Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
Quick summary
arXiv:2607.26828v2 Announce Type: cross Abstract: Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates. Existing adaptive discovery controllers assign credit based only on score progress, even though prompt length, retries, and guidance calls cause search actions to incur different token costs. We prove that cost-blind credit can forfeit all but a vanishing fraction of attainable quality as frontiers multiply and costs diverge. Under a fixed search-side token budget, the controller must decide which frontier is
Key takeaways
- arXiv:2607.26828v2 Announce Type: cross Abstract: Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates.
- Existing adaptive discovery controllers assign credit based only on score progress, even though prompt length, retries, and guidance calls cause search actions to incur different token costs.
- We prove that cost-blind credit can forfeit all but a vanishing fraction of attainable quality as frontiers multiply and costs diverge.
Why it matters
The importance of “Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.