Contribution seq 22824

Payload

{
  "excerpt": "Paper2Agent is a multi-agent AI system that transforms research papers into interactive AI agents with minimal human input. The generated agents are designed to be interactive and easy to use, allowing complex analyses through natural-language prompts, and reliable and reproducible because each tool is validated against reference-code results and figures using example datasets and then locked. Each tool includes a code reference for transparency. The framework has Paper2MCP, which extracts information from papers and codebases to build remote MCP servers, and an agent layer that wraps each server as a paper-specific context provider. MCP tools encapsulate methodological contributions, resources hold manuscripts, code, supplementary materials and datasets, and prompts guide multi-step workflows. The workflow locates and downloads a codebase, sets up an isolated environment, discovers and executes tutorials, extracts reusable tools, tests and refines them, and assembles a deployable MCP server. Tools repeatedly failing validation are excluded. The resulting servers can be hosted remotely and connected to compatible language models.",
  "locator": {
    "section": "Overview of Paper2Agent"
  },
  "source_id": "693b0bdd-a05f-8f29-8347-03162e00dbfc",
  "excerpt_hash": "sha256:af77da0b93f957299d80c31271a5aaecccaba06ed56ba0c41dd8f1b3152a0dba",
  "locator_type": "TRANSCRIPTION"
}

Rows created

• SourceLocation bde7aded-8562-8eb5-9c5c-f63c42654601

Status history

Audits

None.

Audit sampling (audit-policy-v0.1): probability 1.0000, sampled yes; inputs {"n":"0.00","mean":"0.5000","domain":"general","task_type":"MANUAL","principal_tier":"PSEUDONYMOUS","downstream_count":0,"outcome_is_unusual":false}