● Covered by 1 source · 1 reportMedium impact
Fable 5 outperforms GPT-5.6 Sol on NP-hard optimization problem
Key points
- Fable 5 excels in fiber-network design problem
- /goal mode does not significantly improve outcomes
- No single closed-form count for solution space
Overview of the Experiment
The experiment involved testing Claude Fable 5 and GPT-5.6 Sol on a fiber-network design optimization problem. This NP-hard problem, termed KIRO, focuses on connecting distribution points in major French cities while minimizing total cable length.
Performance Results
Fable 5 produced the best overall solution, demonstrating unmatched consistency. In contrast, GPT-5.6 Sol performed adequately but did not reach the same standard. The unique capabilities of Fable 5 highlight its potential efficacy in operations research applications.
The Role of /goal Mode
The /goal mode was tested to see if it would enhance performance. Results indicated that this mode does not serve as a universal improvement tool. It affects the control loop and search path but does not guarantee better outcomes.
Complexity of the Problem Space
The KIRO problem features a massive search space, calculated to be approximately 10^1223, highlighting the complexity of finding optimal solutions in NP-hard scenarios. This immense range underlines the challenge both models faced in achieving effective resolutions.
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Reporting from
Claude Fable 5 achieved superior results in solving a complex NP-hard fiber-network design issue compared to GPT-5.6 Sol. While the introduction of the /goal mode did not significantly enhance performance, Fable 5's consistency was noteworthy.