Foundation Models: The Illusion of Reliability in Navigation Tasks

High success rates in navigation tasks, often touted by developers of foundation models, aren't necessarily an indicator of dependable decision making. Current evaluation metrics gloss over critical limitations, requiring a more meticulous examination of these models’ capabilities in real-world scenarios.

Diagnostic Challenges

Recent evaluations on six diagnostic tasks shed light on the yawning chasm between success metrics and requisite reliability, spanning environments of complete and incomplete spatial information, as well as safety-relevant contexts. These evaluations uncover the murky depths of foundation models' performance, revealing that the current metrics often paint an overly optimistic picture.

Consider GPT-5, achieving a commendable 93% success rate in path-planning scenarios with unknown cells. Yet, lurking beneath this polished surface are fundamental shortcomings. The model's inability to grasp structural spatial understanding, a core necessity for navigation, stands exposed in its failures. What they're not telling you: success rates can be misleading, masking the more nuanced areas where these models falter.

The Reliability Myth

Newer iterations aren't immune from scrutiny. Gemini-2.5 Flash's performance in reasoning under safety-relevant information, particularly in emergency-evacuation tasks, is a case in point. Achieving only 67% success, it actually underperformed compared to its predecessor, Gemini-2.0 Flash, which aced the task with a 100% success rate. This raises a critical question: are newer models genuinely better, or is their reliability merely an illusion crafted by selective metrics?

A closer look reveals that across all diagnostic evaluations, models are plagued by issues such as structural collapse, hallucinated reasoning, constraint violations, and unsafe decision making. These findings fly in the face of the prevailing narrative that newer is always better AI models. Let's apply some rigor here and acknowledge that progress isn't just about iteration, but about meaningful improvement in reliability.

The Path Forward

The implication is clear: foundation models, despite their apparent advancements, exhibit significant inadequacies that must be addressed with targeted, failure-focused analysis. Before we can entrust these models with critical tasks, a fine-grained evaluation is essential to understand and rectify these limitations.

Color me skeptical, but until we confront these glaring issues head-on, claims of reliability remain suspect at best. In the race for AI supremacy, it's easy to get caught up in the allure of progress. Yet, without addressing these foundational concerns, the promise of reliable AI decision making in navigation and safety remains just that, a promise, not yet a reality.