EVA-Bench supplies a simulation engine for bot-to-bot voice dialogues plus two composite metrics (EVA-A for accuracy, EVA-X for experience) evaluated on 213 enterprise scenarios, showing no tested system exceeds 0.5 on both pass@1 scores.
Beyond accuracy: A multi-dimensional framework for evaluating enterprise agentic AI systems
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 3polarities
background 3representative citing papers
Anchor generates consistent long-horizon agent tasks from parametric constraint programs, yielding ERP-Bench of 300 ERP tasks where frontier models reach optimal solutions in 17.4% of trials.
AIT Academy introduces a tripartite curriculum for AI agents across natural science, humanities, and social science domains, with reported gains of 15.9 points in security and 7 points in social reasoning under specific scheduling.
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.
citing papers explorer
-
EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents
EVA-Bench supplies a simulation engine for bot-to-bot voice dialogues plus two composite metrics (EVA-A for accuracy, EVA-X for experience) evaluated on 213 enterprise scenarios, showing no tested system exceeds 0.5 on both pass@1 scores.
-
Anchor: Mitigating Artifact Drift in Agent Benchmark Generation
Anchor generates consistent long-horizon agent tasks from parametric constraint programs, yielding ERP-Bench of 300 ERP tasks where frontier models reach optimal solutions in 17.4% of trials.
-
AIT Academy: Cultivating the Complete Agent with a Confucian Three-Domain Curriculum
AIT Academy introduces a tripartite curriculum for AI agents across natural science, humanities, and social science domains, with reported gains of 15.9 points in security and 7 points in social reasoning under specific scheduling.
-
From Question Answering to Task Completion: A Survey on Agent System and Harness Design
Survey framing LLM agents as model-plus-harness systems, decomposing harness responsibilities, mapping them to tasks, and highlighting open challenges in evaluation, safety, and co-evolution.