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technologyreview.com·2h ago
AI Research
arXiv CS.AI·May 21·5 min read
optimize_anything: A Universal API for Optimizing any Text Parameter
TL;DR
Specialized optimization tools exist for different domains, but require domain-specific engineering. optimize_anything unifies optimization across six diverse tasks using a single LLM-based system that handles single-task search, multi-task learning with knowledge transfer, and generalization to new inputs. The system achieved state-of-the-art results across all tested domains without task-specific customization.
✦ Why It Matters
Engineers can now use one optimization framework across diverse text-based problems instead of maintaining multiple specialized tools.
Key Takeaways
- Use optimize_anything to unify optimization across different text-based domains without retraining.
- Leverage multi-task transfer learning to improve performance on new optimization problems faster.
- Test whether your optimization task fits the text-artifact-plus-scoring-function pattern before adopting.
- Benchmark against domain-specific tools to validate that unified approach meets your performance requirements.
Full Summary
Optimization problems traditionally require specialized tools tailored to each domain—from prompt engineering to code generation to molecule design. optimize_anything addresses this fragmentation by treating diverse optimization tasks as a unified problem: improving text artifacts (prompts, code, molecules) evaluated by scoring functions. The system uses a large language model as a universal optimizer, supporting three capabilities: single-task search (optimizing within one domain), multi-task search with cross-problem transfer (learning patterns across domains to improve performance), and generalization to unseen inputs (applying learned strategies to new problems).
Evaluated across six fundamentally different tasks, the approach matched or exceeded specialized state-of-the-art tools without domain-specific engineering. This demonstrates that LLM-based optimization can serve as a general-purpose alternative to building separate tools for each optimization problem.