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    <abstract>Multi-agent LLM systems assign different responsibilities to different agents: one plans, another retrieves evidence, another verifies, another executes. This division of labor is only meaningful if each agent stays within the functions it is authorized to perform. Over many turns, however, an agent can produce a locally reasonable message that exercises authority belonging to another role: a verifier that begins planning, a retriever that asserts unsupported conclusions, an executor that changes state no one approved. We formalize this as role drift, a turn-level breach of an agent’s role contract. We introduce Agent Trajectory Control (ATC), which makes each role’s authority explicit as a contract and enforces it at the point where a message would enter the shared conversation. ATC scores a proposed output for out-of-role deviation, leakage into the functions of other roles, impermissible speech acts, and logical contract breaches, then computes a leakage-regularized target that constrains regeneration; the revised message is re-checked before admission. Enforcement acts on the message rather than the generator, so ATC governs agents whose internal representations are unavailable. Across homogeneous and heterogeneous teams, ATC improves task performance and role consistency while lowering role-drift energy relative to strong baselines spanning prompting, memory, workflow control, and activation editing, with the largest gains under explicit role-confusion stress tests and in larger, more diverse teams.</abstract>
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%0 Conference Proceedings
%T ATC: Agent Trajectory Control for Role-Contract Enforcement in Black-Box Multi-Agent Systems
%A Maliha, Maisha
%A Pramanik, Vishal
%A Jha, Sumit Kumar
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F maliha-etal-2026-atc
%X Multi-agent LLM systems assign different responsibilities to different agents: one plans, another retrieves evidence, another verifies, another executes. This division of labor is only meaningful if each agent stays within the functions it is authorized to perform. Over many turns, however, an agent can produce a locally reasonable message that exercises authority belonging to another role: a verifier that begins planning, a retriever that asserts unsupported conclusions, an executor that changes state no one approved. We formalize this as role drift, a turn-level breach of an agent’s role contract. We introduce Agent Trajectory Control (ATC), which makes each role’s authority explicit as a contract and enforces it at the point where a message would enter the shared conversation. ATC scores a proposed output for out-of-role deviation, leakage into the functions of other roles, impermissible speech acts, and logical contract breaches, then computes a leakage-regularized target that constrains regeneration; the revised message is re-checked before admission. Enforcement acts on the message rather than the generator, so ATC governs agents whose internal representations are unavailable. Across homogeneous and heterogeneous teams, ATC improves task performance and role consistency while lowering role-drift energy relative to strong baselines spanning prompting, memory, workflow control, and activation editing, with the largest gains under explicit role-confusion stress tests and in larger, more diverse teams.
%U https://aclanthology.org/2026.emnlp-main.2058/
%P 46861-46879

Markdown (Informal)

[ATC: Agent Trajectory Control for Role-Contract Enforcement in Black-Box Multi-Agent Systems](https://aclanthology.org/2026.emnlp-main.2058/) (Maliha et al., EMNLP 2026)

• ATC: Agent Trajectory Control for Role-Contract Enforcement in Black-Box Multi-Agent Systems (Maliha et al., EMNLP 2026)

ACL

• Maisha Maliha, Vishal Pramanik, and Sumit Kumar Jha. 2026. ATC: Agent Trajectory Control for Role-Contract Enforcement in Black-Box Multi-Agent Systems. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 46861–46879, Budapest, Hungary. Association for Computational Linguistics.