The Algorithm Is Not the Behavior: Learned Priors Override Look-Ahead in a Chess-Playing Neural Network
arXiv:2508.21380v3 Announce Type: replace Abstract: Recent mechanistic work has uncovered learned algorithms within neural networks, from modular arithmetic to search and planning in game-playing agents. But does algorithmic structure guarantee algorithmic behavior? We investigate this in Leela Chess Zero, the strongest neural chess engine, where prior work identified learned look-ahead. By extending the logit lens to its move-selecting policy network, we discover that correct puzzle solutions-including immediate checkmates-often appear in intermediate layers but are systematically overridden
This research is emerging as the field of AI interpretability matures and researchers delve deeper into the mechanistic understanding of large neural networks.
It highlights a critical divergence between learned algorithms and actual behavior in complex AI systems, suggesting that even with an identified 'algorithm,' the final decision-making process can be systematically flawed or overridden.
Our understanding of how 'intelligent' behavior emerges from neural networks is refined, showing that identifying internal algorithms does not guarantee predictable or optimal output, which has implications for trust and safety in advanced AI.
- · AI interpretability researchers
- · AI safety organizations
- · Developers of robust AI systems
- · Developers of 'black box' AI
- · High-stakes AI deployment reliant on algorithmic fidelity
Further research will focus on aligning internal algorithms with desired external behaviors in neural networks.
This understanding will lead to new techniques for AI auditing and control, particularly in critical applications.
Increased skepticism about emergent 'general intelligence' will prompt a re-evaluation of current AI development paradigms, emphasizing verifiable mechanistic alignment over pure performance.
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Read at arXiv cs.LG