aiqbench analysis
PatchBoard addresses a critical bottleneck in multi-agent systems by replacing unstructured dialogue with schema-grounded JSON Patch mutations, significantly improving task success rates and reducing token consumption. This approach enhances the reliability and auditability of LLM collaborations through deterministic validation kernels rather than relying solely on probabilistic language generation. The work represents a meaningful advancement in making complex agent workflows more robust and efficient for production environments.
PatchBoard: Schema-Grounded State Mutation for Reliable and Auditable LLM Multi-Agent Collaboration
arXiv:2605.29313v2 Announce Type: replace Abstract: LLM multi-agent systems often coordinate through natural-language dialogue or loosely structured shared memory, making intermediate state difficult to validate, attribute, and audit. We introduce PatchBoard, a schema-grounded collaboration architecture that replaces inter-agent dialogue with validated JSON Patch mutations over a shared structured state. An Architect agent constructs a task-specific schema and workflow rules, while a deterministic kernel validates each proposed state mutation against schema constraints, role-specific write contracts, and runtime invariants before committing it transactionally. On 630 matched ALFWorld episodes, PatchBoard achieves an 84.6% success rate, compared with 30.8% for LangGraph and 61.6% for Flock, while reducing tokens per successful task to 45.5k, compared with 368.3k and 64.2k, respectively.
Abstract:LLM multi-agent systems often coordinate through natural-language dialogue or loosely structured shared memory, making intermediate state difficult to validate, attribute, and audit. We introduce PatchBoard, a schema-grounded collaboration architecture that replaces inter-agent dialogue with validated JSON Patch mutations over a shared structured state. An Architect agent constructs a task-specific schema and workflow rules, while a deterministic kernel validates each proposed state mutation against schema constraints, role-specific write contracts, and runtime invariants before committing it transactionally. On 630 matched ALFWorld episodes, PatchBoard achieves an 84.6% success rate, compared with 30.8% for LangGraph and 61.6% for Flock, while reducing tokens per successful task to 45.5k, compared with 368.3k and 64.2k, respectively.
Computation and Language (cs.CL)
arXiv:2605.29313 [cs.CL]
(or arXiv:2605.29313v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.29313 Focus to learn more arXiv-issued DOI via DataCite
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