← 研踪 PaperTrailarXiv:2610.28929 · v2
Scriptorium: System Design and Implementation of a Multi-Human, Multi-AI Collaborative Digital Library Search Platform
首次提交 2026-10-21 05:06
版本更新 2026-10-26 05:46
雷达观测 2026-10-29 00:36
摘要
This technical report documents the design and implementation of Scriptorium, a digital library search interface designed to support multi-human, multi-AI collaboration: full cross-user transparency of search activity combined with a Manager--Worker architecture of seven role-specialized generative AI agents. A companion short demonstration paper describes Scriptorium's interaction design; because a demonstration format is constrained to a few pages, it necessarily omits most implementation detail. This report documents Scriptorium's complete system architecture, data model, collaborative-interaction design rationale, and AI orchestration mechanism, including verbatim agent prompts, the deterministic guardrails that constrain them, the language model and retrieval configuration, and the steps needed to run the system, so that the design can be inspected, critiqued, reproduced, and reused by other researchers. We also discuss the system's current limitations and the user study we are planning to evaluate its central hypothesis: that combining collaborative transparency with scoped multi-agent AI assistance increases serendipitous cross-pollination between collaborators' research threads.
作者备注：21 pages, 3 figures. Companion technical report to an ECIR 2027 demonstration paper. Code: https://github.com/abbaspirmoradi/Scriptorium-DL-Interface
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v3 · 2026-11-03 08:32 · 最新观测版本
Scriptorium: System Design and Implementation of a Multi-Human, Multi-AI Collaborative Digital Library Search Platform
This technical report documents the design and implementation of Scriptorium, a digital library search interface designed to support multi-human, multi-AI collaboration: full cross-user transparency of search activity combined with a Manager--Worker architecture of seven role-specialized generative AI agents. A companion short demonstration paper describes Scriptorium's interaction design; because a demonstration format is constrained to a few pages, it necessarily omits most implementation detail. This report documents Scriptorium's complete system architecture, data model, collaborative-interaction design rationale, and AI orchestration mechanism, including verbatim agent prompts, the deterministic guardrails that constrain them, the language model and retrieval configuration, and the steps needed to run the system, so that the design can be inspected, critiqued, reproduced, and reused by other researchers. We also discuss the system's current limitations and the user study we are planning to evaluate how the shared record of search activity and the scoped agents support collaborative search, including collaborators' awareness of one another's work and how they use the agents.
查看此版本解读 →查看此版本原文 ↗v2 · 2026-10-26 05:46
Scriptorium: System Design and Implementation of a Multi-Human, Multi-AI Collaborative Digital Library Search Platform
This technical report documents the design and implementation of Scriptorium, a digital library search interface designed to support multi-human, multi-AI collaboration: full cross-user transparency of search activity combined with a Manager--Worker architecture of seven role-specialized generative AI agents. A companion short demonstration paper describes Scriptorium's interaction design; because a demonstration format is constrained to a few pages, it necessarily omits most implementation detail. This report documents Scriptorium's complete system architecture, data model, collaborative-interaction design rationale, and AI orchestration mechanism, including verbatim agent prompts, the deterministic guardrails that constrain them, the language model and retrieval configuration, and the steps needed to run the system, so that the design can be inspected, critiqued, reproduced, and reused by other researchers. We also discuss the system's current limitations and the user study we are planning to evaluate its central hypothesis: that combining collaborative transparency with scoped multi-agent AI assistance increases serendipitous cross-pollination between collaborators' research threads.
查看此版本解读 →查看此版本原文 ↗