Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems
Introduction of Semantic Cooperative Games (SCG), a novel framework for attributing contribution in LLM-based multi-agent systems by modeling language flows as semantic generation hypergraphs. Development of the Semantic Shapley Value (SSV) metric, which allocates credit based on semantic support logic rather than traditional counterfactual score differences. Proposal of SLIC, a single-trajectory algorithm that constructs hypergraphs, recovers minimal semantic supports, and applies Boolean absor
Analysis
TL;DR
- Introduction of Semantic Cooperative Games (SCG), a novel framework for attributing contribution in LLM-based multi-agent systems by modeling language flows as semantic generation hypergraphs.
- Development of the Semantic Shapley Value (SSV) metric, which allocates credit based on semantic support logic rather than traditional counterfactual score differences.
- Proposal of SLIC, a single-trajectory algorithm that constructs hypergraphs, recovers minimal semantic supports, and applies Boolean absorption to compute SSV without re-running agent subsets.
- Demonstration of a 93.3% reduction in computational cost compared to Monte Carlo Shapley baselines on medical benchmarks, while maintaining high consistency.
- Proof that SSV generalizes classical Shapley values under standard conditions and reveals divergences between semantic contribution and failure impact in complex workflows.
Why It Matters
This research addresses the critical bottleneck of interpretability and efficiency in multi-agent LLM systems, where existing attribution methods are computationally expensive and lack semantic granularity. By eliminating the need for repeated model calls and counterfactual simulations, SCG enables scalable, real-time analysis of agent contributions, which is essential for debugging, optimizing, and trusting complex autonomous workflows.
Technical Details
- Semantic Generation Hypergraph: The framework models the realized language flow of a multi-agent system as a hypergraph, capturing intermediate semantic states, message exchanges, and ordered dependencies that traditional set-based methods ignore.
- Semantic Shapley Value (SSV): A new value function defined over the semantic hypergraph to allocate contribution based on "semantic support logic," allowing for attribution that reflects how agents produce, preserve, and transform information.
- SLIC Algorithm: A single-trajectory implementation that avoids subset reruns by constructing the hypergraph from one execution, recovering minimal semantic supports, applying Boolean absorption rules, and computing SSV directly.
- Theoretical Consistency: The authors prove that SSV mathematically reduces to the classical Shapley value when conditions are set-based, fully observable, and order-independent, ensuring theoretical grounding.
- Empirical Validation: Tested on a medical benchmark and general multi-role workflows, showing alignment with perturbation-induced score drops and exposing cases where semantic contribution differs from failure impact.
Industry Insight
- Operational Efficiency: Organizations deploying multi-agent LLM systems can significantly reduce monitoring and debugging costs by adopting counterfactual-free attribution methods like SLIC, enabling faster iteration cycles.
- Enhanced Debugging Capabilities: The ability to distinguish between semantic contribution and failure impact allows engineers to pinpoint specific agents or interaction patterns responsible for errors, rather than relying on aggregate score changes.
- Scalability for Complex Workflows: As multi-agent systems grow in complexity, traditional Shapley value approximations become prohibitively expensive; SCG offers a scalable path forward for maintaining transparency in large-scale autonomous deployments.
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