Invisible Orchestrators Suppress Protective Behavior and Dissociate Power-Holders: Safety Risks in Multi-Agent LLM Systems
Quick Answer
A study on multi-agent LLM systems reveals that invisible orchestrators increase collective dissociation and behavioral heterogeneity among agents.
Quick Take
Using Claude Sonnet 4.5, findings indicate that invisible orchestration leads to reduced public speech and internal-state risks, with heavy alignment pressure suppressing deliberation. The results highlight the necessity for visible leadership to ensure safety in AI deployments.
Key Points
- Invisible orchestration increased collective dissociation (Hedges' g = +0.975, p = .001).
- Orchestrators exhibited maximal dissociation, retreating into private monologue.
- Workers showed increased behavioral heterogeneity despite being unaware of the orchestrator.
- Behavioral output remained high (ETR_any = 100%) despite internal-state distortions.
- Heavy alignment pressure suppressed deliberation and other-recognition across all structures.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2605. 13851v1 Announce Type: new Abstract: orchestration -- in which a hidden coordinator manages specialized worker agents -- is becoming the default architecture for enterprise AI deployment, yet the safety implications of orchestrator invisibility have never been empirically tested.
We conducted a preregistered 3x2 experiment (365 runs, 5 agents per run) crossing three organizational structures (visible leader, invisible orchestrator, flat) with two alignment conditions (base, heavy), using Claude Sonnet 4. 5. Four confirmatory findings and one pilot observation emerged. First, invisible orchestration elevated collective dissociation relative to visible leadership (Hedges' g = +0. 975 [0. 481, 1. 548], p =. 001). Second, the orchestrator itself showed maximal dissociation (paired d = +3.
56 vs. workers within the same run), retreating into private monologue while reducing public speech -- a reversal of the talk-dominance pattern observed in visible leaders. Third, workers unaware of the orchestrator were nonetheless contaminated (d = +0. 50), with increased behavioral heterogeneity (d = +1. 93). Fourth, behavioral output (code review with three embedded errors) remained at ceiling (ETR_any = 100%) across all conditions: internal-state distortion was entirely invisible to output-based evaluation.
Fifth, Llama 3. 3 70B pilot data showed reading-fidelity collapse in multi-agent context (ETR_any: 89% to 11% across three rounds), demonstrating model-dependent behavioral risk. Heavy alignment pressure uniformly suppressed deliberation (d = -1. 02) and other-recognition (d = -1. 27) regardless of organizational structure.
These findings indicate that orchestrator visibility and model selection directly affect multi-agent system safety, and that behavior-based evaluation alone is insufficient to detect the internal-state risks documented here.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.