aiqbench analysis
The paper argues that navigation success alone can hide whether vision-language-action models actually follow their instructions. NavBind tests this by holding the navigation context fixed and changing only the instruction at route forks, then measuring shifts in action likelihoods and visual attention. For readers evaluating embodied AI, the main takeaway is that benchmark scores may overstate instruction-following, so models need diagnostics that test instruction sensitivity directly.
NavBind: Diagnosing Instruction-Action Binding at Route Forks in Navigation VLAs
arXiv:2610.29203v1 Announce Type: new Abstract: Vision-language-action (VLA) navigation models translate visual observations and instructions into actions. Navigation success assesses whether an agent reaches the goal. However, reaching the goal successfully does not establish whether the model genuinely follows the instructions or relies on visual cues or navigation history. We introduce NavBind to diagnose instruction-action binding: whether changing the instruction influences the probabilistic distribution of the actions, with the navigation context fixed. We construct 3,252 route-fork contexts by recombining route-aligned instruction segments from R2R-CE and RxR-CE. Each context pairs full instructions that share the description of the route already traveled but specify different branches. Both are evaluated under the same agent state and navigation history. We compare generated local action sequences, the likelihoods of fixed reference action sequences under both instructions, and changes in visual attention and attribution. Across the evaluated models, higher reported navigation success does not imply that the model more faithfully follows the instructions.
Abstract:Vision-language-action (VLA) navigation models translate visual observations and instructions into actions. Navigation success assesses whether an agent reaches the goal. However, reaching the goal successfully does not establish whether the model genuinely follows the instructions or relies on visual cues or navigation history. We introduce NavBind to diagnose instruction-action binding: whether changing the instruction influences the probabilistic distribution of the actions, with the navigation context fixed. We construct 3,252 route-fork contexts by recombining route-aligned instruction segments from R2R-CE and RxR-CE. Each context pairs full instructions that share the description of the route already traveled but specify different branches. Both are evaluated under the same agent state and navigation history. We compare generated local action sequences, the likelihoods of fixed reference action sequences under both instructions, and changes in visual attention and attribution. Across the evaluated models, higher reported navigation success does not imply that the model more faithfully follows the instructions.
Robotics (cs.RO)
arXiv:2610.29203 [cs.RO]
(or arXiv:2610.29203v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.29203 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 244 words