arXiv:2603.22744v2 Announce Type: replace Abstract: Large language models excel on objectively verifiable tasks such as math and programming, where evaluation reduces to unit tests or a single correct answer. In contrast, real-world enterprise work is often subjective and context-dependent: success hinges on organizational goals, user intent, and the quality of intermediate artifacts produced across long, multi-tool workflows. We introduce LH-Bench, a three-pillar evaluation design that moves beyond binary correctness to score autonomous, long-horizon execution on subjective enterprise tasks.
The proliferation of large language models necessitates better evaluation methods for real-world enterprise applications beyond simple objective tasks.
This development addresses a critical gap in assessing AI agents' performance on complex, subjective business processes, which is essential for broad enterprise adoption.
The focus of agent evaluation shifts from binary correctness to a more nuanced, skill-grounded approach that accounts for context and subjective outcomes.
- · AI agent developers
- · Enterprises adopting AI agents
- · SaaS providers integrating AI agents
- · White-collar professions reliant on repetitive, multi-step workflows
- · Companies slow to adopt agentic workflows
Improved evaluation leads to more robust and reliable long-horizon AI agents.
Enterprise workflows become increasingly automated and optimized by sophisticated AI agents.
The definition of 'work' fundamentally shifts as AI agents assume more complex, multi-faceted tasks previously exclusive to humans.
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Read at arXiv cs.AI