising but preliminary: the pilot is observational and needs larger, randomized or better-controlled studies to establish causality.

low quasi_experimental Summary semantic_scholar

AI-Driven Skill Mapping and Gig Economy Matching Algorithm for Youth Employment within Nairobi's Informal Sector · David Shiala Ongoma · Fetched Mar 18, 2026

Modern AI models — especially ensembles and deep neural networks — predict employee performance more accurately than traditional statistical methods across several public workplace datasets; gains generalize across companies and hinge on engagement, learning agility, tenure and workload signals, suggesting measurable upside for HR decision‑making if firms manage bias and privacy risks.

high descriptive Full text semantic_scholar

Adoption of AI-Based HR Analytics and Its Impact on Firm Productivity, Employment Structure and Wage Dispersion: Evidence from Workforce Data · Richa Sharma, Dr. Neeraj Gupta · Fetched Mar 18, 2026

Firms often use AI as a pretext for cost-cutting driven by short-termist managers and incentive structures; unchecked, such layoffs can erode demand and trigger further automation. Policymakers should consider a phased shortening of the standard workweek—paired with conditional supports—to stabilize employment, sustain consumer purchasing power, and distribute productivity gains more equitably.

low commentary Summary semantic_scholar

A Shorter Workweek as a Policy Response to AI-Driven Labor Displacement: Economic Stabilization in the Age of Automation · Jonathan H. Westover · Fetched Mar 18, 2026

AI is reshaping Albania’s job map rather than causing mass layoffs: routine service and administrative roles are most exposed to displacement, while other occupations gain from AI-enabled productivity; employers now prioritize digital literacy, basic data skills and stronger communication, leaving younger and less-educated workers most vulnerable.

medium correlational Full text semantic_scholar

The AI Transition: Assessing Vulnerability and Structural Reform in Albania’s Labor Market · Bora Bimbari · Fetched Mar 18, 2026

Russia should replace passive AI monitoring with a state-led 'economic cybernetics' system—a National Data Management System running a dynamic intersectoral balance model—to actively steer digitalization for productivity and partial technological sovereignty. The approach promises coordinated gains but raises acute risks: centralized control can deepen social stratification, displace workers, and concentrate data-power absent strong safeguards.

low theoretical Summary semantic_scholar

DIGITAL TRANSFORMATION OF THE RUSSIAN FEDERATION’S SOCIOECONOMIC INFRASTRUCTURE: FROM RISK ANALYSIS TO PROACTIVE MANAGEMENT BASED ON ECONOMIC CYBERNETICS · M. V. Buzmakova · Fetched Mar 18, 2026

A physics-informed offline RL routing system cuts mean voyage CO2 by about 10% and slashes extreme fuel-waste events ninefold in simulated Gulf of Mexico transits, while remaining robust to forecast uncertainty.

medium other Full text arxiv

Physics-informed offline reinforcement learning eliminates catastrophic fuel waste in maritime routing · Aniruddha Bora, Julie Chalfant, Chryssostomos Chryssostomidis · Mar 18, 2026

Provincial AI deployment and tougher environmental rules are linked to lower local circular-economy efficiency in China, though AI’s benefits spill across borders; combined, AI and regulation crowd each other out, implying that smarter, coordinated green governance is needed.

low correlational Full text openalex

How artificial intelligence and environmental regulation influence the efficiency of the circular economy in the context of sustainable development · Yanfeng Guan, Junding Yang, Tong Zhao, Yihong Shan, Yiling Zhu, R. L. Wang · Mar 18, 2026

China’s ageing drag on growth can be blunted where industrial-robot adoption is high; provinces that cross a robot-penetration threshold see much smaller negative effects of ageing on GDP growth, suggesting automation can partly offset demographic headwinds.

low correlational Full text openalex

Nonlinear effects of ageing population and AI on China’s GDP growth: a threshold analysis · Jintao Shi, Jain Yassin, Raina Ginsad, Tongtong Zhang · Mar 18, 2026

AI at work has a double edge: among finance professionals it raises autonomy-driven innovation but also fuels technostress and disengagement; visible, supportive leadership around AI strengthens the upside while blunting the downside.

medium correlational Full text openalex

Autonomous enhancement or emotional depletion? The dual-path mechanism of AI usage on employees’ work behavior · Daokui Jiang, Yaru Liu, Xuan Gu · Mar 18, 2026

Adaptive reinforcement-learning pricing agents can track or outperform rule-based pricing in simulated volatile markets, keeping revenue/price performance within roughly 20% of baselines; the approach looks promising for real-time optimization but lacks field validation and robustness checks.

low descriptive Full text openalex

The Application of Adaptive Reinforcement Learning in Dynamic Pricing Strategies · Lei Li · Mar 18, 2026

Semiconductor rivalry is less a new Silicon-era order than a logistics-driven reinvention of U.S. dominance: control of supply-chain chokepoints and infrastructure sustains geopolitical power. The so-called 'Pax Silica' is fragile—hardware dependencies and networked control, not chip design alone, will shape technological governance and global stability.

n/a theoretical Full text openalex

The Logistics of Hegemony: Semiconductor Chokepoints, Global Production Networks, and the Emergence of 'Pax Silica' · Salem Y. Lakhal · Mar 18, 2026

AI adoption in China correlates with better energy justice on balance, but gains are uneven: richer eastern provinces reap most benefits while distributional fairness can worsen initially; stronger environmental rules and better digital infrastructure amplify positive effects.

medium correlational Full text openalex

Artificial intelligence adoption for advancing energy justice: a multidimensional perspective · Yong Ye, Tingye Huang, Ziyi Shi, Yixuan Luo, Xiaojun Zhang · Mar 18, 2026

AI governance is fragmented and often stuck at high-level ethics or compliance checklists rather than operational controls; synthesizing 95 studies, this paper proposes a policy-ready risk-tiering model that maps ethical principles to auditable technical safeguards for public services and critical infrastructure.

n/a review_meta Summary openalex

AI Governance Risk Tiering for Sustainable Digital Infrastructure: A Systematic Review of Cybersecurity Frameworks · Orjuwan Albulayhi, Ali Alkhalifah · Mar 18, 2026

Chinese listed firms that deploy AI experience materially less executive misconduct and smaller regulatory penalties, alongside lower borrowing costs and higher productivity. The authors attribute these governance and economic gains to AI’s role in tightening internal controls, exposing financial risk, and improving external monitoring.

medium quasi_experimental Summary openalex

The risk-mitigation effects of artificial intelligence adoption: Evidence from executive misconduct · Jie Wu, Siyuan Tang, Ziwei Zhou, Zexuan Qi · Mar 18, 2026

Formalizing developer intent into checkable specifications is the linchpin for reliable AI-generated code; without it, abundant code risks being incorrect. The paper sets out a research agenda—specification validation, interactive test-driven workflows, and verified synthesis—to make AI coding tools dependable in practice.

n/a review_meta Full text arxiv

Intent Formalization: A Grand Challenge for Reliable Coding in the Age of AI Agents · Shuvendu K. Lahiri · Mar 17, 2026

A new MessyKitchens dataset and a Multi-Object Decoder sharply improve single-image multi-object 3D reconstruction—cutting registration errors and object interpenetration across benchmarks; by lowering perception cost and improving physical plausibility, the open release could accelerate robotics, AR/VR and related automation deployments.

medium descriptive Full text arxiv

MessyKitchens: Contact-rich object-level 3D scene reconstruction · Junaid Ahmed Ansari, Ran Ding, Fabio Pizzati, Ivan Laptev · Mar 17, 2026

Parallel Newton algorithms unlock sequence-level parallelism and cut runtime and memory for RNN and MCMC workloads when dynamics are contracting; however, a model's Largest Lyapunov Exponent precisely predicts whether such parallelization will yield real speedups, limiting gains for chaotic or expanding dynamics.

medium theoretical Full text arxiv

Unifying Optimization and Dynamics to Parallelize Sequential Computation: A Guide to Parallel Newton Methods for Breaking Sequential Bottlenecks · Xavier Gonzalez · Mar 17, 2026

A feedback-grounded training method meaningfully boosts long-horizon LLM-agent performance—raising Pass@k up to 14% versus strong baselines—by teaching models how to recover from failures rather than only optimizing final success. The approach increases sample efficiency, implying firms can extract more performance from the same interaction logs and reduce marginal interaction costs.

medium other Full text arxiv

Internalizing Agency from Reflective Experience · Rui Ge, Yichao Fu, Yuyang Qian, Junda Su, Yiming Zhao, Peng Zhao, Hao Zhang · Mar 17, 2026

A 7B model fine-tuned with reinforcement learning produces slide decks nearly as good as a top-tier system (91.2% of Claude Opus 4.6) and improves 33% over its untuned variant, suggesting targeted alignment and tooling deliver large returns that scaling alone would not.

medium descriptive Full text arxiv

Learning to Present: Inverse Specification Rewards for Agentic Slide Generation · Karthik Ragunath Ananda Kumar, Subrahmanyam Arunachalam · Mar 17, 2026

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