Evidence (16496 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
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 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 | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Traditional ads recommendation systems have primarily focused on optimizing for prediction accuracy of click or conversion events using canonical metrics such as recall or normalized discounted cumulative gain (NDCG).
Background/contextual claim about prior work and standard practice; stated in the paper as motivation (no empirical evidence provided in the excerpt).
Current agents are not yet able to reliably produce professional-quality spreadsheets at the level of complexity real-world workflows demand.
Conclusion/interpretation based on the benchmark results and qualitative review reported in the paper (supporting quantitative details not shown in the excerpt).
Even the strongest agents frequently fall short of professional finance standards and degrade sharply as the difficulty increases beyond a few chained calculations.
Empirical observation from the paper's benchmark showing performance declines with task difficulty (no numeric breakdown or sample sizes provided in excerpt).
Existing spreadsheet benchmarks do not measure this advanced capability, focusing instead on question-answering or single-formula edits.
Paper's review/assessment of prior benchmarks (stated in introduction); no comprehensive benchmark list or counts provided in excerpt.
In the absence of communicative and institutional safeguards, individually adaptive delegation aggregates into a systemic collective action problem (modeled as a prisoner's dilemma), producing a sociotechnical lock-in that degrades shared epistemic standards.
Game-theoretic analysis in the paper demonstrating aggregation effects and mapping them to a prisoner's-dilemma–style collective action problem (theoretical modeling, no empirical sample).
The complementarity thesis is an over-simplification of the modalities of human-AI interaction and the possibility-space for both individual and collective action that human-AI interaction potentiates.
Theoretical argumentation and conceptual analysis presented in the paper (no empirical data reported).
AIO is negatively associated with the carbon emission intensity of upstream suppliers.
Authors report a negative association between firms' AIO and the carbon emission intensity of their upstream suppliers in the empirical results using Chinese listed firms (2010–2023).
AIO is negatively associated with the carbon emission intensity of industry peers.
Authors report a negative association between a firm's AIO and the carbon emission intensity of its industry peers based on their empirical analyses of Chinese listed companies over 2010–2023.
Stronger AIO is associated with lower carbon emission intensity within the focal firm.
Empirical association reported between firm-level AIO (measured via LLMs) and firm carbon emission intensity in the authors' analysis of Chinese listed firms (2010–2023); result described as a negative relationship.
Seventy-four percent of task misalignments could be attributed to developers who tended to overfocus on efficiency and speed, especially for systems performing tasks in people-facing occupations such as those in the human resources sector.
Result from comparing traits causing incidents to developers' stated preferences (sample of 197 developers) and computing the proportion of misalignments where developer-desired traits matched the traits causing incidents; noted sectoral concentration in people-facing occupations (e.g., HR).
In most cases, workers wanted systems that are precise, insightful, or personal, but instead received systems that are basic, simple, or general.
Qualitative/quantitative comparison of preferred traits (from 202 workers) versus traits observed in AI systems in incident reports (LLM-coded); reported dominant preference traits versus dominant delivered traits.
As many as 83% of workplace incidents stem from worker-AI misalignments.
Result from comparing LLM-extracted traits of AI systems (from 1,524 incident reports) to the traits preferred by workers (sample of 202); counted incidents where traits did not match worker preferences and reported proportion.
Commercial demand drivers systematically distort finished-goods inventory targets and require integration with sales-and-operations planning for accurate calibration.
Narrative synthesis of studies addressing demand-driver effects on finished-goods targets and recommendations for S&OP integration.
Kamunun Ar-Ge harcamalarının etkin ve verimli kullanılmadığına işaret eden bulgular vardır (kamu Ar-Ge negatif ilişki gösterdiği için).
Negatif ilişkiyi gösteren rassal etkiler regresyon sonuçlarına dayanan çıkarım (G8 + Türkiye, 2010-2020).
Ekonomik büyüme ile yapay zekâ patent sayıları arasında negatif bir ilişki bulunmaktadır.
Panel regresyon (random effects) sonuçları (G8 + Türkiye, 2010-2020) raporlanmıştır; ekonomik büyüme (muhtemelen GSMH büyüme oranı) değişkeninin AI patent sayıları ile negatif ilişki gösterdiği bildirilmiştir.
Kamunun Ar-Ge harcamaları ile yapay zekâ patent sayıları arasında negatif bir ilişki bulunmaktadır.
Rassal etkiler panel regresyonu üzerine raporlanan sonuçlar (G8 + Türkiye, 2010-2020); kamu Ar-Ge harcamaları değişkeninin AI patent sayısı ile negatif ilişki gösterdiği bildirilmiştir.
4.7% of modified files introduce new Bandit findings (security issues).
Static security analysis using Bandit run before and after each refactoring change on the AIDev Python PRs; reported proportion of modified files that gained new Bandit findings.
24.17% of modified files introduce new Pylint issues, predominantly convention-level violations such as long lines.
Domain-independent static analysis using Pylint applied before and after refactoring commits in the AIDev Python PRs; proportion of modified files with new Pylint findings reported.
So far, we lack a sound conceptual basis for categorizing and comparing these arrangements across organizations.
Statement of a gap in the literature based on the authors' literature review (no quantitative measure of literature coverage provided in abstract).
Science-to-technology knowledge flow in AI has been insufficiently examined in a systematic and structural way.
Literature-gap claim in the paper motivating the study.
However, models remain limited in long-horizon reliability and domain-specific planning.
Evaluation results and analysis in paper highlighting failures in maintaining reliability over long-horizon tasks and in planning for domain-specific workflows.
Extensive evaluations reveal that existing agents achieve only 36.0% task success on realistic media editing tasks.
Empirical evaluation reported in paper measuring task success rates of existing GUI agents on the Cutverse benchmark (benchmark size: 186 tasks across 7 apps implied).
Highlighted benchmarks function less as standardized measurement tools and more as flexible narrative devices prioritizing market positioning over scientific evaluation.
Synthesis of quantitative (coverage/reuse statistics) and qualitative analyses (narrative framing, taxonomy mapping) from the Benchmarking-Cultures-25 project; interpretive conclusion drawn by the authors.
Authors of many 'general knowledge application' benchmarks claim to measure knowledge or reasoning broadly, yet mostly evaluate STEM subjects (especially math).
Content analysis of the benchmarks in the dataset showing topical focus (counts/observations indicating predominance of STEM/math topics) versus broader claimed measurement scope.
Qualitative analysis shows many 'general knowledge application' benchmarks deemphasize construct validity, instead framing results as indicators of progress toward AGI.
Qualitative content analysis of benchmark descriptions and builder narratives in the dataset; authors report themes where construct validity is downplayed and AGI progress is emphasized.
38.5% of highlighted benchmarks appear in just one release.
Quantitative analysis of the Benchmarking-Cultures-25 dataset (231 benchmarks); the paper reports the share (38.5%) of benchmarks that appear in only a single model release.
The evaluation landscape is fragmented with limited cross-model comparability: 63.2% of highlighted benchmarks are used by a single builder.
Quantitative analysis of the Benchmarking-Cultures-25 dataset (231 benchmarks). The paper reports the share (63.2%) based on counts of builders per highlighted benchmark.
There is a "Sim-to-Real" gap: synthetic tests maintain constant memory usage but realistic workflows exhibit linear memory growth of about 3 tokens per message, with consolidation quality emerging as the primary scalability bottleneck.
Empirical comparison between synthetic tests and realistic workflows over the reported message corpus (15,000 messages); reported growth rate (~3 tokens/message) and qualitative identification of consolidation quality as bottleneck.
Full-context models fail at 10,000 messages due to context overflow.
Empirical comparison reported in the paper (within the large-scale evaluation), statement that full-context models fail at ~10,000 messages.
Monolithic approaches suffer from quadratic cost scaling and cognitive degradation when used for long scientific workflows.
Author statement contrasting monolithic approaches with the proposed architecture; conceptual/architectural claim rather than a single quantified experiment.
Context window saturation is a critical bottleneck as LLMs evolve into persistent scientific collaborators, because iterative data analysis and hypothesis refinement rapidly saturate even extended contexts with dense technical content.
Author claim based on observed behavior of scientific workflows; contextual motivation in paper (no specific experiment cited for this general statement).
In strategic decision scenarios, individuals may modify their features after deployment, inducing a post-deployment distribution shift; this strategic manipulation creates a mismatch between the non-strategic prior learned during pretraining and the post-manipulation strategic prior, which leads to systematic prediction bias.
Conceptual/theoretical claim stated in the paper that strategic feature manipulation causes distribution shift and mismatch between learned prior and strategic prior; the abstract asserts this as a cause of systematic prediction bias. No empirical sample sizes given in the abstract.
In those same benchmarks, 16 of 84 tasks suffered negative deltas when Skills are introduced.
Reference to the same prior benchmark aggregation that reported task-level deltas (count of tasks with negative deltas = 16 out of 84).
Underspecified prompts can lead to low-quality answers and additional interaction.
Motivating claim in the paper; presented as the problem the study addresses (no sample size or statistical test reported in the abstract).
Content filtering (blocking searches for Gaza War and Tulsa race massacre).
Documented cases of content filtering cited/synthesized in the paper (specific blocked search topics reported).
AI cataloguing failures (26% F1 accuracy for subject headings).
Empirical studies of AI accuracy in cataloguing synthesized by the paper (reported F1 accuracy for subject heading assignment).
Recent generative models show promise, yet they lack explicit mechanisms to balance exploration and safety, relying solely on action perturbations or trajectory guidance without a safety fallback, resulting in inefficient exploration and elevated financial risk for advertising platforms.
Argument in the paper contrasting generative-model-based approaches with the authors' proposed solution (conceptual claim; no quantitative backing given in the excerpt).
Reinforcement Learning approaches modeled bidding as a Markov Decision Process but struggled with long-term dependencies.
Statement in the paper summarizing limitations of prior RL-based bidding work (qualitative claim; no experimental details or sample size provided in the excerpt).
Early rule-based methods lacked adaptability.
Literature/contextual statement in the paper's introduction summarizing prior approaches to automated bidding (no empirical data or sample size reported).
Prompting and AI literacy alone may be insufficient to ensure epistemically independent AI support; system-level approaches are needed to better promote critical engagement in human–AI collaboration.
Authors' interpretation and implication drawn from the experiment's preliminary results: partial improvement from prompt-based training but persistent propagation of contextual errors.
An intervention (prompting training—either general or sycophancy-focused) did not eliminate the propagation of contextual errors from users into AI responses.
Participants received either general or sycophancy-focused prompting training in a pre/post within-subject design; authors report that the propagation of contextual errors persisted after the intervention.
The propagation of user errors into AI responses significantly reduced final user task performance.
Same experiment (n=60); authors report that user errors propagated into AI responses and that this propagation was associated with lower final participant performance on the analytical survival ranking tasks.
The propagation of user errors into AI responses significantly reduced the quality of AI feedback.
Same controlled mixed-design experiment (n=60) with multi-turn human–AI interactions; authors report that when users supplied lower-quality initial responses, those errors were propagated into the AI's responses, reducing AI feedback quality.
LLMs are highly sensitive to user input: lower-quality initial responses lead to poorer AI advice, suggesting that the model mirrors or incorporates user reasoning rather than correcting it or offering better alternatives.
Controlled mixed-design experiment with 60 participants performing multi-turn analytical survival ranking tasks; participants generated individual rankings and then made final decisions after collaborating with an AI assistant. Reported as a preliminary result in the paper.
Twin agents dissolve that boundary, raising a class of trust calibration challenge these frameworks were not designed to handle.
Argument and design observations from the authors' ongoing project presented in the paper; conceptual claim explaining why existing frameworks may be insufficient for twin agents.
When a human colleague doubts a twin agent's output, they face three failure modes (a schema gap, an epistemic gap, and a model artifact) with no reliable attribution path between them.
Conceptual taxonomy derived from the authors' early design observations; presented as an identified set of failure modes in the paper (qualitative, no numeric sample reported in abstract).
Drawing on early design work in an ongoing project, we identify a trust calibration problem specific to this approach.
Based on the authors' early design work (qualitative/design research) described in the paper; no sample size or quantitative metrics reported in the abstract.
Frontier agents struggle with end-to-end completion despite partial progress.
Evaluation experiments reported in the paper showing frontier (state-of-the-art) agents achieving partial progress but failing to reliably complete end-to-end tasks in the OpenComputer benchmark.
Major open challenges for responsible adoption include reliability, bias, privacy, automation bias, transparency, and evaluation.
Authors' identification of risks and open research challenges based on their review/analysis (conceptual synthesis).
Current AI support for code review remains fragmented, with tools focusing on isolated tasks such as reviewer recommendation, PR description generation, or comment suggestion rather than the end-to-end PR review workflow.
Authors' survey/overview of existing AI tooling for code review described in the paper (conceptual / review-based evidence). No quantitative counts provided in the abstract.