JarvisBench: Always-on Intelligence Between Humans and Agents
Long-horizon agents can execute continuously, but human attention remains intermittent and scarce. This creates a bidirectional coordination problem: users may need immediate access to an agent while work continues in the background, whereas agents may encounter consequential decisions that require user judgment after the user has stopped monitoring execution. We posit an always-on attention-coordination layer---\textit{Jarvis}\footnote{Named after the fictional AI assistant in \textit{Iron Man}.}---that mediates this interface and allocates human attention across one or more working agents. We introduce \textit{JarvisBench} to evaluate both directions of this coordination: whether an int...
| Authors | Chen Chen, Zhehuai Chen |
|---|---|
| Primary category | cs.AI |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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