为分歧设计：LLM赋能机器人中的前端护栏用于辅助分配
Designing for Disagreement: Front-End Guardrails for Assistance Allocation in LLM-Enabled Robots
AI总结：
本文提出一种前端模式，通过限制优先级选择、保持交互相关性及提供可争议路径，解决LLM赋能机器人在多用户辅助分配中的分歧问题。
摘要：
LLM赋能的机器人在社交环境中优先分配稀缺援助时面临多元价值观和LLM行为变异：合理的人可能对谁先被帮助存在分歧，而LLM中介的交互策略在提示、上下文和群体间变化多样，难以预知或验证。然而，面向用户的实时多用户辅助分配护栏仍不明确。本文提出有界校准与可争议性，一种前端模式，(i)将优先级限制在治理批准的可接受模式菜单中，(ii)在延迟点保持活跃模式的交互相关术语可见性，(iii)提供结果特定的可争议路径而不重议全球规则。将多元主义和LLM不确定性视为常态，该模式避免了隐含价值偏斜的沉默默认和在时间压力下转移负担的用户可配置'价值设置'。本文通过公共通道机器人场景示例，并概述了以可理解性、程序合法性及可操作性为中心的评估议程，包括自动化偏见风险和可争议渠道的不均等使用风险。
英文摘要：
LLM-enabled robots prioritizing scarce assistance in social settings face pluralistic values and LLM behavioral variability: reasonable people can disagree about who is helped first, while LLM-mediated interaction policies vary across prompts, contexts, and groups in ways that are difficult to anticipate or verify at contact point. Yet user-facing guardrails for real-time, multi-user assistance allocation remain under-specified. We propose bounded calibration with contestability, a procedural front-end pattern that (i) constrains prioritization to a governance-approved menu of admissible modes, (ii) keeps the active mode legible in interaction-relevant terms at the point of deferral, and (iii) provides an outcome-specific contest pathway without renegotiating the global rule. Treating pluralism and LLM uncertainty as standing conditions, the pattern avoids both silent defaults that hide implicit value skews and wide-open user-configurable "value settings" that shift burden under time pressure. We illustrate the pattern with a public-concourse robot vignette and outline an evaluation agenda centered on legibility, procedural legitimacy, and actionability, including risks of automation bias and uneven usability of contest channels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
