aiqbench analysis
This research introduces a novel memory mechanism for GUI agents that relies on action-conditioned bisimulation rather than visual similarity, effectively distinguishing between functionally identical but behaviorally distinct interface states. By freezing the agent and analyzing its empirical predictive state graph without additional training, the method improves task success rates in environments like MiniWoB++ while avoiding common pitfalls of merging dissimilar pages. The approach represents a significant step toward more robust autonomous agents capable of navigating complex graphical user interfaces with precise semantic understanding.
Action Conditioned Bisimulation For GUI Agent Memory
arXiv:2609.38778v1 Announce Type: new Abstract: An agent that remembers what it did on a web page must decide when two pages count as the same. Memories built on observation similarity merge pages that look alike but behave differently, and GUIs are full of such pages: two tabs of one widget or two rows of one menu answer the same click differently. We define the merge rule as an action-conditioned bisimulation over the empirical predictive state graph a frozen agent fills as it acts. Two states merge only when their shared actions lead to agreeing outcomes and successor blocks under an affordance label. Observation similarity never enters the rule, and nothing is trained. It replaces the merge rule of an existing outcome-value memory, so a closed-loop comparison isolates it. On MiniWoB++ it raises success rate over a memoryless agent, while a control taking identical exploratory detours, the prior successor-representation merge, and the same criterion without action conditioning change nothing.
Abstract:An agent that remembers what it did on a web page must decide when two pages count as the same. Memories built on observation similarity merge pages that look alike but behave differently, and GUIs are full of such pages: two tabs of one widget or two rows of one menu answer the same click differently. We define the merge rule as an action-conditioned bisimulation over the empirical predictive state graph a frozen agent fills as it acts. Two states merge only when their shared actions lead to agreeing outcomes and successor blocks under an affordance label. Observation similarity never enters the rule, and nothing is trained. It replaces the merge rule of an existing outcome-value memory, so a closed-loop comparison isolates it. On MiniWoB++ it raises success rate over a memoryless agent, while a control taking identical exploratory detours, the prior successor-representation merge, and the same criterion without action conditioning change nothing.
Artificial Intelligence (cs.AI)
arXiv:2609.38778 [cs.AI]
(or arXiv:2609.38778v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.38778 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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