Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
The emergence of AI agents—systems where large language models serve as the primary reasoning engine, dynamically generating and discarding code as an instrumental resource—constitutes a fundamental restructuring of the software paradigm rather than an incremental improvement.
Argument based on first-principles analysis of complexity scaling and conceptual comparison between traditional software and agentic systems (theoretical analysis presented in the paper).
ALE is intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP-relevant impact.
Author-stated intent and high-level goal of the benchmark.
ALE is designed as a living benchmark: its task pool grows continuously as new workflows and industries are onboarded.
Design and maintenance policy described by the authors.
ALE was developed in collaboration with 250+ industry experts.
Author statement specifying collaborator count.
This paper introduces Agents' Last Exam (ALE), a benchmark designed to evaluate AI agents on long-horizon, economically valuable, real-world tasks with verifiable outcomes.
Description of benchmark introduced by the authors (design claim).
Recent AI systems have achieved strong results on a wide range of benchmarks.
Statement in paper (background/context); refers to existing benchmark results in the literature (no specific benchmarks or datasets named in this excerpt).
The open-source implementation includes audit trails and confidence scoring, providing a replicable foundation for LLM-based actuarial variable extraction in property-casualty insurance.
Authors state the released implementation is open-source and includes audit trail and confidence scoring features; presented as part of the contribution.
Integration with chain ladder reserving demonstrates practical actuarial value: severity-segmented analysis reduced reserve estimation error from 6.5% to 4.0%.
Applied the extracted severity segmentation to chain ladder reserving in an integration experiment; reported reserve estimation error decreased from 6.5% to 4.0%. Sample size/portfolio details not stated in the claim.
We validate 14 core variables using two independent clinical expert reviewers scoring 20 synthetic claims on a five-point Likert rubric, achieving mean scores above 4.0 and a weighted kappa of 0.53.
Validation experiment: two independent clinical expert reviewers scored 20 synthetic claims on a 5-point Likert scale for 14 core variables; reported metrics are mean Likert scores (>4.0) and weighted kappa = 0.53.
A modular four-script Python pipeline processes synthetic FHIR-based claims data and real claims documents, extracting 36 actuarial variables across reserving, ratemaking, and claims management categories.
Authors report implementation of a four-script Python pipeline applied to synthetic FHIR-based claims and real documents, with 36 target variables defined.
We present a proof-of-concept framework using large language models (LLMs) to extract structured actuarial variables from unstructured claims data.
Authors implemented a prototype framework described in the paper (implementation details and pipeline described).
Understanding the evolution of LLM-augmented search is critical for organizations seeking to maintain brand relevance in an AI-augmented information landscape.
Prescriptive concluding claim in paper; based on the authors' synthesis of observed trends and conceptual analysis rather than empirical validation in the provided excerpt.
I have developed LLMbench, a research instrument for the comparative close reading of LLM outputs that visualises token probability distributions, entropy curves, and cross-model divergence.
Description of a tool/method developed by the author (LLMbench); claim about the tool's features as stated in the abstract; no implementation details or evaluation sample sizes provided in the abstract.
Public examples referenced include the reported PocketOS and Replit agentic database-deletion incidents and Moffatt v. Air Canada as an adjudicated output/reliance case.
The paper cites specific public incidents and a legal case as examples supporting its discussion.
The paper makes three contributions: it defines the AI-specific reconstruction problem, operationalizes that problem through CER, and specifies claim-grade evidence for AI reconstruction.
Author-stated contributions in the paper; descriptive of the paper's goals and deliverables.
The paper introduces CER, a use-case-level diagnostic for AI residual risk transfer: C (control boundary) asks whether the system had an enforceable operating envelope; E (evidence reconstruction) asks whether the system state and causal chain can be reconstructed from retained artifacts; R (insurance response) asks whether the reconstructed loss is insured (coverage available and placed, and proof needed to support claim recovery).
Framework introduction and operationalization described in the paper; presented as the paper's primary methodological contribution.
The paper addresses losses in which the insured's AI system is in the causal chain, including externally triggered failures such as prompt injection, retrieval-augmented generation (RAG) poisoning, malicious tool output, credential misuse, and data poisoning.
Scope statement in the paper listing specific failure modes; descriptive rather than empirical.
The relevant question for such losses is not only what loss occurred, but what the system was allowed to do, what it actually did, and whether that reconstructed loss can support insurance claim recovery.
Conceptual framing provided in the paper; presented as the diagnostic/analytic focus rather than backed by empirical data in the excerpt.
AI losses that arise through an insured organization's generative or agentic AI system require state reconstruction, not merely event reconstruction, because the relevant state changes as the system reasons, retrieves, calls tools, and acts.
Argument presented in the paper as a conceptual/theoretical claim about the nature of AI-system-caused losses; no empirical sample or quantitative study reported in the excerpt.
The future of agentic-AI insurance lies not in a single monoline product but in a layered ecosystem of complementary coverages supported by improved governance, transparency, telemetry, and regulatory clarity.
Analytic conclusion/recommendation based on the paper's risk taxonomy, actuarial framework, and parallels to cyber insurance; forward-looking synthesis rather than empirical causal evidence.
A coordinated insurance architecture integrating cyber, technology errors and omissions, product liability, performance-warranty, and affirmative AI-liability coverages with explicit allocation mechanisms and dedicated AI aggregates is proposed.
Design proposal in the paper detailing a layered insurance architecture combining multiple coverages and allocation mechanisms; conceptual design not empirically tested.
The paper proposes an actuarial framework based on exposure assessment, scenario analysis, dependency mapping, and accumulation-risk management, drawing parallels to the evolution of cyber insurance.
Proposed actuarial approach described in the paper, invoking methods like scenario analysis and dependency mapping and analogizing to cyber insurance development; methodological proposal without empirical validation.
The paper develops a framework for understanding underwriting, pricing, reinsurance, and product-design implications for agentic-AI insurance.
Methodological contribution stated in the paper: proposed actuarial/underwriting framework (exposure assessment, scenario analysis, dependency mapping, accumulation-risk management); conceptual development rather than empirical validation.
Large-scale online experiments demonstrate consistent relative improvements in device cold-start engagement.
Reported results from large-scale online experiments in Tubi production (no numerical effect sizes or sample sizes provided in excerpt).
Large-scale online experiments demonstrate consistent relative improvements in impression acquisition.
Reported results from large-scale online experiments in Tubi production (no numerical effect sizes or sample sizes provided in excerpt).
Large-scale online experiments demonstrate consistent relative improvements in promotion speed.
Reported results from large-scale online experiments in Tubi production (no numerical effect sizes or sample sizes provided in excerpt).
Large-scale online experiments demonstrate consistent relative improvements in content cold-start engagement.
Reported results from large-scale online experiments in Tubi production (no numerical effect sizes or sample sizes provided in excerpt).
After training, the learned content encoder generates embeddings for both warm and newly ingested content, enabling implicit graph completion through retrieval of warm surrogate neighbors.
Functional claim based on model training and retrieval behavior described in paper (mechanistic claim; supported by described architecture and training procedure).
The RHS content tower does not use ID-based embeddings, content-side subgraphs, neighbor aggregation, or interaction-derived representations, forcing the content encoder to map intrinsic features into a collaborative-filtering-aware embedding space.
Design choice and intended representational effect described in paper (architectural constraints and claimed representational consequence).
The method is accessible to public entities under budget constraints because it used free AI models.
Author reports that the deployments used free AI models rather than paid services and were implemented within the budgets of the two public units.
The method operates within protocols designed to comply with international and national data-protection law and with the principles of public administration.
Author statement that the method used protocols designed for legal compliance; paper reports no detected incidents and claims protocol adherence.
The analysis is consistent with the hypothesis that the method is portable across agencies with distinct mandates.
Observed positive outcomes in two distinct public-sector units (SES/CONT and UCI/SEDET) after applying the same methodology; author frames this as consistency with portability hypothesis.
UCI/SEDET analyzed cases totaling USD 104.3 million in financial volume during the period examined.
Aggregate monetary total of cases analyzed reported from SEI-GDF official indicators in the paper.
UCI/SEDET issued 288 formal recommendations to public managers during the examined period.
Count of formal recommendations reported from SEI-GDF official indicators as presented in the paper.
UCI/SEDET recorded a 92% increase in technical-report production during the period examined.
Quantitative production figures from SEI-GDF official indicators reported in the paper for UCI/SEDET.
Official indicators from SEI-GDF recorded an average processing time fall of 50% at UCI/SEDET during the period examined.
Quantitative before–after measurement from SEI-GDF official indicators for UCI/SEDET as reported in the paper.
Official indicators from the Electronic Information System of the Federal District Government (SEI-GDF) recorded an average processing time fall of 18.2% at SES/CONT during the period examined.
Quantitative before–after measurement from SEI-GDF official indicators for SES/CONT as reported in the paper.
The method was applied in two distinct units: the Sectoral Internal Control Office of the Federal District Department of Health (SES/CONT) throughout 2024, and the Internal Control Unit of the Federal District Department of Economic Development, Labor and Income (UCI/SEDET) throughout 2025.
Paper reports implementation timelines and unit names; described as auditable cases.
The author developed a four-layer structured pedagogical methodology for teaching generative-AI use in the public sector.
Author description of the methodology in the paper; applied in two case units.
Digital learning platforms and AI-based training tools are increasingly used as central mechanisms to support continuous skill acquisition and professional growth.
Synthesis of prior studies and thematic literature discussed in the editorial (Bankins et al., 2024a; other cited works).
Adoption of STARA increases the need to upskill and reskill workers across skill levels, with even high-skilled workers expected to integrate new digital competencies into their professional trajectories.
Literature synthesis and cited empirical/conceptual studies (e.g. Hani et al., 2025; Ibrahim and Abiddin, 2024; Singh and Chandra, 2026; Tariq, 2026).
The Talent pillar exerts a significant positive effect on tourism’s GDP share with a one-year lag.
Lagged specification (one-year lag) in fixed-effects panel models on 33 countries (2017–2023); reported coefficient β = 0.183, p = 0.025.
The Policy and Governance pillar is a significant positive driver of tourism’s GDP share.
Pillar decomposition with fixed-effects estimation on panel data (33 countries, 2017–2023); reported coefficient β = 0.353, p = 0.037; result robust to alternative SE and two-way fixed effects.
The AI-related R&D pillar is a significant positive driver of tourism’s GDP share.
Pillar decomposition using fixed-effects models on the same 33-country panel (2017–2023); reported coefficient β = 1.811, p = 0.005; effect robust to alternative standard errors and two-way fixed effects.
Journalists and editors exercise bounded and situational agency through local adaptation, self-training, and development of ethical guardrails that institutionalise responsible AI use.
Based on in-depth interviews with newsroom staff (journalists, editors, technical personnel) at Al-Masry Al-Youm; qualitative accounts of local practices such as self-training and the creation of internal ethical rules. Sample size not reported in the excerpt.
The synthesized mixed-objective program retains most of the profit-oriented baseline's funds.
Reported comparison in simulation between the synthesized program and a profit-oriented baseline showing the synthesized program keeps most of the baseline funds while reducing gaming behaviors.
The synthesized mixed-objective program halves rejection.
Results from the LLM-guided evolutionary search experiment reported in the paper: the synthesized program reduces rejection by half in the simulation.
LLM-guided evolutionary code search synthesizes an inspectable mixed-objective program that eliminates up-coding.
Experiment using LLM-guided evolutionary search over the rule-program space within Medi-Sim; the synthesized program reportedly eliminates up-coding behavior in the simulation.
A single audit lever exposes pressure migration: closing the coding channel more than doubles low-complexity selection.
Targeted simulation experiment in Medi-Sim where an audit intervention closes the coding channel; reported effect is >2x increase in low-complexity patient selection.
An incentive sweep recovers classical health-economics findings as adjacent regimes -- up-coding and low-complexity-patient selection under profit pressure.
Simulation experiments (an 'incentive sweep') run in Medi-Sim showing regimes with up-coding and selection of low-complexity patients when profit incentives are increased.