arXiv:2605.29126v1 Announce Type: new Abstract: A linear probe can decode a representation almost perfectly and yet be completely irrelevant to how the model uses it. On calendar-date duration reasoning in language models, a $\sin$/$\cos$ probe recovers day-of-year from a layer's activations, yet ablating its direction has no effect on the model's answers -- while ablating a four-dimensional subspace found by Distributed Alignment Search (DAS) at the same layer collapses performance entirely. We measure the angle between these two subspaces -- the \emph{readout-mediator angle} -- and find it i
This research is emerging as AI model interpretability becomes a critical bottleneck for further development and trusted deployment, especially in complex reasoning tasks.
Understanding how AI models process temporal information is crucial for building more reliable and transparent AI, impacting applications from scheduling to scientific discovery.
We now have a quantifiable methodology, the 'readout-mediator angle,' to better distinguish between superficial model understanding and genuine, integrated representational use, challenging prior interpretability assumptions.
- · AI researchers
- · AI interpretability tools
- · Developers of temporal AI systems
- · Overly simplistic AI interpretability methods
Improved model interpretability tools will emerge, focusing on identifying true 'mediator' subspaces.
This methodology could lead to more robust and less 'brittle' AI models that truly generalize temporal reasoning.
Deeper understanding of AI's internal mechanisms could accelerate progress in advanced AI systems, potentially impacting the timeline for general AI capabilities.
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Read at arXiv cs.LG