Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents

Researchers introduce CoSee, an auditing framework for analyzing failure modes in collaborative visual reasoning systems using resource-constrained language models (4B-8B parameters). The study reveals that shared working memory architectures paradoxically amplify hallucinations rather than improve performance, identifying two critical failure modes: noise reinforcement and policy collapse.

This research addresses a fundamental challenge in scaling modular AI systems: the assumption that collaborative reasoning improves output quality often proves incorrect in resource-constrained environments. The counterintuitive finding that shared workspaces can degrade performance challenges conventional wisdom in multi-agent AI architecture design.

The paper emerges from the growing adoption of modular visual reasoning pipelines that coordinate smaller models through shared intermediate states. As teams deploy 4B-8B parameter models for cost efficiency, the quality degradation from accumulated errors becomes a practical bottleneck. CoSee's trace-level diagnostics reveal that the bottleneck isn't computational reasoning depth but rather communication fidelity—how accurately information transfers between agents.

The identified failure modes carry significant implications for production systems. Noise Reinforcement explains how unverified information becomes self-reinforcing through reuse, while Policy Collapse demonstrates how additional context can paradoxically push models toward oversimplified outputs. These dynamics suggest that increasing compute without verification mechanisms creates negative returns, fundamentally altering cost-performance calculations for developers building constrained systems.

For teams implementing modular AI systems across document processing, chart analysis, and web understanding tasks, these findings suggest that verification layers provide better returns than raw model scaling. The research provides mechanistic baselines for designing more reliable collaborative architectures, shifting focus from model capacity to communication integrity. Future development should prioritize explicit verification protocols and bounded context windows over naive parameter expansion.

• →Shared working memory in resource-constrained visual agents amplifies hallucinations rather than resolving them through collaboration
• →Noise Reinforcement and Policy Collapse represent two dominant failure modes limiting collaborative reasoning effectiveness
• →Communication fidelity, not reasoning depth, emerges as the primary bottleneck for resource-constrained multi-agent systems
• →Increased compute without explicit verification mechanisms correlates negatively with performance in collaborative architectures
• →CoSee framework enables trace-level diagnostics for designing more reliable modular agent systems