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).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 claims
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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
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Firm-level AI adoption raises executive compensation in Chinese A-share listed companies (2007–2023).
Baseline panel regressions on a panel of Chinese A-share listed firms (2007–2023) linking a firm-level AI application indicator to executive compensation, controlling for standard firm controls and fixed effects.
From interview-based evidence the authors constructed a conceptual framework that integrates empirical insights with existing theories to explain how human–AI interaction alters design cognition.
Synthesis of qualitative interview findings with literature on creative cognition and design thinking; framework presented as an output of the study (framework construction described in paper).
The paper issues a research agenda for economists: empirically develop instruments linking first‑person temporal reports with behavioral and neural proxies; theoretically incorporate subjective temporality into models of utility, human capital, attention economics, and platform competition; and evaluate policy accounting for temporal‑experience externalities.
Explicitly stated research agenda and methodological recommendations in the paper; no empirical follow‑up included.
Economists will need new empirical measures: validated instruments translating phenomenological constructs (e.g., Chronons) into observable proxies or composite indices for welfare and labor studies, facing standardization and comparability challenges.
Methodological recommendation and discussion in the paper; no empirical measure development or validation reported.
The paper proposes candidate mappings from subjective reports to neural/behavioral signatures (e.g., neural markers of attentional episodes, temporal binding windows) and suggests experimental paradigms to operationalize temporal units.
Methodological proposals and suggested experimental agendas in the paper; no implemented experiments or sample sizes reported.
The framework situates itself at the intersection of neurophenomenology, computational phenomenology, brain–computer interfaces, and human–AI teaming research.
Cross-disciplinary literature synthesis and conceptual mapping in the paper; descriptive claim with no empirical sampling (N/A).
The paper introduces symbolic operators—Chronons, Hexachronons, Metachronos—as theoretical units intended to bridge first-person phenomenology of temporal experience with third‑person neurotechnology descriptions.
Theoretical proposal and definitional introduction within the paper (conceptual development); no experimental validation or sample (N/A).
XChronos is a philosophical-epistemological framework arguing that transhumanism must place subjective temporality (lived time, presence, attention, meaning) at the center of design and evaluation.
Conceptual/philosophical analysis and literature synthesis presented in the paper; no empirical sample or dataset (N/A).
A Random Survival Forest built on curated cancer‑death‑related genes (CDRG‑RSF) achieved the best long‑term prognostic performance among 14 tested ML algorithms for pancreatic cancer, with 3‑ and 5‑year AUCs > 0.7.
Comparison of 14 ML survival algorithms on curated prognostic genes; Random Survival Forest (CDRG‑RSF) reported superior 3‑ and 5‑year AUCs exceeding 0.7 (exact sample sizes/cohort details not provided in summary).
Experimental knockdown of PSME3 reduced proliferation and invasion and increased apoptosis in LUAD cells, implicating the PI3K/AKT/Bcl‑2 pathway as a mediator.
Functional assays (gene knockdown experiments) reported in the PIGRS study showing decreased proliferation/invasion and increased apoptosis after PSME3 knockdown, with pathway analyses implicating PI3K/AKT/Bcl‑2.
Deep neural networks (DNNs) better captured cross‑study differential expression (DEA) signals when predicting miRNA from mRNA than sparse linear models (LASSO); for HIV the cross‑study log2 fold‑change (log2FC) correlation was approximately R ≈ 0.59 for the DNN approach.
Analysis on seven paired viral infection datasets (including WNV and HIV); compared DNNs vs. LASSO for mRNA→miRNA prediction; reported cross‑study log2FC correlation R ≈ 0.59 for HIV for the DNNs. Methods included differential expression signal recovery across studies.
An AI‑powered pipeline (EPheClass) produced a parsimonious saliva microbiome classifier for periodontal disease with AUC = 0.973 using 13 features.
EPheClass pipeline using ensemble ML (kNN, RF, SVM, XGBoost, MLP), centred log‑ratio (CLR) transform and Recursive Feature Elimination (RFE); reported performance AUC = 0.973 for periodontal disease model with 13 features (sample size not specified in summary).
About 78% of the included studies document productivity increases related to digital transformation initiatives.
Quantitative summary across the 145 included studies indicating the proportion reporting productivity gains (~78%).
A systematic review of 145 empirical studies (published 2020–2025) finds a consistent positive association between digital transformation and work productivity.
Systematic review following PRISMA 2020 of 145 included empirical studies identified and screened from searches (see Methods); inclusion period 2020–2025; productivity outcomes extracted from each study.
A PaaS layer enables industry-specific customization (complex contract logic, milestone handling, multi-entity consolidation).
Paper's architectural proposal; described as the role of PaaS in the hybrid framework. This is a design claim, not a measured outcome in the summary.
A SaaS layer should provide standardized accounting, invoicing, and reporting workflows for the EPC industry.
Architectural proposition in the paper: design recommendation rather than an empirically isolated test. The claim is descriptive of the proposed architecture.
Core supply‑chain management challenges targeted by simulation are production layout, product strategy, and managing volume and variety.
Survey and critique of simulation applications presented in the paper; conceptual taxonomy of application areas.
The paper proposes a 'manufacturing operation tree'—an organizationally structured framework—to guide development of more realistic, validated, and industry‑relevant simulation models.
Conceptual/modeling output in the paper (diagram and explanation of the manufacturing operation tree); theoretical development rather than empirical testing.
Econometric and causal-inference tools (difference-in-differences, instrumental variables, randomized encouragement designs) are needed to estimate long-term effects of personalized robot interventions.
Recommended methodological agenda for AI economists in the paper; no applied causal studies presented.
Research and deployment will require new datasets: longitudinal multimodal interaction logs, user preference surveys, simulated user populations, and ethically annotated datasets for fairness and safety evaluation.
Data & Methods recommendations based on identified empirical needs; no dataset release or analysis in this paper.
Measuring welfare impact of personalized robots requires going beyond engagement to include non-market outcomes such as well-being, autonomy, and mental health.
Methodological recommendation in the implications and evaluation sections; no empirical measures provided.
A/B testing and longitudinal field studies are necessary for real-world validation of robot personalization, and metrics should include welfare-oriented outcomes (well-being, trust) in addition to engagement.
Recommended evaluation strategy drawing from HRI and RS experimental standards; no field trials reported in this work.
Prior to live trials, offline RS evaluation metrics (precision/recall, NDCG), counterfactual/off-policy estimators, and simulated users should be used to validate personalization policies.
Methodological recommendation based on RS evaluation practices; no empirical comparison with live trials in robots presented.
Contextual bandits and counterfactual/off-policy learning can enable safe exploration and off-policy evaluation when adapting robot interactions from logged data.
Methodological synthesis referencing contextual bandit and counterfactual learning techniques from RS and causal inference; no robotic implementation experiments reported.
Sequence-aware recommenders (RNNs, Transformers, Markov/session-based models) are suitable for modeling session dynamics and short-term preference shifts in robot interactions.
Survey of sequence/temporal RS models and their typical use cases; conceptual recommendation only.
RS tooling covers long-term user profiles, short-term/session signals, context-awareness, multi-objective ranking, and evaluation methods suited for personalization at scale.
Review of recommender-systems methods and tooling in the literature; conceptual synthesis without empirical new data.
Recommender systems are specialized in representing, predicting, and ranking user preferences across time and contexts (e.g., collaborative filtering, content-based models, sequential/session models).
Established RS literature surveyed and cited as the basis for the claim; conceptual argument, no new experiments.
Digital trade development raises city-level house prices in China in a robust, linear manner.
City-level panel regressions using a constructed digital trade index (entropy-TOPSIS aggregation of multiple indicators). Authors report tests for nonlinearity (none found) and multiple robustness checks. Sample: Chinese cities (years and exact sample size not specified in the summary).
Breakthroughs in structure prediction arise from end‑to‑end deep models that combine evolutionary information (MSAs, coevolutionary signals), geometric constraints and equivariant architectures, and large‑scale pretraining on sequence databases.
Paper describes methodological components: end‑to‑end architectures using MSAs, SE(3)/E(3)-equivariant layers, transformer‑based pretraining on UniRef/UniProt/metagenomic catalogs; no quantitative ablation studies are provided in the text.
Creators explicitly name advertising, direct sales, affiliate marketing, and revenue-sharing models as common monetization channels for GenAI-enabled content.
Explicit references to these monetization channels appeared repeatedly across the 377 videos and were extracted during thematic coding.
XAI analyses (e.g., SHAP / feature importance) indicate that forecasted features are among the top contributors to model predictions.
Feature attribution experiments described in the paper using SHAP or similar methods showing high importance scores for TSFM-generated forecasted features in the downstream regression.
The forecasted features produced by a frozen TSFM drive most of the predictive gains.
Ablation studies reported in the paper that remove forecasted features and measure performance degradation, plus XAI analyses (feature importance / SHAP) showing forecasted features rank highly.
Practitioners adopt methodological adaptations — including adaptive/longitudinal designs, versioning/documentation, stratification/moderation analyses, robustness checks, mixed methods, deployment-stage monitoring, and pre-analysis plans — to mitigate validity threats.
Reported mitigation strategies aggregated from the 16 semi-structured interviews and described in the paper's 'Practitioner solutions' section.
A hybrid architecture where cross-domain integrators encapsulate complex subgraphs into well-structured “resource slices” reduces price volatility (approximately 70–75%) without losing throughput.
Ablation experiments comparing baseline decentralised market vs hybrid integrator architecture across simulation configurations (subset of the 1,620 runs, multiple random seeds per configuration). The paper reports ~70–75% reduction in measured price volatility metrics for hybrid vs non-hybrid cases while throughput remained statistically indistinguishable.
Agents detected up to 65% of vulnerabilities in some experimental settings.
Reported detection rate maxima from the study's experiments on certain model/scaffold/task combinations.
The authors constructed a contamination-free dataset of 22 real-world smart-contract security incidents that postdate every evaluated model's release.
Curation procedure described in the methods: 22 incidents selected to occur after all model release dates to prevent leakage.
This study expanded the evaluation matrix to 26 agent configurations spanning four model families and three scaffolding approaches.
Methods reported in this study specifying 26 agent configurations, four model families, and three scaffolds.
EVMbench (OpenAI, Paradigm, OtterSec) reported agents detecting up to 45.6% of vulnerabilities and achieving exploitation on 72.2% of a curated subset.
Reported metrics from the original EVMbench paper/benchmark (as summarized in this study).
Under NFD, agents are initialized with minimal scaffolding and grown through structured conversational interaction with domain practitioners, with the Knowledge Crystallization Cycle consolidating tacit dialogue into structured, reusable knowledge assets.
Architectural specification and operational formalism in the paper; supported by a detailed case study (iterative co-development with financial analysts, logged interaction transcripts and produced artifacts). Sample size for the case study is not specified.
Label changes across rounds concentrate on statements judged as ambiguous; statement ambiguity drives most label changes.
Participants provided labeling rationale and self-reported uncertainty for each of the 30 statements per round; analyses showed higher change rates for statements with higher self-reported uncertainty/ambiguous wording.
The penalized framework induces centroid estimation and dataset-specific shrinkage whose strength is controlled by a penalty parameter, enabling tunable information sharing.
Method formulation in the paper: penalized likelihood with KL term; derivation showing centroid estimated from pooled datasets and penalty parameter governing shrinkage magnitude; discussion of tuning.
The KL-penalized estimators achieve provably lower mean squared error (MSE) than dataset-specific maximum likelihood estimators.
Non-asymptotic and/or asymptotic analyses provided in the paper that compare MSE of KL-penalized estimators to MLEs (mathematical proofs/sketches in theoretical section).
The KL-based shrinkage estimators adapt to the true degree of shared information across datasets (i.e., they automatically perform partial pooling when appropriate).
Theoretical characterization of the estimator's dependence on the penalty strength and centroid, plus simulation studies varying degree/structure of heterogeneity to show adaptive behavior.
A KL-divergence penalty that shrinks dataset-specific distributions toward a learned centroid yields simple closed-form estimators for linear models.
Methodological development in the paper: formulation of a penalized likelihood/objective using KL divergence; algebraic derivations producing closed-form solutions for the centroid and shrunken dataset estimates (closed forms presented in the paper).
The learned adaptive policy outperformed a fixed-wrench baseline by an average of 10.9% across five material setups.
Empirical evaluation: comparison between learned adaptive policy and a fixed-wrench policy on five different material setups; the paper reports an average improvement of ~10.9% (the exact performance metric formulation and per-setup statistics are not provided in the summary).
Integrating AI (notably ML and NLP) meaningfully automates routine software engineering tasks across requirements management, code generation, testing, and maintenance.
Systematic literature review of prior AI-for-SE work combined with an empirical survey of software engineering professionals reporting usage and examples of tool-supported automation; sample size for the survey not specified in the summary.
A speculative WikiRAT instantiation on Wikipedia illustrates RATs' design and potential uses.
The paper presents WikiRAT as a speculative prototype/illustration; no large-scale deployment or user study of WikiRAT is reported.
RATs record sequences of interaction: traversal (what is read and in what order), association (links and connections the reader forms), and reflection (annotations, notes, time spent), producing inspectable, shareable trajectories.
Design specification within the paper and description of data types RATs would collect (ordered page/navigation logs, hyperlinks followed, time-on-page, annotations, saved excerpts, tags, notes). This is a definitional claim about the proposed system rather than empirical measurement.
An autoencoder-based ODE emulator that maps parameter values to latent trajectories can flexibly generate different solution paths conditioned on parameters.
Architecture and experiments: authors present a novel encoder/decoder ODE emulator that learns latent representation of trajectories and maps parameter vectors to latent trajectories; empirical examples provided (details not in summary).
A quantile emulator trained conditional on MCMC parameter draws can produce conditional quantile predictions without training a Bayesian neural network.
Method and empirical demonstration: paper describes and implements a quantile emulator (network trained to predict conditional quantiles across parameter draws).