How WriterAgent Generates Formatted Academic Papers from Modeling Results in MathModelAgent

The WriterAgent transforms raw modeling outputs into competition-ready academic manuscripts by orchestrating a multi-turn LLM conversation that automatically invokes literature search tools, injects citations, and enforces markdown formatting rules defined in system prompts.

The WriterAgent in the jihe520/mathmodelagent repository serves as the final automated author in a multi-agent mathematical modeling pipeline. After the Modeler and Coder agents produce computational results, this specialized component synthesizes the findings into structured, citation-rich academic papers suitable for mathematical modeling competitions.

Agent Initialization and System Prompt Construction

The agent's behavior is established during instantiation in WriterAgent.__init__ (lines 19-36 of backend/app/core/agents/writer_agent.py). The constructor accepts a task_id, an LLM instance, a formatting enum defaulting to FormatOutPut.Markdown, and an optional OpenAlexScholar wrapper for literature retrieval.

Crucially, the constructor generates a specialized system prompt by calling get_writer_prompt(format_output) from backend/app/core/prompts.py (lines 37-88). This prompt encodes the writer role definition, citation formatting rules, and markdown structure requirements that constrain the LLM's output format.

The Execution Pipeline: From Raw Data to Formatted Manuscript

The WriterAgent.run method (lines 53-142) implements a sophisticated multi-turn conversation loop that handles dynamic literature retrieval and citation integration.

Step 1: Prompt Enrichment with Visual Assets

When image file names are provided via the available_images parameter, the agent automatically appends markdown-style image URL references to the user prompt (lines 59-66). This allows the LLM to reference figures and charts generated by upstream modeling agents.

Step 2: Initial LLM Invocation with Tool Access

The first LLM call (lines 70-78) passes the enriched history to self.model.chat with tools=writer_tools and tool_choice="auto". The writer_tools schema defined in backend/app/core/functions.py exposes the search_papers function to the LLM, enabling autonomous literature requests when the draft requires theoretical backing.

Step 3: Automated Literature Retrieval via OpenAlex

When the LLM response contains tool_calls requesting search_papers (lines 84-122), the agent executes a closed-loop retrieval sequence:

1. Publishes a status message via redis_manager for front-end progress tracking
2. Extracts the search query from the tool call arguments
3. Invokes self.scholar.search_papers(query) through the OpenAlexScholar wrapper in backend/app/tools/openalex_scholar.py
4. Formats the returned bibliography using self.scholar.papers_to_str
5. Appends a tool message containing the formatted citations back into the chat history

Step 4: Citation Integration and Final Output Generation

Following literature injection (lines 124-137), a second LLM call incorporates the retrieved papers into the draft. The final content is stored in response_content, wrapped in a WriterResponse object (lines 138-142), and returned to the caller with an optional footnotes list reserved for custom annotations.

Practical Implementation Example

The following demonstrates instantiation and execution:

from app.core.llm.llm import LLM
from app.core.agents.writer_agent import WriterAgent
from app.tools.openalex_scholar import OpenAlexScholar

# Configure dependencies

llm = LLM(...)
scholar = OpenAlexScholar()

# Initialize agent

writer = WriterAgent(
    task_id="task-1234",
    model=llm,
    format_output=FormatOutPut.Markdown,
    scholar=scholar,
)

# Generate paper with image references

paper = await writer.run(
    prompt="请基于模型结果撰写完整的竞赛论文，包括理论背景、模型描述和结果分析。",
    available_images=["20250420-173744-9f87792c/1_分布.png"],
)
print(paper.response_content)  # Markdown-formatted academic paper

Optional Summarization Capabilities

Beyond full manuscript generation, the WriterAgent.summarize method (lines 43-58) provides concise synthesis of the writing exchange using the same chat-based architecture, enabling executive summaries of complex modeling workflows.

Summary

• The WriterAgent in backend/app/core/agents/writer_agent.py serves as the automated authorial component in the mathmodelagent architecture.
• System prompt construction via get_writer_prompt establishes markdown formatting and citation rules during initialization (lines 19-36).
• Multi-turn conversation loops handle dynamic literature requests through the search_papers tool integration (lines 84-122).
• OpenAlexScholar integration in backend/app/tools/openalex_scholar.py provides real-time academic paper retrieval without direct API coupling.
• WriterResponse objects encapsulate the final formatted content and metadata for downstream consumption.
• Redis status updates enable real-time front-end progress tracking during literature searches.

Frequently Asked Questions

What output format does WriterAgent generate by default?

By default, the agent outputs Markdown format as specified by the FormatOutPut.Markdown enum passed during initialization. The system prompt generated by get_writer_prompt in backend/app/core/prompts.py enforces specific markdown structure rules for academic paper sections, including headers, equations, and citation blocks.

How does WriterAgent handle citations without direct API calls?

The agent delegates literature searches to the OpenAlexScholar wrapper class in backend/app/tools/openalex_scholar.py. When the LLM requests citations via the search_papers tool, the agent calls self.scholar.search_papers() and formats results using papers_to_str(), then injects these as tool messages into the conversation history for the LLM to reference.

Can WriterAgent include figures and charts in the generated paper?

Yes. By passing file paths through the available_images parameter in WriterAgent.run, the agent appends markdown-style image references to the prompt (lines 59-66), enabling the LLM to reference visual assets generated by upstream modeling components such as distribution plots or result charts.

What happens if the LLM does not request literature searches?

If the LLM generates content without invoking the search_papers tool, the agent skips the retrieval loop and returns the draft immediately. The tool_choice="auto" setting in the chat call allows the LLM to determine whether theoretical citations are necessary based on the prompt content and subject matter.

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