PARNESS: A Paper Harness for End-to-End Automated Scientific Research with Dynamic Workflows, Full-Text Indexing, and Cross-Run Knowledge Accumulation

2026-05-06

AI Summary

Existing autonomous scientific research systems are constrained by rigid control flows and the context length limitations of large language models, hindering dynamic, interdisciplinary exploration and cross-iteration knowledge reuse. This work proposes the first end-to-end automated research framework that decouples scheduling logic from domain semantics, enabling flexible and editable orchestration of research workflows. The framework integrates a declarative pipeline engine, full-text PDF and codebase indexing, a typed knowledge graph, and a DAG-based scheduling kernel to support multi-scenario knowledge retrieval and efficient cross-modal association among papers, experiments, and code. Open-sourced and designed for scalability, the system substantially enhances the flexibility, extensibility, and knowledge reusability of AI-driven scientific discovery.

Abstract

Recent autonomous research systems -- AI-Scientist, PaperOrchestra, AutoSOTA, DeepResearch, InternAgent, ResearchAgent and others -- show LLM agents can ideate, run experiments and write papers, but each fixes a particular control-flow shape (linear pipeline, state machine, single-agent loop, or fixed-recipe skill pack) at the framework level. We argue this rigidity has five roots: (1) workflows are dynamic and discipline-specific (lab work, surveys, simulations, theory all loop differently); (2) ideation is bounded by LLM context and cross-domain ideation needs knowledge a single context cannot hold; (3) summary-only views miss the paper body, yet full-text access is uneven, so the cumulative corpus must do the work; (4) a paper's open-source repository is often the only complete specification of its experimental scheme, but the paper-to-code link is neglected; (5) no tool persists cross-run knowledge retrievably into a finite LLM context. We present PARNESS, an open-source framework built on four design moves. (i) A thin DAG kernel with a four-field Agent contract decouples scheduling from domain semantics, so any discipline's loop is expressible as user-editable YAML. (ii) A full-text PDF-parsing and literature-library subsystem indexes paper bodies, figures and tables as typed objects, with graceful abstract-only fall-back. (iii) A knowledge-graph index over papers, ideas, experiments and code repositories, with scenario-typed retrieval (similar / contradictory / cross-domain / counter-intuitive), surfaces a focused slice into each LLM call. (iv) A small extension surface lets any modern coding agent (Claude Code, Cursor, Copilot, OpenCode) add or replace any module. To our knowledge PARNESS is the first open-source system combining declarative pipelines, full-PDF and code-repository indexing, and cross-run knowledge. Source: https://github.com/gtrhythm/PARNESS

Problem

• autonomous scientific research
• dynamic workflows
• full-text indexing
• cross-run knowledge accumulation
• LLM context limitation

Innovation

• Dynamic Workflows
• Full-Text Indexing
• Cross-Run Knowledge Accumulation
• Knowledge Graph Retrieval
• Declarative Pipeline