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CANTANTE introduces a contrastive credit attribution method to optimize multi-agent LLM systems by decomposing global rewards into per-agent signals, enabling automated prompt tuning. It outperforms baselines on programming, math, and retrieval benchmarks, achieving up to +18.9 points improvement without increased inference cost.
Researchers apply contrastive LRP-based attribution to analyze why LLMs fail on realistic benchmarks, finding the method gives useful signals in some cases but is not universally reliable.