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Large language models often treat stored user preferences as global rules rather than context‑dependent signals, leaking or applying preferences in third‑party contexts; stronger personalization improves correct tailoring but also raises harmful misapplication, and prompt‑based fixes only partially mitigate the problem.
medium descriptive arxiv
BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs · Sangyeon Yoon, Sunkyoung Kim, Hyesoo Hong, Wonje Jeung, Yongil Kim, Wooseok Seo,... · Mar 17, 2026
Limit, label and let users contest: the paper argues that LLM-powered robots allocating scarce, real-time help should use a governance-vetted menu of prioritization modes, clearly communicate which mode is active, and offer an easy contest pathway; this reduces hidden value skews and ad-hoc user configuration but requires governance, adjudication capacity, and careful design to avoid new inequities.
n/a descriptive arxiv
Designing for Disagreement: Front-End Guardrails for Assistance Allocation in LLM-Enabled Robots · Carmen Ng · Mar 17, 2026
Time-series foundation models trained on low-frequency data stumble on millisecond-scale 5G network dynamics, even after fine-tuning. Firms that collect and pretrain on high-resolution telemetry will have a practical edge in latency-sensitive services, potentially concentrating rents among incumbents.
medium descriptive arxiv
Bridging the High-Frequency Data Gap: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models · Subina Khanal, Seshu Tirupathi, Merim Dzaferagic, Marco Ruffini, Torben Bach Ped... · Mar 17, 2026
A strategy-execution split stabilises LLM agents on simulated retail operations but fails under real-world-scale complexity: ESE improves operational stability and efficiency on RetailBench, yet performance collapses as non-stationarity and multi-factor interactions rise, implying current LLMs remain fragile for fully autonomous long-horizon commercial management.
medium descriptive arxiv
RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments · Linghua Zhang, Jun Wang, Jingtong Wu, Zhisong Zhang · Mar 17, 2026
A European-focused sparse model claims 8B–16B-level performance with far less compute: EngGPT2’s MoE design promises large reductions in training data and inference energy, potentially lowering barriers for EU adopters—though the model release omits key benchmarking and compute details needed to verify those savings.
low descriptive arxiv
EngGPT2: Sovereign, Efficient and Open Intelligence · G. Ciarfaglia, A. Rosanova, S. Cipolla, J. Bartoli, A. Di Domenico, C. Fioroni, ... · Mar 17, 2026
SlideFormer enables fine‑tuning 100B‑plus models on a single consumer GPU, boosting throughput by 1.4–6.3× and roughly halving memory needs; by lowering the hardware barrier it could democratize model specialization and shift demand away from multi‑GPU clusters.
medium descriptive arxiv
An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU · Ruijia Yang, Zeyi Wen · Mar 17, 2026
Focusing model training on explicit prohibitions and dispreferred examples can yield safer, more stable behavior than preference-based RLHF because constraints are easier to verify and falsify; if correct, this could shift labeling budgets toward 'constraint datasets', reshape demand for human feedback labor, and concentrate power in curators of rule libraries.
medium theoretical arxiv
Via Negativa for AI Alignment: Why Negative Constraints Are Structurally Superior to Positive Preferences · Quan Cheng · Mar 17, 2026
Qatar's Fanar 2.0 builds an Arabic‑first generative AI stack on 256 H100 GPUs using a curated 120B‑token corpus and targeted continual pre‑training/model‑merging, reporting double‑digit benchmark gains while using far less pre‑training than its predecessor. The project shows language‑specific, quality‑focused strategies can be a cost‑effective alternative to the global scale arms race, enabling sovereign control and niche competitiveness for underrepresented languages.
medium descriptive arxiv
Fanar 2.0: Arabic Generative AI Stack · FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad, Minhaj Ahmad, Abdulaziz Al-Hom... · Mar 17, 2026
Exchanging generative modules, not model weights, lets siloed clients recover centralized accuracy in one communication round and supports exact deletion of a client's contribution. The method reduces reliance on pretrained foundation models, lowering coordination costs and creating opportunities (and regulatory questions) for marketplaces in certified generative priors.
medium other arxiv
FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios · Andrea Moleri, Christian Internò, Ali Raza, Markus Olhofer, David Klindt, Fabio ... · Mar 17, 2026
India's solar boom is polarizing employment: mid-level operations roles are shrinking as high-skilled AI and data positions rise, while low-wage installation jobs expand—raising concerns about regional inequality and the need for targeted re-skilling.
medium review_meta semantic_scholar
Job Polarization in Solar Power Plants: A Systematic Literature Review · U. Srividya, U. Krishna · Fetched Mar 17, 2026
Firms that install AI products patent more and patent better — their inventions are more cited, more original and span new technological domains — and these innovation gains translate into higher R&D, productivity and market value, implying roughly a 1.5% boost to aggregate TFP.
medium quasi_experimental semantic_scholar
AI and Productivity: The Role of Innovation · Viral V. Acharya, Xuehua Shao, Zhaoxia Xu · Fetched Mar 17, 2026
AI is accelerating pharmaceutical R&D in pilots—claims include cutting discovery timelines from years to weeks, slashing screening time and improving patient-selection accuracy—but the evidence is fragmented, often proprietary, and constrained by data bias, interpretability gaps and regulatory ambiguity.
n/a review_meta semantic_scholar
THE AI REVOLUTION IN PHARMACEUTICALS: INNOVATIONS, CHALLENGES, AND FUTURE PROSPECTS – AN OVERVIEW · R. Vignesh, M. S. Umashankar, Damodharan Narayanasamy · Fetched Mar 17, 2026
AI adoption helps SME founders spot market opportunities and speeds product innovation while automating routine work, but benefits are uneven and accompanied by displacement and access concerns.
low correlational semantic_scholar
Entrepreneurship in the Era of Artificial Intelligence: Redefining Opportunity Recognition, Labour Substitution, and Innovation Processes · Muhammad Sarfraz Latif, Mansoor Ahmed Soomro, Asif Ahmad, Muhammad Irfan Syed · Fetched Mar 17, 2026
Integrated AI across planning, execution and performance management is linked to measurable gains in supply‑chain forecasting, efficiency and responsiveness; execution capabilities matter most and performance‑management systems strengthen the planning-to-operations pathway.
low correlational semantic_scholar
Smart Supply Chain Ecosystems: Artificial Intelligence Enabled Integration of Planning, Execution, and Performance Management · Zujaj Ahmed, Jauhar Abbas, Ahsan Hussain · Fetched Mar 17, 2026
AI-enhanced ESG analytics appear to boost equity portfolio performance and resilience compared with traditional ESG ratings, delivering higher returns and lower downside risk in the reported backtests; however, the evidence is associative and hinges on proprietary model choices and backtest assumptions.
low correlational semantic_scholar
Green Intelligence in Finance: Artificial Intelligence-Driven ESG Analytics and Sustainable Investment Performance · Hind Gatoi, Islam Belhaoua, Kashf Akhtar, Miqdad Qadir, Nida Mohammad, Muhammad ... · Fetched Mar 17, 2026
AI-powered FinTech speeds cross-border payments and trims trade transaction costs, materially raising international trade efficiency; gains come mainly from better credit evaluation and fraud monitoring that lower uncertainty in trade finance.
low correlational semantic_scholar
Artificial Intelligence in FinTech and Its Implications for International Trade Efficiency · Ali Raza, Muhammad Ali · Fetched Mar 17, 2026
Small businesses in underserved US communities that report higher AI adoption also report substantially better financial literacy, decision-making and higher profit growth (9.5% vs 5.8%), but gains are curtailed by low training uptake, uneven education and demographic barriers.
low correlational semantic_scholar
The role of artificial intelligence in enhancing financial literacy, decision-making and growth, for small businesses in underserved communities · Gifty Akuffo · Fetched Mar 17, 2026
Generative AI has not produced broad job or wage losses yet, but demand for automatable and entry-level roles has fallen while AI-complementary occupations are growing; the net long-term impact will depend on policy, reskilling, and governance.
medium review_meta semantic_scholar
The Impact of Generative AI on the Future of Employment: Opportunities and Challenges · Devansh · Fetched Mar 17, 2026
AI and digital transformation are remaking human capital from static credentials into dynamic learning ecosystems; continuous, data-driven reskilling — coordinated across schools, firms and government — is essential to reduce disruption and preserve competitiveness.
n/a descriptive semantic_scholar
EDUCATIONAL AND PROFESSIONAL STRATEGIES FOR PREPARING HUMAN CAPITAL FOR DIGITAL TRANSFORMATION · Artem Horilyi, Nataliia Ivanova · Fetched Mar 17, 2026
State-of-the-art LLMs sharply boost human task performance—GPT‑4o increases accuracy by ~29 percentage points and Llama‑3.1‑8B by ~23—yet these gains hinge on a distinct collaborative ability rooted in perspective-taking, not just raw problem‑solving skill.
medium correlational semantic_scholar
Quantifying and Optimizing Human-AI Synergy: Evidence-Based Strategies for Adaptive Collaboration · Jonathan H. Westover · Fetched Mar 17, 2026
An AI ensemble that alternates between a trust-building specialist and a performance-focused specialist improves human decision accuracy more than single-model assistants; a simple, provably near-optimal routing rule decides which specialist to use based on context.
medium other semantic_scholar
Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration · H. Amin, Ming Yin, Rajiv Khanna · Fetched Mar 17, 2026
Widespread AI use is producing 'AI brain fry'—employees facing cognitive overload report higher turnover intentions and falling productivity. Employers must redesign work systems, training and governance to make AI a productivity enhancer rather than a drain on human attention.
medium review_meta semantic_scholar
When AI Assistance Becomes Cognitive Overload: Understanding and Managing "Brain Fry" in the Modern Workplace · Jonathan H. Westover · Fetched Mar 17, 2026
A new Trust–Complementarity model argues organisations make better knowledge‑intensive decisions when calibrated trust in AI is paired with complementary human–machine capability use, creating feedback loops that amplify learning; the framework gives executives levers (training, metrics, team design) but remains untested empirically.
n/a theoretical semantic_scholar
Optimising Human– AI Decision Performance: A Trust and Capability Framework for Knowledge Management · Eduardo Carlos Dittmar, Martin Sposato · Fetched Mar 17, 2026
Demand for AI skills in knowledge work has surged since ChatGPT—appearing in 27.8% of job listings and linked to a 17.7% advertised wage premium—while adoption varies from 43.2% in high-tech to 9.7% in the public sector. Conventional appraisal systems miss hybrid human–AI capabilities, so the paper proposes a three-part performance framework (AI Tool Mastery, Collaborative Work Quality, Human–AI Synergy) for firms redesigning work.
low correlational semantic_scholar
Reconstruction of knowledge worker performance evaluation system in the ChatGPT era: an exploratory study based on human-AI collaborative work model · Zhixin Yu, Zhicheng Yu · Fetched Mar 17, 2026
Divergent depths of strategic reasoning among AI agents undermine teamwork, but agents that infer and adapt to partners’ Theory-of-Mind depth recover most coordination losses across matrix, navigation and Overcooked tasks.
medium descriptive arxiv
Adaptive Theory of Mind for LLM-based Multi-Agent Coordination · Chunjiang Mu, Ya Zeng, Qiaosheng Zhang, Kun Shao, Chen Chu, Hao Guo, Danyang Jia... · Mar 17, 2026