How Much Does Correctness Cost? Budgeted Placement of Strong Correctors in a Weak Multi-Agent Swarm
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Abstract (arXiv)
A cheap swarm of unreliable agents can be steered to a correct consensus by a few strong, expensive "oracle" correctors. We ask how much one must spend, and where to place the oracles. We model the swarm as a consensus on a graph in which each oracle pins one node toward the truth at a cost-coupled, concave strength, and measure quality by the coherence H(R)=tr M(R)^{-1}. Our first result is that H stays submodular (each added oracle helps less than the last) even when the oracles differ in strength, so a cost-benefit greedy comes within 1-1/e of the best placement at any budget. Inverting the budget gives the budget-correctness frontier B*(eps), the least spend that guarantees an eps-correct consensus: closed-form on the complete graph, and a minimal oracle count k* when oracles cost the same. Whether a budget then buys a few strong oracles or many medium onese curvature of the cost-quality law: diminishing returns favour spreadsharply increasion. Measured onthe Qwen3 ladder (0.6-32B), the law is concave for math verificatio convex foremergent code tracing, so the verdict is genuinely task-dependent.https://github.com/YehudaItkin/budgeted-oracle-placemen
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 7 Jul 2026.
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References · 8
- 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 791
- 2025Correlated Errors in Large Language Modelscited by 42
- 2025When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQAcited by 13
- 2025Automated Composition of Agents: A Knapsack Approach for Agentic Component Selectioncited by 10
- 2026Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placementcited by 4
- 2026Phase Transition for Budgeted Multi-Agent Synergycited by 3
- 2017Submodular Optimization for Consensus Networks with Noise-Corrupted Leaderscited by 2
- 2013Algorithms for leader selection in stochastically forced consensus networkscited by 1
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