Evidence (3103 claims)
Adoption
5267 claims
Productivity
4560 claims
Governance
4137 claims
Human-AI Collaboration
3103 claims
Labor Markets
2506 claims
Innovation
2354 claims
Org Design
2340 claims
Skills & Training
1945 claims
Inequality
1322 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 378 | 106 | 59 | 455 | 1007 |
| Governance & Regulation | 379 | 176 | 116 | 58 | 739 |
| Research Productivity | 240 | 96 | 34 | 294 | 668 |
| Organizational Efficiency | 370 | 82 | 63 | 35 | 553 |
| Technology Adoption Rate | 296 | 118 | 66 | 29 | 513 |
| Firm Productivity | 277 | 34 | 68 | 10 | 394 |
| AI Safety & Ethics | 117 | 177 | 44 | 24 | 364 |
| Output Quality | 244 | 61 | 23 | 26 | 354 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 168 | 74 | 37 | 19 | 301 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 89 | 32 | 39 | 9 | 169 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 106 | 12 | 21 | 11 | 151 |
| Consumer Welfare | 70 | 30 | 37 | 7 | 144 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 75 | 11 | 29 | 6 | 121 |
| Training Effectiveness | 55 | 12 | 12 | 16 | 96 |
| Error Rate | 42 | 48 | 6 | — | 96 |
| Worker Satisfaction | 45 | 32 | 11 | 6 | 94 |
| Task Completion Time | 78 | 5 | 4 | 2 | 89 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 17 | 9 | 5 | 50 |
| Job Displacement | 5 | 31 | 12 | — | 48 |
| Social Protection | 21 | 10 | 6 | 2 | 39 |
| Developer Productivity | 29 | 3 | 3 | 1 | 36 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Skill Obsolescence | 3 | 19 | 2 | — | 24 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Labor Share of Income | 10 | 4 | 9 | — | 23 |
Human Ai Collab
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Historical precedents from past technological revolutions suggest that innovation tends to expand, rather than shrink, the scope of economic activity and employment in the long run.
Paper draws on analysis of economic history (qualitative historical analysis implied; no specific historical datasets or sample sizes provided in the abstract).
The paper studies principal-agent alignment using revealed preference techniques.
Stated methodological approach in the abstract; implies analytical use of revealed-preference methods for identification.
The AI's alignment (similarity of human and AI preferences) can be generically identified in the field setting, where only AI choices are observed.
Analytical/theoretical identification result presented in the paper using revealed preference techniques (as stated in abstract); no empirical sample reported in the abstract.
The AI's alignment (similarity of human and AI preferences) can be generically identified in the laboratory setting, where both human and AI choices are observed.
Analytical/theoretical identification result presented in the paper using revealed preference techniques (as stated in abstract); no empirical sample reported in the abstract.
The paper introduces the Luce Alignment Model, where the AI's choices are a mixture of two Luce rules, one reflecting the human's preferences and the other the AI's.
Paper proposes and defines a new theoretical model (model specification described in abstract).
Human decision makers increasingly delegate choices to AI agents.
Stated as motivation in the abstract; no empirical data or sample described in the provided text.
By formalizing the end-to-end transaction model together with its asset and incentive layers, EpochX reframes agentic AI as an organizational design problem focused on infrastructures where verifiable work leaves persistent, reusable artifacts and value flows support durable human-agent collaboration.
Theoretical framing and normative claim in the paper; no empirical evaluation demonstrating that this reframing yields measurable benefits.
Credits lock task bounties, allow budget delegation, settle rewards upon acceptance, and compensate creators when verified assets are reused.
Functional description of the credit mechanics and settlement rules within the proposed EpochX marketplace; presented as part of system design without empirical settlement or user-behavior data.
EpochX introduces a native credit mechanism to make participation economically viable under real compute costs.
Proposed economic/incentive mechanism described in the paper; no empirical cost analysis, pricing model validation, or participant economic outcomes reported.
These assets are stored with explicit dependency structure, enabling retrieval, composition, and cumulative improvement over time.
Design-level assertion about data model/asset graph in the EpochX proposal; no empirical results demonstrating retrieval/composition or measured cumulative improvement.
Each completed transaction can produce reusable ecosystem assets, including skills, workflows, execution traces, and distilled experience.
Architectural claim about artifacts produced per transaction in EpochX; described as a design goal rather than backed by empirical evidence or deployment data.
Claimed tasks can be decomposed into subtasks and executed through an explicit delivery workflow with verification and acceptance.
Design description of the workflow and verification/acceptance mechanisms in the proposed EpochX architecture; no empirical testing or metrics reported.
EpochX treats humans and agents as peer participants who can post tasks or claim them.
Architectural/design specification in the paper describing participant roles and interactions; no empirical validation provided.
We introduce EpochX, a credits-native marketplace infrastructure for human-agent production networks.
System/design description in the paper (architectural proposal); no deployment, user study, or evaluation results reported.
Google has been pioneering machine learning usage across dozens of products.
Contextual statement in the abstract about the organization's activity; asserted without empirical detail in abstract.
The techniques and approaches described can be generalized for other framework migrations and general code transformation tasks.
Authors' stated expectation/generalization claim in the abstract; no empirical evidence or cross-framework experiments reported in the abstract.
The system creates a virtuous circle where effectively AI supports its own development workflow.
Conceptual claim supported by the system's design and reported improvements that enable iterative AI-assisted development; described qualitatively in the paper.
Our approach dramatically reduces the time (6.4x-8x speedup) for deep learning model migrations.
Quantitative speedup figure reported in the paper's abstract (6.4x-8x); likely based on measured migration times on demonstrated cases, though the abstract does not state sample size or exact experimental setup.
The system accelerates code migrations in a large hyperscaler environment on commercial real-world use-cases.
Reported demonstration and evaluation in a hyperscaler (commercial) environment using real-world cases as described in the paper; no detailed sample size given in abstract.
We define quality metrics and AI-based judges that accelerate development when the code to evaluate has no tests and has to adhere to strict style and dependency requirements.
Design and implementation of quality metrics and AI-based judges described in the paper; claimed acceleration of development workflow (no numeric quantification in abstract).
We built an AI-based multi-agent system to support automatic migration of TensorFlow-based deep learning models into JAX-based ones.
System implementation and description in the paper; demonstration on real-world code migration tasks in a hyperscaler environment (qualitative description in abstract).
The dataset, contexts, annotations, and evaluation harness are released publicly.
Paper states that dataset, contexts, annotations, and evaluation harness are released publicly (release / open-source claim).
A structured 2,000-token diff-with-summary prompt outperforms a 2,500-token full-context prompt (enriched with execution context, behaviour mapping, and test signatures) across all 8 models.
Direct prompt/context-size comparison across the 8 models on SWE-PRBench; reported that the 2,000-token diff-with-summary prompt yields better performance than the 2,500-token full-context prompt with extra enrichments.
The LLM-as-judge framework used for evaluation is validated at kappa = 0.75.
Inter-judge validation reported in paper (agreement metric kappa reported as 0.75). Specific validation sample size not stated in the excerpt.
Pull requests are drawn from active open-source repositories, filtered from 700 candidates using a Repository Quality Score.
Dataset curation procedure reported: initial pool of 700 candidate repositories/PRs filtered by a Repository Quality Score to produce the final benchmark.
We introduce SWE-PRBench, a benchmark of 350 pull requests with human-annotated ground truth for evaluating AI code review quality.
Dataset construction described in paper: benchmark contains 350 pull requests with human annotations. Pull requests drawn from active open-source repositories and filtered from 700 candidates using a Repository Quality Score.
The paper concludes by articulating expected outcomes for management practice and proposes a research agenda calling for future mixed-methods validation of the framework.
Stated conclusion and explicit call for mixed-methods validation; no validation results provided in this paper.
The review derives constructs, hypothesized links among them, and governance implications for managing and institutionalizing workplace AI.
Paper reports that reviewed sources were used to derive constructs and governance implications; this is a conceptual derivation rather than empirical testing.
The framework and synthesis can be used to diagnose patterns of disengagement and pilot-to-production failure in corporate AI initiatives.
Proposed analytical structure derived from literature synthesis and conceptual mapping; intended as a diagnostic tool but not empirically validated within this paper.
The paper integrates adoption frameworks (TAM and TOE) with evidence on human-AI interaction to produce a scaling-oriented conceptual framework for diagnosing disengagement and pilot-to-production failures.
Comparative conceptual analysis and framework building based on reviewed literature; no new empirical validation reported.
Integrating technological, human, and organizational capabilities is important to maximize the benefits of AI in smart manufacturing.
Conclusion based on thematic patterns in interviews, observations, and document analysis from purposively sampled supply chain and production professionals; identified as an implementation implication.
Firms adopting AI-driven forecasting and inventory strategies can achieve higher operational agility, better strategic resource alignment, and maintain a competitive advantage in dynamic manufacturing contexts.
Synthesis and implications drawn from thematic analysis of interviews, site visits, and documents from purposively sampled industry practitioners; presented as study conclusions rather than quantitatively tested outcomes.
AI supports sustainability initiatives within manufacturing operations.
Thematic analysis of practitioner interviews and organizational documentation where respondents linked AI-based forecasting/inventory optimization to sustainability outcomes (e.g., waste reduction).
AI improves supply chain coordination among partners and internal functions.
Interview and document-based thematic findings from purposively sampled supply chain managers and industry experts reporting enhanced coordination following AI adoption.
AI contributes to operational resilience in manufacturing supply chains.
Qualitative evidence from interviews and organizational documents indicating that AI-enabled forecasting and inventory controls improve firms' ability to adapt to disruptions; thematic analysis produced resilience as a reported benefit.
Organizational readiness, skilled personnel, data quality, and robust technological infrastructure are critical factors influencing AI effectiveness.
Recurring themes identified via thematic analysis of semi-structured interviews with supply chain and production professionals, corroborated by observational site visits and organizational documents from purposive sample.
AI reduces excess inventory levels in manufacturing firms.
Thematic findings from interviews, site visits, and documents from industry experts and practitioners who reported decreased excess inventory following AI-driven forecasting and inventory optimization.
AI reduces stockouts in manufacturing supply chains.
Practitioner accounts and organizational document evidence from purposive qualitative sampling and thematic analysis indicating fewer stockouts associated with AI-driven forecasting and inventory controls.
AI adoption reduces operational inefficiencies in manufacturing processes.
Thematic analysis of qualitative data (semi-structured interviews, site observations, organizational documents) from purposively sampled industry practitioners reporting reductions in inefficiencies after AI implementation.
AI supports proactive decision-making among supply chain and production stakeholders.
Qualitative reports from interviews and document review with supply chain managers, production planners, and industry experts; thematic analysis identified proactive decision-making as a theme associated with AI use.
AI enables adaptive inventory management in manufacturing operations.
Findings from thematic analysis of semi-structured interviews with supply chain managers, production planners, and industry experts, plus observational site visits and organizational documents (purposive sampling).
AI technologies enhance forecasting accuracy in smart manufacturing.
Qualitative evidence from purposive sample of supply chain managers, production planners, and industry experts gathered via semi-structured interviews, observational site visits, and organizational documents; analyzed using thematic analysis.
Our dataset is available at https://guide-bench.github.io.
Paper's statement providing a URL for dataset access.
Graphical User Interface (GUI) agents have the potential to assist users in interacting with complex software (e.g., PowerPoint, Photoshop).
Motivating claim in the paper's introduction/abstract, based on prior work and the authors' argument about potential application domains.
Providing user context significantly improved the performance, raising help prediction by up to 50.2pp.
Experimental comparison reported in the paper showing differences in Help Prediction performance with and without provided user context; reported improvement magnitude of up to 50.2 percentage points.
GUIDE defines three tasks - (i) Behavior State Detection, (ii) Intent Prediction, and (iii) Help Prediction that test a model's ability to recognize behavior state, reason about goals, and decide when and how to help.
Paper's benchmark/task definitions describing three evaluation tasks and their goals.
GUIDE consists of 67.5 hours of screen recordings from 120 novice user demonstrations with think-aloud narrations, across 10 software.
Paper's dataset description: dataset construction of screen recordings, number of demonstrations, duration, participant expertise (novice), and inclusion of think-aloud narrations across 10 software.
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).