arXiv subjects

Yi Fan

Publications and source records attributed to Yi Fan.

At least 19 recordsLinked to original sources

arXiv2026

Delayed Retirement, Intergenerational Transfers, and Fertility

Population aging and fertility decline are occurring simultaneously worldwide, straining labor supply and pension systems. China began a gradual delayed-retirement (GDR) reform in 2025, raising statutory retirement ages on different schedules for men, female cadres, and female workers across birth cohorts. To evaluate its effects on older individuals and within the family, we develop a model of older adults' retirement, intergenerational transfers, and adult children's fertility decisions, with parameters estimated via fuzzy regression discontinuity at the pre-2025 statutory retirement ages. Counterfactual simulations show that, for mature reform cohorts, the GDR reform increases older employment and monetary transfers to adult children but reduces grandparental childcare. Because the reduction in childcare outweighs the increase in monetary transfers, fertility and older adults' welfare decline, with particularly large losses among lower-income women. Fully flexible retirement, mandatory partial retirement, and mandatory full-time work do not raise employment without similarly reducing fertility and welfare. Childcare subsidies can offset the declines in fertility and welfare, while restricting eligibility to women or lower-income women requires higher subsidy rates but reduces average fiscal expenditure. These findings suggest that evaluations of retirement policy should consider not only its direct effects on older adults' labor supply, but also its fertility effects through intergenerational transfers; childcare support can help mitigate these negative intergenerational effects of delayed retirement.

econ.GN↗

arXiv2026

Testing Black Holes with Interstellar Missions: I. Orbiting Probes

Recently, we showed that the possibility of an interstellar mission to the closest black hole, while highly speculative and extremely challenging, is not completely unrealistic within the next few decades. Since such a mission might last around a century and require significant financial and human resources, it is crucial to assess whether it can truly study black holes and test General Relativity at levels unattainable by observational facilities in the Solar System for many years. In this manuscript, we assume the capability to decelerate the spacecraft and present a preliminary study of how probes orbiting a black hole could test the nature of the compact object.

gr-qc↗

arXiv2026

Testing Black Holes with Interstellar Missions: II. Flyby Probes

Recently, we demonstrated that while an interstellar mission to the nearest black hole remains highly speculative and extraordinarily challenging, it is not entirely implausible within the coming decades. Given that such a mission would likely take about a hundred years and require substantial financial and human investment, it is essential to assess whether it could investigate black holes and test General Relativity to a degree that cannot be achieved by Solar System observatories for the foreseeable future. In Paper I, we assumed the capability to decelerate the spacecraft and presented a preliminary study of how orbiting probes could test the nature of the compact object. In this second paper, we study how the black hole can be tested without decelerating the spacecraft, using flyby probes.

gr-qc↗

arXiv2026

Machine learning modularity

Based on a transformer based sequence-to-sequence architecture combined with a dynamic batching algorithm, this work introduces a machine learning framework for automatically simplifying complex expressions involving multiple elliptic Gamma functions, including the $q$-$θ$ function and the elliptic Gamma function. The model learns to apply algebraic identities, particularly the SL$(2,\mathbb{Z})$ and SL$(3,\mathbb{Z})$ modular transformations, to reduce heavily scrambled expressions to their canonical forms. Experimental results show that the model achieves over 99\% accuracy on in-distribution tests and maintains robust performance (exceeding 90\% accuracy) under significant extrapolation, such as with deeper scrambling depths. This demonstrates that the model has internalized the underlying algebraic rules of modular transformations rather than merely memorizing training patterns. Our work presents the first successful application of machine learning to perform symbolic simplification using modular identities, offering a new automated tool for computations with special functions in quantum field theory and the string theory.

hep-th↗

arXiv2026

Bayes-Sufficient Compression Is Not Enough: How Does Communication Help Multi-Agent Systems?

Multi-agent LLM systems pair a sender with broad context and an executor with a limited local view. We study when a short message improves the executor's next decision, when raw context is preferable, and when a stronger sender helps. Our framework, \emph{receiver-relative bounded coordination}, expresses message utility as receiver gain minus protocol tax. Compression beats raw context when tax savings exceed losses from omitted information and decoder mismatch. Even \emph{Bayes-sufficient} compression can fail when a bounded executor cannot use its surface form. A three-stage decomposition separates externalization, absorption, and \emph{action closure}, explaining how errors remain after the correct content reaches the receiver. Under a single-crossing condition, sender upgrades help above a receiver-burden threshold. Across six benchmarks, the same Qwen protocol raises ContextBench joint accuracy from $0.633$ to $0.775$ but lowers ToolSandbox from $0.889$ to $0.653$. Fixed-message replay reveals closure failures despite correct artifact recovery. These results guide an inference-time selector that improves the accuracy-cost frontier on the evaluated communication regimes.

cs.LG↗

arXiv2026

Bricks or Cash? Externalities of Housing Upgrading in High-density Cities

We estimate housing externalities in a high-density city, exploiting the staggered rollout of Singapore's nationwide Main Upgrading Programme for public housing. Controlling for nonrandom neighborhood exposure, we find that upgrading raises treated buildings' prices by 11.5% upon completion and neighboring buildings' resale prices by about 2% within 500 meters, decaying to zero beyond. A model with distance-decaying externalities shows that in dense settings spillovers justify the distortions of in-kind provision; this advantage diminishes and reverses at lower densities. Administrative data on over 2 million residents show that upgrading disproportionately retains older incumbents, suggesting age-specific amenities as an underexplored externality channel.

econ.GN↗

arXiv2026

A Tale of Two Cities: The Announcement Effect of Northern Metropolis Plan

Using the October 2021 announcement of Hong Kong's Northern Metropolis Plan as a quasi-natural experiment, we examine its impacts on households, firms, and demographics. Leveraging 100,576 housing transactions, 4.7 million consumption records, and district-level demographic data, difference-in-differences estimates show housing prices in the treated region rose by 3.9% within one year, alongside a 4.1% increase in consumption. Firm entry expanded, with inflows of higher-income, higher-educated households. We find a mild, lagged spillover to Shenzhen's housing market but no significant firm response. While the policy narrows cross-region inequality, it increases within-region inequality and raises affordability concerns for lower-SES households.

econ.GN↗

arXiv2026

Intergenerational Mobility in China

In this chapter, we provide a synthesis of empirical work on the intergenerational mobility in China, examining how much a child's success depends on their parents' success. We start with an overview of data sources and measure of intergenerational mobility in China's context, followed by discussion on the empirical findings in five categories of intergenerational mobility: income, education, social class, wealth, and health. Major channels of transmission of socioeconomic status across generations are investigated, including human and social capital, fertility and migration decision, housing price and trade liberalization in the market transition, and belief. Finally, we provide policy implications to promote upward mobility for children born to less advantaged parents in China's context.

econ.GN↗

arXiv2026

Power Up Consumption: Impact of Electric Vehicle Infrastructure on Household Finance

To achieve carbon neutrality, electrifying vehicles in a cleaner electricity grid has become a key pathway for global cities. While extensive research has examined the environmental benefits of electric vehicles (EVs), their financial impacts remain underexplored. Using Singapore-a smart city-state - as a social laboratory, we combine high-resolution data on the geographic distribution of EV chargers with nearly 1 million individual-level monthly expenditure records from a major commercial bank and about 65,000 housing transaction records, spanning January 2019 to March 2021. Our results show that proximity to charging stations during the initial phase of EV infrastructure rollout significantly increases household consumption by 1-3%. This effect is driven primarily by public housing residents, who are generally more financially constrained than their private housing counterparts. We identify three channels underlying this increase in consumption: appreciation in housing wealth, catalysts for commercial activity and discretionary spending around EV infrastructure, and budget reallocation from fuel expenses to other consumption categories. Notably, hybrid car owners exhibit the strongest trade-offs-reducing fuel spending while increasing expenditure on entertainment or necessity goods among public housing residents. Overall, our findings demonstrate that green infrastructure can shape economic behavior and enhance consumer welfare, extending its impact beyond environmental objectives.

econ.GN↗

arXiv2026

Retrieval-Constrained Policy Optimization for Attack Technique Extraction from Cyber Threat Intelligence

Mapping cyber threat intelligence (CTI) text to MITRE ATT&CK techniques is essential for structured threat analysis, yet manual annotation is costly and does not scale. The ATT&CK taxonomy comprises several hundred attack techniques, and a single CTI passage may describe multiple techniques, making accurate and complete extraction challenging. Existing automated approaches fall short in different ways: multi-label classifiers struggle with severe class imbalance and the large label space, while LLM-based methods--retrieval pipelines and fine-tuned generators--optimize token-level objectives that treat technique annotation as sequence generation rather than set prediction, lacking direct supervision on whether the predicted technique set is correct and complete. We propose TTP-R1, a two-stage framework that combines retrieval-augmented supervised fine-tuning (SFT) with reinforcement learning using verifiable rewards (RLVR). A hybrid retriever first narrows the large label space to a candidate set, and a fine-tuned LLM learns to select the correct techniques. We then apply Group Relative Policy Optimization with a decomposed reward that directly supervises the precision, recall, and output format of the predicted technique set. Across four CTI benchmarks, TTP-R1 achieves the best average F1, improving sub-technique-level F1 by 7.4 percentage points over Claude Sonnet 4.5 with retrieval augmentation, while running 28x faster when served as an 8B-parameter model on a single GPU.

cs.CR↗

arXiv2026

Embedded quantum computing for many-body surface reaction

Predictive simulations of catalytic interfaces require correlated electronic-structure treatments that describe localized chemical transformations while retaining the influence of the extended metallic environment. We introduce QC-DFET, a quantum-computing density-functional embedding framework that maps surface-reaction active spaces to compact, environment-aware qubit Hamiltonians. A reaction-consistent active-space protocol preserves orbital continuity along reaction coordinates, while quantum-selected configuration interaction based on measurements from the Zuchongzhi superconducting quantum processor and strongly contracted perturbation theory capture static and dynamic correlation. On Cu(111), QC-DFET treats active spaces up to 28 qubits and is validated through a hierarchy of experimentally constrained surface-chemistry challenges. H2 dissociation/desorption tests balanced bond breaking and recombination barriers, CO adsorption tests site selectivity and metal-adsorbate bonding, and formate hydrogenation tests competing hydrogenation branches with different kinetic and thermodynamic signatures. Across these cases, QC-DFET reproduces bidirectional H2 barriers, recovers the observed top-site preference and adsorption strength of CO, and reconciles the experimentally benchmarked H2COO* reverse barrier with the lower forward barrier to HCOOH*. These results establish embedded quantum computing as a practical route to correlated surface-reaction energetics.

quant-ph↗

arXiv2026

A High-Performance Pauli-Algebra Framework for Large-Scale Quantum Simulations

Efficient manipulation of Pauli-algebraic objects is a key bottleneck in the classical emulation and benchmarking of quantum algorithms for chemistry and many-body physics. This bottleneck appears in Hamiltonian construction, variational ansatz preparation, expectation-value and gradient evaluation, and real-time propagation, all of which require repeated Pauli-algebra operations. Here, we present a high-performance Pauli-algebra framework tailored to quantum many-body and quantum-chemical simulations. The framework combines compact binary symplectic encoding, canonical coefficient reduction, and grouped sparse operator representations that exploit shared bit-flip patterns among Pauli strings. The resulting Julia/C\texttt{++} implementation accelerates Pauli multiplication, Hamiltonian construction, and operator--state multiplication in sparse and symmetry-adapted many-electron spaces. Benchmarks demonstrate efficient Hamiltonian construction, large-active-space VQE and ADAPT-VQE calculations, and real-time variational dynamics on modern multicore CPU and GPU architectures. These results show that structure-aware Pauli-algebra engines provide a scalable classical backend for developing and benchmarking quantum algorithms in quantum chemistry and many-body simulation.

quant-ph↗

arXiv2026

The Price of Quietness: How a Pandemic Affects City Dwellers' Response to Road Traffic Noise

Using the outbreak of COVID-19 in Singapore as a quasi-natural experiment, we investigate tenants' changing responses to road traffic noise in the rental housing market, using 46,980 transaction records between 2006 and 2022. Our difference-in-differences estimates show that road traffic noise decreases housing rents by 3.8% immediately after the pandemic outbreak and further declines by 12.7% in the subsequent year-equivalent to 186.7 US dollars per month. The results are robust to parallel trend analysis, permutation placebo tests, and tests using alternative distance thresholds or distance to the nearest main road. Then, we adopt a machine learning text analysis of 10,425 rental housing advertisements, showing that tenants' preference for quietness increases by approximately 10% from 2019 into 2020. The new work-from-home business model and rising traffic from delivery services can explain for this pattern. To the best of our knowledge, this is the first paper using a large volume of transaction records to quantify city dwellers' willingness to pay for quietness in the COVID-19 context. Our results have policy implications for other nations and post-pandemic era on the interaction among urban planning, transport networks, and human settlements, and shed light on the pathway to achieve sustainable development goals.

econ.GN↗

arXiv2026

Noise Pollution and Household Sustainability: An Economic Approach

Examining the economic impact of noise pollution from a lens of household is a burgeoning field in the study of environmental sustainability. Economics studies cover the source, measure, consequence of noise pollution, as well as the econometric methods used to identify the causal impact of noise pollution on socioeconomic welfare. There are broadly four major noise origins along with the industrial growth and urban development, which are airport, railway, urban traffic, and neighborhood. Four general kinds of measures or data sources are used in economics studies to capture the noise variations, namely, proximity to noise origins, real-time noise monitor records, household surveys, and administrative records on noise complaints. The socioeconomic consequences of noise pollution span from physical or mental health to happiness, violence and suicide, housing market capitalization, and inequality. In economics studies, generally three types of econometric methods are used to identify causal impact of noise pollution on the household's welfare, which are instrumental variable estimation, difference-in-difference estimation, randomized and quasi-natural experiments. The causal impact of noise pollution on household's socioeconomic welfare derived from economics studies can help guide policy efforts in allocating resources for noise elimination and conduct cost-benefit analysis. The economics research contributes to the general noise research from both conceptual and methodological perspectives: It expands the scope of research from sound-poof technology or site layout planning to human welfare, and endeavors to isolate the causal impact of noise pollution from other confounding factors. Future studies are warranted along the lines of environmental injustice of noise pollution and socioeconomic consequences in less developed countries when the data become more available.

econ.GN↗

arXiv2026

False Positives Raised by Quantum Readout Error Mitigation

Quantum readout error mitigation is essential for noisy intermediate-scale quantum devices to achieve reliable data. The conventional approaches, conflating initialization errors with measurement errors, not only suppress the influence of measurement errors, but also strengthen that of initialization errors, which is a systematic bias grows exponentially with the qubit number. Here, we have proved that this effect causes severe fidelity overestimation for all stabilizer states and might lead to false positives in large-scale entangled state characterization. Similarly, the results from algorithms like the variational quantum eigensolver and time evolution also deviate negatively, and cover up other errors in the quantum circuit. These findings highlight the critical need for rigorous benchmarking and careful management of initialization errors. Consequently, we establish an upper bound for the tolerable initialization error rate to ensure effective error mitigation at a given system scale.

quant-ph↗

arXiv2026

Social Integration and Housing Behaviours of Immigrants: Evidence from Singapore's Public Housing Market

This study investigates the impact of social integration on immigrants' housing behaviours from a temporal perspective, using Singapore's differential public housing policies on immigrants as a quasi-natural experiment. With the support of a local town council, we conducted a survey on social integration among 1,128 immigrant and local households living in public housing estates. In the public open rental housing market - primarily accommodating yet-to-integrate immigrants - we find immigrant renters live up to 3.04% farther from their workplace and pay lower rents up to 0.67% per additional year of residency. Such impacts are more substantial among minority ethnic groups. The results remain robust when using alternative subjective or objective measures of social integration. However, in the public resale housing market - primarily accommodating native and well-integrated naturalised citizens - we find that naturalised citizens face no price premiums relative to native homebuyers, implying no further effect of integration on housing prices after well-integration. This study extends the literature of spatial assimilation focusing on ethnic residential segregations and is generalizable to cities with few ethnic enclaves.

econ.GN↗

arXiv2026

Ageing in which place? Spatial analytical framework for evaluating ageing-in-place practices

Over the past decade, governments around the world have made significant investments in creating elderly-friendly urban environments within local neighborhoods. However, the lack of a standardized evaluation framework for Ageing-in-Place (AIP) practices makes it challenging to generalize these experiences. First, we compare the AIP models of the U.S.- San Francisco, Japan-Tokyo, and Singapore using a cost-benefit analysis, demonstrating the comparative advantage of the Singapore model in terms of low cost and high accessibility for the independent ageing population. Second, we propose a spatial analytics framework to visualize and quantify the degree of alignment between a basket of ageing facilities and the active ageing population, enabling a data-driven, timely evaluation of the effectiveness of Singapore's AIP policies. Singapore's AIP model, either in its entirety or as a hybrid with other models, can be generalized to other global cities, providing valuable insights for optimal elderly-friendly urban planning.

econ.GN↗

arXiv2026

FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization

Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines. Existing benchmarks are limited to small or simplified examples far below real-world scale and complexity. We introduce FrontierOR, among the first benchmarks to systematically evaluate LLM-based efficient algorithm design for realistic large-scale optimization problems. FrontierOR includes 180 tasks derived from methodologically diverse papers published in top-tier operations research venues, each with standardized instances and a hidden, expert-verified evaluation suite. We evaluate seven LLMs spanning frontier, cost-effective, and open-source models both in one-shot and test-time evolution settings. The results reveal that frontier models still struggle to move from executable formulations to efficient optimization algorithms: the strongest one-shot model outperforms Gurobi in only 31% of cases in both solution quality and computational efficiency, and even strong coding agents with test-time evolution achieve only 50% on selected hard tasks. FrontierOR establishes a practical evaluation platform for LLM-based optimization algorithm design, which enables future LLMs and agents to be systematically tested on whether they can move beyond correct formulation toward a feasible, high-quality, and efficient algorithm. Code and data are publicly released at https://github.com/Minw913/FrontierOR.

cs.AI↗