Paper: Structured Authority Injection (SAI) — Persistent Human Control for LLM Agents

Forum topic · 小凯 · 2026-10-19

Summary

Structured Authority Injection (SAI) is a protocol that gives human operators persistent control over LLM-based agents operating as autonomous orchestrators in distributed and multi-agent systems. It addresses a protocol-level control gap: existing free-text override instructions lack schema validation, leading to ambiguous interpretation, and suffer from authority erosion as they lose influence across accumulating context and planning cycles. SAI routes operator corrections through typed, schema-validated Model Context Protocol (MCP) tool calls and maintains a ranked authority context prepended to each prompt. It defines three override classes—Constraint, Priority, and Strategy—with distinct persistence semantics and explicit precedence enforced via a directed authority channel inaccessible to the planning process. The method requires no fine-tuning or weight access, working with instruction-tuned LLMs supporting MCP tool use. Evaluation across nine LLMs and eight agent failure-mode scenarios showed reduced directive ambiguity, mitigated authority decay across planning iterations, and up to 38% lower prompt overhead on the strongest models, though benefits varied substantially by model. arXiv:2610.22373.

Paper Overview

Field: ML Authors: Ryan Tran, YueMing Zhang, Lingfeng Tao, Nan Yang, Shengjie Xu, Yu Yin, Shuai Xu, Wenlu Zhang, Hailu Xu Published: 2026-10-19 arXiv: 2610.22373

Abstract

LLM-based agents are increasingly used as autonomous orchestrators in distributed and multi-agent systems, yet a control gap remains: no protocol-level mechanism allows human operators to inject binding directives that persist across planning cycles. Free-text overrides suffer from ambiguous interpretation due to lack of schema validation and from authority erosion as operator instructions lose influence within accumulating context.

Structured Authority Injection (SAI) addresses these issues by routing operator corrections through typed, schema-validated Model Context Protocol (MCP) tool calls and maintaining a ranked authority context prepended to each LLM prompt. SAI defines three override classes (Constraint, Priority, and Strategy) with distinct persistence semantics and an explicit precedence order enforced via a directed authority channel inaccessible to the planning process. The method requires no model fine-tuning or access to model weights and applies to instruction-tuned LLMs supporting MCP tool use.

Evaluation across nine LLMs and eight scenarios targeting agent failure modes shows reduced ambiguous directive interpretation and mitigated authority decay across planning iterations, with lower prompt overhead in the strongest models (up to 38% depending on model and scenario), though benefit varies substantially across models. SAI provides a general design pattern for agent systems requiring explicit and persistent human control.

Key Contributions

Identifies a protocol-level control gap: no existing mechanism lets operators inject directives that persist across planning cycles
Typed, schema-validated overrides via MCP tool calls eliminate ambiguous interpretation of free-text instructions
Ranked authority context prepended to each prompt mitigates authority erosion over long contexts
Three override classes with explicit precedence: Constraint, Priority, Strategy
Enforced through a directed authority channel the planning process cannot access
No fine-tuning or model-weight access required

Evaluation Results

Tested across 9 LLMs and 8 scenarios targeting common agent failure modes
Reduced ambiguous interpretation of operator directives
Mitigated authority decay across planning iterations
Up to 38% lower prompt overhead in the strongest models (model- and scenario-dependent)
Benefits vary substantially across models

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