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 |
China's legal environment may offer certain advantage in terms of access to training data.
Stated as an analytical conclusion in the chapter based on comparative legal/regulatory assessment of data regimes; no empirical sample or quantitative evidence reported in the provided excerpt.
This work demonstrates how energy considerations can be embedded directly into AI-assisted coding workflows, supporting developers as they engage with energy implications through actionable feedback.
Concluding claim based on the system implementation and evaluation described (benchmarks and controlled study).
EcoAssist reduced per-website energy by 13-16% on average.
Reported result from the benchmark evaluation of 500 websites (effect size reported as 13-16%).
We introduce EcoAssist, an energy-aware assistant integrated into an IDE that analyzes AI-generated frontend code, estimates its energy footprint, and proposes targeted optimizations.
Description of the system introduced by the authors (implementation claim).
AI assistance improves short-term performance on tasks (people do better while using the AI).
Randomized controlled trials (N = 1,222) showing better immediate task outcomes when participants used AI assistance.
Market-Bench provides a reproducible testbed for studying how LLMs interact in competitive markets.
Paper presents the benchmark design and logging mechanisms intended to enable reproducible experiments of multi-agent market interactions.
Market-Bench logs complete trajectories of bids, prices, slogans, sales, and balance-sheet states, enabling automatic evaluation with economic, operational, and semantic metrics.
Paper states that the benchmark captures full transaction trajectories and exposes economic/operational/semantic metrics for automatic evaluation.
In the retail stage, LLMs set retail prices, generate marketing slogans, and provide them to buyers through a role-based attention mechanism for purchase.
Methodological description of the retail-stage tasks and the role-based attention mechanism used to present offers to buyers.
In the procurement stage, LLMs bid for limited inventory in budget-constrained auctions.
Design specification of the benchmark describing procurement-stage mechanics (auction/bidding mechanism, budget constraints).
We construct a configurable multi-agent supply chain economic model where LLMs act as retailer agents responsible for procuring and retailing merchandise.
Methodological description in the paper detailing the simulated multi-agent supply chain environment and the role of LLMs as retailer agents.
We introduce Market-Bench, a comprehensive benchmark that evaluates the capabilities of LLMs in economically-relevant tasks through economic and trade competition.
Paper describes the design and release of Market-Bench as a benchmark/testbed (methodological contribution).
Analyses use fixed-effects regression and structural equation modeling (SEM) on panel data from OECD countries.
Methods statement in the paper indicating use of fixed-effects and SEM applied to OECD-country panel data.
This paper provides the first cross-country empirical validation of AI-augmented scientific evaluation systems.
Authors' stated novelty claim that prior work lacked cross-country empirical quantification and that their OECD panel study is the first such validation.
A one standard deviation increase in AIRC is associated with an 18–25% increase in scientific productivity.
Reported point estimate/range from regression/SEM results linking a 1 SD change in the constructed AIRC to productivity outcomes in the OECD panel.
AI-assisted evaluation significantly enhances scientific productivity.
Fixed-effects regression and structural equation modeling (SEM) applied to panel data from OECD countries; reported association between AIRC and research output.
We construct a novel AI Review Capability Index (AIRC).
Paper reports creation of a new composite index (AIRC) to measure national-level AI capability in peer review; constructed and applied to panel data from OECD countries.
China's 'Global Community of Shared Future' white paper and Putin's 2024 Valdai address provide empirical evidence for an articulated alternative vision to the Western‑led global order.
Qualitative textual/readings of the cited official documents (the white paper and the Valdai address) used in the paper as empirical support; no quantitative content analysis or sample coding is reported.
Technical workers' potential for progressive transformation lies not just in their strategic importance and specialized knowledge but in their ability to build solidarity across the broader ecosystem of AI labour while operating between otherwise incommensurable philosophical and infrastructural systems.
Normative/theoretical claim combining philosophical analysis (Chinese Marxism, Bauman) with empirical literature on hidden AI labour and infrastructure competition (Muldoon et al., 2024); offered as an interpretive synthesis rather than empirically validated causal finding.
Technical workers occupy a strategic position at the intersection of competing infrastructural systems and alternative visions of global order, making them potentially crucial actors in determining the outcome of the current interregnum.
Argumentative claim supported by secondary empirical literature cited in the paper (Muldoon, Graham, and Cant, 2024) on hidden labour supporting AI systems and on geopolitical competition over digital infrastructure; presented as qualitative/interpretive evidence rather than primary quantitative measurement.
The semi-core's challenge to Western hegemony creates unique conditions for systemic transformation.
The paper advances this as a theoretical argument synthesizing World‑Systems theory, Demirel (2024), Bauman's philosophical work, and interpretive readings of official Chinese and Russian documents; no quantitative causal test is reported.
The emergence of a 'semi-core' is represented most prominently by China and Russia.
The paper cites Ege Demirel (2024) as the primary conceptual source and draws on textual evidence from China's 'Global Community of Shared Future' white paper and Putin's 2024 Valdai address; presented via World‑Systems theoretical framing and qualitative/discourse analysis.
AI agents autonomously plan, invoke external tools, and execute multi-step action chains with reduced human involvement.
Definitional framing provided by the authors describing the technical/functional characteristics of 'AI agents' as used in the paper.
The provider's foundational compliance task is an exhaustive inventory of the agent's external actions, data flows, connected systems, and affected persons.
Authors' recommendation/practical conclusion derived from the regulatory mapping (prescriptive guidance rather than empirical measurement).
We propose a twelve-step compliance architecture and a regulatory trigger mapping connecting agent actions to applicable legislation.
Paper asserts it includes a proposed 12-step compliance architecture and a mapping between agent actions and regulatory triggers (explicit step count provided).
We present a practical taxonomy of nine agent deployment categories mapping concrete actions to regulatory triggers.
Paper states it includes a taxonomy comprising nine deployment categories (explicit count provided).
This paper provides the first systematic regulatory mapping for AI agent providers integrating (a) draft harmonised standards under Standardisation Request M/613 to CEN/CENELEC JTC 21 as of January 2026, (b) the GPAI Code of Practice published in July 2025, (c) the CRA harmonised standards programme under Mandate M/606 accepted in April 2025, and (d) the Digital Omnibus proposals of November 2025.
Author claim about the paper's contribution and scope (novelty/first-of-its-kind mapping integrating specified standards and documents).
AI agents - i.e. AI systems that autonomously plan, invoke external tools, and execute multi-step action chains with reduced human involvement - are being deployed at scale across enterprise functions ranging from customer service and recruitment to clinical decision support and critical infrastructure management.
Author assertion in the paper's introductory framing; no empirical sample size or quantified deployment statistics provided in the excerpt.
Ablation experiments and scalability analysis verify the effectiveness of each core module of HGA-MADDPG.
Ablation study and scalability analysis reported in the paper; experiments removing or altering core modules and reporting comparative performance.
HGA-MADDPG maintains a cost reduction rate of 21.5% in a 120-node ultra-large-scale supply chain.
Scalability experiments reported in the paper on a 120-node simulated supply chain; reported cost reduction rate of 21.5% for HGA-MADDPG.
In the same extreme scenario of triple perturbation, HGA-MADDPG achieves a recovery time of 58 hours, outperforming existing methods.
Simulation experiments under triple perturbation reported in the paper; reported recovery time of 58 hours and stated superior performance relative to baselines.
In an extreme scenario of triple perturbation, HGA-MADDPG achieves a cost deviation rate of 29.6%, which is significantly better than existing methods.
Simulation experiments under an extreme scenario (triple perturbation) reported in the paper; comparison with existing methods and reported cost deviation rate of 29.6%.
In the same baseline scenario, HGA-MADDPG controls the stockout rate at 3.2%.
Simulation experiments reported in the paper (baseline four-level supply chain using real data), reporting a stockout rate of 3.2% for HGA-MADDPG compared to baselines.
In the same baseline scenario, HGA-MADDPG achieves a service level improvement rate of 42.8% compared with eight baseline algorithms.
Simulation experiments reported in the paper (baseline four-level supply chain using SCDL and WSN data), compared to eight baselines; reported 42.8% service level improvement.
In a baseline scenario (four-level supply chain, dynamic environment driven by real data from SCDL and WSN) and compared with eight baseline algorithms, HGA-MADDPG achieves a total cost reduction rate of 26.2%.
Simulation experiments reported in the paper: four-level supply chain baseline scenario driven by real data (SCDL and WSN), compared to eight baseline algorithms; reported aggregate result of 26.2% total cost reduction.
The paper constructs an adversarial disturbance and resilient training architecture that models three types of disturbances (demand mutation, node failure, transportation delay), adversarial agent injection, a dynamic environment replay buffer, and a two-stage training strategy.
Methodological description and implementation details of the training architecture and disturbance models in the paper.
An adaptive fusion weight based on marginal returns is designed to dynamically balance local and global credit.
Methodological description (design and incorporation of adaptive fusion weight in algorithm).
The algorithm quantifies the contribution of individual actions to sub-chain objectives and system-level indicators through local and global credit networks.
Methodological description and algorithm design (local and global credit networks described in the paper).
HGA-MADDPG introduces a hierarchical graph attention mechanism to dynamically represent the state of the supply chain network topology.
Methodological description and algorithm design presented in the paper (development and implementation of the hierarchical graph attention mechanism).
Rather than indiscriminate collection of context-relevant data, researchers and practitioners should adopt interactional practices to embed generative AI systems more appropriately into users' contexts of use.
Normative conclusion/provocation drawn from the paper's empirical findings and analysis of failure modes; presented as a recommendation (not an empirical effect; based on qualitative synthesis).
Users deploy concrete strategies to address failures of generative AI systems to account for context.
Empirical observations from interviews describing user-devised workarounds and strategies; qualitative cases/examples (sample size not provided).
We hypothesize the emergent necessity of a 'Compliance Premium,' indicating wage resilience increasingly tied to risk-absorption capacity.
Hypothesis proposed by authors based on observed institutional/business risk differentials from HITL validation and OAI patterns; framed as a forward-looking interpretation rather than demonstrated empirical result.
Non-routine cognitive roles highly dependent on symbolic manipulation (e.g., Data Scientists) face unprecedented exposure, with OAI ≈ 0.70.
Reported OAI value for example occupation(s) (Data Scientists) derived from the algorithmic aggregation across DWAs; claim presented as a key empirical finding.
We utilize a multi-agent LLM ensemble to score both technical feasibility and business risk for DWAs.
Method description: deployment of a multi-agent LLM ensemble to produce scores on technical feasibility and business risk per DWA. Specific ensemble composition and hyperparameters not provided in the excerpt.
We introduce a Tech-Risk Dual-Factor Model that jointly scores technical feasibility and business risk to re-evaluate occupational exposure to LLMs.
Methodological contribution described in the paper (model specification). Implementation details described elsewhere in paper (see multi-agent scoring and aggregation), but claim itself is the introduction of the model.
All code, infrastructure, and benchmark data are released to facilitate future research in realistic computer-use agents.
Statement of release in paper (availability claim).
Applying the same auditing principle at test time — a separate VLM reviews completed trajectories and provides feedback — improves Gemini-3-Flash on CUA-World-Long from 11.5% to 14.0%.
Experimental result reported in paper: evaluation of Gemini-3-Flash with/without test-time VLM auditing on CUA-World-Long, reported scores 11.5% -> 14.0%.
Distilling successful trajectories from the training split into a 2B vision-language model outperforms models 2× its size.
Modeling experiments reported in paper: distilled 2B VLM evaluated against larger models (2× size). Exact evaluation metrics and baseline model sizes not specified in excerpt.
CUA-World-Long is a challenging long-horizon benchmark with tasks often requiring over 500 steps, far exceeding existing benchmarks.
Benchmark description in paper reporting typical task lengths ("often requiring over 500 steps") and comparison to existing benchmarks.
The result is CUA-World, a collection of over 10K long-horizon tasks spanning domains from medical science and astronomy to engineering and enterprise systems, each configured with realistic data along with train and test splits.
Dataset release / creation claim specifying >10,000 tasks and train/test splits.
Using a taxonomy of economically valuable occupations grounded in U.S. GDP data, we apply this pipeline to 200 software applications with broad occupational coverage.
Dataset creation procedure and reported coverage claim (200 software applications), taxonomy derived from U.S. GDP data as stated.