[R] Guiding LLM agents via game-theoretic feedback loops

Abstract-style summary

We introduce a closed-loop method for guiding LLM-based agents using explicit game-theoretic feedback. Agent interaction logs are transformed into structured graphs, a zero-sum attacker–defender game is solved on the graph (Nash equilibrium), and the resulting equilibrium statistics are injected back into the agent’s system prompt as a strategic control signal.

Method • Automatic graph extraction from agent logs • Effort-based scoring replacing static probabilities • Nash equilibrium computation on dynamically inferred graphs • Periodic feedback into the agent’s planning loop

Results • Success rate: 20.0% → 42.9% (44-run benchmark) • Tool-use variance: −5.2× • Expected time-to-success: −2.7×

Paper (PDF): https://arxiv.org/pdf/2601.05887

Code: https://github.com/aliasrobotics/cai