Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies

TL;DR

This paper introduces a spectral diagnostic framework based on the successor representation to predict and analyze the robustness, consensus, and drift behaviors of multi-agent LLM communication topologies.

Contribution

It develops a novel spectral analysis method for multi-agent LLM communication graphs, linking spectral quantities to failure modes and validating predictions empirically.

Findings

Condition number predicts perturbation robustness with perfect correlation.

Spectral gap partially predicts consensus dynamics.

Spectral radius inversely correlates with cumulative error.

Abstract

Practitioners deploying multi-agent large language model (LLM) systems must currently choose between communication topologies such as chain, star, mesh, and richer variants without any pre-inference diagnostic for which topology will amplify drift, converge to consensus, or remain robust under perturbation. Existing evaluation answers these questions only post hoc and only for the task measured. We introduce a structural diagnostic for multi-agent LLM communication graphs based on the successor representation $M = (I - \gamma P)^{-1}$ of the row-stochastic communication operator, and we connect three of its spectral quantities, the spectral radius $\rho(M)$, the spectral gap $\Delta(M)$, and the condition number $\kappa(M)$, to three distinct failure modes. We derive closed-form spectra for the chain, star, and mesh under row-stochastic normalization, and validate the predictions on a 12-step structured state-tracking task with Qwen2.5-7B-Instruct over 100 independent trials. The condition number is a perfect rank-order predictor of empirical perturbation robustness ($r_s = 1.0$); the spectral gap partially predicts consensus dynamics ($r_s = 0.5$); and the spectral radius is perfectly \emph{inverted} with respect to cumulative error ($r_s = -1.0$). We trace this inversion to a regime in which linear spectra are blind to non-contracting bias drift, and we propose an affine-noise extension of the predictive map that recovers the empirical ordering. We read this as a first step toward representational, drift-aware structural diagnostics for multi-agent LLM systems, sitting alongside classical spectral and consensus theory.