Agents
CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution [R]
CANTANTE addresses the challenge of optimizing LLM-based multi-agent systems where system-level performance scores are available but individual agent parameters cannot be directly optimized. The frame
CANTANTE addresses the challenge of optimizing LLM-based multi-agent systems where system-level performance scores are available but individual agent parameters cannot be directly optimized. The framework uses contrastive credit attribution to decompose system-level rewards into per-agent signals by comparing multiple agent configurations, with instantiation for prompt optimization. CANTANTE is evaluated on programming, mathematical reasoning, and multi-hop question answering tasks against competing optimization methods.
Source: r/MachineLearning | 2026-05-20