AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical.
Hugging Face · Daily Papers
·Zichen Ding, Jiaye Ge
·
·▲ 37 upvotes
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Zichen Ding, Jiaye Ge, Shufan Jiang, Kai Chen, Mo Li, Qingqiu Li
- 37 upvotes da comunidade
Resumo
Resumo original (em inglês), extraído do paper:
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To address this, we introduce AgentCompass, an open-source, lightweight, and extensible infrastructure for evaluating LLM-based agents. AgentCompass organizes the evaluation process around three independent components, namely Benchmark, Harness, and Environment, thereby enabling flexible configurations without requiring the reimplementation of complex execution logic. Furthermore, it features a fault-tolerant asynchronous runtime and comprehensive trajectory analysis tools to transparently diagnose nuanced failure modes like reward-hacking. Natively supporting over 20 benchmarks across five capability dimensions, AgentCompass provides the community with a scalable and reproducible infrastructure for advancing agent research.Onde ler
// relacionados
Leia também
Editorial
Kimi K3: a China lança o maior modelo aberto do mundo — e ele não é o maior por acaso
Blog
Modelos de peso aberto agora igualam o desempenho cibernético de ponta de apenas quatro meses atrás por uma fração do custo
Blog
O novo manual de IA do Pentágono trata a adoção lenta como um risco maior do que o alinhamento imperfeito
Blog