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[2602.21061] Tool Building as a Path to "Superintelligence"
Summary
The paper explores how Large Language Models (LLMs) can achieve superintelligence through the Diligent Learner framework, emphasizing the importance of tool design and precise tool calls in logical reasoning tasks.
Why It Matters
As AI continues to evolve, understanding the pathways to superintelligence is crucial for developing robust AI systems. This research highlights the significance of tool-building capabilities in LLMs, which could influence future AI architectures and applications.
Key Takeaways
- LLMs can achieve superintelligence via test-time search with sufficient step-success probability.
- The Diligent Learner framework is essential for measuring LLM performance on complex tasks.
- Tool design is critical for LLMs to perform logical reasoning effectively.
- Frontier models show partial robustness in reasoning tasks, indicating potential for improvement.
- Successful reasoning at scale requires precise integration of information.
Computer Science > Artificial Intelligence arXiv:2602.21061 (cs) [Submitted on 24 Feb 2026] Title:Tool Building as a Path to "Superintelligence" Authors:David Koplow, Tomer Galanti, Tomaso Poggio View a PDF of the paper titled Tool Building as a Path to "Superintelligence", by David Koplow and 2 other authors View PDF HTML (experimental) Abstract:The Diligent Learner framework suggests LLMs can achieve superintelligence via test-time search, provided a sufficient step-success probability $\gamma$. In this work, we design a benchmark to measure $\gamma$ on logical out-of-distribution inference. We construct a class of tasks involving GF(2) circuit reconstruction that grow more difficult with each reasoning step, and that are, from an information-theoretic standpoint, impossible to reliably solve unless the LLM carefully integrates all of the information provided. Our analysis demonstrates that while the $\gamma$ value for small LLMs declines superlinearly as depth increases, frontier models exhibit partial robustness on this task. Furthermore, we find that successful reasoning at scale is contingent upon precise tool calls, identifying tool design as a critical capability for LLMs to achieve general superintelligence through the Diligent Learner framework. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.21061 [cs.AI] (or arXiv:2602.21061v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.21061 Focus to learn more arXiv-issued DOI via DataCite ...