Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents
arXiv:2605.31354v1 Announce Type: cross Abstract: Modular visual reasoning systems increasingly rely on shared working memory for multi-step collaboration, yet the failure dynamics of intermediate state evolution in low-capacity regimes remain underexplored. We study failure modes of collaborative reasoning with weak learners (4B--8B models) through the lens of noise accumulation. We introduce CoSee, an auditing framework that formalizes the read-write-verify loop to trace information flow in document visual question answering. Across multi-page, chart, and web-based benchmarks, we find a coun
This research addresses a critical and emerging challenge in current AI development: the collaboration and failure modes of modular, resource-constrained AI systems, particularly as developers push towards more agentic architectures.
Understanding the failure dynamics of shared-state collaboration in visual agents is crucial for building reliable and scalable AI systems, directly influencing the feasibility and safety of autonomous AI agents.
This research provides an auditing framework (CoSee) to diagnose failure modes, shifting the focus from simply building agents to systematically understanding and mitigating their inherent weaknesses in collaborative tasks.
- · AI safety researchers
- · Developers of agentic AI systems
- · Companies building visual reasoning applications
- · AI auditing tool providers
- · Developers ignoring systematic failure analysis
- · Applications reliant on brittle AI collaboration
- · Early monolithic AI architectures
Improved reliability and robustness of multi-modal and multi-agent AI systems become achievable goals.
Increased trust and adoption of AI agents in complex, high-stakes environments due to better predictability.
The development of standardized auditing and verification protocols for AI agent collaboration, creating a new sub-industry for AI compliance and quality assurance.
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