RESEARCH · RESEARCH · #2194
Structured Authority Injection (SAI): protocol for persistent human overrides in LLM agents
The paper proposes Structured Authority Injection (SAI), a protocol that routes operator corrections through typed, schema-validated Model Context Protocol (MCP) tool calls and maintains a ranked authority context prepended to each LLM prompt. SAI defines three override classes (Constraint, Priority, Strategy) with distinct persistence and precedence enforced via a directed authority channel, requires no model fine-tuning, and evaluation across nine LLMs and eight scenarios shows reduced ambiguous directive interpretation and mitigated authority decay (with prompt overhead reductions up to ~38% in the strongest models), though benefits vary by model and scenario.
KEY POINTS
- The paper proposes Structured Authority Injection (SAI), a protocol that routes operator corrections through typed, schema-validated Model Context Protocol (MCP) tool calls and maintains a ranked authority context prepended to each LLM prompt.
- SAI defines three override classes (Constraint, Priority, Strategy) with distinct persistence and precedence enforced via a directed authority channel, requires no model fine-tuning, and evaluation across nine LLMs and eight scenarios shows reduced ambiguous directive interpretation and mitigated authority decay (with prompt overhead reductions up to ~38% in the strongest models), though benefits vary by model and scenario.
- SAI matters because it provides a protocol-level, schema-validated way to make human directives binding and persistent across agent planning cycles without model changes, addressing a key control gap in multi-agent LLM systems.
WHY IT MATTERS
SAI matters because it provides a protocol-level, schema-validated way to make human directives binding and persistent across agent planning cycles without model changes, addressing a key control gap in multi-agent LLM systems.