This paper proposes pAI-Econ-claude, a gated, human-in-the-loop multi-agent architecture for AI-assisted economic theory development, integrating inspectable intermediate records and targeted quality gates. [page::0] The design emphasizes interception at visible decision points, non-certifying gates, and irreversible human checkpoints to improve auditability without replacing formal verification. [page::0] In a matched-task evaluation across five economic-theory challenges, the gated workflow outperformed ungated baselines on four tasks, with substantial reductions in reported failure severity and higher usefulness. [page::0] A notable success occurred when a reality check rejected a false market-structure premise and a subsequent proof review corrected a welfare claim, while Task 1 shows limits where extra structure may compress key mechanisms. [page::0] The results support the claim that bounded gated oversight enhances interpretability and auditability of AI-assisted theory, with human judgment concentrated where irreversibility is highest. [page::0] The code and workflow are publicly available at the linked GitHub repository. [page::0]
This paper introduces a dynamic boarding group assignment framework for commercial aircraft, formulating the problem as a finite-horizon MDP and solving it with proximal policy optimization (PPO) using a CNN-based state encoder. The model integrates seat location, carry-on luggage declarations, and travel-companion constraints. In simulations across six layouts, the dynamic policy consistently outperforms strong static benchmarks (back-to-front with optimized group sizes) on both total boarding time and average individual boarding time, achieving up to 9.8% and 22.8% improvements respectively. The policy generalizes to varying load factors, companion distributions, and luggage loads, and a Pareto frontier is reported by sweeping the objective weight. These results suggest online dynamic grouping at check-in can meaningfully reduce turnaround time while improving passenger experience [page::6][page::7].
Digital search is undergoing a fundamental transformation from a human-driven process of discovery to an agent-mediated system of delegated decision-making. In an AI-native world, users articulate goals in natural language, agents interpret these intents, and outcomes are delivered as recommendations or executed decisions. This shift redefines search from a visible list of links to an embedded system component that governs information access, trust, incentives, and market structure. The paper identifies four structural shifts—ranked lists to delegated decisions, attention economy to preference economy, evaluating performance to verifying delegation, and retrospective study to prospective research design—and argues that the future of search will be determined by open, transparent, and competitive agentic systems that regulate how decisions are made and markets operate. Early experimental evidence from agent-mediated marketplaces and theoretical work suggest that small design choices in information access, option surface, and action execution have first-order effects on efficiency, competition, and welfare for both consumers and firms. The authors emphasize the need for agent-native infrastructure, auditable signals of quality, and governance mechanisms to sustain trust and competition over time [page::0]
本研究通过高时空分辨的欧洲能源系统模型在严格的化石气供应上限下对各部门进行耦合优化,发现降低进口依赖在经济上具有可行性:通过替代发电、低温工业热和建筑热,欧洲有望在2030–35年前将天然气消费减半至约200 bcm/年,与欧洲生产能力相匹配。但独立供应并不能完全保护消费者免受全球天然气价格波动影响,因为 gas 在边际电价形成中的作用仍然显著,需辅以其他政策工具来对冲价格冲击 [page::0]
This paper introduces an occupation-level execution share by scoring 19,265 O*NET task statements under fixed execution and AI-capability rubrics, enabling a direct comparison with AI capability and linking to employment trends. The execution share is shown to be reproducible across model coders and O*NET vintages, distinct from AI-capability and routine-task intensity, and adds only modest incremental power beyond existing evaluation measures. In harmonized panels, employment growth is lower in execution-heavy white-collar occupations across multiple periods, with a secular, between-family gradient rather than a pure AI-era shock; the vintage-valid capability gradient steepens after 2022, though the change is dated and not causally attributed to AI. The results establish a workable measure and a chronology for potential labor-market effects, emphasizing measurement framework and future causal-testing needs rather than asserting a causal AI-driven decline.
We provide a large-scale empirical audit of DEX routing using 2.98 million WETH–USDC swaps on Ethereum. We compare realized routes with optimized benchmarks, finding an average shortfall of 2.02 bps per trade, amounting to about \$24 million on roughly \$120 billion of volume. We introduce three reproducible optimal benchmarks—SCO (activated pools only), FVO (all pools, no gas), and G-FVO (gas-aware)—and a scalable bisection-based routing algorithm to compute these benchmarks. The study highlights two regularities: information timeliness critically affects routing outcomes, and inefficiency is heavy-tailed with a few extreme outliers driving most of the dollar losses. Sandwiching attacks emerge as a concrete channel for routing sub-optimality. The framework provides a rigorous, reproducible basis for timing-aware, gas-aware routing improvements [page::1]