arXiv:2605.09169v2 Announce Type: replace-cross Abstract: A Mamba state-space model trained only for next-step prediction appears to recover Granger-causal structure through a simple readout $S = |W_{out} W_{in}|$, with early experiments suggesting the phenomenon generalized across architectures and benefited from interventional data at $p < 10^{-5}$. We package the protocol used to test that claim -- standardized synthetic generators (VAR/Lorenz/CauseMe-style), three intervention semantics ($do(X=c)$, soft-noise, random-forcing), edge-provenance cards on three real datasets, and size-matched
This paper offers a critical re-evaluation of how Mamba state-space models, a new class of deep learning architecture, learn causal structures, coming at a time when 'autonomous agents' are foregrounded.
Understanding the actual mechanisms by which modern AI models interpret causality is crucial for building reliable and trustworthy AI systems, especially for high-stakes applications.
This research refines our understanding of what 'prediction bottlenecks' in AI models truly represent, shifting the focus from direct causal discovery to more nuanced forms of structural recognition.
- · AI researchers
- · Developers of interpretable AI
- · Autonomous agent developers
- · Oversimplified interpretations of AI causality
- · Developers relying solely on direct readout for causal inference
More robust and theoretically grounded methods for AI model interpretation and diagnosis are likely to emerge.
This refined understanding could accelerate the development of agentic AI systems that interact more safely and predictably with complex environments.
Long-term, this could lead to more efficient and less resource-intensive AI models for causal inference, impacting various scientific and industrial applications.
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Read at arXiv cs.AI