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 main results are robust to inclusion of firm, industry, and year fixed effects, DID identification using the 2018 SCD pilot, and multiple robustness checks addressing potential confounders and endogeneity.
Authors report baseline regressions with firm/industry/year fixed effects, DID specifications exploiting the 2018 Supply Chain Innovation and Application Pilot Program as a quasi-natural experiment, and a battery of robustness tests (alternative specifications, controls, and checks).
The positive effect of SCD on green innovation is stronger for substantive green innovation (actual environmentally beneficial R&D and technologies) than for strategic green innovation (symbolic/labeling or reputation‑oriented activities).
Heterogeneous outcome analysis splitting green innovation into 'substantive' (e.g., green patents, technological R&D outputs) versus 'strategic' (signaling/compliance indicators); regression and DID estimates show larger and statistically significant coefficients for substantive measures compared to smaller or weaker effects on strategic measures.
Supply chain digitalization (SCD) significantly increases corporate green innovation among Chinese A-share listed firms (2012–2022).
Panel analysis of Chinese A-share listed firms over 2012–2022 using regression models with firm, industry, and year fixed effects; difference-in-differences (DID) identification exploiting the 2018 Supply Chain Innovation and Application Pilot Program as an exogenous shock to SCD; firm-level controls included; multiple robustness checks reported.
Algorithmic transparency and interpretability are important so investors and regulators can understand how ESG inputs affect automated decision systems.
Normative recommendation grounded in literature on model risk, accountability, and regulatory needs; not an empirical finding but a consensus implication of reviewed work.
Research priorities include empirically quantifying AI's effects on productivity, wages, inequality, and environmental costs; developing standardized sustainability and governance metrics; and evaluating regulatory impacts on innovation and welfare.
Stated research agenda based on gaps identified in the narrative review; identifies directions for future empirical work rather than presenting new empirical findings.
AI has progressed from symbolic systems to data-driven, generative architectures and large-scale computational infrastructures, becoming a foundational technology across sectors.
Narrative synthesis of historical and technical literature across AI research and innovation studies; qualitative tracing of architectural shifts (symbolic → statistical → deep learning/generative models) and increased deployment across industries. No original empirical measurement or sample size reported in this paper.
MYRIAD-EU synthesizes progress and remaining challenges and proposes concrete directions for continued research and practice in multi-hazard, multi-risk DRR.
Overall project scope: synthesis and reflection on interdisciplinary research and practice conducted across MYRIAD-EU (2021–2025), as reported in the paper.
MYRIAD-EU conducted in-depth, place-based case studies co-produced with local stakeholders to test methods and tools for multi-risk assessment.
Reported methods include in-depth place-based case studies co-produced with local stakeholders as part of MYRIAD-EU activities (2021–2025).
The main results are robust to inclusion of controls and a range of heterogeneity and moderation checks, supporting that findings are not driven by simple time trends or obvious confounders.
Reported robustness checks in the staggered-DID framework (control variables, alternative specifications, subgroup tests) and discussion of parallel-trends assumption.
Implementation of urban green data center pilot policies leads to measurable improvements in firms' energy utilization efficiency.
Staggered-adoption difference-in-differences (DID) using an unbalanced firm–year panel of Chinese A-share listed firms linked to prefecture-level cities (2012–2024); treatment is timing/location of urban green data center pilot designation; results reported as statistically significant and robust to controls and alternative specifications.
Mechanisms linking digital services to export performance include reduced transaction and search costs, platform network and scale effects, data as an input improving service quality and customization, and task‑level specialization changing comparative advantage.
Conceptual/theoretical synthesis drawing on multiple strands of literature and illustrative case studies presented in the review (no new causal identification).
Digital services trade is shifting from traditional cross‑border delivery toward online, platform‑based models, with cross‑border data flows a core input and determinant of competitiveness.
Integrative literature and policy review synthesizing domestic and international studies; theoretical/conceptual synthesis and cited case examples (no new econometric analysis or primary microdata).
Policy recommendations include standards on explainability, audit trails, certification for finance/tax AI systems, stronger data governance, and public–private coordination to update regulatory guidance.
Paper's policy and governance recommendations drawn from case findings and literature synthesis; prescriptive content rather than evaluated interventions.
Deployments should build governance, explainability, and auditability into systems and start with pilots on high-volume, well-structured tasks before scaling.
Paper recommendations based on case experience and analytic framing; advocated strategy rather than empirically validated at scale within the paper.
To mitigate risks and realize benefits, AI systems in finance/tax should combine AI with human-in-the-loop controls and clear escalation paths.
Prescriptive recommendation grounded in case lessons and literature on safe AI deployment; presented as a best-practice guideline rather than tested intervention.
Technical building blocks leveraged in these deployments include large language models (LLMs), OCR plus structured information extraction, retrieval-augmented generation (RAG) and knowledge bases, and process automation/RPA.
Explicit technical characteristics section and case descriptions in the paper identify these components as core to implementations.
Generative AI is used for risk control and audit functions, including real-time monitoring, fraud detection, KYC/AML screening, and automated exception reporting.
Reported use-cases in the two case organizations and corroborating industry reports discussed in the literature review portion of the paper.
For tax declaration, generative AI enables extraction of tax-relevant facts from invoices and contracts, drafting of tax returns, compliance checks, and scenario simulations.
Case examples and literature synthesis describing OCR + information extraction and LLM-assisted drafting workflows used in practice.
Generative AI is applied to fund management tasks such as cashflow forecasting, anomaly detection, and automated workflows for payments and collections.
Case descriptions and technical mapping in the paper showing implementations at the sharing center and professional services firm level.
Accounting automation use-cases include automated bookkeeping, reconciliations, journal entry suggestion, and error detection using LLMs and document understanding.
Detailed scope mapping and case examples in Xiaomi and Deloitte illustrating these accounting applications; supported by literature review of technical capabilities.
Realizing those AI-driven gains in Vietnam requires legal and institutional redesigns.
Close reading of Vietnam's constitutional provisions, administrative statutes, procedural rules and judicial doctrine (doctrinal legal analysis) combined with comparative lessons from other jurisdictions; no quantitative data.
A supplemental theological differentiator probe achieved perfect rank-order agreement between the two ceiling judges (Spearman rs = 1.00), supporting judge reliability for the ceiling probe.
Reported Spearman rank correlation rs = 1.00 between Gemini Pro and Copilot Pro on the theological differentiator probe used as a reliability check.
Rigorous research priorities include randomized controlled trials with long-run follow-ups, cost-effectiveness studies, structural adoption models, and validated metrics for feedback quality and learning durability.
Actionable research recommendations produced by the 50-scholar interdisciplinary meeting; prescriptive synthesis rather than empirical results.
CABP (Context-Aware Broker Protocol) extends JSON-RPC with identity-scoped request routing via a six-stage broker pipeline to ensure correct identity and policy propagation.
Design and protocol specification included in the paper; formal description and broker-pipeline semantics documented as a deliverable.
Different model families (Sonnet 4.6 vs. Opus 4.6) exhibit stable, systematic differences in methodological preferences and choice patterns—distinct empirical 'styles'.
Comparison of choice patterns and methodological decisions across agents instantiated with Sonnet 4.6 versus Opus 4.6 within the 150-agent experiment, showing consistent between-family differences in measure selection and estimation procedures.
Agents split on measure choice (e.g., autocorrelation vs. variance-ratio tests; dollar-volume vs. share-volume measures), producing different substantive estimates from the same raw data and hypotheses.
Observed categorical divergences in measure selection across the 150 agents during independent analyses of SPY TAQ (2015–2024); documented alternative test/measure families and corresponding divergent effect estimates for the six hypotheses.
AI-to-AI variation (nonstandard errors, NSEs) across autonomous coding agents produces substantial uncertainty in empirical results analogous to human researcher heterogeneity.
Experimental results from 150 autonomous Claude Code agents (two model families: Sonnet 4.6 and Opus 4.6) independently analyzing the same SPY TAQ data (NYSE TAQ, 2015–2024) on six pre-specified hypotheses; recorded agent-to-agent variation in methodological choices and resulting effect estimates (dispersion measured via IQR and related diagnostics).
Observations span multiple agent platforms (Moltbook, The Colony, 4claw) with more than 167,000 agents interacting as peers.
Author-reported coverage from naturalistic observations across the named platforms during the one-month observation window; count reported as ≈167k agents.
The mechanism generalizes to another field: models trained on economics publication records reach ~70% accuracy on a similar benchmark.
Analogue of the management experiment performed in economics: models fine-tuned on economics journal publication records were evaluated on an economics benchmark and achieved approximately 70% accuracy. (Exact dataset sizes, benchmarks, and train/test splits not specified in the provided text.)
Fine-tuned models trained on publication records each outperform every frontier model and the expert panel; the best single model achieves 59% accuracy on the benchmark.
Language models fine-tuned on historical journal accept/reject records were evaluated on the held-out four-tier benchmark; reported performance shows each fine-tuned model exceeds the frontier-model average and the human-panel baseline, with the best model at 59% accuracy. (Exact training set size and benchmark sample count not specified here.)
Panels of journal editors and editorial board members reach 42% accuracy by majority vote on the same four-tier benchmark.
Human baseline obtained by soliciting judgments from journal editors and editorial board members on the held-out benchmark and computing majority-vote accuracy (reported as 42%). (Number of human raters and benchmark size not given in supplied text.)
Fine-tuning language models on historical journal publication decisions recovers an evaluative "scientific taste" that frontier (zero-shot) models and expert editor panels cannot reliably reproduce.
Fine-tuned models were trained on years of journal publication decisions (institutional accept/reject records) and evaluated on a held-out four-tier benchmark of management research pitches; performance compared to zero-shot evaluations of frontier models and to panels of journal editors (majority-vote). (Sample sizes for training records and held-out benchmark not specified in the provided text.)
An asynchronous sliding-window engine treats the GPU as a sliding compute window and overlaps GPU computation with CPU-side parameter updates and multi-tier I/O to hide data movement and synchronization overheads.
System design and implementation described in the paper: an asynchronous runtime that coordinates GPU kernels, CPU updates, and multi-tier I/O. This is a design/implementation claim rather than a measured outcome; the summary links the design to performance improvements.
The A-ToM mechanism operates by estimating a partner's likely ToM order from interaction history and using that estimate to predict the partner's next action which then informs the agent's policy choices.
Method description and implementation details provided in the paper: estimator over ToM orders based on past interactions + conditional action prediction feeding into decision-making; validated in the reported experiments.
Empirical evaluation was performed across four coordination environments: a repeated matrix game, two grid navigation tasks, and an Overcooked task.
Methods section describes these four benchmark environments used for all reported comparisons between fixed-order agents and A-ToM agents; evaluation metrics were joint payoffs and task-specific success measures.
Modular outputs (question histories, security checks, rubric scores, summaries) enable post-hoc review and explainability.
Architectural design and output artifacts described in the paper (logs and structured outputs per agent); these artifacts provide material for explanation and audit.
Adaptive difficulty and multidimensional evaluation allow dynamic tailoring of questions to candidate performance.
Implementation of adaptive testing logic within the workflow described in the paper, with experiments involving dynamic difficulty adjustment; detailed metrics of adaptation effectiveness are not provided in the summary.
Operating as a pre-processor (rather than modifying the generator) enables modular integration with existing LLMs and provides an explicit decision point for clarification.
Novelty/architecture claim in the paper explaining that C.A.P. runs before generation and therefore can be plugged into existing LLM pipelines; described design rationale (no empirical integration study presented).
C.A.P. verifies semantic alignment between the current expanded prompt and the weighted history and triggers a structured clarification protocol when similarity is below a threshold.
Component-level description: alignment verification via semantic embeddings (cosine similarity) or learned classifiers and threshold-based decision branching to initiate clarification; described protocol templates (no empirical validation provided).
C.A.P. retrieves dialogue history using a time-weighted decay so recent context is prioritized (approximating human conversational focus).
Design description of a 'time-weighted context retrieval' component; authors propose temporal decay functions (e.g., exponential decay, half-life parameter) applied to dialogue-turn embeddings or metadata (no empirical results reported).
C.A.P. is a pre-generation module that expands user utterances to recover omitted premises and implications.
Architecture and methods description in the paper specifying a 'semantic expansion' component; suggested implementations via knowledge-bases or small LLM prompts to generate premises, paraphrases, and implications (no empirical evaluation reported).
Structured argumentation frameworks make chains of inference inspectable and machine-checkable, improving transparency and verifiability of AI outputs.
Argument from formal properties of AFs and representation; no empirical user studies but relies on known formal semantics.
Computational argumentation offers formal, verifiable reasoning representations (argumentation frameworks, attack/support relations).
Established literature on formal argumentation (e.g., Dung-style AFs) and the paper's conceptual description; no new empirical data reported.
The development artifacts are fully transparent and reproducible: the repository includes an archive of 229 human prompts and a git history with 213 commits.
Paper reports counts of prompts (229) and git commits (213) and states these archives are public; these are concrete repository metrics (n=1 development repository).
The Lean kernel provided full machine verification of all formalized statements in the development.
Paper reports 'Full verification by the Lean kernel' for the Lean 4 development; supported by availability of the Lean 4 repository and verified theorem artifacts (n=1 project).
A specialized prover (Aristotle) automatically closed 111 lemmas during the development.
Quantitative verification metric reported in the paper: 111 lemmas automatically closed by Aristotle; claim tied to the Lean development and prover logs (single project count).
The AI-assisted pipeline combined an AI reasoning model (Gemini DeepThink) to generate the proof, an agentic coding tool (Claude Code) to translate the proof to Lean, a specialized automated prover (Aristotle) that closed 111 lemmas, and the Lean kernel to fully verify the result.
Project workflow description and verification metrics in the paper; reported counts and named components (Gemini DeepThink, Claude Code, Aristotle, Lean kernel); repository and logs purportedly document toolchain usage (n=1 project; 111 lemmas closed by Aristotle reported).
A complete formalization in Lean 4 of the equilibrium characterization for the Vlasov–Maxwell–Landau (VML) system was produced through an AI-assisted pipeline.
Single-project artifact: a Lean 4 development containing formal statements, proof scripts and verified theorems reported by the paper (n=1 project); authors report full machine verification by the Lean kernel and provide the repository as public evidence.
In the human–human benchmark, repeated pre-play communication substantially increases cooperation.
Reference benchmark data from Dvorak & Fehrler (2024), human–human sample n = 108, showing higher cooperation under repeated communication relative to less frequent communication; comparison reported in the paper.
Evaluation metrics for the benchmark include task-specific metrics such as win-rate for battling and completion time for speedruns, as well as strategic robustness measures.
Paper's evaluation section lists metrics used: win-rate, completion time, strategic robustness; describes how they are computed and used to compare agents.