Evidence (6491 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Human Ai Collab
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At a 20x compression ratio, DPM improves reasoning coherence by +0.53 (Cohen's h=1.13, p=0.0034) compared to summarization-based memory (paired permutation, n=10).
Paired permutation test over 10 cases at a 20x compression ratio; reported effect +0.53 with Cohen's h=1.13 and p=0.0034.
At a 20x compression ratio, DPM improves factual precision by +0.52 (Cohen's h=1.17, p=0.0014) compared to summarization-based memory (paired permutation, n=10).
Paired permutation test over 10 cases at a 20x compression ratio; reported effect +0.52 with Cohen's h=1.17 and p=0.0014.
On ten regulated decisioning cases at three memory budgets, DPM matches summarization-based memory at generous budgets and substantially outperforms it when the budget binds.
Empirical evaluation on 10 decisioning cases across three memory budgets; comparison between DPM and summarization-based memory as reported in the paper (n=10).
We propose Deterministic Projection Memory (DPM): an append-only event log plus one task-conditioned projection at decision time.
Method/architectural proposal described in the paper.
Presumptuousness in legal AI is systematic but addressable, and addressing it is a necessary step towards systems that reliably support, rather than supplant, human judgment wherever decisions must await sufficient evidence.
Synthesis conclusion in paper based on the benchmark experiments, comparisons across prompting methods, and SPEC results.
SPEC achieves 89% overall accuracy, while appropriately deferring when evidence is insufficient.
Empirical evaluation of SPEC reported in paper: overall accuracy reported as 89% and behavior of proper deferral on insufficient-evidence cases.
We introduce SPEC (Structured Prompting for Evidence Checklists), a structured framework requiring explicit identification of missing information before any determination.
Methodological contribution described in paper: new prompting/framework (SPEC) that enforces explicit missing-information identification prior to decision.
Through a collaboration with the Colorado Department of Labor and Employment, we secured access to official training materials and guidance to design a novel benchmark that systematically varies information completeness.
Methodological description in paper: collaboration with state agency and dataset/benchmark construction using official training materials and guidance.
Long-term prospects of agentic AI include catalyzing accelerated innovation in physical design via autonomous algorithm discovery, continuous tool improvement, and closed-loop learning from large design corpora.
Forward-looking conclusion in the paper; framed as the authors' projection based on survey synthesis rather than as an empirically demonstrated outcome in the abstract.
Interfaces between agentic systems and traditional EDA frameworks are a key area of focus and enable tighter integration of agent capabilities into existing design workflows.
Survey highlights interfaces between agents and EDA frameworks as a focus area; claim is descriptive of research direction rather than reporting empirical outcomes.
Autonomous agents can explore heuristic spaces for placement, routing, and partitioning, enabling autonomous exploration of design heuristics.
Presented as an emphasized capability/area of research in the survey; the abstract asserts this possibility but does not report empirical benchmarks or sample sizes.
Tool-integrated agents can be used for algorithm evolution, debugging, and workflow automation in physical design R&D.
Paper emphasizes this as a primary area of application in the survey; rationale and examples are discussed but no quantitative trial sizes are given in the abstract.
Agentic AI systems can comprehend user specifications, modify code, run EDA tools, analyze results, perform multi-step reasoning, and iteratively refine design heuristics—unlike earlier ML uses that focused narrowly on prediction or optimization subroutines.
Descriptive claim in the paper contrasting agentic AI capabilities with earlier ML approaches; presented as an overview of functional capabilities rather than empirical measurement.
Recent advances in large language models (LLMs) and tool-using autonomous agents present new opportunities for accelerating research and development in physical design.
Stated as a central thesis in the paper's abstract/survey; based on the authors' synthesis of recent advances and emerging applications (no empirical sample or quantified evaluation reported in the abstract).
The framework is applied to Canada's 2025-2026 national AI Strategy consultation with n = 5,253 respondents across two independent policy topics.
Empirical application reported in the paper; dataset description gives sample size and two policy topics.
This paper introduces 'participatory provenance': a measurement framework grounded in optimal transport theory, causal inference and semantic analysis that tracks how individual public submissions are transformed, filtered or lost through AI-mediated summarization.
Methodological contribution described in the paper (framework design combining optimal transport, causal inference, semantic analysis).
AI systems currently provide more consistent fraud warnings than lay humans in an identical advisory role.
Aggregate comparison from the preregistered experiment showing humans had nonzero endorsement and higher suppression rates while all tested LLMs showed 0% endorsements and lower suppression under pressure (human n=1,201; AI conversations n=3,360).
Human advisors endorsed fraudulent investments at baseline rates of 13-14%.
Human benchmark of 1,201 participants run in the preregistered experiment; reported baseline endorsement rates for fraudulent scenarios.
Motivated investor framing did not suppress AI fraud warnings; if anything, it marginally increased them.
Preregistered experiment across seven leading LLMs and twelve investment scenarios; 3,360 AI advisory conversations analyzed comparing motivated vs. baseline investor framings.
Under these conditions (alignment of forces and AI-driven ideation cost reductions), PIM offers a framework for organising governed discovery in real time and provides the methodological foundation for later applied work.
The paper presents PIM as a proposed framework and positioning statement for future applied research and implementations (theoretical proposal; no applied trials reported).
Organised attacks on complex problems can generate an epistemic mode transition: a shift from predominantly Knightian uncertainty toward probabilistically characterisable innovation dynamics as relevant structures become more visible, decomposed, coordinated, and testable.
The paper states and formalises this methodological claim within PIM as a central proposition (theoretical argumentation; no empirical validation reported).
When problem-relevant causal, informational, and coordinative forces become sufficiently aligned, the epistemic character of search changes and open-ended uncertainty can be progressively transformed into structured probabilistic search.
The claim is presented as the central theoretical argument and formalised within the PIM conceptual framework (theoretical/model-based argumentation; no empirical sample).
The same user study (n=32) reports improvements in subjective measures including fluency and user preference for RAPIDDS over non-adaptive systems.
User study (n=32) reporting subjective questionnaire/ratings (fluency, preference) comparing RAPIDDS vs non-adaptive baselines.
A user study (n=32) shows significant plan improvement compared to non-adaptive systems across objective metrics such as efficiency and proximity.
User study reported in paper with sample size n=32 comparing RAPIDDS to non-adaptive systems on objective metrics (efficiency, proximity); significance claimed.
An ablation study in simulation and a physical robot scenario demonstrates the importance of dual (task + motion) adaptation.
Ablation experiments reported in paper (simulation and physical robot experiments comparing full RAPIDDS to ablated variants).
RAPIDDS jointly adapts task schedules and steers diffusion models of robot motions to maximize efficiency and minimize proximity accounting for individualized models.
Algorithmic method described in paper combining schedule optimization with motion steering (method section).
At the country level, digitalisation and workplace training provision steepen the exposure–adoption gradient.
Country-level heterogeneity analysis using the 2024 EWCS (35 countries) linking national measures of digitalisation and prevalence of workplace training to stronger occupational exposure–adoption relationships.
Individual skills, non-routine cognitive job content within occupations, and employee say in organisational decisions steepen the exposure–adoption gradient.
Interaction and stratified analyses from the 2024 EWCS showing stronger exposure–adoption associations among workers with higher individual skills, more non-routine cognitive job content (within occupations), and greater employee influence over organisational decisions; sample >36,600 workers.
Occupational exposure strongly predicts uptake.
Associational/regression analysis using the 2024 EWCS linking occupation-level measures of AI exposure to individual-level self-reported adoption; sample >36,600 workers across 35 countries.
Adoption averages 12% but ranges from under 3% to 25% across countries.
Descriptive analysis of the 2024 European Working Conditions Survey (EWCS), sample of more than 36,600 workers in 35 countries; country-level tabulations of self-reported generative AI adoption.
ClawNet enables multiple users to collaborate securely through their respective agents.
Capability claim about the instantiated system (authors assert that ClawNet enables secure multi-user collaboration; excerpt contains no empirical security evaluation or user study).
We instantiate this paradigm in ClawNet, an identity-governed agent collaboration framework that enforces identity binding and authorization verification through a central orchestrator.
Implementation claim: authors state they built ClawNet as an instantiation of their paradigm (paper describes framework/architecture; no experimental evaluation included in excerpt).
Action-level accountability logs every operation against its owner's identity and authorization, ensuring full auditability.
Design claim describing an accountability primitive (paper asserts logging and auditability as a property; no audit or verification evidence shown in excerpt).
Scoped authorization enforces per-identity access control and escalates boundary violations to the owner.
Design/specification claim describing the scoped authorization governance primitive in the proposed paradigm (no empirical or security evaluation provided in excerpt).
The paradigm rests on three governance primitives: (1) a layered identity architecture that separates a Manager Agent from multiple context-specific Identity Agents; the Manager Agent holds global knowledge but is architecturally isolated from external communication.
Architectural/design claim describing the proposed layered identity primitive (presentation of design; no empirical validation in excerpt).
We propose a human-symbiotic agent paradigm in which each user owns a permanently bound agent system that collaborates on the owner's behalf, forming a network whose nodes are humans rather than agents.
Design proposal / conceptual architecture presented in the paper (no large-scale deployment or empirical evaluation described in excerpt).
The next frontier for AI agents lies not in stronger individual capability, but in the digitization of human collaborative relationships.
Normative/strategic claim advanced by the authors as the central thesis (conceptual argument, no empirical test reported).
Human productivity rests on the social and organizational relationships through which people coordinate, negotiate, and delegate.
Theoretical/argumentative claim presented as background motivation (conceptual reasoning, citation not provided in excerpt).
Time Series Augmented Generation (TSAG) enables LLM agents to delegate quantitative tasks to verifiable external tools.
Description of TSAG framework in paper stating delegation mechanism to external verifiable tools for quantitative computations.
We publicly release the evaluation framework and empirical insights to foster standardized research on reliable financial AI.
Paper states that the framework, benchmark, and empirical results are released publicly by the authors.
The results demonstrate that capable agents can achieve near-perfect tool-use accuracy with minimal hallucination, validating the tool-augmented paradigm.
Empirical results from the authors' experiments on the 100-question benchmark across multiple agents; paper states agents achieve 'near-perfect' tool-use accuracy and 'minimal' hallucination.
We apply this methodology in a large-scale empirical study using our framework, Time Series Augmented Generation (TSAG), where an LLM agent delegates quantitative tasks to verifiable, external tools.
Paper reports applying the TSAG framework in an empirical study in which agents call external tools to perform quantitative computations; described as 'large-scale' and implemented by the authors.
We introduce a novel evaluation methodology and benchmark designed to rigorously measure an LLM agent's reasoning for financial time-series analysis.
Paper describes a new methodology and benchmark (Time Series Augmented Generation, TSAG) developed by the authors for evaluating LLM reasoning on financial time-series tasks.
Effective evaluation-driven loop scaling is a central axis for advancing LLM-driven scientific discovery, and SimpleTES provides a simple yet practical framework for realizing these gains.
High-level claim supported by the aggregate experimental results and discussion in the paper.
When post-trained on successful trajectories, models not only improve efficiency on seen problems but also generalize to unseen problems, discovering solutions that base models fail to uncover.
Experiments in which models were post-trained on successful SimpleTES trajectories and evaluated on both seen and unseen problems (paper claim of improved efficiency and generalization).
SimpleTES produces trajectory-level histories that naturally supervise feedback-driven learning.
Methodological claim and supporting experiments where SimpleTES generates solution trajectories that are then used as supervision for learning.
We discovered new Erdos minimum overlap constructions that surpass the best-known results.
Reported novel combinatorial constructions (Erdos minimum overlap) in the experiments that improve on prior best-known results.
We designed quantum circuit routing policies that reduce gate overhead by 24.5%.
Experimental results reported for quantum circuit routing tasks showing a 24.5% reduction in gate overhead when using SimpleTES-designed policies.
We sped up the widely used LASSO algorithm by over 2x.
Benchmarking experiment reported in the paper comparing LASSO runtime/performance with and without SimpleTES (paper states >2x speedup).
SimpleTES consistently outperforms both frontier-model baselines and sophisticated optimization pipelines.
Comparative experimental evaluation vs. frontier-model baselines and optimization pipelines across the reported problems (paper claim).