Evidence (7278 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
9047 claims
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Productivity
8066 claims
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Governance
7278 claims
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Human-AI Collaboration
6912 claims
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Org Design
4439 claims
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Innovation
4359 claims
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Labor Markets
3652 claims
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Skills & Training
3018 claims
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Inequality
2160 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 | 795 | 210 | 105 | 955 | 2131 |
| Governance & Regulation | 886 | 414 | 197 | 126 | 1654 |
| Organizational Efficiency | 826 | 204 | 129 | 87 | 1257 |
| Technology Adoption Rate | 681 | 259 | 128 | 110 | 1189 |
| Research Productivity | 464 | 138 | 65 | 349 | 1028 |
| Output Quality | 503 | 196 | 61 | 53 | 813 |
| Decision Quality | 351 | 180 | 84 | 51 | 673 |
| AI Safety & Ethics | 238 | 288 | 71 | 34 | 637 |
| Firm Productivity | 455 | 58 | 92 | 20 | 631 |
| Market Structure | 186 | 172 | 123 | 25 | 511 |
| Task Allocation | 222 | 70 | 76 | 34 | 407 |
| Innovation Output | 238 | 28 | 48 | 18 | 334 |
| Skill Acquisition | 177 | 62 | 62 | 17 | 318 |
| Employment Level | 107 | 57 | 108 | 13 | 287 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Firm Revenue | 172 | 50 | 28 | 5 | 256 |
| Consumer Welfare | 121 | 68 | 45 | 12 | 246 |
| Task Completion Time | 183 | 33 | 10 | 13 | 240 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 95 | 74 | 23 | 12 | 204 |
| Error Rate | 77 | 98 | 11 | 4 | 190 |
| Regulatory Compliance | 84 | 73 | 17 | 7 | 181 |
| Automation Exposure | 61 | 61 | 27 | 14 | 166 |
| Training Effectiveness | 98 | 21 | 14 | 19 | 154 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 23 | 1 | 119 |
| Hiring & Recruitment | 53 | 8 | 8 | 3 | 72 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 50 | 6 | 1 | 62 |
| Labor Share of Income | 17 | 20 | 17 | — | 54 |
| Worker Turnover | 15 | 15 | — | 3 | 33 |
| Industry | — | — | — | 1 | 1 |
Governance
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The nonparametric quantile causality test shows a unidirectional causal relationship from AI to EPU.
Nonparametric quantile causality test applied to time-series data on AI and Economic Policy Uncertainty (EPU) in China (method reported; sample size not stated in the provided text).
The proposed policy framework contributes to establishing a foundation for Vietnam to proactively embrace the Agent Economy safely and effectively.
Claim in abstract about the intended contribution/impact of the proposed framework; no empirical evaluation or measured outcomes presented.
The Agent Economy promises substantial gains in productivity and innovation.
Asserted in paper abstract as an anticipated outcome; no empirical measurement, sample size, or quantified effect provided.
This study proposes a Workforce Resilience Governance Framework (WRGF) that includes task-level exposure assessment, human augmentation design, reskilling, redeployment, transparent communication, psychological safety, workforce impact accountability, and policy alignment.
Conceptual framework proposed by the authors in the paper (design/proposal; no empirical test described in the excerpt).
The paper concludes with policy recommendations for accelerating human-centred AI integration in public-sector HRM.
Stated conclusion and policy recommendations section in the paper; recommendations derived from empirical findings.
Access to modern digital tools positively moderates AI uptake.
Reported moderation/interaction effects in regression/path analysis indicating that access to modern digital tools is associated with higher AI adoption/uptake; exact effect size not specified in summary.
Holding a managerial position is the strongest predictor of active AI adoption (OR = 1.609).
Reported odds ratio from the binary logistic regression for role/position predictor (managerial status) predicting active AI adoption; OR = 1.609.
Internal HR factors exert a stronger influence on perceived HR effectiveness (β = 0.463) than external factors (β = 0.227).
Reported standardized (?) path/regression coefficients from OLS/path analysis linking internal and external HR quality indices to perceived HR effectiveness; coefficients β = 0.463 and β = 0.227 respectively.
We evaluate collaborative performance from consensus-based routing among self-interested heterogeneous agents in AgentSociety on real-world datasets.
Empirical evaluation / experiments using real-world datasets to measure collaborative performance under consensus-based routing among heterogeneous agents.
We characterize the Nash equilibrium showing that agent payoffs are reflective of their marginal contributions.
Analytical game-theoretic characterization/proof of Nash equilibrium in the paper.
The mechanism incentivizes agents to selectively disclose information to their neighbor agents when doing so aligns with their self-interest, in order to garner influence.
Theoretical analysis and mechanism design arguments (and possibly supporting simulations) within the paper.
Delegation to more competent neighbor agents is incentive compatible and naturally generates multi-agent routing path by consensus.
Formal theoretical proof/analysis presented in the paper (analytical/theoretical result).
We propose AgentSociety, a mechanism that enables decentralized agentic collaboration grounded in liquid democracy and information diffusion from social choice theory.
Description and design of the AgentSociety mechanism in the paper (mechanism proposal / system design).
LLM-assisted systems make candidate generation, code comprehension, harness construction, proof-of-impact drafting, and report preparation cheaper at codebase scale.
Argument supported by analysis using public data from Anthropic's Mythos Preview and Mozilla Firefox collaborations (qualitative and illustrative examples; no sample size reported in the provided text).
Case studies demonstrate exact power-water consistency between virtual attributions and physical generation-side withdrawals.
Simulation results on IEEE 30-bus and 118-bus test systems reported in the paper claiming exact consistency (two test systems used).
Case studies on the IEEE 30-bus and 118-bus test systems demonstrate reliable convergence of the method.
Simulation experiments reported in the paper using two standard test systems (IEEE 30-bus and IEEE 118-bus). Sample size: 2 test systems.
Combined with fixed-point coordination, the framework enforces consistency between virtual water attribution and physical generation-side withdrawals.
Methodological claim about algorithmic properties (fixed-point coordination used to align attributions with physical withdrawals); supported by theoretical description and later case-study demonstrations.
The framework represents dispatch optimization as a differentiable optimization layer embedded within a deep learning architecture, enabling efficient end-to-end learning of coordination policies while preserving operational feasibility.
Methodological description claiming an implementation approach (differentiable optimization layer within deep learning); evidence likely from algorithmic implementation and simulation experiments described later in the paper.
This paper develops an operational electricity-computation-water (ECW) nexus framework that internalizes virtual water impacts directly into power system dispatch.
Primary methodological contribution described in the paper (development and formulation of an ECW framework; implementation details implied but not quantified in the excerpt).
The expansion of data centers (DCs) drives a sustained increase in electricity demand and associated water withdrawals at generation sites.
Background assertion in paper introduction; general empirical observation motivating the work (no specific dataset or sample size reported in the excerpt).
The contribution is a benchmark-ready evaluation framework for runtime actuarial control of autonomous-agent side effects.
Paper presents the AAI, Authority Frontier, metrics (C_full, Capital@k), taxonomy, implementations and experimental traces; authors present it as benchmark-ready.
We report a live Postgres panel in which three Azure-hosted models propose actions through the same contract.
Live-panel experiment described in the paper using three Azure-hosted models interacting with a Postgres panel under the AAI contract.
We instantiate AAI across four agentic environments (database mutation, customer-service refund, and the public tau-bench retail and airline tool-use traces).
Empirical instantiation described in the paper across four named environments/traces.
The framework provides (i) a deterministic quote-bind-commit protocol with toll-bounded capability tokens; (ii) a universal seven-class action taxonomy mapping heterogeneous tool calls to comparable authority units; (iii) replay determinism and pathwise reserve coverage under alpha-spending; (iv) cross-domain normalization via full reserve demand C_full and capital metrics Capital@k.
System design and theoretical specification in the paper; described as implemented across experiments.
We develop the Authority Frontier, an evaluation primitive measuring how much autonomous authority the runtime releases at each level of reserve capital.
Methodological contribution (definition and formulation of the Authority Frontier) described in the paper; subsequently instantiated empirically in experiments.
We propose the Actuarial Action Interface (AAI), a deterministic runtime contract that prices each such action against a contractually fixed safe default under a time-consistent risk mapping, and gates execution against a per-boundary reserve capital budget.
Methodological design and proposal described in the paper (no empirical test reported for the claim itself).
The paper proposes a policy architecture for 'shared gains' centered on learning equity, transition protections, accountable algorithmic management, and distribution-sensitive metrics beyond GDP.
Paper's normative policy proposal presented in abstract, based on the integrative framework and synthesis of secondary sources; no empirical sample size reported.
India's macro growth remains robust.
Statement in abstract referencing official Indian statistics (MoSPI–NSO GDP estimates, 2025); no numerical sample size provided in abstract.
Evidence indicates accelerating AI adoption among firms in advanced economies.
Abstract cites validated secondary sources including OECD (2026) and other global reports; no primary sample size reported in paper abstract.
AI is increasingly embedded in production, services, and workforce management.
Statement in paper's abstract supported by integrative socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF). No primary sample size reported.
Future A2A collaboration networks cannot rely on unverified self-reporting alone; scalable collaboration requires mechanisms that balance open participation with verifiable execution and trustworthy evaluation.
Paper's concluding recommendation based on the empirical problems documented (low reuse, ranking manipulation, vacuous validations).
EvoMap's credit economy rewards agents for publishing valuable assets, encouraging participation at scale.
Description and analysis of the platform's reward mechanism and observed high participation (agent counts); empirical linkage between reward rules and publishing behavior discussed in the paper.
A human-centred approach underpinned by ongoing reskilling and ethical governance is vital for sustainable workforce evolution in the Indian IT sector.
Authors' policy/recommendation derived from their literature synthesis and thematic analysis (qualitative conclusion).
The paper introduces a conceptual framework for hybrid intelligence within the Indian IT sector.
Authors present a new conceptual framework as part of this qualitative research article (conceptual contribution).
Collaboration between humans and AI enhances decision-making, efficiency, and innovation.
Reported result from thematic evaluation of literature and secondary data (qualitative synthesis). No sample size or quantified effect provided.
AI improves overall organisational productivity.
Authors' synthesis of peer-reviewed studies and secondary data indicating productivity impacts (qualitative literature review). No quantitative sample size reported.
AI increases human capacities.
Conclusion from comprehensive analysis of peer-reviewed literature and thematic evaluation of secondary data (literature review). No primary sample size reported.
Policy responses should prioritise governance frameworks that emphasise equity, accountability, and inclusive distribution of value to address concentrated digital power.
Normative policy recommendations derived from the paper's conceptual analysis and synthesis of recent literature (policy prescription, no empirical evaluation reported).
Together, these results bring individual-level LLM-based resident simulation within reach of resource-constrained local administrations, enabling community-governance decisions to be systematically pre-evaluated in silico before real-world deployment.
Aggregate of dataset creation, benchmark results, algorithm (curriculum-LoRA) efficiency gains, and system integration reported in the paper; claim is a stated implication/claim about practical feasibility for local administrations.
The system integrates curriculum-LoRA into a closed-loop policy-evaluation pipeline.
System-level description and implementation in the paper that embeds curriculum-LoRA within a closed-loop pipeline for policy evaluation and iteration.
Curriculum-LoRA Pareto-dominates every configuration tested.
Empirical comparisons across the tested configurations in the experiments reported in the paper; curriculum-LoRA outperforms or matches all other configurations on the fidelity-versus-cost Pareto frontier.
Curriculum-LoRA is a parameter-efficient personalization framework that, by closing the fidelity-cost gap, matches the strongest baseline's fidelity at roughly 10x lower per-call cost.
Experimental evaluation comparing curriculum-LoRA to baselines on fidelity and per-call cost metrics; reported result that curriculum-LoRA attains comparable fidelity while reducing per-call cost by about a factor of ten.
Adding rich life-history profiles meaningfully raises fidelity above the no-profile baseline.
Benchmark comparisons between prompting strategies that include rich life-history profiles versus a no-profile baseline across the evaluated LLMs, using the interview-derived dataset to assess fidelity.
The dataset comprises approximately 1.2 million characters of first-person narrative collected through two-hour semi-structured interviews with each of 92 residents in an urban community, organized around nine community-governance domains.
Reported dataset construction: two-hour semi-structured interviews with each of 92 residents (92 interviews), organized around nine governance domains; reported total text volume ~1.2 million characters.
Recommendations for adapting employment policy to AI transformation conditions have been proposed.
Policy recommendations derived from the paper's analysis of statistical data, industry reviews, and regulatory/legal documents; recommendations are proposed by the authors (not empirically validated within the paper).
In 2024-2025, the labor market of Uzbekistan is characterized by duality: there is an increasing demand for IT specialists and workers with digital skills.
Analysis of 2024–2025 labor market statistics and industry reviews cited in the paper (no numerical sample size or survey sampling reported).
The aim is to keep autonomous agency composable while keeping accountability non-negotiable, so that coordination itself can become shared infrastructure for a human-AI society that is open, pluralistic, and governable.
Stated design/ethical objective in the paper; normative claim about intended social and governance outcomes rather than an empirically validated result.
FP is designed to wrap and bridge existing protocols rather than replace them, enabling incremental adoption while reducing integration and governance overhead.
Design rationale/claim in the paper about interoperability and incremental adoption strategy; no empirical deployment, integration case studies, or measured overhead reductions presented.
FP treats policy, provenance, and audit as first-class concerns.
Design/architectural claim in the paper stating that policy, provenance, and audit are prioritized within FP; no empirical compliance or audit trials presented.
FP provides economic primitives for metering, receipts, and settlement.
Design claim in the paper listing economic primitives as part of FP; no deployment or economic experiments reported.