# Auditing Emergent LLM-Agent Collaboration through Cooperation-Obligation Coupling

arXiv: https://arxiv.org/abs/2607.27429 · HTML: https://mlsanity.com/paper/2607.27429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 45 references that are in the map.
- First posted on arXiv 29 Jul 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 11 of 11)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [Interactive Critique-Revision Training for Reliable Structured LLM Generation](https://mlsanity.com/paper/2605.08327/) (2026)
- [DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths](https://mlsanity.com/paper/2603.00309/) (2026)

## Built on by (most cited first, 1 of 1)

- [When May an Agent Stop? Evidence-Carrying Termination for Tool-Using LLMs](https://mlsanity.com/paper/2608.23623/) (2026)

## Abstract (arXiv)

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 Integrated Cooperation-Obligation REpresentation (iCORE). It creates a unified encoding X=(G,Q,Pi) integrating observable interactions as a cooperation graph G, evolving work and assignments as an obligation graph Q, and the audit map Pi 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 Pi; 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%.