Evidence (4131 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Innovation
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Adaptation-oriented AI research (modifying AI models for domain-specific problems) is associated with relatively higher object-based creativity.
Subgroup/heterogeneity analysis in the OpenAlex dataset classifying AI publications by research mode (Adaptation-oriented) and comparing object novelty outcomes across modes.
Tool-oriented AI research (applying existing AI models to domain tasks) is associated with the largest gains in recombinant-based creativity.
Subgroup/heterogeneity analysis in the OpenAlex dataset classifying AI publications by research mode (Tool-oriented) and comparing recombinant novelty outcomes across modes.
AI publications have a 5.5 to 10.2 percentage point higher likelihood to rank in the top creativity decile.
Reported quantitative effect from the paper comparing top-decile creativity probabilities between AI and non-AI publications in the OpenAlex sample.
AI publications are significantly more likely to achieve top-decile creativity relative to non-AI publications.
Observational statistical analysis comparing AI-labeled vs non-AI publications across novelty and impact measures using the >1M OpenAlex dataset (novelty measured as recombinant and object novelty; impact measured as 3-year and 10-year citation impact).
GIPs enhance urban industrial chain resilience by promoting industrial structure optimization.
Mechanism analysis in the study showing industrial structure optimization as a channel linking GIP implementation to improved UICR; based on the 281‑city panel and specified empirical tests.
GIPs enhance urban industrial chain resilience mainly by fostering green technological innovation.
Mechanism analysis reported in the paper identifying green technological innovation as a primary mediator through which GIPs improve UICR; based on empirical mediation/analysis within the panel and DML framework.
The resilience‑enhancing effect of GIPs is more pronounced in resource‑based cities.
Heterogeneity analysis reported in the study indicating larger GIP effects on UICR in cities classified as resource‑based; derived from the 281‑city panel analysis.
The resilience‑enhancing effect of GIPs is more pronounced in eastern cities.
Regional heterogeneity analysis reported in the paper showing stronger estimated impacts in eastern region cities within the 281‑city panel.
The resilience‑enhancing effect of GIPs is stronger in cities with stronger AI computing power.
Heterogeneity analysis in the study indicating larger GIP effects on UICR in cities with higher AI computing power measures; based on the same panel dataset and statistical methods.
The resilience‑enhancing effect of GIPs is stronger in cities with more advanced digital economies.
Heterogeneity analysis reported in the paper showing larger estimated impacts of GIPs on UICR in cities with more developed digital economy indicators; based on the 281‑city panel.
The resilience‑enhancing effect of GIPs is stronger in cities with higher openness.
Heterogeneity analysis reported in the study indicating larger estimated effects in subsamples or interaction models for cities with greater openness; based on the 281‑city panel (2005–2022).
The positive effect of GIPs on UICR is robust across alternative sample specifications, estimation algorithms, variable definitions, and controls for parallel policies.
Reported robustness checks in the study (alternative samples, estimation algorithms, variable definitions, and adjustments for parallel policies); based on same panel of 281 cities and DML framework.
The implementation of national Green Industrial Parks (GIPs) significantly improves urban industrial chain resilience (UICR).
Panel data analysis of 281 Chinese cities (2005–2022), treating establishment of national GIPs as a quasi‑natural experiment and estimating effects using a double machine learning approach. Statistical significance asserted in results.
This analysis provides practical insights for politicians and corporate strategists as they navigate significant transformations in capital, labor, and innovation.
Claim about the applied relevance of the paper's findings; presented as an asserted contribution rather than a quantified outcome.
AI facilitates a polycentric, resilient production topology.
Central theoretical claim of the paper's 'Cognitive Economic Geography' framework, linked to observed capital investment patterns and the four mechanisms identified in the empirical analysis.
This transition is evidenced by the significant relocation of high-value production to the Midwest, South, and Great Plains.
Empirical claim based on capital investment data (2018-2024) for EV battery factories, semiconductor fabs, and additive manufacturing sites showing relocation patterns toward those U.S. regions.
An empirical investigation of capital investment (2018-2024) in electric-vehicle battery factories, semiconductor fabrication facilities, and additive manufacturing sites identifies four bled mechanisms that facilitate a significant spatial-economic inversion.
Paper reports an empirical investigation covering capital investment in specified facility types over 2018-2024 and claims identification of four mechanisms (paper does not list numeric sample size in the provided text).
A novel spatial calculus has emerged, emphasizing the cost structures of interiors, land availability, and energy infrastructure.
Conceptual assertion in the paper, argued in relation to observed capital investment patterns (2018-2024) across EV battery, semiconductor, and additive manufacturing projects.
The American Interior is not nostalgically resurrecting antiquated factories but is instead evolving into a new, AI-driven industrial entity.
Stated thesis of the paper supported by the paper's conceptual argument and referenced empirical investigation of capital investment (2018-2024) in EV battery factories, semiconductor fabs, and additive manufacturing sites.
The deployment and online lifts demonstrate the approach's industrial value.
Paper statement linking observed online metric improvements from production deployment to industrial value; based on reported online lifts on Tmall.
Deployed via an efficient hybrid architecture, it achieves significant online lifts (+0.13% UCTR, +0.25% UCTCVR).
Online A/B (production) deployment on Tmall; reported online metric lifts: +0.13% UCTR and +0.25% UCTCVR. (No sample size or statistical significance numbers provided in the excerpt.)
Extensive experimental results on Tmall's production data show that our proposed approach has achieved better results, improving offline AUC by +1.54%.
Offline experiments reported on Tmall production data; improvement reported as +1.54% in offline AUC. (No sample size or test details given in the excerpt.)
Hierarchical prefix matching between query and item SIDs yields discriminative features that perfectly complement dense signals.
Methodological claim in paper describing feature construction and asserted complementarity with dense embeddings; paper includes experiments to evaluate overall model performance.
We explore generative LLMs on the query side to explicitly predict item SIDs from text, resolving tail queries and intent ambiguity.
Methodological claim: use of generative large language models for predicting item SIDs from queries; paper claims this helps tail queries and intent ambiguity.
We present a query-bridged contrastive quantization approach on the item side, injecting query-item interaction supervision into Residual Quantization to actively learn relevance-aware semantic partitions.
Methodological claim describing the proposed quantization approach (algorithmic design). Supported in paper by methodological exposition; experimental validation referenced elsewhere in paper.
We propose a Discrete Semantic Identifier Relevance Model (DSIRM) that explicitly models discrete relevance features for e-commerce search.
Methodological description in the paper presenting DSIRM (model proposal). No numerical evaluation data in this sentence; overall paper includes experiments on Tmall production data.
Network composition analysis of 8,012 workers shows all have inference-capable hardware.
Network composition analysis covering 8,012 workers; hardware capability inferred from worker-reported or probed specifications.
Artificial intelligence significantly facilitates carbon mitigation.
Empirical analysis on prefecture-level panel data (2005–2023) showing AI development is associated with reductions in carbon emissions or improved carbon mitigation indicators (authors state 'significantly facilitates ... carbon mitigation').
Artificial intelligence significantly facilitates pollution reduction.
Empirical results from prefecture-level panel analysis (Guanzhong Plain, 2005–2023) report AI development is associated with reductions in pollution indicators (authors state 'significantly facilitates pollution reduction').
Artificial intelligence promotes the growth of urban ecological resilience through the channel of green technological innovation.
Mediation/mechanism analysis using prefecture-level panel data (2005–2023); authors identify green technological innovation as a significant mediating channel in the relationship between AI development and ecological resilience.
Artificial intelligence promotes the growth of urban ecological resilience through the channel of green finance.
Mediation/mechanism analysis in the paper using the same prefecture-level panel data (2005–2023); authors report that green finance is a statistically significant channel linking AI development to higher ecological resilience.
The development of artificial intelligence exerts a positive effect on ecological resilience.
Empirical analysis using prefecture-level panel data for cities in the Guanzhong Plain Urban Agglomeration (2005–2023); authors construct an urban ecological resilience index (three dimensions) and estimate the relationship between AI development and the index using panel econometric methods.
A tool-augmented agentic AI method (equipped with analytical tools, structured DIKW reasoning agents, and transparent evidence chains) can automatically learn from experimental data to generate new interventions and produce superior interventions compared to Human + Chatbot co-design.
Two-stage field experiments in healthcare prescription messaging comparing Stage 1 (Human + Chatbot: 13 message variants, 444,691 patient visits) to Stage 2 (Tool-Augmented Agentic AI: 17 AI-generated variants, 248,448 patient visits).
The best AI-generated message achieved a 69.8% CTR (+6.5 percentage points over baseline).
Stage 2 field experiment in healthcare prescription messaging where AI-generated message variants were tested; result reported directly in paper.
Taiji demonstrates robust scalability in web-scale environments.
Assertion supported by deployment at large scale and claimed daily user numbers (paper's scalability claim).
Taiji yields significant commercial revenue.
Claim in the paper that the deployed system produces notable commercial revenue (no quantitative revenue figures provided in abstract).
Taiji currently serves over 400 million users daily.
Operational usage statistic reported in the paper (daily active users served).
Taiji has been deployed on Kuaishou's advertising platform since May 2026.
Deployment statement reported in the paper (operational deployment date claimed).
Extensive offline evaluations and online A/B tests validate the effectiveness of Taiji.
Empirical evaluation section claiming extensive offline experiments and online A/B testing validate the system (no numeric details in abstract).
Theoretically, POPO achieves an optimal trade-off between the semantic world knowledge of LLMs and the collaborative ID features representing online user preferences.
Theoretical analysis/claims in the paper asserting optimality of the proposed POPO method (presumably proofs or propositions).
To resolve the RL alignment issue, Taiji proposes Pareto Optimal Policy Optimization (POPO), which adaptively adjusts cross-domain reward weights.
Algorithmic contribution described in the paper introducing POPO as an RL method for adaptive reward-weighting.
To overcome the SFT bottleneck, Taiji utilizes reverse-engineered reasoning and open-ended rejection sampling to generate high-quality, domain-specific chain-of-thought (CoT) data.
Methodological description in the paper detailing reverse-engineered reasoning and open-ended rejection sampling as data-generation techniques.
We present Taiji, a novel LLM-as-Enhancer framework designed for industrial recommender systems.
Paper's core contribution: proposal of a new framework described in the manuscript.
Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.
Framing statement in the paper's introduction/abstract asserting industry trend (literature/industry observation).
The paper constructs firm-level indicators of artificial intelligence and new quality productive forces for new energy vehicle firms.
Authors state they constructed firm-level indicators as part of their empirical approach on the Yangtze River Delta panel dataset.
Artificial intelligence affects firms' new quality productive forces through improvement of innovation output.
Mechanism tests reported by the authors showing empirical evidence that AI improves innovation output (e.g., measured innovation outcomes) which is linked to higher new quality productive forces.
Artificial intelligence affects firms' new quality productive forces through optimization of R&D personnel structure.
Mechanism tests reported by the authors using the constructed indicators and panel data; empirical evidence cited that links AI to changes in R&D personnel structure which in turn link to new quality productive forces.
The promoting effect of artificial intelligence on new quality productive forces is more pronounced among small-sized enterprises.
Heterogeneity tests by firm size in the panel data; authors report stronger positive effects for small-sized firms.
The promoting effect of artificial intelligence on new quality productive forces is more pronounced in Jiangsu and Zhejiang provinces.
Heterogeneity tests on the Yangtze River Delta panel data comparing regional subsamples; authors report stronger positive effects in Jiangsu and Zhejiang.
The positive effect of artificial intelligence on firms' new quality productive forces remains robust after addressing endogeneity concerns and conducting robustness checks.
Authors report endogeneity-corrected estimations and multiple robustness checks on the same panel dataset and constructed firm-level indicators; specific endogeneity correction methods and robustness checks are not detailed in the excerpt.