Researchers prove computational arbitrage in AI model marketplaces is generally NP-hard
A new arXiv paper formalizes computational arbitrage in AI model marketplaces as an allocation problem and proves it is generally NP-hard. The authors show that optimal allocation can be computed efficiently under a diminishing-returns condition on model performance scaling with inference spending. They also develop a sample-efficient method to estimate model performance from limited data,…
1 source primary source
Key points
- Computational arbitrage in AI model marketplaces is proven to be generally NP-hard
- Optimal allocation becomes tractable under diminishing-returns performance scaling
- Sample-efficient method reduces cost of estimating model performance from limited data
Read the original at arXiv cs.AI · by Nastaran Okati, Ricardo Olmedo, Moritz Hardt primary sourceOpen source ↗
Topics · follow one to build your own front page
Olmedo et al.
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
More in Research
All →- SemiAnalysis releases GDPx benchmark with Claude Opus 5.5 leading at 12% · 3 src
- Gemma-3-12b fails to detect misinformation when poisoned claims dominate retrieved evidence · 1 src
- Researchers release BILAL-BENCH to test Bengali AI models’ Islamic text fabrication · 1 src
- Researchers prune 729 attention heads to cut GPT-2-XL’s memorisation by 91.5% · 1 src
- arXiv paper finds shortcuts distort RAG benchmarking accuracy · 1 src
Comments
via GitHub Discussions