Unifying Temporal and Structural Credit Assignment in LLM-Based Multi-Agent Prompt Optimization
Wenwu Li, Yuran Song, Mingze Zhao, Bo Jin, Wenhao Li
Why It Matters
What makes this one worth your time
This work addresses a significant challenge in MAS optimization, potentially leading to more efficient and interpretable collaborative AI systems.
A new method for optimizing Multi-Agent Systems through targeted credit assignment.
Summary
The paper proposes a novel approach to optimizing Multi-Agent Systems (MAS) by introducing a method for temporal and structural credit assignment, which aims to improve the efficiency of agent interactions and reduce query complexity in reasoning tasks.
Key contributions
- Proposes a dual-axis credit assignment framework for MAS optimization.
- Introduces a discrete block coordinate descent algorithm tailored for role prompt optimization.
- Demonstrates substantial reductions in query complexity across diverse reasoning benchmarks.
Notable insights
- The introduction of state-space bottlenecks for temporal credit assignment is a clever way to identify critical interaction rounds.
- Using LLM-generated 'proxy gradients' for targeted updates could enhance the interpretability and efficiency of the optimization process.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.30227v1 Announce Type: cross Abstract: While Multi-Agent Systems (MAS) empower Large Language Models to tackle complex reasoning tasks through collaborative interaction, optimizing their dynamics remains a formidable challenge due to the discrete, non-differentiable nature of the computation graph and the sparsity of global supervisory signals. Existing black-box optimizers struggle to attribute trajectory-level failure to specific local components, resulting in inefficient, high-variance exploration. We argue that tractable MAS optimization needs structural inductive biases to disentangle error signals. We propose temporal and structural credit assignment, which decomposes the objective along two axes: (i) temporal credit, using state-space bottlenecks to identify critical rounds, and (ii) structural credit, using stationary role policies to isolate agent contributions. Leveraging these decomposed signals, we introduce a discrete, verbalized block coordinate descent algorithm for iterative refinement. Rather than indiscriminate global updates, it alternates between optimizing role prompts and aggregation protocols, using LLM-generated "proxy gradients" to target only the identified weak links. Across diverse reasoning benchmarks, our approach substantially reduces query complexity while improving performance, providing a principled and interpretable path toward self-improving MAS.