Evidence (14922 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
9047 claims
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Productivity
8066 claims
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Governance
7278 claims
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Human-AI Collaboration
6912 claims
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Org Design
4439 claims
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Innovation
4359 claims
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Labor Markets
3652 claims
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Skills & Training
3018 claims
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Inequality
2160 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 | 795 | 210 | 105 | 955 | 2131 |
| Governance & Regulation | 886 | 414 | 197 | 126 | 1654 |
| Organizational Efficiency | 826 | 204 | 129 | 87 | 1257 |
| Technology Adoption Rate | 681 | 259 | 128 | 110 | 1189 |
| Research Productivity | 464 | 138 | 65 | 349 | 1028 |
| Output Quality | 503 | 196 | 61 | 53 | 813 |
| Decision Quality | 351 | 180 | 84 | 51 | 673 |
| AI Safety & Ethics | 238 | 288 | 71 | 34 | 637 |
| Firm Productivity | 455 | 58 | 92 | 20 | 631 |
| Market Structure | 186 | 172 | 123 | 25 | 511 |
| Task Allocation | 222 | 70 | 76 | 34 | 407 |
| Innovation Output | 238 | 28 | 48 | 18 | 334 |
| Skill Acquisition | 177 | 62 | 62 | 17 | 318 |
| Employment Level | 107 | 57 | 108 | 13 | 287 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Firm Revenue | 172 | 50 | 28 | 5 | 256 |
| Consumer Welfare | 121 | 68 | 45 | 12 | 246 |
| Task Completion Time | 183 | 33 | 10 | 13 | 240 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 95 | 74 | 23 | 12 | 204 |
| Error Rate | 77 | 98 | 11 | 4 | 190 |
| Regulatory Compliance | 84 | 73 | 17 | 7 | 181 |
| Automation Exposure | 61 | 61 | 27 | 14 | 166 |
| Training Effectiveness | 98 | 21 | 14 | 19 | 154 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 23 | 1 | 119 |
| Hiring & Recruitment | 53 | 8 | 8 | 3 | 72 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 50 | 6 | 1 | 62 |
| Labor Share of Income | 17 | 20 | 17 | — | 54 |
| Worker Turnover | 15 | 15 | — | 3 | 33 |
| Industry | — | — | — | 1 | 1 |
CoMAI implements multi-layered defenses against prompt-injection and other prompt-level attacks via a dedicated security agent and constrained state transitions.
System design (a dedicated security/validation agent and a finite-state machine enforcing information flow) and reported security testing that included prompt-injection/adversarial inputs to probe defenses.
Candidate satisfaction with CoMAI was 84.41%.
Reported experimental metric in the paper summary; likely derived from post-interview surveys, but survey design, sample size, and response rates are not specified in the summary.
In experiments CoMAI achieved 83.33% recall.
Reported experimental metric in the paper summary; no information provided on how recall was computed (e.g., per-class vs. overall), sample sizes, or confidence intervals.
In experiments CoMAI achieved 90.47% accuracy.
Reported experimental metric in the paper summary. The underlying dataset size, class balance, and baseline comparison details are not provided in the summary.
CoMAI outperforms monolithic LLM-based assessments on robustness, fairness, and interpretability.
Comparative framing and reported experiments in the paper claiming improved robustness, fairness, and interpretability relative to single-agent LLM baselines; however, baseline specifics, dataset sizes, and statistical tests are not disclosed in the provided summary.
The clarification protocol elicits missing premises or confirms intent rather than producing an ill-aligned response.
Paper describes structured clarification templates (binary checks, multi-choice scaffolds, short clarifying questions) intended to elicit missing information; this is a design assertion without reported user-study evidence.
There are potential welfare gains from improved decision quality and trust in automation, particularly where human oversight remains required.
Conceptual welfare analysis; no welfare quantification or simulations provided.
Structured AFs can reduce information asymmetry by making reasoning traceable, thereby lowering search and verification costs in transactions and contracting.
Economic reasoning drawing on information-asymmetry theory; no empirical transaction-cost measurements given.
Firms offering argumentatively transparent AI can obtain competitive advantage and charge premium prices for verifiability and auditability.
Economic reasoning and market-structure inference; no empirical pricing or demand elasticity studies provided.
Demand will shift toward AI systems that provide verifiable, contestable reasoning in regulated/high‑stakes sectors (healthcare, law, finance, public policy).
Economic argument and market prediction in the paper; speculative without market data or forecasting models presented.
This approach supports collaborative reasoning ('with' humans) rather than opaque automation 'for' humans, improving uptake in high‑stakes settings.
Conceptual argument about human-in-the-loop workflows and collaborative roles; no empirical uptake or deployment data presented.
Framing decisions as contestable and revisable (via dialectical challenge and update) increases robustness and trust in AI-supported decision-making.
Conceptual claim arguing that contestability/revision improve robustness and trust; no experimental evidence or user studies provided.
Running formal dialectical/acceptability semantics and dialogue protocols over AFs enables agents that reason with humans through structured debates and revisions.
Conceptual integration of formal semantics (Dung-style, bipolar, weighted) and dialogue protocols; no human-subject studies or system evaluations reported.
Argumentation Framework Synthesis: mined fragments can be combined into coherent formal argumentation frameworks (AFs) with explicit semantics enabling verification and automated inference.
Conceptual algorithmic proposal (graph synthesis, canonicalization, formal semantics); no empirical synthesis results or benchmarks presented.
Argumentation Framework Mining: LLMs and NLP pipelines can be used to extract claims, premises, relations (attack/support), and provenance from text corpora.
Proposed methodological pipeline (fine-tuning/prompting LLMs and IE pipelines); conceptual proposal without implementation details or experimental results.
Combining formal argument structures with LLMs’ ability to mine and generate rich, contextual arguments from unstructured text promises human-aware, verifiable, and trustable AI for high‑stakes domains.
Conceptual synthesis of computational argumentation (formal AFs) and LLM capabilities; no empirical validation or quantified metrics provided.
Integrating computational argumentation with large language models (LLMs) creates a new paradigm—Argumentative Human-AI Decision‑Making—where AI agents participate in dialectical, contestable, and revisable decision processes with humans.
Conceptual / design argument presented in the paper; no empirical implementation or sample; draws on prior work in computational argumentation and capabilities of LLMs.
There will likely be growth in complementary markets for model verification, provenance tracking, legal-AI audits, and human-in-the-loop workflow services.
Market foresight based on identified unmet needs (explainability, verification) and illustrative examples; no market-sizing data.
The project demonstrates that high-skill, knowledge-intensive tasks (formal mathematics) can be substantially automated with a heterogeneous AI toolchain, reducing human coding labor while retaining supervisory oversight.
Inference from project outcomes: AI tools produced formal Lean code and discharged lemmas while the reported human supervisor did not write code; single-project evidence (n=1), qualitative and quantitative logs support partial automation.
The formalization finished prior to the final draft of the corresponding informal math paper.
Timing claim reported in the paper comparing formalization completion date to the final draft date of the related math paper (self-reported for the single project).
Effective practices included splitting proofs into abstract (high-level reasoning) and concrete (formalization) parts, having agents perform adversarial self-review, and targeting human review to key definitions and theorem statements.
Process-level recommendations drawn from the project's workflow; paper reports these practices as successful for this single development (n=1 project) based on qualitative assessment.
One mathematician supervised the process over approximately 10 days, reported a human cost of about $200, and wrote no code.
Self-reported human-role summary in the paper: single supervisor, ~10 days supervision time, reported monetary cost ≈ $200, and assertion that the human wrote no code (n=1 human supervisor for the project).
Clear agent identity and provenance simplify liability attribution and enable markets for certified components, attestation services, and compliance tooling.
Legal/economic reasoning about traceability and liability plus systems design suggestions; no legal case analysis or market data presented.
Lifecycle service models (leasing, 'agent as a service', update/maintenance contracts) will become economically important to manage long‑lived physical assets with fast‑moving AI stacks.
Business model reasoning and analogy to service models in other capital‑intensive sectors; no market empirical study or business case analysis provided.
Observability and attestation reduce uncertainty for insurers and regulators, lowering risk premia and insurance costs for agent deployments.
Argument from information economics/insurance theory and analogy to fields where observability reduces asymmetric information; no empirical insurance cost data or pilot programs reported.
Open interoperability standards and agent identities can lower entry barriers, increase competition, and accelerate complementary innovation.
Economic and policy reasoning referencing benefits of standards/open ecosystems; no empirical intervention or controlled comparison provided.
Design choices will shape capital intensity and replacement cycles; architectures that support upgradeability and modularity lower expected upgrade costs and stranded‑asset risk.
Economic reasoning and analogy to modular design benefits in other industries; conceptual argument without empirical capital‑allocation data or simulations.
Architectural components such as agentic identity and attestation, secure communication protocols, semantic layers and interchange formats, policy engines, and observability pipelines are necessary to enable safe, economic multi‑agent ecosystems.
Architectural blueprint proposed via conceptual systems design; justification by analogy to existing security/identity/semantic frameworks; no empirical testing reported.
Design principles — modularity, clear agentic identity, secure agent‑to‑agent communication, policy‑governed runtimes, semantic interoperability, and observability/governance frameworks — will mitigate the architectural risks identified.
Normative systems design proposition grounded in systems engineering reasoning and historical lessons; no experimental validation or deployment studies provided.
New capabilities (edge hardware, sensing, connectivity, and AI) now enable agents that not only sense/report but also perceive, reason, and act autonomously and cooperatively in real time.
Technological trend synthesis and systems reasoning; examples of mature edge hardware and advances in real‑time ML are used illustratively; no experimental validation provided.
Treating evolution, trust, and interoperability as first‑class requirements (rather than afterthoughts) is essential to avoid costly lock‑in, premature ossification, fragmentation, and negative externalities observed with IoT.
Normative prescription motivated by historical/comparative analysis of Internet and IoT (qualitative examples of fragmentation and lock‑in); no controlled study or quantitative validation presented.
The next phase of the Internet will be the "Internet of Physical AI Agents" — distributed, long-lived, embodied systems that perceive, reason, and act autonomously in real time.
Predictive/conceptual argument based on observed technological trends (advances in edge hardware, sensing, connectivity, and AI). Position paper with historical/comparative reasoning and illustrative examples; no primary empirical dataset or quantified projection.
Governance should be hybrid and structured: legal/regulatory frameworks (e.g., EU AI Act), technical standards (ISO safety norms), and crisis-management practices must be combined to allocate responsibilities and intervention authority.
Policy and standards synthesis drawing on EU AI Act, ISO standards, and crisis-management literature; prescriptive argument without empirical testing.
Robust resilience stems from 'bounded autonomy': constraining what an AI may decide and when humans must intervene.
Normative proposal grounded in synthesis of safety standards, crisis-management practices, and conceptual arguments; specification of autonomy dimensions (authority scope, temporal limits, performance envelopes, fail-safes).
Human–AI chat logs contain more explicit strategy commitments (stated rules) than human–human chats.
Content analysis / coding of natural-language chat logs from the human–AI experiment (human–AI n = 126) and the human–human benchmark (n = 108); coding counts show higher frequency of explicit commitments/statements of rules in human–AI messages.
Human–human subjects converge to Tit‑for‑Tat under one condition and to unconditional cooperation under the repeated-communication condition.
Strategy-estimation and behavioral trajectory analysis from the human–human benchmark (Dvorak & Fehrler 2024; n = 108) reported in the paper, showing condition-dependent convergence to Tit‑for‑Tat and to unconditional cooperation under repeated communication.
Strategy estimation indicates human–AI subjects tend to favor Grim Trigger when allowed pre-play communication.
Strategy-estimation/classification applied to subjects' choices in the human–AI condition with pre-play chat (subset of the human–AI n = 126); inferred strategy prevalence shows elevated assignment to Grim Trigger-type rules.
Extensive simulation experiments across different network topologies and attacker/defense scenarios validate both the FJ modeling of LLM-MAS and the effectiveness of the trust-adaptive defense.
Multiple simulation studies reported in the paper that vary network density, trust matrices, attacker stubbornness/persuasiveness, and defense strategies; validation claims stem from consistent patterns observed across these simulated settings. (The summary does not list the number of experimental runs or statistical reporting.)
A trust-adaptive defense that dynamically reduces trust in agents suspected of adversarial behavior can limit adversarial influence while preserving cooperative performance better than static trust-lowering strategies.
Implemented a trust-adaptive mechanism and evaluated it in simulation experiments across multiple network topologies and attack/defense scenarios, reporting reductions in adversarial sway with preserved task performance compared to naïve trust reduction. (Exact experimental counts and numeric effect sizes not provided in the summary.)
Increasing the number of benign agents dilutes an adversary's relative influence and thereby reduces the probability and magnitude of persuasion cascades.
Simulation experiments varying the count of benign agents in networks while measuring adversarial sway and collective opinion outcomes across different topologies. (Summary does not report exact counts or statistical summaries.)
The Friedkin–Johnsen opinion-dynamics model (innate opinions + interpersonal influence weights + stubbornness) closely captures LLM-MAS behavior across settings, both theoretically and empirically.
Modeling: derivation of FJ dynamics for LLM-MAS; Empirical: simulation experiments comparing FJ model predictions to observed LLM-MAS opinion trajectories and final consensus under varied topologies and trust matrices. (Exact goodness-of-fit metrics and sample counts not provided in the summary.)
Open-source orchestration and evaluation harnesses plus a self-contained evaluation pipeline improve reproducibility for the Speedrunning Track.
Paper claims and documents the release of orchestration and evaluation code and describes the self-contained pipeline designed for deterministic reproducible evaluation.
Version 1.0 marks integration into operational workflows and establishes a base for future capabilities.
Authors report that v1.0 has been used in verification and mask-refinement loops for real datasets (MeerKAT, ASKAP, APERTIF); no detailed deployment metrics provided.
Immersive inspection tools like iDaVIE are complements to automated ML pipelines by helping generate higher-quality labels and curated training examples.
Paper argues conceptual complementarity and cites iDaVIE's use for mask refinement and curated subcube export; no experimental comparison of label quality or downstream ML performance provided.
iDaVIE accelerates inspection-driven parts of astronomy workflows (e.g., mask refinement, verification).
Reported use cases where iDaVIE was used to refine masks and verify sources in real datasets; no measured time-per-task or throughput statistics provided.
iDaVIE has already been integrated into real pipelines (MeerKAT, ASKAP, APERTIF) and used to improve quality control, refine detection masks, and identify new sources.
Author statement of integration and use cases citing verification of HI data cubes from MeerKAT, ASKAP and APERTIF; no quantitative deployment counts or independent validation provided in the text.
There is a need for policies supporting workforce transitions (retraining, portability of skills) and safety/regulation for embodied agents operating in public spaces.
Policy recommendation grounded in anticipated labor and safety risks; proposed but not empirically evaluated.
Benchmarks and tasks that mix observation and intervention (imitation with sparse feedback, active imitation, transfer under domain shift, continual learning streams) are required to evaluate the architecture.
Proposal for evaluation tasks and benchmarks; not empirically validated in the paper.
Embodied robotics experiments are necessary to evaluate real-world constraints such as sample efficiency, physical affordances, and motor learning.
Methodological recommendation recognizing simulation-to-real gaps; no experiments reported.
Simulated environments (procedural, nonstationary), multi-agent social domains, and open-world 3D simulators are appropriate for scalable iteration to test the proposed architecture.
Methodological recommendation and suggested experimental approaches; not tested in the paper.