Research
CANTANTE Solves Multi-Agent Prompt Optimization Through Credit Attribution
A new method treats agent prompts as learnable parameters rather than hand-tuned configurations, automatically decomposing system-level rewards into per-agent signals to optimize complex multi-agent LLM systems.
1 min read
Multi-agent LLM systems are powerful but frustratingly opaque to optimize. When you tweak one agent's prompt, the entire system's output shifts—and you have no clear way to know whether that agent helped or hurt the final result. This is the credit assignment problem, and it's been a persistent fric...
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Method & sources
- Source type
- Community signal (Reddit) — our summary + analysis
- Source link
- Reddit · reddit-machinelearning
- Published
- UTC
- Byline
- By the gotcontext.ai team (editorial standards)
- Correction?
- corrections@gotcontext.ai