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• Leading training-free LLM adaptation methods (GEPA and ACE) achieve only marginal gains on the FAMMA financial reasoning benchmark.result 70%
• ASDA achieves up to +17.33% improvement on arithmetic reasoning and +5.95% on non-arithmetic reasoning on the FAMMA benchmark.result 70%
• ASDA generates structured skill artifacts through iterative error-corrective learning without modifying model weights.method 70%
• ASDA's skill artifacts are human-readable, version-controlled, and compatible with the Agent Skills open standard.technical 70%
• ASDA offers a practical and auditable path to domain adaptation for organizations without weight access or retraining.market 70%
• Dependency on high-quality labeled domain datasets is a caveat for ASDA's performance.limitation 70%
• Integration challenges with existing LLM infrastructure and potential latency from dynamic skill injection are potential caveats.limitation 70%
• ASDA's moat is derived from proprietary skill artifacts, error clustering algorithms, and integration with the Agent Skills standard.market 70%

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