CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution [R]

2026-05-20 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Advanced, quick

Summary

CANTANTE is a novel algorithm designed to optimize LLM-based multi-agent systems by addressing the credit assignment problem, a key bottleneck in automating their configuration. Unlike traditional manual, trial-and-error prompt tuning, CANTANTE treats agent prompts as parameters learned from task rewards. Its method involves local optimizers suggesting configurations, evaluating these on queries to capture reasoning traces and system scores, and then using an attributer to compare rollouts and assign per-agent credit. These credits are fed back to local optimizers, such as CAPO, for continuous improvement. Evaluated against DSPy-solutions GEPA and MIPROv2 on benchmarks like MBPP, GSM8K, and HotpotQA, CANTANTE achieved the best average rank, beating the strongest baseline by +18.9 points on MBPP and +12.5 points on GSM8K, all while maintaining inference time cost.

Key takeaway

For AI Engineers developing multi-agent LLM systems, CANTANTE offers a path beyond manual prompt tuning. If you are struggling with credit assignment in complex agentic workflows, consider implementing contrastive credit attribution to automate prompt optimization. This approach can significantly improve performance, as demonstrated by +18.9 points on MBPP, without increasing inference costs. It enables more robust and autonomous system development.

Key insights

CANTANTE optimizes multi-agent LLM systems by learning agent prompts from task rewards through contrastive credit attribution.

Principles

• Decompose global reward to per-agent signals.
• Treat prompts as learnable parameters.
• Credit assignment enables autonomous systems.

Method

Local optimizers suggest configurations; evaluate rollouts to capture traces and scores; an attributer assigns per-agent credit by comparing rollouts; feed credits to local optimizers.

In practice

• Apply credit attribution to prompt tuning.
• Use CAPO for prompt optimization.
• Benchmark against DSPy-solutions.

Topics

• Multi-agent Systems
• LLM Optimization
• Credit Assignment
• Prompt Engineering
• Contrastive Learning
• Automated Tuning

Code references

• finitearth/cantante

Best for: Research Scientist, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.