Evidence (13827 claims)
Adoption
8454 claims
Productivity
7544 claims
Governance
6789 claims
Human-AI Collaboration
6327 claims
Org Design
4126 claims
Innovation
4058 claims
Labor Markets
3520 claims
Skills & Training
2924 claims
Inequality
2057 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 195 | 97 | 889 | 1979 |
| Governance & Regulation | 815 | 391 | 188 | 121 | 1539 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 624 | 233 | 123 | 96 | 1084 |
| Research Productivity | 410 | 121 | 56 | 331 | 929 |
| Output Quality | 466 | 177 | 59 | 47 | 749 |
| 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 | 166 | 122 | 24 | 495 |
| Task Allocation | 206 | 64 | 70 | 31 | 376 |
| Skill Acquisition | 165 | 57 | 60 | 17 | 299 |
| Innovation Output | 201 | 27 | 41 | 18 | 288 |
| Employment Level | 105 | 51 | 107 | 13 | 278 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 149 | 46 | 26 | 3 | 224 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 61 | 20 | 12 | 182 |
| Error Rate | 69 | 91 | 10 | 2 | 172 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 92 | 19 | 13 | 19 | 145 |
| 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 |
| Skill Obsolescence | 5 | 45 | 6 | 1 | 57 |
| Creative Output | 31 | 16 | 7 | 2 | 57 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
We propose Strategic Prior-data Fitted Network (SPN), an inference-time strategy-aware framework that adapts tabular foundation models to strategic environments without retraining.
Methodological contribution described in the paper: SPN is introduced as an inference-time framework that modifies behavior without retraining. This is a description of the proposed method rather than quantified empirical evidence; no sample sizes reported in the abstract.
Tabular foundation models based on pretrained prior-data fitted networks (PFNs) have shown strong generalization on diverse tabular tasks, but they are typically designed for non-strategic settings where data distributions are independent of deployed classifiers.
Statement in the paper situating PFN-style tabular foundation models as having strong generalization in prior work and noting their design assumption of non-strategic, classifier-independent data distributions; no dataset/sample sizes provided in the abstract.
Agent Skills, structured packages of procedural knowledge loaded into an LLM agent at inference time, are widely reported to improve task pass rates by an average of 16.2 percentage points across diverse domains.
Authors cite prior Skills benchmarks / aggregated reports (benchmark summary referenced in paper); average improvement reported as 16.2 percentage points across tasks in those benchmarks (implied sample of tasks from the referenced benchmark).
Software products and software R&D contributed 50 percent of the 1.2 percentage point acceleration in nonfarm business labor productivity (2017–2024 relative to 2012–2017).
Empirical decomposition comparing productivity growth rates across periods (2017–2024 vs 2012–2017) in the paper; the authors attribute half of the observed 1.2 percentage point acceleration to software products and software R&D.
Software products and software R&D contributed 50 percent of the 2 percent average growth rate in nonfarm business labor productivity from 2017 to 2024.
Empirical decomposition of nonfarm business labor productivity growth in the United States for the period 2017–2024 reported in the paper (the authors attribute shares of the observed 2% average growth to components including software products and software R&D).
AI is already materially affecting official productivity measures in the United States.
Empirical decomposition of U.S. productivity data reported in the paper that attributes portions of measured productivity growth to software-related channels linked to AI.
Using a framework that separates upstream innovation from downstream production suggests that AI boosts both upstream total factor productivity and intangible capital use downstream.
Model/framework decomposition in the paper (theoretical separation of upstream vs downstream, combined with empirical application to productivity data); the paper reports results consistent with increases in upstream TFP and downstream intangible capital use.
Code cleanliness joins model choice, harness, and prompting as a factor that materially affects agent behaviours.
Conclusion drawn from experimental findings that cleanliness materially influenced agent operational metrics (tokens and revisits) even when pass rates were unchanged.
Traditional maintainability principles remain highly relevant in the era of AI-driven development, shaping the computational cost and navigational efficiency of coding agents.
Interpretation based on experimental results showing token and navigational efficiency gains on cleaner code (7–8% fewer tokens, 34% fewer revisitations) despite unchanged pass rates.
Agents working on cleaner code reduce file revisitations by 34%.
Empirical measurement across the same experimental trials comparing agent file-revisitation counts between clean and messy repo variants; reported 34% reduction in file revisitations on cleaner code.
Agents working on cleaner code use 7 to 8% fewer tokens.
Empirical measurement across trials (660 trials with Claude Code) comparing token consumption between clean and messy repository variants; reported decrease of 7-8% in tokens when working on cleaner code.
We author 33 tasks across six such pairs, evaluated through hidden tests at the application's public surface.
Reported experimental design: 33 authored tasks spanning six repository pairs; evaluation used hidden tests executed at the application's public surface.
The pairs are constructed in both directions, by agent pipelines that either degrade a clean repository or clean a messy one.
Method description: authors constructed pairs bidirectionally using agent pipelines that modify repositories to create matched clean/messy variants.
We introduce an evaluation protocol built around minimal pairs: repositories that match on architecture, dependencies, and external behaviour, but differ on static-analysis rule violations and cognitive complexity.
Methodological description in paper: construction of paired repositories controlling for architecture, dependencies, and external behaviour while varying static-analysis violations and cognitive complexity.
A simple prompt checklist can improve LLM responses while reducing unnecessary interaction.
Authors' interpretation/conclusion drawn from the experimental comparisons and rubric scores reported in the paper's results.
Checklist prompts produced the best quality-effort tradeoff, using fewer average tokens than both raw and clarifying prompts.
Reported comparative statement in the results that checklist prompts used fewer average tokens and produced a better quality-effort tradeoff (no token counts, sample size, or statistical tests reported in the abstract).
Checklist-improved prompts achieved the highest mean rubric score, 7.50 out of 8, compared with 5.67 for raw prompts and 6.67 for clarifying-question prompts.
Reported mean rubric scores for each prompt condition in the paper's results (no sample sizes or significance tests provided in the abstract).
The authors open-source optimize_anything with support for multiple backends as part of the GEPA project at https://github.com/gepa-ai/gepa.
Explicit statement and provided GitHub URL in the paper excerpt.
Multi-task search outperforms independent optimization given equivalent per-problem budget through cross-task transfer, with benefits scaling with the number of related tasks.
Reported experiments comparing multi-task search versus independent per-problem optimization under equal per-problem budget; observed cross-task transfer benefits and that benefits increase with more related tasks.
Ablations across three domains reveal that actionable side information yields substantially higher final scores than score-only feedback.
Same ablation studies across three domains as above; reported higher final optimization scores when using actionable side information compared to only score feedback.
Ablations across three domains reveal that actionable side information yields faster convergence than score-only feedback.
Paper reports ablation studies in three domains comparing optimization with actionable side information versus score-only feedback and finds faster convergence with side information.
The system outperforms AlphaEvolve's reported circle packing solution (n=26).
Direct comparison reported to AlphaEvolve's circle packing solution with sample size notation n=26 provided in the excerpt; implies evaluation over 26 instances or trials.
The system generates CUDA kernels where 87% match or beat PyTorch.
Reported evaluation of generated CUDA kernels against PyTorch implementations; paper states 87% of generated kernels match or outperform PyTorch.
The system finds scheduling algorithms that cut cloud costs by 40%.
Paper reports that its discovered scheduling algorithms reduce cloud costs by 40%; presumably measured by evaluating cost of scheduled workloads before/after optimization.
The system discovers agent architectures that nearly triple Gemini Flash's ARC-AGI accuracy (32.5% to 89.5%).
Reported comparison to Gemini Flash on the ARC-AGI benchmark with explicit accuracy numbers (32.5% baseline to 89.5% after optimization). Method: discovered agent architectures via LLM-based search; benchmark evaluation on ARC-AGI.
A single AI-based optimization system achieves state-of-the-art results across six diverse tasks.
Paper reports experiments applying a single LLM-based optimization system to six diverse tasks and claims SOTA results across them; no further per-task details provided in the excerpt.
The framework extends platform capitalism theory to professional service contexts.
Theoretical contribution claimed in the paper, integrating platform capitalism literature with sociology of professions and critical information science.
Resistance requires collective organising, alternative infrastructure development, and recognition that current AI implementations conflict with core professional values.
Normative conclusion drawn from the paper's critical qualitative analysis and theoretical framing; prescriptive recommendations rather than empirical measurement.
Vendor monopolies (84% ARL member institutions market share at peak concentration).
Market concentration data synthesized in the paper (reported peak share among ARL member institutions).
In real-world deployment, GUIDE achieves notable gains: +4.10% ad GMV, +1.40% ad clicks, +1.66% ad cost, and +3.52% ad ROI.
Reported quantitative results from large-scale online deployment on Taobao (real-world A/B/test deployment; exact sample size, duration, and statistical significance are not stated in the excerpt).
Results show GUIDE consistently outperforms state-of-the-art baselines across all scenarios.
Aggregate claim summarizing experimental comparisons versus state-of-the-art baselines across the reported evaluations (public datasets, simulations, and live deployment). Specific baselines, metrics, and statistical details are not provided in the excerpt.
GUIDE employs a Decision Transformer (DT) to jointly model historical bidding actions and environmental state transitions, a Q-value module to guide DT exploration via regularization constraints, and an Inverse Dynamics Module (IDM) that leverages DT-predicted future states to infer robust behaviorally consistent actions as a safe policy fallback.
Detailed methodological description of GUIDE components and their intended roles in the paper (architectural claim).
We propose GUIDE (Generative Auto-Bidding with Unified Modeling and Exploration), a framework that synergistically integrates directed exploration with a safe fallback mechanism.
Methodological contribution described in the paper (design/proposal of the GUIDE framework).
The intervention significantly improved AI advice by reducing the direct mirroring of incorrect user rankings.
In the same controlled experiment (n=60) with pre/post prompting training, authors report a statistically significant improvement in AI advice after training, characterized by reduced direct mirroring of participants' incorrect rankings.
We introduce the concept [of twin agents], distinguish it from digital twins, and outline the research questions this new class of agent demands.
Stated contribution of the paper (conceptual development and research agenda); content claim about what the paper contains rather than an empirical finding.
Cognitive forcing functions and related frameworks address overreliance effectively in contexts where there is a clear boundary between the AI and the human decision-maker.
Claim based on literature and frameworks cited or discussed by the authors (asserted effectiveness in boundary-defined contexts); the abstract does not provide empirical evaluation details or sample sizes.
The next role on that list is more personal: you — digital twins of each individual (twin agents) representing their knowledge, perspective, and communicative style to colleagues when they are unavailable.
Proposed argument supported by the authors' early design work in an ongoing project; conceptual proposal rather than reported empirical validation in the abstract.
Agentic AI has taken on the role of assistant, collaborator, and decision-support tool.
Asserted in the paper's framing/introduction; based on synthesis of prior work and the authors' characterization of current agentic-AI deployments (no empirical sample or quantitative data reported in the abstract).
The evaluation harness records full trajectories and computes auditable partial-credit rewards.
System description in the paper specifying that the evaluation harness captures full action trajectories and implements an auditable partial-credit reward computation.
OpenComputer's hard-coded verifiers align more closely with human adjudication than LLM-as-judge evaluation, especially when success depends on fine-grained application state.
Experimental comparison reported in the paper between hard-coded verifiers and LLM-as-judge evaluations, measured against human adjudication (presumably over the benchmark tasks).
OpenComputer integrates four components: (1) app-specific state verifiers that expose structured inspection endpoints over real applications, (2) a self-evolving verification layer that improves verifier reliability using execution-grounded feedback, (3) a task-generation pipeline that synthesizes realistic and machine-checkable desktop tasks, and (4) an evaluation harness that records full trajectories and computes auditable partial-credit rewards.
System design description presented in the paper (architectural claim listing four components and their intended functions).
In its current form, OpenComputer covers 33 desktop applications and 1,000 finalized tasks spanning browsers, office tools, creative software, development environments, file managers, and communication applications.
System description / reported inventory in the paper (explicit counts of covered applications and finalized tasks).
The paper offers a research agenda for more effective human-AI collaboration in software engineering.
Authors' concluding recommendations and agenda presented in the paper (conceptual / prescriptive contribution).
Humans are retained at key decision points in the workflow to preserve judgment, accountability, and team-level understanding.
Authors' design rationale / argument for human-in-the-loop controls within their proposed workflow (conceptual justification).
The proposed framework spans five stages: PR Creation, PR Augmentation, Reviewer Selection, AI-Assisted Code Review, and PR Retrospective.
Authors' explicit description of their framework stages in the paper (conceptual/design content).
We present a vision for an AI-powered code review workflow combining specialized agents with human-controlled quality gates.
Paper authors' proposed conceptual framework / design contribution (framework description rather than empirical validation).
The rise of Artificial Intelligence (AI) coding assistants has increased code production velocity.
Authors' summary statement about observed effects of AI coding assistants; based on prior literature/observations rather than a reported experiment in this paper's abstract.
Compute expansion increases data-centre electricity pressure.
Public institutional data on compute expansion and data-centre electricity demand analyzed with growth indicators (CAGR, relative growth) showing rising electricity demand associated with compute capacity expansion.
Industrial robots represent persistent cyber-physical action capacity (as evidenced by installations and operational stock).
Use of public data on robot installations and operational stock, summarized via stock-flow ratios and related indicators to characterize persistent robotic action capacity.
AI investment signals broad capital allocation.
Public institutional data on AI investment examined with indicators such as growth multipliers, CAGR and concentration ratios to infer capital allocation patterns.