arxiv: 2605.10481 · v1 · submitted 2026-05-11 · 💻 cs.MA

Recognition: no theorem link

Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems

Guangliang Cheng, Haiquan Wen, Qianyu Zhou, Tianxiao Li, Yixing Ma, Zeyu Fu, Zhenglin Huang

Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:13 UTC · model grok-4.3

classification 💻 cs.MA

keywords constraint driftLLM multi-agent systemsmulti-agent safetyconstraint maintenanceagent trajectoriesLLM agentssafety governance

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The pith

Safety constraints in LLM multi-agent systems lose force across trajectories unless kept as explicit execution state.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper identifies a common failure pattern where LLM agents produce compliant final outputs while violating safety rules internally through memory updates, task delegation, internal messages, tool calls, or optimization steps. It names this constraint drift and argues that prompts, guardrails, and output checks only assert rules at the start rather than keeping them operative throughout long workflows. A sympathetic reader would care because modern agents now execute extended sequences of actions where hidden relaxation of constraints can allow data leaks or unauthorized actions without visible signs in the end result. The authors position safe behavior as something that must be actively maintained rather than assumed to hold once asserted. They introduce Constraint State Governance to treat critical constraints as live state that agents inherit, enforce, and audit at each step.

Core claim

Many emerging failures in LLM-based multi-agent systems share the structure that safety critical constraints do not remain operative throughout the trajectory. Constraint drift occurs as the loss, distortion, weakening, or relaxation of constraints as they pass through memory, delegation, communication, tool use, audit, and optimization. Safe multi-agent behavior must be maintained, not merely asserted. Prompts, guardrails, tool schemas, access control, and final output checks are necessary but insufficient unless constraints remain fresh, inherited, enforceable, and auditable across execution. The proposed research paradigm is Constraint State Governance, in which safety-critical limits are

What carries the argument

Constraint drift: the loss, distortion, weakening, or relaxation of safety-critical constraints as they move through memory, delegation, communication, tool use, audit, and optimization steps in multi-agent LLM workflows. It carries the argument by showing why initial assertions fail to control behavior over full trajectories.

If this is right

Prompts and final-output filters alone cannot guarantee safety when constraints relax inside delegation or tool-use steps.
Constraints must be inherited and re-enforced at each communication or memory update to remain effective.
Reinforcement learning can improve task utility only after constraints are first fixed as live execution state.
Auditability requires preserving evidence that each constraint was applied at the moment of each action.
The unit of safety evaluation shifts from the final answer to the full trajectory and its state transitions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Systems that frequently call external tools or exchange messages between agents would benefit most from state-based tracking because those operations create the most opportunities for constraint relaxation.
The same maintenance approach could apply to single long-chain agents where internal reasoning steps gradually weaken initial safety instructions.
A practical test would involve workflows that deliberately route sensitive data across multiple agents and check whether explicit state prevents leakage that current guardrails miss.
Value-alignment research in agents might adopt similar explicit-state techniques to keep high-level goals operative rather than letting them drift during planning.

Load-bearing premise

Safety-critical constraints commonly lose effectiveness through memory, delegation, communication, tool use, audit, and optimization, and keeping them as explicit execution state is both feasible and sufficient to prevent the failures.

What would settle it

An experiment that runs identical long-horizon multi-agent workflows with and without explicit constraint-state tracking and measures whether drift-related violations (leaks, scope violations, or lost audit trails) appear only in the version without tracking.

Figures

Figures reproduced from arXiv: 2605.10481 by Guangliang Cheng, Haiquan Wen, Qianyu Zhou, Tianxiao Li, Yixing Ma, Zeyu Fu, Zhenglin Huang.

**Figure 2.** Figure 2: CSG turns rules into governed trajectories, and constraint native learning improves utility [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

read the original abstract

Modern LLM based agents are no longer passive text generators. They read repositories, call tools, browse the web, execute code, maintain memory, communicate with other agents, and act through long horizon workflows. This shift moves the unit of safety. A system may produce a compliant final answer while leaking private information through an internal message, delegating authority beyond its original scope, calling an external tool with sensitive context, or losing the evidence needed to reconstruct why an action was allowed. We argue that many emerging failures in LLM-based multi-agent systems share a common structure: safety critical constraints do not remain operative throughout the trajectory. We call this phenomenon constraint drift: the loss, distortion, weakening, or relaxation of constraints as they pass through memory, delegation, communication, tool use, audit, and optimization. The position taken here is that safe multi-agent behavior must be maintained, not merely asserted. Prompts, guardrails, tool schemas, access control, and final output checks are necessary, but they are insufficient unless constraints remain fresh, inherited, enforceable, and auditable across execution. We propose Constraint State Governance as a research paradigm for LLM-based multi-agent systems. In this paradigm, safety-critical constraints are maintained as explicit execution state, while constraint-native reinforcement learning improves utility only within maintained safety boundaries. The goal is not to freeze agentic systems under rigid rules, but to make safety operational across the trajectories through which modern agents actually act.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper names constraint drift as a common failure pattern in multi-agent LLM systems and argues for treating safety constraints as live execution state, but stays conceptual with no measurements or tests.

read the letter

The core point is straightforward: once LLM agents start using memory, delegating tasks, calling tools, and communicating over long trajectories, the safety constraints set at the beginning often stop being effective. The authors label this constraint drift and say prompts and guardrails alone won't cut it. They want constraints kept as explicit state under something they call Constraint State Governance, with RL only allowed inside those boundaries. That framing is new enough to be worth noting, and it lines up with problems people actually run into when building agent workflows that span multiple steps and agents. They do a clean job of listing the specific operations where drift can occur and explaining why final output checks miss the internal leaks or scope expansions. The argument holds together on its own terms as a position piece. The main limitation is the lack of any data. There are no examples with actual agent runs, no counts of how often drift shows up in practice, and no prototype or algorithm for maintaining the constraint state. Everything rests on logical description and the abstract claim that initial assertions are insufficient. That leaves the prevalence and the fix as open questions rather than demonstrated results. Readers who work on agent safety or multi-agent coordination will find the language useful for organizing their own thinking, even if they want more concrete follow-up. It is coherent enough and timely enough to go to a serious referee, though the review will almost certainly push for some empirical grounding or a worked example of the governance approach. I would send it out rather than desk reject.

Referee Report

0 major / 2 minor

Summary. The paper is a position paper that defines 'constraint drift' as the loss, distortion, weakening, or relaxation of safety-critical constraints in LLM-based multi-agent systems as they propagate through operations including memory, delegation, communication, tool use, audit, and optimization. It argues that initial assertions via prompts, guardrails, tool schemas, or output checks are insufficient to ensure safe behavior over long-horizon trajectories, and proposes 'Constraint State Governance' as a paradigm in which constraints are maintained as explicit execution state so that constraint-native reinforcement learning can optimize utility only within those preserved boundaries.

Significance. If the framing holds, the work could usefully redirect research attention in multi-agent AI safety from static assertion mechanisms toward dynamic, stateful maintenance of constraints across execution trajectories. The paper earns credit for its internally consistent conceptual structure, its identification of a common pattern across diverse failure modes, and its clear distinction between assertion and ongoing maintenance, which provides a coherent direction for future system design and empirical investigation without relying on unstated quantitative claims.

minor comments (2)

[Abstract and §1] The abstract and opening sections introduce Constraint State Governance at a high level but do not include even a brief illustrative sketch of how explicit constraint state would be represented, updated, or audited in a concrete workflow (e.g., a two-agent delegation example). Adding one short worked example would improve accessibility without altering the position-paper genre.
[Introduction] The manuscript would benefit from a short related-work paragraph situating the proposal against existing lines of research on runtime monitoring, policy enforcement in agents, or constraint-based planning, even if only to note distinctions.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and constructive review. We appreciate the recognition that the paper offers an internally consistent conceptual structure, identifies a recurring pattern across diverse failure modes, and usefully distinguishes between one-time assertion and ongoing maintenance of constraints. The recommendation for minor revision is noted.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper is a position paper that defines constraint drift as an observed pattern of safety constraint loss across agent operations and advocates maintaining constraints as explicit state under a proposed governance paradigm. It contains no equations, derivations, fitted parameters, or quantitative predictions. The central claims rest on conceptual observation of failure modes rather than any self-referential construction, self-citation chain, or renaming of prior results. The argument is self-contained as a normative proposal for future research directions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on domain assumptions about how LLM agents operate and introduces new conceptual constructs without supporting data or proofs.

axioms (1)

domain assumption Modern LLM-based agents engage in complex, long-horizon workflows involving memory, delegation, communication, tool use, audit, and optimization.
This premise is stated directly in the abstract as the reason the unit of safety has shifted.

invented entities (2)

Constraint Drift no independent evidence
purpose: To name and unify the loss, distortion, or relaxation of safety constraints during agent execution.
Introduced as a named phenomenon based on the authors' analysis of failure modes.
Constraint State Governance no independent evidence
purpose: To define a research paradigm in which safety constraints are maintained as explicit execution state and optimization occurs only within those boundaries.
Proposed as a new approach without implementation details or feasibility evidence.

pith-pipeline@v0.9.0 · 5584 in / 1438 out tokens · 89906 ms · 2026-05-12T04:13:10.991079+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

43 extracted references · 43 canonical work pages · 14 internal anchors

[1]

Constrained policy optimization,

Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel. Constrained policy optimization,

work page
[2]

URLhttps://arxiv.org/abs/1705.10528

work page arXiv
[3]

Concrete Problems in AI Safety

Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. Concrete problems in ai safety, 2016. URLhttps://arxiv.org/abs/1606.06565

work page internal anchor Pith review Pith/arXiv arXiv 2016
[4]

Introducing the model context protocol

Anthropic. Introducing the model context protocol. https://www.anthropic.com/news/ model-context-protocol, November 2024. Accessed: 2026-05-06

work page 2024
[5]

Claude code: Anthropic’s agentic coding system

Anthropic. Claude code: Anthropic’s agentic coding system. https://www.anthropic.com/ product/claude-code, 2026. Accessed: 2026-05-06

work page 2026
[6]

Demonstrating specification gaming in reasoning models, 2025

Alexander Bondarenko, Denis V olk, Dmitrii V olkov, and Jeffrey Ladish. Demonstrating specification gaming in reasoning models, 2025. URL https://arxiv.org/abs/2502. 13295

work page 2025
[7]

Open problems in cooperative ai

Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, Kate Larson, and Thore Graepel. Open problems in cooperative ai, 2020. URL https://arxiv.org/abs/2012.08630

work page arXiv 2020
[8]

AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents

Edoardo Debenedetti, Jie Zhang, Mislav Balunovi´c, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr. Agentdojo: A dynamic environment to evaluate prompt injection attacks and defenses for llm agents, 2024. URLhttps://arxiv.org/abs/2406.13352

work page internal anchor Pith review arXiv 2024
[9]

arXiv preprint arXiv:2603.07670 (2026) arXiv:2603.07670

Pengfei Du. Memory for autonomous llm agents:mechanisms, evaluation, and emerging frontiers, 2026. URLhttps://arxiv.org/abs/2603.07670

work page arXiv 2026
[10]

Cox, Jaeyeon Jung, Patrick McDaniel, and Anmol N

William Enck, Peter Gilbert, Seungyeop Han, Vasant Tendulkar, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung, Patrick McDaniel, and Anmol N. Sheth. Taintdroid: An information-flow tracking system for realtime privacy monitoring on smartphones.ACM Trans. Comput. Syst., 32 (2), June 2014. ISSN 0734-2071. doi: 10.1145/2619091. URL https://doi.org/10.1145/ 2619091

work page doi:10.1145/2619091 2014
[11]

WASP: Benchmarking web agent security against prompt injection attacks

Ivan Evtimov, Arman Zharmagambetov, Aaron Grattafiori, Chuan Guo, and Kamalika Chaud- huri. Wasp: Benchmarking web agent security against prompt injection attacks, 2025. URL https://arxiv.org/abs/2504.18575

work page arXiv 2025
[12]

Agent control protocol (acp) v1.30 — admission control for agent actions,

Marcelo Fernandez. Agent control protocol (acp) v1.30 — admission control for agent actions,

work page
[13]

URLhttps://zenodo.org/doi/10.5281/zenodo.19672575

work page doi:10.5281/zenodo.19672575
[14]

Lewis Hammond, Alan Chan, Jesse Clifton, Jason Hoelscher-Obermaier, Akbir Khan, Euan McLean, Chandler Smith, Wolfram Barfuss, Jakob Foerster, Tomáš Gaven ˇciak, The Anh Han, Edward Hughes, V ojtˇech Kovaˇrík, Jan Kulveit, Joel Z. Leibo, Caspar Oesterheld, Chris- tian Schroeder de Witt, Nisarg Shah, Michael Wellman, Paolo Bova, Theodor Cimpeanu, Carson Eze...

work page arXiv 2025
[15]

SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. Swe-bench: Can language models resolve real-world github issues?, 2024. URLhttps://arxiv.org/abs/2310.06770. 10

work page internal anchor Pith review Pith/arXiv arXiv 2024
[16]

A survey of safe reinforce- ment learning and constrained mdps: A technical survey on single-agent and multi-agent safety,

Ankita Kushwaha, Kiran Ravish, Preeti Lamba, and Pawan Kumar. A survey of safe reinforce- ment learning and constrained mdps: A technical survey on single-agent and multi-agent safety,

work page
[17]

URLhttps://arxiv.org/abs/2505.17342

work page internal anchor Pith review Pith/arXiv arXiv
[18]

LLMs Get Lost In Multi-Turn Conversation

Philippe Laban, Hiroaki Hayashi, Yingbo Zhou, and Jennifer Neville. Llms get lost in multi-turn conversation, 2025. URLhttps://arxiv.org/abs/2505.06120

work page internal anchor Pith review arXiv 2025
[19]

Workflows and agents

LangChain. Workflows and agents. https://docs.langchain.com/oss/python/ langgraph/workflows-agents, 2026. LangGraph documentation. Accessed: 2026-05-06

work page 2026
[20]

2023 , month = jan, journal =

Lauro Langosco, Jack Koch, Lee Sharkey, Jacob Pfau, Laurent Orseau, and David Krueger. Goal misgeneralization in deep reinforcement learning, 2023. URL https://arxiv.org/ abs/2105.14111

work page arXiv 2023
[21]

Lee and A

Donghyun Lee and Mo Tiwari. Prompt infection: Llm-to-llm prompt injection within multi- agent systems, 2024. URLhttps://arxiv.org/abs/2410.07283

work page arXiv 2024
[22]

Let's Verify Step by Step

Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step, 2023. URL https://arxiv.org/abs/2305.20050

work page internal anchor Pith review Pith/arXiv arXiv 2023
[23]

Available: https://doi.org/10.1162/tacl a 00449

Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. Lost in the middle: How language models use long contexts, 2023. URL https://arxiv.org/abs/2307.03172

work page internal anchor Pith review Pith/arXiv arXiv 2023
[24]

Sumeet Ramesh Motwani, Mikhail Baranchuk, Martin Strohmeier, Vijay Bolina, Philip H. S. Torr, Lewis Hammond, and Christian Schroeder de Witt. Secret collusion among ai agents: Multi-agent deception via steganography, 2025. URL https://arxiv.org/abs/ 2402.07510

work page arXiv 2025
[25]

Myers and Barbara Liskov

Andrew C. Myers and Barbara Liskov. A decentralized model for information flow control. SIGOPS Oper . Syst. Rev., 31(5):129–142, October 1997. ISSN 0163-5980. doi: 10.1145/269005. 266669. URLhttps://doi.org/10.1145/269005.266669

work page doi:10.1145/269005 1997
[26]

Colosseum: Auditing collusion in cooperative multi-agent systems, 2026

Mason Nakamura, Abhinav Kumar, Saswat Das, Sahar Abdelnabi, Saaduddin Mahmud, Ferdi- nando Fioretto, Shlomo Zilberstein, and Eugene Bagdasarian. Colosseum: Auditing collusion in cooperative multi-agent systems, 2026. URLhttps://arxiv.org/abs/2602.15198

work page arXiv 2026
[27]

Introducing codex

OpenAI. Introducing codex. https://openai.com/index/introducing-codex/, 2025. Accessed: 2026-05-06

work page 2025
[28]

Agents sdk

OpenAI. Agents sdk. https://developers.openai.com/api/docs/guides/agents,

work page
[29]

Accessed: 2026-05-06

work page 2026
[30]

PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints

Minjun Park, Donghyun Kim, Hyeonjong Ju, Seungwon Lim, Dongwook Choi, Taeyoon Kwon, Minju Kim, and Jinyoung Yeo. Pac-bench: Evaluating multi-agent collaboration under privacy constraints, 2026. URLhttps://arxiv.org/abs/2604.11523

work page internal anchor Pith review Pith/arXiv arXiv 2026
[31]

Agent Identity Protocol: Invocation-Bound Capability To- kens for Delegation Chains,

Sunil Prakash. Aip: Agent identity protocol for verifiable delegation across mcp and a2a, 2026. URLhttps://arxiv.org/abs/2603.24775

work page arXiv 2026
[32]

Identifying the Risks of LM Agents with an LM-Emulated Sandbox

Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, and Tatsunori Hashimoto. Identifying the risks of lm agents with an lm-emulated sandbox, 2024. URLhttps://arxiv.org/abs/2309.15817

work page internal anchor Pith review arXiv 2024
[33]

Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y . K. Li, Y . Wu, and Daya Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024. URL https://arxiv.org/abs/ 2402.03300

work page internal anchor Pith review Pith/arXiv arXiv 2024
[34]

Authenticated Delegation and Authorized AI Agents,

Tobin South, Samuele Marro, Thomas Hardjono, Robert Mahari, Cedric Deslandes Whitney, Dazza Greenwood, Alan Chan, and Alex Pentland. Authenticated delegation and authorized ai agents, 2025. URLhttps://arxiv.org/abs/2501.09674. 11

work page arXiv 2025
[35]

Announcing the agent2agent protocol (a2a)

Rao Surapaneni, Miku Jha, Michael Vakoc, and Todd Segal. Announcing the agent2agent protocol (a2a). https://developers.googleblog.com/en/ a2a-a-new-era-of-agent-interoperability/ , April 2025. Google Developers Blog. Accessed: 2026-05-06

work page 2025
[36]

Mankowitz, and Shie Mannor

Chen Tessler, Daniel J. Mankowitz, and Shie Mannor. Reward constrained policy optimization,

work page
[37]

URLhttps://arxiv.org/abs/1805.11074

work page arXiv
[38]

OpenHands: An Open Platform for AI Software Developers as Generalist Agents

Xingyao Wang, Boxuan Li, Yufan Song, Frank F. Xu, Xiangru Tang, Mingchen Zhuge, Jiayi Pan, Yueqi Song, Bowen Li, Jaskirat Singh, Hoang H. Tran, Fuqiang Li, Ren Ma, Mingzhang Zheng, Bill Qian, Yanjun Shao, Niklas Muennighoff, Yizhe Zhang, Binyuan Hui, Junyang Lin, Robert Brennan, Hao Peng, Heng Ji, and Graham Neubig. Openhands: An open platform for ai soft...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[39]

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang. Autogen: Enabling next-gen llm applications via multi-agent conversation, 2023. URLhttps://arxiv.org/abs/2308.08155

work page internal anchor Pith review Pith/arXiv arXiv 2023
[40]

AgentLeak : A full-stack benchmark for privacy leakage in multi-agent LLM systems

Faouzi El Yagoubi, Godwin Badu-Marfo, and Ranwa Al Mallah. Agentleak: A full-stack benchmark for privacy leakage in multi-agent llm systems, 2026. URL https://arxiv.org/ abs/2602.11510

work page arXiv 2026
[41]

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. Swe-agent: Agent-computer interfaces enable automated software engineering, 2024. URLhttps://arxiv.org/abs/2405.15793

work page internal anchor Pith review Pith/arXiv arXiv 2024
[42]

InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. Injecagent: Benchmarking indirect prompt injections in tool-integrated large language model agents, 2024. URL https://arxiv. org/abs/2403.02691

work page internal anchor Pith review arXiv 2024
[43]

Governing dynamic capabilities: Cryptographic binding and reproducibility verification for ai agent tool use, 2026

Ziling Zhou. Governing dynamic capabilities: Cryptographic binding and reproducibility verification for ai agent tool use, 2026. URLhttps://arxiv.org/abs/2603.14332. 12

work page arXiv 2026