Evidence (8974 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
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 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 | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Productivity
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Empirical evaluation was performed across four coordination environments: a repeated matrix game, two grid navigation tasks, and an Overcooked task.
Methods section describes these four benchmark environments used for all reported comparisons between fixed-order agents and A-ToM agents; evaluation metrics were joint payoffs and task-specific success measures.
Operating as a pre-processor (rather than modifying the generator) enables modular integration with existing LLMs and provides an explicit decision point for clarification.
Novelty/architecture claim in the paper explaining that C.A.P. runs before generation and therefore can be plugged into existing LLM pipelines; described design rationale (no empirical integration study presented).
C.A.P. verifies semantic alignment between the current expanded prompt and the weighted history and triggers a structured clarification protocol when similarity is below a threshold.
Component-level description: alignment verification via semantic embeddings (cosine similarity) or learned classifiers and threshold-based decision branching to initiate clarification; described protocol templates (no empirical validation provided).
C.A.P. retrieves dialogue history using a time-weighted decay so recent context is prioritized (approximating human conversational focus).
Design description of a 'time-weighted context retrieval' component; authors propose temporal decay functions (e.g., exponential decay, half-life parameter) applied to dialogue-turn embeddings or metadata (no empirical results reported).
C.A.P. is a pre-generation module that expands user utterances to recover omitted premises and implications.
Architecture and methods description in the paper specifying a 'semantic expansion' component; suggested implementations via knowledge-bases or small LLM prompts to generate premises, paraphrases, and implications (no empirical evaluation reported).
Structured argumentation frameworks make chains of inference inspectable and machine-checkable, improving transparency and verifiability of AI outputs.
Argument from formal properties of AFs and representation; no empirical user studies but relies on known formal semantics.
Computational argumentation offers formal, verifiable reasoning representations (argumentation frameworks, attack/support relations).
Established literature on formal argumentation (e.g., Dung-style AFs) and the paper's conceptual description; no new empirical data reported.
The development artifacts are fully transparent and reproducible: the repository includes an archive of 229 human prompts and a git history with 213 commits.
Paper reports counts of prompts (229) and git commits (213) and states these archives are public; these are concrete repository metrics (n=1 development repository).
The Lean kernel provided full machine verification of all formalized statements in the development.
Paper reports 'Full verification by the Lean kernel' for the Lean 4 development; supported by availability of the Lean 4 repository and verified theorem artifacts (n=1 project).
A specialized prover (Aristotle) automatically closed 111 lemmas during the development.
Quantitative verification metric reported in the paper: 111 lemmas automatically closed by Aristotle; claim tied to the Lean development and prover logs (single project count).
The AI-assisted pipeline combined an AI reasoning model (Gemini DeepThink) to generate the proof, an agentic coding tool (Claude Code) to translate the proof to Lean, a specialized automated prover (Aristotle) that closed 111 lemmas, and the Lean kernel to fully verify the result.
Project workflow description and verification metrics in the paper; reported counts and named components (Gemini DeepThink, Claude Code, Aristotle, Lean kernel); repository and logs purportedly document toolchain usage (n=1 project; 111 lemmas closed by Aristotle reported).
A complete formalization in Lean 4 of the equilibrium characterization for the Vlasov–Maxwell–Landau (VML) system was produced through an AI-assisted pipeline.
Single-project artifact: a Lean 4 development containing formal statements, proof scripts and verified theorems reported by the paper (n=1 project); authors report full machine verification by the Lean kernel and provide the repository as public evidence.
iDaVIE's modular architecture supports extensibility (planned features include subcube loading, advanced render modes, video scripting, and collaborative VR sessions).
Paper describes modular architecture and lists planned/possible future features; this is a software design claim rather than an empirical result.
Because iDaVIE is open-source and extensible, software licensing costs are low and marginal adoption costs fall over time.
Paper states iDaVIE is open-source and designed for community-driven enhancements; economic claim based on general properties of open-source software rather than empirical cost accounting.
iDaVIE includes interaction features such as selection, cropping/subcube tools, catalogue overlays, and export back to existing pipelines.
Feature list in paper describing selection, cropping, overlays, in-VR metrics and export functionality; demonstrated integration to export edited masks/subcubes.
Streaming and downsampling pipelines implemented as Unity plug-ins make large volumes interactively viewable in VR while preserving needed detail for inspection.
Technical description of custom Unity plug-ins for streaming/downsampling and on-the-fly statistics; tested on HI cubes (telescopes listed) per the paper.
iDaVIE (v1.0) is a working VR software suite that lets astronomers import, render, inspect, and interactively edit very large 3D data cubes in real time.
Described implementation of iDaVIE v1.0 built on Unity/SteamVR with custom plug-ins for parsing/downsampling and real-time rendering; tested on large 3D spectral (HI) cubes from radio telescopes (MeerKAT, ASKAP, APERTIF) as reported in the paper.
Personalized LLM coaching produced a statistically significant increase in alignment with the normative empathic taxonomy relative to both the video-based non-personalized feedback and control arms.
Pre-registered randomized experiment with three arms; pre-registered analysis reported statistically significant differences favoring personalized coaching on the primary alignment outcome.
A brief, personalized coaching intervention delivered by a large language model significantly improves participants' alignment with normative, idiomatic empathic communication patterns.
Pre-registered randomized controlled trial with three arms (personalized LLM coaching, video-based non-personalized feedback, control). Outcome measured as alignment to a data-driven normative taxonomy via coding/automated measures. Overall corpus and sample context: 968 participants, 2,904 conversations, 33,938 messages used in the study.
HindSight reveals a large, real difference between systems that is missed by LLM-based judging (i.e., HindSight detects the retrieval-augmentation advantage while LLM-judged metrics do not).
Combined empirical results: HindSight shows a 2.5× advantage (p < 0.001) for retrieval augmentation while LLM-as-Judge reports no significant difference (p = 0.584).
Experiments in the paper cover 10 AI/ML research topics and use a 30-month forward evaluation window.
Experimental setup reported in the paper: scope explicitly stated as 10 AI/ML topics and a 30-month forward window after cutoff T.
Generated ideas can be algorithmically compared to future publications and matched items can be assigned scores reflecting downstream impact (citation counts and venue acceptance).
Method section: description of algorithmic matching procedure and scoring rules that use citation counts and venue acceptance as impact proxies.
A retrieval-augmented idea generator produces 2.5× higher-scoring ideas than a vanilla generator according to HindSight (p < 0.001).
Empirical comparison reported in the paper across the specified experiments (10 AI/ML topics, time-split at T, 30-month forward window); statistical test reporting a 2.5× difference with p < 0.001.
HindSight is a time-split, retrospective evaluation that (1) restricts idea generation to pre-cutoff literature (time T), (2) compares generated ideas to papers published in the following 30 months, and (3) scores matches by downstream impact (citation counts and venue acceptance).
Method described in paper: time-split protocol with a temporal cutoff T, a 30-month forward window, algorithmic matching of generated ideas to later publications, and scoring based on downstream impact metrics (citations and venue acceptance).
The paper introduces a Multi-Object Decoder (MOD) that extends SAM 3D to jointly reconstruct multiple objects from a single image, targeting physically plausible, non-penetrating object configurations and realistic contacts.
Method section: MOD is described as an extension of the single-object SAM 3D architecture to jointly decode multiple object shapes and poses from a monocular image; the method explicitly aims to reduce inter-object penetration and model contacts.
LEAFE achieves up to a 14% absolute improvement on Pass@128 versus the strongest baselines.
Empirical result explicitly reported in the paper: maximum observed improvement 'up to +14% Pass@128' in comparisons to baselines on the experimental tasks.
Compared with outcome-driven methods (e.g., GRPO) and experience-based baselines (e.g., Early Experience), LEAFE yields consistent gains in Pass@1 and Pass@k under fixed interaction budgets.
Head-to-head experimental comparisons reported between LEAFE and baselines GRPO and Early Experience on the task suite; fixed interaction-budget experimental regime; Pass@1 and Pass@k used as evaluation metrics.
LEAFE substantially improves long-horizon agentic performance by internalizing recovery behavior learned from environment feedback.
Reported experiments on a suite of long-horizon interactive tasks (multi-step coding and agentic tasks) comparing LEAFE to baselines; evaluation using Pass@k metrics under fixed interaction budgets; qualitative description that LEAFE internalizes recovery behavior from environment feedback.
The RL fine-tuned Qwen2.5-Coder-7B improves 33.1% over the same base 7B model without RL fine-tuning.
Head-to-head comparison between the tuned model and its untuned base across the 48 evaluation briefs; reported improvement of +33.1%.
Fine-tuning a parameter-efficient 7B model (Qwen2.5-Coder-7B) via reinforcement learning in an OpenEnv-compatible environment yields near-state-of-the-art automated slide-generation: the tuned 7B model reaches 91.2% of Claude Opus 4.6’s quality.
Empirical evaluation on 48 diverse business briefs comparing six models; reported relative quality score of tuned Qwen2.5-Coder-7B = 91.2% of Claude Opus 4.6.
Managing captures, traces, and replay sessions from a unified single design database ensures consistency across replay targets and sessions.
Method description emphasizes a single design database coordinating captures and replays across simulation and emulation for the demonstrator system. (Operational claim demonstrated in the implementation; no metrics on error reduction provided.)
The captured traces can be deterministically replayed across different execution targets (software/hardware simulation and hardware emulation), reducing cross-platform setup complexity and discrepancies.
The same captured waveforms/traces were replayed on both simulation and emulation environments for the ODIN demonstrator; cross-target replay was part of the described method. (Demonstrated on the single reported system; no broad cross-toolchain study provided.)
BATQuant significantly outperforms prior post-training quantization (PTQ) methods on MXFP4 microscaling floating-point formats under aggressive quantization.
Comparative experiments against rotation-based PTQ techniques and other existing PTQ baselines on the described multimodal and language tasks; improvements shown in benchmark metrics and recovery percentages in the paper's experimental section.
BATQuant recovers up to 96.43% of full-precision performance under aggressive W4A4KV16 quantization on MLLMs and LLMs.
Empirical evaluation reported in the paper: experiments on multiple multimodal large language models (MLLMs) and standard LLMs using an aggressive W4A4KV16 quantization setup; performance reported as percentage of full-precision performance recovered (specific models, benchmark names, and exact sample sizes not enumerated in the summary).
The paper provides concrete, regulation-inspired policy examples (e.g., content prohibition, sensitive data exfiltration) showing how they map into the Policy function.
Worked, illustrative examples included in the paper mapping regulatory constraints to the Policy(agent_id, partial_path, proposed_action, org_state) formalism.
Runtime policy evaluation can intercept, score, log, allow/modify/block actions, and update organizational state as part of an agent's execution loop (reference implementation architecture).
Reference implementation design described in the paper (runtime policy evaluator hooks, logging, enforcement actions); architectural reasoning and pseudo-workflows provided; no production deployment data.
Policies can be formalized as deterministic functions p_violation = Policy(agent_id, partial_path, proposed_action, org_state) that return a probability or score of violation for a proposed next action.
Formal definition and mapping in the paper; worked examples showing how regulatory-style constraints map into this function; no large-scale empirical validation.
Effective governance for agentic LLM systems requires treating the execution path as the central object and performing runtime evaluation of proposed next actions given the partial path.
Theoretical argument and formal proposal of runtime policy evaluator that takes (agent_id, partial_path, proposed_action, org_state) and returns a violation probability; reference architecture described; illustrative examples.
The surrogate-driven inverse-design pipeline transfers to physical hardware — designs produced by the CNN+GA pipeline were realized and validated experimentally.
Two fabricated prototypes implemented the optimized pixelated combiners and GaN HEMT Doherty PAs; measured performance metrics correspond to the designs, demonstrating transfer from surrogate-driven design to hardware.
Under a 20 MHz 5G-NR-like waveform (9 dB PAPR) with digital predistortion (DPD), each prototype reached average PAE greater than 51% while meeting ACLR ≤ −60.8 dBc.
Realistic waveform testing described: a 20 MHz 5G‑NR-like signal with 9 dB PAPR was applied to the prototypes, DPD was used, and measurements reported average PAE > 51% and ACLR ≤ −60.8 dBc for each prototype.
Each prototype demonstrated drain efficiency greater than 52% at 9 dB back-off.
Back-off efficiency measurements reported for the fabricated prototypes showing drain efficiency > 52% at 9 dB back-off.
Each prototype produced output power exceeding 44.1 dBm at 2.75 GHz.
Measured output power reported from RF characterization of the two fabricated prototypes; reported value > 44.1 dBm at the test frequency.
Each fabricated prototype achieved peak drain efficiency greater than 74%.
Measured RF characterization reported for the two prototypes showing peak drain efficiency > 74%; measurements conducted on fabricated hardware at 2.75 GHz.
A genetic-algorithm (GA) blackbox optimizer paired with the CNN surrogate can effectively search the discrete multi-port pixel layout space to synthesize output combiners for Doherty amplifiers.
Method description: CNN surrogate embedded in a blackbox Doherty framework and used within a GA to select pixelated combiner layouts; successful designs were produced and taken to fabrication.
The parallel associative scan enables the reductions required by Newton-style updates across time steps, thereby enabling parallelism across sequence length.
Algorithmic construction and implementation details in the thesis showing how associative scan operations aggregate intermediate Jacobian/ update information across time; examples provided in implementation section.
The thesis proves linear convergence rates for a family of fixed-point/Newton-like solvers, with rates depending on approximation accuracy and stability properties of the chosen method.
Mathematical proofs and convergence theorems provided in the theoretical analysis section establishing linear rates under stated assumptions (bounds on approximation error, stability metrics).
Evaluation of dynamical systems can be cast as solving a system of nonlinear equations, enabling parallel solution methods.
Methodological framing and derivation in the thesis showing recurrent updates and Markov transitions can be represented as a global nonlinear root-finding problem; algorithmic constructions follow from this representation.
Explicit enforcement of signal constraints in DeePC provides a safety/operational advantage over many pure learning approaches that do not explicitly enforce hard constraints.
Algorithmic formulation includes constraints in the optimization; paper contrasts this with unconstrained learning-based controllers and demonstrates constrained, feasible actuation in simulation.
DeePC can compute traffic-light actuation sequences that respect hard operational and safety constraints (e.g., phasing, minimum/maximum green times).
Formulation of DeePC as a constrained optimization problem in the paper with explicit constraint terms for signal phasing and safety; implemented in simulation experiments where constraints are enforced in the controller optimization.
Reframing urban traffic dynamics with behavioral systems theory allows system evolution to be learned and predicted directly from measured input–output data (no explicit model identification).
Theoretical exposition in the paper showing that traffic trajectories can be represented as linear combinations of past measured trajectories via Hankel/data matrices; used as the basis for predictive control (DeePC).