Evidence (8974 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
10085 claims
Filter claims →
Productivity
8974 claims
Filtered →
Governance
8062 claims
Filter claims →
Human-AI Collaboration
7749 claims
Filter claims →
Org Design
5057 claims
Filter claims →
Innovation
4896 claims
Filter claims →
Labor Markets
4088 claims
Filter claims →
Skills & Training
3372 claims
Filter claims →
Inequality
2377 claims
Filter claims →
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 | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Productivity
Remove filter
We establish a comprehensive readability model that synthesizes textual, structural, program, and visual features of code.
Description in paper of a newly constructed readability model combining textual, structural, program, and visual features; model development is presented as a methodological contribution (no numeric effect size).
The study demonstrates that recent archival case evidence can be used rigorously to analyze an emerging strategic phenomenon without reducing the study to a purely descriptive literature review.
Methodological claim supported by the paper's demonstration of within-case coding and cross-case pattern matching applied to recent archival documents for the four firms.
The paper develops a process view of AIECI built on sensing, interpretation, and orchestration as the sequence through which AI inputs are transformed into competitive intelligence capability, intelligence-informed decisions, and economic outcomes.
Theoretical contribution synthesized from cross-case analysis and conceptual development within the paper.
Competitive intelligence (the process of sensing, interpreting, and orchestrating responses) rather than AI as a standalone automation tool is the strategic mechanism through which value is created.
Theoretical argument supported by within-case coding and cross-case synthesis of archival materials from four firms demonstrating how AI functions as part of an intelligence infrastructure rather than as isolated automation.
Across the four cases, AIECI delivered strategic speed under uncertainty (faster, better-timed decisions in uncertain environments).
Archival case evidence (public disclosures and corporate materials) showing firms using AI-enabled intelligence to accelerate decision cycles and respond more quickly to market signals.
Across the four cases, AIECI improved allocation quality (better targeting and resource allocation decisions).
Within- and cross-case coding of corporate materials from the four sampled firms reporting improvements in campaign targeting, budget allocation, and resource deployment linked to AI-driven intelligence.
Across the four cases, AIECI produced efficiency gains and cost relief for firms.
Cross-case evidence from archival corporate disclosures and reports for Walmart, Unilever, Sprinklr, and DoubleVerify showing operational/marketing efficiencies and cost savings linked to AI-enabled competitive intelligence.
Across the four cases, AIECI generated value through revenue acceleration.
Cross-case findings from a qualitative comparative multiple-case design using public archival evidence (annual reports, 10-Ks, earnings releases, corporate materials) for four firms (Walmart, Unilever, Sprinklr, DoubleVerify).
Policy options should centre on building institutional capacity for AGI situational awareness, strengthening Europe's position in the AI value chain, and developing frameworks for international stability in an era of increasingly capable AI systems.
Paper's recommended policy agenda derived from its assessment of risks and gaps (as stated in abstract); the abstract does not report empirical testing of these options or quantified expected effects.
These findings point to a need for a coordinated European preparedness agenda.
Paper's synthesis and policy recommendation based on the identified capability and governance gaps (as stated in abstract); recommendation not supported by quantified impact estimates in the abstract.
A plausible window for AGI emergence falls between 2030 and 2040, or potentially earlier, though substantial uncertainty remains.
Paper's synthesis of empirical trends in AI capabilities, expert forecasting surveys, and policy analysis (as stated in abstract). No specific sample size or survey details provided in the abstract.
Visualizing spatial (localization) uncertainty in the annotation interface improves human-in-the-loop annotation (i.e., localization uncertainty is a lever to improve annotation quality/efficiency).
Synthesis/interpretation in the paper based on the controlled study results (120 participants) and box-level analysis showing improved label quality and reduced time when uncertainty cues were shown.
A box-level analysis confirms that the uncertainty cues redirect annotator effort toward high-uncertainty predictions and away from well-localized boxes.
Box-level analysis reported in paper comparing annotator behavior across predicted boxes with differing localization uncertainty; analysis shows effort reallocation toward boxes labeled as high-uncertainty.
In the same controlled study, participants who received uncertainty cues were faster overall (reduced annotation time).
Same controlled user study with 120 participants comparing interfaces with and without spatial-uncertainty visualizations; paper reports that participants with cues were faster overall.
In a controlled study with 120 participants, those receiving uncertainty cues achieve higher label quality.
Controlled user study reported in the paper; 120 participants; comparison between annotators who received visualized spatial-uncertainty cues via a purpose-built interface and those who did not; paper reports label quality outcomes.
The model identifies simple measures/conditions that characterize when productivity paradoxes and skill polarization arise.
Theoretical derivations and analytical characterizations within the model yielding threshold conditions and measures parameterizing when paradoxical outcomes occur (model-based; no empirical validation).
Sustainable progress requires collaborative integration of humans and machines, rather than replacement.
Normative conclusion/recommendation stated in the paper based on study findings (argument for augmented intelligence over replacement).
This research presents the innovative Marketing Intelligence Operations (MIO) Framework and a practical AI Adoption Readiness Scorecard, enabling leaders to manage the operational balance between transformative efficiency improvements and human capital vulnerability.
Paper states that it introduces a new framework and a practical scorecard as deliverables of the research (descriptive claim about the paper's contributions).
AI-integrated Marketing Intelligence Operations (MIO) quantitatively improves campaign Return on Investment (ROI) by 47%.
Reported as an empirical result from the paper's mixed-methods study (the paper states use of audits, surveys, and NLP analysis to evaluate MIO outcomes).
Deploying LegalCheck in the Municipality of Amsterdam demonstrated substantial efficiency gains, improved legal consistency, and positive user acceptance.
Summary claim based on the real-world deployment outcomes described in the paper (timing improvements, consistency/factual accuracy statements, and reported positive reception by professionals); specific quantitative metrics and sample sizes are not fully reported in the excerpt.
The system produced explainable outputs based on actual regulations and prior cases, providing citations/explainability that support legal reasoning.
Paper describes retrieval from curated legal knowledge bases and generation of outputs grounded in regulations and prior cases during the Amsterdam deployment; presented as a feature of the system and supported by expert review.
LegalCheck uses a combination of Retrieval-Augmented Generation (RAG) and Context-Augmented Generation (CAG) with curated legal knowledge bases and controlled prompting to retrieve relevant laws and precedents and incorporate case-specific details into coherent drafts.
System architecture and methodology described in the paper (design/implementation claim).
Legal professionals found that the system ensured a consistent application of legal standards without replacing human judgment.
Reported qualitative feedback from professionals in the Municipality of Amsterdam deployment and the system design that includes an expert-in-the-loop review; no formal measurement of 'replacement' was reported.
Legal professionals found that the system reduced their workload.
Reported user feedback from legal professionals during the Municipality of Amsterdam deployment; qualitative statements that professionals experienced workload reduction (no numeric workload metrics or sample size reported).
The system's output captured the vast majority of required legal reasoning—often 80% to 100% of essential content.
Reported coverage statistic from the deployment/evaluation described in the paper (phrased as 'often 80% to 100% of essential content'); exact evaluation method, sample size, and measurement protocol are not provided in the excerpt.
LegalCheck maintained high legal consistency and factual accuracy when generating draft letters.
Evaluation during real-world deployment with expert-in-the-loop review and feedback from legal professionals in the Municipality of Amsterdam; claims of high consistency and factual accuracy are reported but no formal numeric accuracy metric or sample size is provided in the text.
LegalCheck produced near-final advice letters in minutes rather than hours.
Reported results from a real-world deployment within the Municipality of Amsterdam; system logs / timing comparisons between human drafting time (hours) and LegalCheck-assisted drafting time (minutes) are described in the paper (no explicit numeric sample size reported).
We outline a research program for the runtime systems that foundation-model software agents will require.
Paper claims to present a forward-looking research agenda or program (stated in abstract); this is a conceptual contribution rather than an empirical finding.
Applied to a controlled validation task, the framework yields episode packages whose evidence structure varies systematically with harness level: lower levels produce only a final patch, while higher levels produce reproduction logs, failure attributions, deterministic requirement checks, and structured verification reports.
Empirical application described in the abstract: framework applied to a controlled validation task showing systematic variation in episode-package evidence structure across harness levels. The abstract does not report sample size or statistical measures.
We propose a trace-based evaluation protocol that converts each agent run into an auditable episode package.
Methodological proposal described in the abstract proposing a trace-based protocol and an auditable episode package format; no quantitative evaluation details provided in the abstract.
We operationalize the harness through a four-level ladder (H0–H3) that progressively exposes runtime support to the agent.
Design contribution described in the paper (abstract) introducing a four-level ladder (H0–H3) as an operationalization of the harness concept.
Foundation models have transformed automated code generation.
Statement in paper's abstract referring to broad impact of foundation models on automated code generation; likely supported by citations and literature overview within the paper (no sample size or quantitative study reported in the abstract).
Authorship preservation should be a design priority for AI tools deployed in identity-relevant, behavior-dependent tasks.
Authors' recommendation based on experimental results showing negative motivational and behavioral consequences of delegating authorship to LLMs despite improved objective goal quality.
Mediation analyses identified psychological ownership as the mechanism: it mediated the authorship effect on every downstream motivational and behavioral outcome, while objective goal quality did not.
Mediation analyses reported in the preregistered experiment (authors tested psychological ownership and objective goal quality as mediators of authorship effects on multiple downstream outcomes); preregistered N = 470.
At two-week follow-up, 72.8% of self-authored participants had acted on two or more of their goals, compared to 46.6% in the LLM condition.
Behavioral follow-up measure collected two weeks after the intervention in the preregistered experiment; percentages reported in the paper/abstract. (Follow-up completion N not specified in the abstract.)
LLM-generated goals scored higher on SMART criteria (specificity, measurability, achievability, relevance, and time-boundedness).
Preregistered randomized experiment comparing self-authored vs LLM-authored goals derived from a personal reflection; reported effect size d = 2.26; total preregistered N = 470.
The Agent-First paradigm is orthogonal and complementary to transport-layer standards such as MCP, operating as the semantic application layer above existing tool discovery and invocation protocols.
Conceptual argument and mapping presented in the paper asserting interoperability/orthogonality with transport-layer standards (e.g., MCP).
Agent-First APIs improve autonomous error recovery by 5.8x (compared to optimized CRUD baselines).
Reported comparative experiments on 50 real operational tasks measuring autonomous error recovery capability.
Agent-First APIs reduce required human interventions by 72.7% (compared to optimized CRUD baselines).
Same set of comparative experiments on 50 real operational tasks reported in the paper.
Comparative experiments on 50 real operational tasks demonstrate that Agent-First APIs achieve 88% end-to-end task success rate versus 64% for optimized CRUD baselines (+37.5%).
Empirical comparative experiments reported in the paper on 50 real operational tasks, comparing Agent-First APIs to optimized CRUD baselines.
The paradigm is implemented and validated in a production multi-tenant SaaS platform serving 85 registered tools across 6 business domains.
Reported production implementation and deployment statistics (platform with 85 registered tools spanning 6 business domains).
We propose the Agent-First Tool API paradigm, comprising three integrated mechanisms: (1) a Six-Verb Semantic Protocol that decomposes tool interactions into search, resolve, preview, execute, verify, and recover phases; (2) a Normalized Tool Contract (NTC) providing structured decision-support metadata including confidence scores, evidence chains, and suggested next actions; and (3) a dual-layer governance pipeline combining static capability policies with dynamic risk escalation.
Design and specification presented in the paper (proposed architecture and components).
LLMs can help generate more correct and functional code compared to participant-generated solutions.
Comparative analysis of generated solutions reported in the paper (no sample-size for solutions explicitly stated in the abstract). The paper states LLM-assisted solutions were more correct/functional.
Qualitative analysis of participants' interactions and interviews revealed four different human-LLM collaboration modes supporting various problem-solving strategies.
Qualitative analysis of interaction logs and retrospective interviews from the study participants (N=20) reported in the paper; identification of four collaboration modes described.
We conducted a within-subject study followed by retrospective interviews with programmers (N=20).
Stated methods in the paper: within-subject experimental design plus retrospective interviews; sample size explicitly given as N=20.
Organizations classified as 'Proactive Integrators' can reduce the risk of obsolescence by up to 53%.
Subgroup finding reported in the study (reduction estimate for organizations labeled 'Proactive Integrators'); specific subgroup sample not provided in abstract.
AI-assisted engineering teams can achieve a 24% increase in productivity.
Empirical finding reported by the study, derived from the mixed-methods analysis (survey of 320 orgs, Delphi with 40 experts, and case studies of 5 industries as described in abstract).
Entities that strategically implement AI can enhance their innovation cycles by up to 30%.
Statement in paper (presented as a forecast/estimate; no specific study or sample detailed in abstract).
AwareLLM opens new avenues for Human-AI collaboration where technology adapts to users' needs rather than users adhering to technological constraints.
Authorial/conceptual claim based on the proposed framework and study results; presented as a broader implication rather than a direct empirical finding.
Participants described AwareLLM's personalized interventions as timely and relevant, helping them boost their confidence and deepen engagement with their work.
Qualitative user feedback reported in the study (participant descriptions); sample size 20. No coding details or counts provided in the abstract.