Evidence (8974 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Productivity
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IDS is 17% cheaper than SOTA agents.
Cost comparison reported in the paper between IDS and the evaluated SOTA coding agents across the same 7 specs, yielding a 17% cost reduction for IDS.
IDS is roughly 200x faster than expert effort.
Comparison in the paper between IDS runtime (hours) and the typical expert effort (described as months to years) required for mechanized formal verification of similar distributed-system specifications; reported multiplicative speedup (~200x).
IDS costs $106 per spec on average.
Reported monetary cost computed for IDS runs averaged across the 7 specs in the evaluation.
IDS achieves 7/7 (succeeds on all 7 specs) in about 6.8 hours per spec on average.
Empirical evaluation of IDS on the same suite of 7 distributed key-value-store specifications, with runtime (wall-clock) measured and averaged over the 7 specs.
The paper presents a comprehensive empirical study of key design choices — including alignment objectives, embedding dimensionality, model scale, architecture, and optimization strategies — to identify configurations that are most effective in production settings.
Authors report an empirical study covering multiple design axes; details of experiments, datasets, and sample sizes are not included in the excerpt.
HARNESS-LM (HLM) is a three-phase training framework for transferring the capabilities of large-scale retrievers into compact, cost-efficient models: (1) train a high-performance reference ('teacher') retriever by fine-tuning a billion-parameter-scale SLM; (2) align query representations via an L2 objective to distill knowledge into a sub-600M parameter student encoder; (3) apply a final contrastive refinement stage to optimize the student for retrieval performance.
Methodological description of the HLM training recipe and model sizes provided in the paper; supported by subsequent empirical evaluations reported in the paper.
Online A/B testing on Bing Ads shows a +0.4% Click uplift over the current ensemble of retrievers running in production with the deployed 190M parameter model.
Live online A/B testing on Bing Ads comparing HLM deployment to the production ensemble using the 190M parameter model; exact experiment details not provided in excerpt.
Online A/B testing on Bing Ads shows a +0.6% Impression uplift over the current ensemble of retrievers running in production with the deployed 190M parameter model.
Live online A/B testing on Bing Ads comparing HLM deployment to the production ensemble using the 190M parameter model; exact experiment details not provided in excerpt.
Online A/B testing on Bing Ads shows a +1% Revenue uplift over the current ensemble of retrievers running in production with the deployed 190M parameter model.
Live online A/B testing on Bing Ads comparing HLM deployment to the production ensemble using the 190M parameter model; exact experiment duration, traffic allocation, and statistical significance not provided in excerpt.
HLM delivers up to 20x higher throughput on NVIDIA A100 GPUs.
Throughput benchmarking on NVIDIA A100 GPUs comparing HLM student encoder to baseline/reference encoders; exact workload and measurement details not provided in excerpt.
HLM delivers up to 27x lower online query-encoder latency on NVIDIA A100 GPUs.
Measured inference latency on NVIDIA A100 GPUs comparing HLM student encoder to baseline/reference encoders; exact measurement procedure and number of runs not provided in excerpt.
On a real-world Bing Ads evaluation benchmark, HLM recovers over 98% of the reference retriever's precision across multiple settings.
Empirical evaluation on a real-world Bing Ads retrieval benchmark comparing HLM student retriever to a high-performance reference (teacher) retriever; exact benchmark dataset and number of test queries not reported in excerpt.
FastKernels is released (code available) as a stepping stone toward kernel agents whose benchmark gains translate directly into production throughput improvements.
Statement of code release with GitHub URL provided in abstract.
The FastKernels kernels collectively subsume those of 96.2% (409/425) of HuggingFace Transformers architectures.
Coverage analysis comparing FastKernels' kernels to HuggingFace Transformers architectures (numbers given in abstract: 409/425).
FastKernels is built around a minimal set of 46 representative architectures spanning 8 categories.
Design of the benchmark as described in the paper; explicit counts provided in abstract.
This paper provides new evidence on AI adoption from a non-US context by leveraging German firm-level data (ifo Business Survey).
Use of a large German business survey (ifo Business Survey) and analysis of AI adoption patterns among German firms.
AI is expected to have positive long-term productivity impacts for different sectors of the German economy.
Assessment of potential productivity impacts using firm-level survey responses about expected long-term benefits of AI (forward-looking/expectation-based analysis).
The increase in AI usage from 2023 to 2024 was particularly pronounced in manufacturing and services sectors.
Sectoral breakdown of ifo Business Survey firm-level data showing higher increases in reported AI usage for manufacturing and services compared with other sectors.
There was a significant increase in AI usage among German firms from 2023 to 2024.
Firm-level responses from the ifo Business Survey comparing reported AI usage in 2023 versus 2024 (cross-sectional/descriptive trend analysis).
To support sustainable human–AI collaboration, the authors emphasize adopting a human-centered approach that prioritizes transparency, explainability, and user autonomy.
Authors' policy/research/practice recommendation grounded in the review synthesis of the interdisciplinary literature.
Well-designed AI systems have the potential to increase cognitive efficiency and job satisfaction.
Synthesis of findings across reviewed studies indicating positive associations between human-centered AI design and outcomes like cognitive efficiency and job satisfaction.
The successful integration of AI-driven EPM systems relies on the synergy between AI technologies and human judgment, allowing healthcare organizations to cultivate a more dynamic, innovative and responsive workforce.
Normative/concluding statement in the scoping review based on synthesis of included studies (n=29).
AI-driven EPM systems mark a significant advance in accessing real-time performance data and provide considerable progression when utilized within appropriate guidelines.
Conclusion drawn in the paper from the scoping review of 29 empirical studies; phrased as an overall assessment.
Predictive analytics help manage high rates of burnout.
Reported in the scoping review as a benefit across included studies (n=29).
Predictive analytics optimize operations.
Stated as an operational benefit in the scoping review (29 studies).
Predictive analytics assist in assessing labor shortages.
Reported use-case in the scoping review synthesizing empirical studies (n=29).
Predictive analytics are vital in orchestrating healthcare organizations’ strategic and operational activities.
Claim derived from the scoping review's conclusions across included studies (n=29).
AI-powered EPM produces significant time savings for managers.
Reported as a benefit in the scoping review synthesis (29 studies); no numerical magnitude given in the excerpt.
AI-powered EPM helps identify potential leaders.
Summarized outcome across empirical studies in the scoping review (n=29).
AI-powered EPM heightens employee engagement.
Reported as an aggregated finding in the scoping review of 29 empirical studies.
AI-powered EPM increases the frequency of feedback to employees.
Stated as a benefit in the scoping review synthesis across included studies (n=29).
AI-powered EPM platforms result in considerable improvements in efficiency, including increased frequent feedback, heightened employee engagement, identification of potential leaders and significant time savings for managers.
Synthesis claim from the scoping review of 29 empirical studies; no quantitative effects reported in the excerpt.
The delivery of high-quality healthcare depends essentially on the effective functioning of personnel, who are the vital resource for maintaining reputation, fostering a culture of continuous improvement, and ensuring the overall effective operation of the healthcare sector.
Conceptual assertion in the paper supported by literature synthesis in the scoping review (29 studies).
Modern methodological assessment emphasizes the importance of recording individual contribution in various areas, assessing not only the fulfillment and quality of assignments, but also aspects such as collaboration, creativity, innovative behavior and professional growth.
Descriptive conclusion from the scoping review synthesizing themes across 29 empirical studies (2020–2025).
Employee Performance Management (EPM) systems are undergoing a pivotal shift from annual manual data collection ... into more agile human research operations.
Claim summarized from the scoping review of 29 empirical studies (PRISMA-ScR adherence stated).
Developing an integrated national AI strategic framework is critically necessary to position Georgia as a regional technological leader.
Method: policy recommendation derived from the paper's sectoral analysis and comparative study of successful national strategies; argumentative/ normative claim rather than experimental evidence.
An effective AI ecosystem requires an adaptive regulatory framework, infrastructural investments, the integration of ethical standards, and cross-sectoral coordination.
Method: synthesis of findings from the comparative policy analysis and literature; policy prescription based on observed patterns across successful national strategies.
Countries such as Singapore, the United Kingdom, Canada, and France have achieved AI policy success through institutional flexibility and targeted policies independent of the dominant USA and China models.
Method: comparative analysis of national AI strategies and institutional arrangements across the four named countries; qualitative assessment (no numeric sample size).
The paper analyzes the sectoral economic effects of AI using projections from Goldman Sachs, McKinsey, Penn Wharton, and the IMF, and assesses the potential for technology integration in Georgia's finance, healthcare, and education sectors.
Method: synthesis/analysis of published projections from Goldman Sachs, McKinsey, Penn Wharton, and IMF applied to Georgia's sectoral context; comparative assessment of applicability to finance, healthcare, education in Georgia. (No sample size reported.)
We survey recent open-world evaluations, identify their strengths and limitations, and conclude with recommendations for designing and reporting open-world evals.
Paper content promise: literature/methods survey and synthesis; detailed recommendations included in conclusions (qualitative content).
Open-world evaluations can provide early warning of capabilities that may soon become widespread.
Inference drawn by authors based on the reported open-world experiment (the iOS app trial) and a survey of recent open-world evaluations; claim is presented as a suggested benefit rather than proven at scale.
The agent completed the task with only a single avoidable manual intervention.
Direct observation from the paper's described experiment (single-agent trial producing the iOS app and publishing it; authors report occurrence of one avoidable manual intervention). Sample size = 1.
As a first instance, we task an AI agent with developing and publishing a simple iOS application to the Apple App Store.
Empirical demonstration described in the paper: a single experimental task in which an AI agent was assigned to develop and publish a simple iOS app. Sample size implied by description: 1 trial/instance.
We introduce CRUX (Collaborative Research for Updating AI eXpectations), a project for conducting such [open-world] evaluations regularly.
Paper describes the CRUX project as a proposed/introduced initiative (project description); no empirical trial-size or rollout numbers reported in the abstract.
We advocate for a complementary class of evaluations, which we term open-world evaluations: long-horizon, messy, real-world tasks assessed through small-sample qualitative analysis rather than benchmark-scale automation.
Methodological proposal/definition presented in the paper (conceptual argument and rationale); described as design recommendation rather than empirically validated at scale.
Benchmark-based evaluation remains important for tracking frontier AI progress.
Conceptual assertion in paper's introduction/abstract and literature context; no empirical sample reported for this claim (position statement).
Data and code are available at https://anonymous.4open.science/r/AgroVG-5172/ .
Availability statement in the paper (link to repository).
AgroVG provides task-specific protocols for box-set matching and query-level mask coverage.
Methodological contribution described in the paper (evaluation protocols designed for the benchmark).
AgroVG supports bounding-box grounding (T1) across all six families and instance-mask grounding (T2) on sources with reliable instance-level pixel annotations, with queries covering single-target, multi-target, and target-absent regimes.
Dataset/task specification described in the paper (task types T1 and T2 and query regime coverage).
AgroVG contains 10,071 annotation-grounded image-query pairs from ten source datasets across six target families: crop/weed, fruit, wheat head, pest, plant disease, and tree canopy.
Dataset construction / reported dataset statistics in the paper (explicit count and composition).