Auditing Emergent LLM-Agent Collaboration through Cooperation-Obligation Coupling

2026-07-29

AI Summary

Existing collaborative auditing methods for large language model (LLM) agents struggle to simultaneously capture pending tasks, responsibility attribution, and evidence of state transitions, leading to verification blind spots. This work proposes iCORE, a novel framework that unifies the modeling of cooperative structure, obligation allocation, and verifiable evidence through a coupled representation of a cooperation graph \(G\), an obligation graph \(Q\), and an audit mapping \(\Pi\), thereby enabling formal verification of collaborative processes. iCORE introduces local-to-global rationality reasoning and regret-bound analysis for obligation assignment, allowing rigorous certification of task rationality and agent stability. Experimental results demonstrate that iCORE-Audit improves trajectory quality by 11.5% and 26.4%, and boosts end-task performance by 15.1% and 31.0%, in controlled and real-world LLM environments, respectively.

Abstract

LLM-agent systems can solve complex tasks through dynamic self-organization and emergent cooperation. Auditing this process is essential because plausible intermediate or final outputs can conceal incomplete or unsupported work and poorly allocated responsibility, ultimately compromising response quality. While existing approaches may record messages, tool calls, provenance, or task dependencies, an auditability gap exists as they do not jointly represent what work remains, who is responsible for it, and what evidence justifies each work-state transition. We address this auditability gap by proposing \emph{Integrated Cooperation-Obligation REpresentation} (iCORE). It creates a unified encoding $X=(G,Q,Π)$ integrating observable interactions as a cooperation graph $G$, evolving work and assignments as an obligation graph $Q$, and the audit map $Π$ linking them with verifiable properties and evidence. This iCORE representation enables the auditor to certify two complementary properties: {Work soundness}, where every active decision-relevant work assertion must have a finite justification through $G$ and $Π$; and {Agent-assignment stability}, which requires that no feasible alternative agent improve the declared contribution value for an evaluated obligation by more than $ε$. We establish local-to-global soundness and assignment-regret guarantees and a performance bound under stated conditions. iCORE is an instrumentation layer over workflows. Numerical results show that the full coupled state exactly reconstructs soundness and assignment defects in two execution modes and that, relative to passive full-state observation, iCORE-Audit yields absolute trajectory-quality improvements of $11.5\%$ and $26.4\%$ in controlled and real-LLM execution, respectively, with corresponding absolute terminal-performance improvements of $15.1\%$ and $31.0\%$.

Problem

• LLM-agent collaboration
• auditability gap
• cooperation-obligation coupling
• work soundness
• agent-assignment stability

Innovation

• LLM-agent collaboration
• auditability
• cooperation-obligation coupling
• iCORE
• work soundness