Grounding Spatial Relations in a Compact World Model: Instruction Leakage and a Goal-Free Dynamics Fix
arXiv:2607.06925v1 Announce Type: new Abstract: Compact world models that condition on a language goal promise to ground relations such as ``put the red block left of the blue block'' using a sparse set of explicit \emph{reference anchors}. We ask when such references actually ground a relation, and identify a trap: a goal-conditioned predictor reaches a striking $0.90$ relation-readout accuracy, yet this is \emph{instruction transcription}, not perception. Withholding the goal collapses it to chance ($0.90\!\to\!0.27$, three seeds) and a counterfactual instruction makes the predicted anchors
This research provides a critical insight into the current limitations of goal-conditioned AI models, highlighting a potential over-reliance on proxies rather than true grounding, which is timely as AI systems become more complex.
A strategic reader should care because it exposes a fundamental challenge in AI's ability to truly perceive and ground relations, rather than merely 'transcribe' instructions, which has implications for the robustness and autonomy of AI agents.
This research indicates that perceived high accuracy in grounded AI systems might be misleading, suggesting a need for more robust evaluation metrics and architectural changes to ensure genuine understanding rather than superficial instruction following.
- · AI Safety Researchers
- · Developers of foundational AI models
- · Companies investing in robust AI testing
- · AI companies over-promising 'grounded' or autonomous capabilities
- · Investors funding AI based on superficial benchmarks
Increased scrutiny on evaluation methodologies for AI systems, particularly those claiming spatial or relational understanding.
A shift in AI research focus towards developing truly grounded perception systems that are robust to instruction leakage and context changes.
Potential for a 'winter' effect in specific subfields of AI if current capabilities are demonstrably less robust than advertised, impacting funding and public perception.
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Read at arXiv cs.AI