Represented Is Not Computed: A Causal Test of Candidate Algorithmic Intermediates in a Transformer
arXiv:2605.22488v1 Announce Type: new Abstract: Structured prompts require integrating components according to task-relevant relations. How a network implements this integration is often hard to judge in language or vision, where those relations are rarely specified precisely enough to define a candidate internal algorithm. Arithmetic offers a cleaner setting. We study a Transformer trained on base-digit extraction: given $N$, $B$, and $D$, it must report the coefficient of $B^D$ in the base-$B$ expansion of $N$. The closed-form solution, $\lfloor N/B^D \rfloor \bmod B$, provides explicit cand
This paper leverages improved interpretability techniques and the controlled environment of arithmetic tasks to probe the internal workings of Transformers, a critical area given the rapid advancement and deployment of large language models.
Understanding how AI models arrive at their decisions, rather than just what they decide, is crucial for developing robust, reliable, and auditable AI systems, especially for high-stakes applications.
This research provides a methodology for causally testing hypothesized internal algorithms within deep learning architectures, moving beyond correlational analysis towards a more mechanistic understanding of AI cognition.
- · AI safety researchers
- · AI interpretability tools developers
- · High-reliability AI sectors
- · Black-box AI development approaches
Improved methods for verifying the internal logic of AI models become available.
This could accelerate the development of AI models that are inherently more transparent or 'glass-box' in their operation.
Increased trust and regulatory acceptance for AI systems, potentially broadening their application in sensitive domains.
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