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 |
As a secondary external check, we evaluate the consistency of reconstructed signals against stock-price data for a multi-firm dataset of AI-related news titles (November 2024 to February 2026).
Empirical evaluation reported in the paper using reconstructed signals compared to stock-price time series over the specified date range; described as a 'multi-firm' dataset (exact number of firms not stated in the abstract).
Because ground-truth longitudinal sentiment labels are typically unavailable, we introduce a label-free evaluation framework based on signal stability diagnostics, information preservation lag proxies, and counterfactual tests for causality compliance and redundancy robustness.
Methodological contribution described in the paper (evaluation framework proposal).
We present a modular three-stage pipeline that (i) aggregates article-level scores onto a regular temporal grid with uncertainty-aware and redundancy-aware weights, (ii) fills coverage gaps through strictly causal projection rules, and (iii) applies causal smoothing to reduce residual noise.
Description of proposed algorithm/pipeline in the paper (design/implementation claim).
Rather than treating this as a classification challenge, we propose to frame it as a causal signal reconstruction problem: given probabilistic sentiment outputs from a fixed classifier, recover a stable latent sentiment series that is robust to the structural pathologies of news data such as sparsity, redundancy, and classifier uncertainty.
Methodological proposal presented in the paper (conceptual framing and problem statement).
Automatic speech recognition (ASR) has shown increasing potential to assist in the transcription of endangered language data.
Background claim in the paper, referring to advances in ASR and prior work suggesting utility for endangered-language transcription; stated as motivation rather than a novel empirical finding in this paper.
We train an ASR model that achieves a character error rate as low as 15%.
Reported quantitative evaluation of the trained ASR model on the constructed Ikema dataset (character error rate = 15%). Exact evaluation protocol, test set size, and train/test split not provided in the abstract.
We construct a {\totaldatasethours}-hour speech corpus from field recordings.
Stated in paper as an outcome of the authors' data-collection and corpus-construction effort from field recordings; no numeric value resolved in the provided text (placeholder present).
With calibrated oversight that aligns accountability to real-world risks, AI can secure the profession’s future.
Normative/prognostic claim in the Article (argument that appropriate governance will preserve or strengthen the legal profession).
With calibrated oversight that aligns accountability to real-world risks, AI can improve service quality in legal services.
Normative/prognostic claim in the Article (argument that governance plus AI yields quality improvements). No empirical effect sizes reported in the excerpt.
While the risks of AI are real, they must not eclipse the opportunity: with calibrated oversight that aligns accountability to real-world risks, AI can expand access to legal services.
Normative claim and projected benefit argued by the authors (theoretical/argumentative; no empirical evidence in excerpt).
Using agentic financial transactions as an example, we demonstrate how governments and regulators can use this monitoring method to extend oversight beyond model outputs to the tool layer to monitor risks of agent deployment.
Paper includes a case demonstration (agentic financial transactions) showing application of MCP monitoring to identify and assess risky tool deployments and to inform regulatory oversight.
The share of 'action' tools rose from 27% to 65% of total usage over the 16-month period sampled.
Time-series usage/download data from MCP servers across the 16-month sample (paper reports increase in share of action tools from 27% to 65%).
Software development accounts for 90% of MCP server downloads.
Download metrics from monitored MCP servers stratified by tool domain indicating 90% of downloads are for software development tools (paper statement).
Software development accounts for 67% of all agent tools.
Categorisation of the 177,436 monitored agent tools by task domain (O*NET mapping) yielding 67% in software development.
We evaluated 177,436 agent tools created from 11/2024 to 02/2026 by monitoring public Model Context Protocol (MCP) server repositories.
Empirical monitoring of public MCP server repositories; dataset of 177,436 agent tools collected over the period 11/2024–02/2026 (as stated in paper).
The framework provides a roadmap for coordinated response across educational institutions, government agencies, and industry to ensure workforce resilience and domestic leadership in the emerging agentic finance era.
Authors' proposed integrated roadmap (prescriptive recommendation; no empirical testing or outcome measurement reported in the provided text).
We develop a comprehensive government policy framework including: 1) Federal AI literacy mandates for post-secondary business education; 2) Department of Labor workforce retraining programs with income support for displaced financial professionals; 3) SEC and Treasury regulatory innovations creating market incentives for workforce development; 4) State-level workforce partnerships implementing regional transition support; and 5) Enhanced social safety nets for workers navigating career transitions during the estimated 5-15 year transformation period.
Author-presented policy framework and recommendations (policy design proposals and an asserted 5–15 year transformation timeframe; no empirical evaluation reported).
We propose a multi-layered integration strategy for higher education encompassing: 1) Foundational AI literacy modules for all business students; 2) A specialized "Agentic Financial Planning" course with hands-on labs; 3) AI-augmented redesign of core courses (Investments, Portfolio Management, Ethics); 4) Interdisciplinary project-based learning with Computer Science; and 5) A governance and policy module addressing regulatory compliance (NIST AI RMF, SEC regulations).
Proposed curricular framework presented by the authors (recommendation/proposal, not empirically tested within the paper).
The ultimate competitive edge lies in an organization's ability to treat AI not as a standalone tool, but as a core component of sustainable, long-term corporate strategy.
Concluding normative claim in the paper; presented as an interpretation/synthesis rather than supported by cited empirical evidence in the abstract.
Successful global expansion is no longer predicated solely on physical presence but on the deployment of scalable, localized AI models that navigate diverse regulatory, linguistic, and cultural landscapes.
Argumentative claim in the paper describing a strategic determinant for global expansion; no empirical sample or quantified outcomes presented in the abstract.
AI hyper-personalizes customer engagement.
Declarative claim in the paper about AI's effect on customer engagement personalization; no experimental or observational data reported in the abstract.
AI acts as an internal engine for operational agility by compressing R&D cycles.
Claim made in the paper asserting R&D cycle compression due to AI; no empirical data, sample size or quantitative measures provided in the abstract.
The strategic focus has transitioned from mere process automation to autonomous orchestration, where multi-agent systems independently manage complex, cross-border operations and real-time decision-making.
Analytic statement from the paper describing an observed/argued shift in strategic focus; no empirical methodology or sample reported.
Organizations leverage agentic workflows and domain-specific intelligence to catalyse strategic innovation and facilitate global expansion in the digital era.
Conceptual claim in the paper describing how organizations use specific AI capabilities; no empirical design or sample described in the abstract.
The rapid evolution of Artificial Intelligence (AI) has shifted from a disruptive trend to the fundamental operating layer of the modern enterprise.
Statement/assertion in the paper (conceptual/positioning claim); no empirical method, sample size, or statistical analysis reported in the abstract.
The analysis provides a transparent measurement framework and baseline statistics for tracking the emerging shift from AI discussion to action-oriented, agentic deployments in finance.
Methodological contribution claim: presentation of an auditable dictionary-and-context approach plus reported baseline statistics (percentages by year).
Autonomy evidence focuses on regions with higher control density, consistent with governance maturity serving as a prerequisite for action-taking deployments.
Comparative text-as-data analysis showing agentic/autonomy references concentrated in disclosure windows with higher measured controls density; interpretive claim linking this pattern to governance maturity as a prerequisite.
Agentic disclosures are absent in 2021–2023, appear in 2024 (0.4% of firm-years), and increase in 2025 (1.6% of firm-years), indicating a late but accelerating diffusion phase.
Empirical counts/percentages reported from the assembled panel; per-year denominators are 500 firm–year observations (500 firms per year).
We implement an auditable dictionary-and-context approach that flags agentic references and then quantifies the surrounding 'controls density' (governance and safety language) within the same local disclosure window.
Methods description: dictionary-and-context text-as-data approach and a quantified 'controls density' metric applied to filings.
We assemble a balanced panel of 2,500 firm–year observations (500 firms per year) from 2021–2025.
Stated dataset construction in the paper: balanced panel across years with 500 firm–year observations per year, total 2,500 firm–years.
Agentic artificial intelligence (AI) systems can execute actions rather than merely generate content.
Conceptual/definitional statement in the paper framing agentic AI as systems that execute actions (not an empirical test).
Transparency’s effectiveness in promoting data-sharing is amplified by, and dependent upon, user trust; fostering trust in AI may be a more vital prerequisite for data-sharing than implementing transparent designs.
Synthesis of experimental findings (N=240): transparency increased willingness only among users with pre-existing trust; null effect of transparency alone on actual sharing; authors conclude that trust moderates transparency effects and recommend focusing on trust-building.
Immediate sharing decisions were largely driven by intuitive System 1 processing rather than deliberative evaluation (System 2).
Interpretation of the pattern in experimental data (N=240): high, similar sharing rates across conditions despite differing stated willingness-to-share and measured privacy concerns; authors attribute this to dual-process dynamics (System 1 driving immediate behavior).
The positive effect of transparency on willingness to share was contingent on pre-existing user trust in AI, particularly for white-box systems.
Moderation analyses reported from the experiment (N=240): interaction between transparency (white-box vs black-box) and measured pre-existing trust in AI showed increased willingness-to-share only among users with higher trust, with the effect most pronounced for white-box systems.
We conducted a pre-registered online experiment (N=240) where participants interacted with a fictional sleep-optimization app and were randomly assigned to scenarios where data was processed by either a human expert, a transparent white-box AI, or an opaque black-box AI.
Pre-registered online experimental design described in paper; random assignment to three processing-entity conditions (human, white-box AI, black-box AI); sample size reported as N=240; measured outcomes included actual data-sharing and willingness to share, plus trust and privacy concerns.
A Metacognitive Co-Regulation Agent (in CRDAL) assists the Design Agent in metacognition to mitigate design fixation, thereby improving system performance for engineering design tasks.
Mechanistic claim supported by the paper's experimental results on the battery pack design problem showing CRDAL outperforming SRL and RWL; detailed measures of fixation reduction not provided in the excerpt.
The CRDAL system navigated through the latent design space more effectively than both SRL and RWL.
Empirical analysis on the battery pack design task comparing latent-space trajectories/exploration between CRDAL, SRL, and RWL; details on how 'more effectively' was quantified and sample size are not provided in the excerpt.
The CRDAL system achieves better design performance without significantly increasing the computational cost compared to SRL and RWL.
Empirical claim based on experiments on the battery pack design problem comparing computational cost across CRDAL, SRL, and RWL; exact computational metrics and sample size not provided in the excerpt.
In the battery pack design problem examined here, the CRDAL system generates designs with better performance compared to a plain Ralph Wiggum Loop (RWL) and the metacognitively self-assessing Self-Regulation Loop (SRL).
Empirical comparison on a battery pack design task between CRDAL, SRL, and RWL reported in the paper; exact number of test instances or runs not stated in the excerpt.
We propose a novel Co-Regulation Design Agentic Loop (CRDAL), in which a Metacognitive Co-Regulation Agent assists the Design Agent in metacognition to mitigate design fixation.
Methodological contribution presented in the paper (proposed system architecture). No empirical sample size reported for the proposal itself.
We propose a novel Self-Regulation Loop (SRL), in which the Design Agent self-regulates and explicitly monitors its own metacognition.
Methodological contribution presented in the paper (proposed system architecture). No empirical sample size reported for the proposal itself.
When models are used in research, potential threats to inference should be systematically identified alongside the steps taken to mitigate them, and specific justifications for model selection should be provided.
Prescriptive recommendation in the paper (normative guidance) based on the authors' analysis of threats to inference; no empirical testing reported in abstract.
The inferential issues that closed models present can be resolved or mitigated by certain measures.
Paper's analytic discussion of mitigation strategies and ways to resolve or reduce threats to inference; no empirical validation or quantified results provided in the abstract.
EcoThink offers a scalable path toward a sustainable, inclusive, and energy-efficient generative AI Agent.
Concluding claim in the paper asserting broader impact and scalability of the proposed method (position/interpretive claim based on reported results).
Extensive evaluations were performed across 9 diverse benchmarks.
Statement in the paper that evaluations were run on 9 benchmarks (as stated in the abstract).
EcoThink employs a lightweight, distillation-based router to dynamically assess query complexity, skipping unnecessary reasoning for factoid retrieval while reserving deep computation for complex logic.
Methodological description of the proposed framework in the paper (design/architecture claim).
EcoThink reduces inference energy by up to 81.9% for web knowledge retrieval.
Experimental result reported in the paper (maximum observed reduction for the web knowledge retrieval benchmark, as stated in the abstract).
EcoThink reduces inference energy by 40.4% on average across 9 diverse benchmarks.
Experimental evaluations reported in the paper across 9 benchmarks comparing inference energy of EcoThink versus baseline (as stated in the abstract).
Policy efficacy varies significantly across corporate profiles, with the strongest effects observed in non-state-owned enterprises, high-tech firms, and firms located in eastern regions.
Heterogeneity analyses reported in the study (subgroup analysis by ownership, technology intensity, and geographic region).
The estimated positive effect of the pilot zones on corporate NQPF is robust across a comprehensive battery of robustness and endogeneity tests.
Paper reports multiple robustness and endogeneity checks (details not provided in abstract) that reportedly do not overturn main findings.