ScholarStack: Layered Research Asset Orchestration and Cross-Task Reuse for Scientific Agents

2026-09-20

AI Summary

本文提出ScholarStack框架，通过构建可重用、分层的研究资产来解决科学代理任务中的重复处理和知识复用问题。

Abstract

Scientific agents support a range of literature-based research tasks, such as retrieval, question answering, evidence-grounded generation, and claim assessment. Most existing systems, however, are organized around individual tasks: the same papers are repeatedly retrieved, segmented, and interpreted, and the understanding built in one task is difficult to reuse in the next. We present ScholarStack, a layered research asset framework that compiles a paper collection into reusable, versioned, and provenance-preserving assets at three complementary levels: source-grounded paper-level statements, domain-level organization, and evidence-grounded cross-paper syntheses. A common access interface returns task-specific views at the evidence granularity each task requires, preserving study conditions, source traceability, and verification status. We instantiate the framework on four task families spanning ten task settings, comparing agents that use the compiled assets with task-specific baselines under matched base models. Quality gains concentrate on tasks that require cross-paper evidence, such as multi-paper question answering and literature review generation, and query-time token cost falls on every task where it is measured, with assets compiled once and reused across tasks. These results suggest that layered research assets can serve as shared infrastructure for scientific agents, shifting literature-based assistance from isolated document processing toward cumulative, evidence-grounded workflows.

Problem

• Scientific Agents
• Research Tasks
• Reusability
• Cross-Task
• Evidence-Grounded

Innovation

• Layered Research Assets
• Cross-Task Reuse
• Evidence-Grounded Syntheses
• Scientific Agents