Evidence (8807 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
9875 claims
Filter claims →
Productivity
8807 claims
Filtered →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filter claims →
Skills & Training
3308 claims
Filter claims →
Inequality
2332 claims
Filter claims →
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 | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Productivity
Remove filter
Historical collaboration patterns (CPs) can be represented as knowledge-graph episodic memories and used for reuse via graph representation learning with a node-classification objective to identify a representative and effective memory.
Methodological description in the paper: authors construct knowledge-graph episodic memories from prior CPs and apply graph representation learning with a node-classification objective to select memories for reuse; no external validation beyond the study's experimental tests.
The study uses cross-country evidence from OECD Productivity, OECD STAN, OECD Patents, INTAN-Invest, and Functional Urban Areas (FUAs) databases and combines descriptive analysis with panel and robust regression techniques.
Methods statement from the paper (datasets named and methods described in abstract/introduction).
AI patent intensity has weak and statistically insignificant associations with aggregate Total Factor Productivity (TFP).
Cross-country panel and robust regression analysis using OECD Productivity and OECD Patents data (descriptive analysis + panel/robust regressions reported in paper).
The dataset contains large high-resolution images of dimensions 1280x959 and 960x703, which increase the complexity of the annotation task.
Dataset description in the paper specifying image dimensions (1280x959 and 960x703) and noting annotation complexity due to image size.
LLM guidance did not increase the total number of victims saved (no increase in total victims saved relative to baseline).
Same experimental comparison (two LLM-guided conditions vs no-LLM) in the simulated SAR environment; behavioral measure of total victims saved reported.
A 2015-2017 backward extension (224 firms, 601 observations) supplies pre-treatment data and provides evidence against pre-existing upward-trend confounds in SG&A-to-revenue.
Additional panel extension covering 2015-2017 with 224 firms and 601 firm-year observations, used to test pre-trends.
South Korea exemplifies national-scale under-augmentation: high human capital (H), substantial AI (A), but low convergence capacity (C) produce phi = 0.
Case/example presented in the paper as an illustrative national example (descriptive/case-study evidence).
The identification strategy exploits the staggered establishment of National Supercomputing Centers (NSCs) as quasi-natural experiments and uses a staggered difference-in-differences model for causal identification.
Methodological design as described in the paper.
The study sample consists of Chinese A-share listed companies from 2012 to 2023.
Paper sample description.
The study contributes theoretically by integrating perspectives from productivity economics, public administration, and systemic risk within a sociotechnical systems framework; empirically by providing a comprehensive synthesis of evidence on AI and public sector productivity; and methodologically by applying transparent PRISMA 2020 review procedures.
Author-stated contributions supported by the paper's literature integration, systematic review (68 studies), and use of PRISMA 2020 methods.
This study systematically reviews 68 peer reviewed empirical studies published between 2015 and 2025 using PRISMA 2020 methodology.
Methods statement in the paper describing the systematic review procedure and sample of included studies.
Digital transformation, AI adoption, and foreign direct investment (FDI) do not display statistically significant direct effects on export performance in the baseline specification.
Null statistical significance reported for these predictors in the study's baseline pooled OLS / fixed-effects regressions (abstract statement); no specific coefficients reported in abstract.
Across the subsequent ~150 sessions after deploying Baseline-Log Physical Separation, no recurrence of Index Sickness was observed.
Authors' observational report from Bang-v3 following deployment: subsequent ~150 collaborative sessions with no observed recurrence.
We used action research methods in a real software project (Bang-v3) spanning approximately one month and 391 collaborative sessions.
Paper statement of study design: action research in Bang-v3, duration ~1 month, 391 collaborative sessions.
Whether wear-aware placement improves task value remains open — χ is measured against a value proxy, and the non-monotone optimum, while proven, is not yet observed in data.
Explicit caveat in the paper noting measurement limitations (value proxy) and that the proven non-monotone optimal policy has not been observed empirically. This is a statement about the limits of current empirical evidence.
The paper proposes an integration framework covering use case suitability, autonomy levels, technical integration, governance, security, employee enablement, and measurable impact.
Paper presents a proposed framework (descriptive; the existence of the proposal is internal to the paper).
Unlike traditional automation or conversational AI, agentic systems can interpret goals, plan multi-step tasks, access tools, interact with enterprise systems, and execute workflows with varying degrees of autonomy.
Descriptive definition and capability listing in the paper (conceptual/technical description). No empirical validation provided.
Agentic AI marks a new phase of enterprise automation.
Author's high-level claim in the paper (conceptual/position statement). No empirical data or sample reported.
We provide a candid assessment of the problems Mojo does and does not yet solve.
Paper claims to include evaluative discussion of limitations and unsolved problems; descriptive statement about paper content.
Larger-scale GPU workload results are projections calibrated from published benchmarks.
Paper states that larger GPU results are not directly measured but are projections calibrated using published benchmarks; no calibration dataset size given in the excerpt.
We benchmark four core financial AI workloads: Monte Carlo option pricing, LLM sentiment inference, multi-asset backtesting, and portfolio Value at Risk.
Paper reports that these four workloads were benchmarked (method: benchmarking); number of distinct workload types = 4.
A qualitative analysis of 384 stratified patches informs a syntactic taxonomy of eight oracle signal categories.
Manual qualitative coding/analysis of a stratified sample of 384 test-file patches, resulting in an eight-category syntactic taxonomy.
We conduct an empirical study of 86,156 test-file patches from 33,596 agent-authored PRs across 2,807 GitHub repositories produced by five coding agents: OpenAI Codex, GitHub Copilot, Devin, Cursor, and Claude Code.
Dataset collection and descriptive statistics reported by the study (counts of patches, PRs, repositories, and agents analyzed).
The production technology is multiplicative and cognitive capital functions as collateral that determines the return to AI adoption.
Analytic model specification and derivation inside the paper (formal mechanism). No empirical data.
The model features two state variables per agent, cognitive capital and cognitive debt.
Formal theoretical model presented in the paper (model specification). No empirical sample; analytic construction.
A pilot deployment in Newham's secure environment evaluated operational performance relative to manual workflows.
Paper reports a pilot deployment and an operational evaluation comparing DOMUS to existing manual workflows (method: pilot deployment; specifics such as duration or sample size not stated in provided text).
The authors evaluate general-purpose vision-language models, specialized GUI agent models, and advanced agentic frameworks at both subtask and end-to-end levels on LabOSBench.
Paper reports an evaluation suite covering multiple model classes and evaluation granularities (subtask and end-to-end); specific model identities and counts are not provided in the excerpt.
LabOSBench constructs 96 subtasks across eight instrument simulators, covering workflows from sample loading, alignment, parameter tuning, and data acquisition to result inspection.
Explicit specification in the paper of benchmark breadth: 96 subtasks and 8 simulators including the listed workflow stages.
Scientific instrumentation scenarios require coordinated control over complex interfaces, and feedback-driven parameter adjustment.
Conceptual claim in the paper describing the nature of scientific-instrument operation; not supported by empirical data in the excerpt.
Current computer-use benchmarks primarily focus on software operation tasks in virtualized systems.
Statement in paper framing the problem; based on literature/field observation rather than reported experiments or data in this excerpt.
In evaluation runs, the evaluated model controls one coffee roaster while the remaining firms are controlled by fixed reference agents.
Experimental setup described in the paper: one roaster is controlled by the model under test; other five firms use fixed reference agents.
Each firm in CoffeeBench seeks to maximize cumulative net income through communication and transactions while managing cash, inventory, and pricing.
Specification of agent objectives and state variables in the benchmark design (cumulative net income objective; resources: cash, inventory; decision variables: pricing and transactions).
CoffeeBench simulates an economy of two farmers, two roasters, and two retailers operating autonomously over a 90-day simulation.
Environment description in the paper specifying the number and types of firms and the 90-day simulation horizon.
Frontier costs have stayed relatively stable between 2024 and 2026.
Authors' reported trend analysis of frontier model costs across the 2024–2026 period; excerpt lacks numeric cost trend data and sample size.
Every output is valuable (in H), trivial (in F \ H), or a hallucination (not in F).
Definition/assumption in the paper's formal model classifying outputs into three mutually exclusive categories relative to the formal language F and the valuable language H.
The empirical analysis used archival microdata from 770 large Spanish firms and employed staged OLS regression models.
Statement of data source and method in the paper's abstract.
The complementarity between AI deployment depth and breadth offers a configurational explanation for the AI productivity paradox.
Theoretical interpretation plus empirical finding of a positive interaction between depth and breadth in staged OLS analyses of archival microdata from 770 large Spanish firms.
AI capability can be conceptualized as two-dimensional: AI deployment depth (technological variety of AI implementations) and AI deployment breadth (organizational scope of AI diffusion).
Theoretical framing drawing on Resource-Based Theory and organizational search theory; conceptual argument presented in the paper.
Those preliminary experiments do not establish behavior preservation, scaling economics, or verified-change cost.
Authors' explicit limitation statement following the preliminary QLoRA experiments.
The review focuses on three core dimensions of impact: employee attitudes (job satisfaction, motivation, adaptability), workplace behaviours (performance, creativity, technology adoption), and organisational dynamics (leadership, trust, team cohesion).
Stated scope and focus areas in the abstract describing the review's analytical framework.
The study contributes a structured framework that clarifies the role of emotional AI in organisational contexts and outlines actionable, scalable strategies for real-world application.
Authors claim to have developed and presented a framework and strategies as part of the review paper (a descriptive/conceptual contribution rather than empirical evidence).
The study identifies key patterns, methodological trends, and underexplored areas in research on emotional AI systems in organisational contexts.
Authors report findings of their comparative analysis of the state-of-the-art literature (exact patterns/trends and counts are detailed in the full review).
This study follows the PRISMA framework to conduct a systematic evaluation and comparative analysis of the state-of-the-art literature on emotional AI in organisations.
Statement of methods in the abstract that the review used PRISMA; implies structured search, screening, and selection procedures described in the full paper.
The literature on AI-powered emotional intelligence systems is fragmented and insufficiently synthesised.
Authors' assessment based on a systematic literature review conducted following the PRISMA framework (details of databases, search terms, and included studies reported in the paper); exact number of studies not stated in the abstract.
The paper derives tight conditions that determine whether the economy is partially versus fully automated in the long run.
Analytical characterization in the model: derivation of necessary and sufficient (tight) conditions separating long-run partial automation from full automation (mathematical proofs within the paper).
Data accumulates endogenously as a byproduct of economic activity.
Model assumption and mechanism in the theoretical dynamic model: data generation is modeled as an endogenous outcome of agents' economic activity (analytical model specification).
Data is heterogeneous and task-specific.
Model assumption stated in the paper's setup: the model is built with data that varies across tasks and is task-specific (analytical model specification).
Density-normalized outcomes (e.g., smells per LOC) can mislead when treatment affects system size; raw counts and explicit decomposition are required for causal mining studies of AI tool adoption.
Interpretation and methodological recommendation derived from the observed pattern (unchanged smell counts + increased LOC leading to lower density) in the paper's empirical results.
Per-type estimates and robustness checks (wild cluster bootstrap, Lee bounds, stale-observation sensitivity) corroborate the main pattern; pre-trends are flat (Wald p = 0.90), consistent with the parallel trends assumption.
Placebo and robustness analyses reported in the paper (per-type breakdowns and multiple sensitivity checks) applied to the 151-repository panel; pre-trend test result reported as Wald p = 0.90.
Total architectural smell counts are essentially unchanged after adoption (+1.1%, p = 0.82).
Estimated treatment effect from staggered DiD / Borusyak imputation on total smell counts using the 151-repository panel (74 treated, 77 controls).