Search and filter through extracted claims from AI researchers.
Showing 701-720 of 4537 claims
"Enterprise AI deployment is a coordination problem across business units, application and AI teams, testing, platform engineering, infrastructure, security, operations, and data governance."
"Use-case benchmarks show whether one agent completes one task, but not how changing capabilities, models, runtime mechanisms, capacity, and enterprise data should be owned, changed, admitted, or evidenced together."
"We present four responsibility objects as shared organizational contracts: Skill (reusable, versioned capability and workflow asset), Harness (runtime compiler and governor), Scaffold (execution/control boundary and NFR owner), and a stack-external data substrate under independent CIO-governed semantics and telemetry."
"within a declared operating region, changing activated capability preserves the capacity-response interaction within a preregistered equivalence margin, while changing compatible Scaffold capacity preserves capability semantics up to a non-inferiority margin, and the required controls stay within a declared enforcement budget."
"The approach achieves Semantic Graph Similarity of 0.8692, Term-Typing F1 of 0.9200, and Taxonomy Discovery F1 of 0.8540 on Task B"
"We propose a cluster-period randomized crossover experiment (balanced order, reset/washout, repeated seeds and failure regimes, cluster-aware uncertainty) with a four-state verdict: supported, falsified, conditional-engineering, or inconclusive."
"It reports no completed implementation, experiment, dataset, or measured result."
"advanced plant characterization requires the integration of active physiological sensing modali- ties such as chlorophyll fluorescence"
"The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging"
"the proposed system provides a robotics-driven approach to high-resolution plant phenotyping and opens new directions for autonomous agricultural inspection and plant-aware manipulation"
"Results indicate that SPEA-2 achieved higher precision and recall than both multi-objective and single-objective baseline methods."
"The proposed recommender system successfully identified buggy classes or files for 88.5\% of bug reports within the top 10 recommendations and 94\% within the top 20."
"The effectiveness of the model was further validated on an industrial Android project written in Kotlin, demonstrating its adaptability across programming languages."
"In addition, relying solely on lexical similarity between source code and bug reports is often insufficient due to the natural language nature of bug descriptions."
"Most deep-learning segmentation work for OCT is validated only in-domain, leaving generalization to clinical data collected under different acquisition protocols largely untested."
"reaching Dice scores of 0.76 to 0.82 with strong volumetric and surface calibration (r vol, r surf greater than or equal to 0.97 across all four pipelines) on an in-domain validation set"
Ensemble composition is the most consistent driver of improvement in OCT lesion segmentation models
"identifies ensemble composition as the most consistent driver of improvement"
"Predictions track clinical biomarkers outside the training distribution, though less strongly than in-domain -- evidence for, not validation of, automated lesion-burden tracking as a clinical tool."
"Ontology learning from text remains challenging despite significant progress in Large Language Models (LLMs)"
LLMs favor hierarchical relations over associative relations in ontology learning tasks
"favor hierarchical over associative relations"
Pipeline data may be stale or degraded.
Last synthesis: 2026-09-20. 8,964 pending.