Causal state binding predicts action control in language agents
Autonomous language agents increasingly expose traces, memories, plans and constraints, but existing evaluations rarely test whether these state variables are bound to final actions. We introduce causal state binding, an intervention-coupled evaluation framework that measures whether actions change with the event-specific decisive state while remaining invariant to irrelevant cues. The primary readout is a hidden-target finite-action benchmark in which scorer-side intervention targets are assigned before generation and withheld from the model-visible prompt. Across 57,816 scored records in seven corpus-level units, structured-agent conditions exceeded high-randomness controls and targeted...
| Authors | Xiao Jia |
|---|---|
| Primary category | cs.AI |
| Published | 10 May 2026 |
| Last updated at source | 1 June 2026 |
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