explanation on how the iterative refinement process works on https://arxiv.org/abs/2505.18105 using gemini-2.5-pro on 20250619

1-prompt.md

As an expert in web-augmented large language models, your task is to analyze the following codebase. Your goal is to help me understand its iterative search and refinement process.

Please follow these steps:

1. IDENTIFY THE REFINEMENT MECHANISM Explore the codebase and pinpoint the exact functions, classes, or code blocks responsible for self-correction, iterative search, or any refinement loop. I need to see where this is explicitly implemented. Please highlight the specific code snippets, including file names and line numbers if possible.

2. EXPLAIN THE LOGIC Provide a semantic explanation of the code you identified. Infer what the developers are trying to do. Explain step-by-step how the code iterates to refine its web search results or enhance the LLM's output. How does the loop work? What triggers a new iteration? What is the goal of each cycle?

3. PROVIDE EXPERT ANALYSIS Based on your analysis, share your expert opinion on this implementation. Explain the general principles for effectively executing an iterative refinement process for LLM-powered web searches. Is the approach in this codebase effective? What are its strengths and weaknesses? What are the best practices they are following or missing?

Here is the codebase:

https://uithub.com/RUCAIBox/ManuSearch?accept=text%2Fhtml&maxTokens=50000&ext=py

2-response.md

Of course. As an expert in web-augmented LLMs, I'd be happy to analyze this codebase and explain its iterative refinement process. The architecture here is a sophisticated example of a multi-agent system designed for complex question-answering.

Let's break it down.

1. IDENTIFY THE REFINEMENT MECHANISM

The core of the iterative search and refinement process is located in the SearchAgent class, specifically within its iterative method. This method orchestrates a loop between a Planner agent and a Searcher agent. A secondary, nested refinement loop exists within the Searcher agent itself.

Here are the key code snippets:

Primary Refinement Loop (Orchestration):

• File: searchagent/models/searchagent.py
• Code Block: The iterative method.

# /searchagent/models/searchagent.py:

 77 |     def iterative(self, query):
 78 |         """
 79 |         Executes the search process iteratively, refining the search results over multiple turns.
 ...
 87 |         planner_message = queue.Queue()
 88 |         planner_message.put(query)
 ...
 94 |         for turn in range(self.max_turn):
 95 |             # Plan the one search step.   User search query --> Overall search steps
 96 |             logging.info('planner planning....')
 97 |             with timeit("Planner"):
 ...
100 |                 for response in self.planner.plan(
101 |                     message=message,
102 |                     recorder=self.recorder
103 |                 ):
104 |                     current_plan = parse_resp_to_json(response.content)
 ...
121 |             if not finish_condition(current_plan) and current_plan['actions'] == 'extract_problems':
122 |                 current_subquery = current_plan['content']
...
129 |                     for tool_name, search_result, references_url in self.searcher.search(
130 |                         question=current_subquery, 
131 |                         recorder=self.recorder,
132 |                     ):
...
152 |                     step_message.append(
153 |                         AgentMessage(
154 |                             sender="searcher",
155 |                             content=search_result if search_result else 'can not find realted information!',
156 |                             formatted=copy.deepcopy(_graph_state)
157 |                         )
158 |                     )
159 | 
160 |                 planner_message.put(step_message)
...
162 |             elif finish_condition(current_plan):
163 |                 response.formatted = _graph_state
164 |                 return response

Secondary Refinement Loop (Sub-task Execution):

• File: searchagent/models/searcher.py
• Code Block: The search method.

# /searchagent/models/searcher.py:

 58 |     def search(
 59 |         self,
 60 |         question, # 当前问题 
 61 |         recorder, 
...
118 |         for turn in range(self.max_turn):
119 |             if turn == self.max_turn-1:
...
129 |                 for response in super().forward(messages, tools=self.tools_schema, tool_choice="auto", session_id=session_id):
...
170 |                 if not isinstance(response.content, str):
173 |                     for tool_call in response.content.tool_calls:
174 |                         name = tool_call.function.name
175 |                         args = load_multiple_dict(tool_call.function.arguments)
176 |                         if name:
177 |                             if name.lower() == 'googlesearch':
...
188 |                                 search_results = self.collected_tools.execute(name=name, tool_input=args)
...
221 |                             elif name.lower() == 'final_answer' and not ignore:
...
232 |                                 return references

The "Brain" of the Loop (Planner's Instructions):

• File: searchagent/prompt/planner.py
• Code Block: PLANNER_ITERATIVE_PROMPT_EN / _CN

# /searchagent/prompt/planner.py:

 62 | Your decomposition process should be iterative. Based on the current state of problem-solving, each step should break down a subproblem that can be answered with a single search (i.e., a single-hop subproblem). After this subproblem is resolved, proceed to decompose the next subproblem requiring a search.
...
 71 | 1. RESPONSE FORMAT: Your output format must always be a JSON object containing the following fields:
 72 | {{
 73 |     "evaluation_previous_goal": "Success|Failed|Unknown - A brief analysis of the current state...",
 74 |     "actions": "Indicate the action you will perform now. If you want to continue decomposing the question, fill in 'extract_problems'. If you want to make a final response, fill in 'final_response'...",
...
 77 |     "content": "Fill in one sub-question you decompose this step or the final response to the main problem" 
 78 | }}

---

2. EXPLAIN THE LOGIC

The developers have implemented a sophisticated, two-level iterative refinement process based on the Plan-and-Solve agent architecture. The system decomposes a complex query, executes searches for each sub-part, and uses the results to inform the next step.

Here is the step-by-step logic:

High-Level Orchestration Loop (SearchAgent.iterative)

This is the main control loop that manages the overall problem-solving strategy.

1. Initialization: The user's initial query is placed into a planner_message queue. The loop is set to run for a maximum of max_turn iterations.

2. Step 1: Planning (planner.plan)

   • In each turn, the SearchAgent takes a message from the queue. For the first turn, this is the user's query. For subsequent turns, it's the result of the previous search action.
   • This message is passed to the Planner agent. The Planner is an LLM guided by the PLANNER_ITERATIVE_PROMPT.
   • Goal: The Planner's job is to act as a strategist. It must: a. Evaluate: Look at the history and the result of the last action (evaluation_previous_goal). b. Decide: Choose the next action (actions). This is the critical decision point. c. Act: Generate the content for that action (content).

3. Step 2: Loop Control (Decision Point)

   • The SearchAgent inspects the Planner's JSON response.
   • Continue Iteration: If the Planner's action is "extract_problems", it means more information is needed. The content of the plan contains a new sub-question. This is the trigger for a new refinement cycle.
   • Terminate Loop: If the action is "final_response", the Planner has determined that it has enough information to answer the user's original query. The finish_condition becomes true, and the loop terminates, returning the final answer.

4. Step 3: Execution (searcher.search)

   • If the Planner decided to continue, the SearchAgent passes the new sub-question to the Searcher agent.
   • The Searcher's job is to take this specific, single-hop sub-question and find an answer for it using its available tools (like GoogleSearch).

5. Step 4: Closing the Loop

   • Once the Searcher completes its task and returns an answer to the sub-question, the SearchAgent packages this result into a new AgentMessage.
   • This message is put back into the planner_message queue (planner_message.put(step_message)).
   • The main loop then repeats from Step 1, but this time the Planner has the new information (the answer to the last sub-question) to inform its next strategic decision.

Nested Refinement Loop (Searcher.search)

Within a single turn of the main loop, the Searcher has its own internal loop. This allows it to refine its own process for answering a single sub-question.

• Goal: To answer one sub-question from the Planner.
• Process: The Searcher LLM is prompted to use tools. It can call GoogleSearch to get a list of web pages. It can then analyze the results and decide if it needs to search again with a different query or if it has enough information to call final_answer (for that sub-question). This allows it to self-correct, for example, by re-querying if the initial search results are poor, all before returning control to the main SearchAgent loop.

---

3. PROVIDE EXPERT ANALYSIS

This implementation demonstrates a mature and effective approach to building a web-augmented LLM agent. It follows several best practices while also having some potential areas for enhancement.

General Principles for Effective Iterative Refinement:

An effective iterative process for LLM-powered web search requires:

1. State Management: A clear way to track what has been asked, what has been found, and what the current context is.
2. Decomposition: The ability to break a complex, multi-step problem into a series of smaller, manageable tasks.
3. Action & Execution: A robust mechanism to perform actions in the external world (e.g., call a search API).
4. Evaluation & Reflection: A step where the agent assesses the outcome of its last action and decides if it's closer to its goal.
5. Synthesis & Termination: The ability to recognize when enough information has been gathered and to synthesize it into a final, coherent answer.

Analysis of the Codebase:

Strengths:

• Excellent Separation of Concerns: The architecture cleanly separates the high-level strategist (Planner) from the tactical executor (Searcher). This is a powerful pattern that makes the system more robust and easier to debug. The Planner doesn't need to know how to search, only what to search for.
• LLM-Driven Control Flow: This is a key strength. Instead of hard-coding complex if/else logic to decide when to stop, the system offloads this decision to the Planner LLM. The structured JSON output ("actions": "extract_problems" | "final_response") is a best practice for reliable agentic control.
• Stateful and Context-Aware: The Recorder class, with its WebSearchGraph, provides a structured way to manage state. By feeding the results of previous turns back to the Planner, the agent can make increasingly informed decisions as it gathers more information.
• Nested Refinement: The Searcher's internal loop is a sophisticated feature. It allows for localized self-correction without needing to go back to the high-level Planner for every minor adjustment, making the process more efficient.
• Robust Tooling: The use of a ToolCollection and Pydantic models for tool schemas is a clean and scalable way to manage the agent's capabilities.

Weaknesses and Potential Improvements:

• Sequential Execution: The current implementation appears to be strictly sequential, tackling one sub-question at a time. The Planner could be enhanced to identify and output a set of independent sub-questions that could be executed in parallel by multiple Searcher instances, significantly speeding up the process.
• Basic Error Correction: The PLANNER_ITERATIVE_PROMPT mentions re-executing failed actions, but the logic for handling failure seems limited. A more advanced system could try to diagnose the reason for failure (e.g., "the search query returned no results," "the website was inaccessible") and prompt the Planner to form a new, corrective plan (e.g., "rephrase the search query," "find an alternative source").
• Static Loop Termination: The loop is hard-capped by max_turn. While this prevents infinite loops, a more dynamic approach could be beneficial. For instance, the system could track a confidence score or a "completeness" metric, or the Planner could be explicitly asked if it believes it can answer the question with high confidence.
• Context Summarization: As the number of turns increases, the history of Q&A pairs fed back to the Planner can become very long and exceed context window limits. The system could benefit from an intermediate "summarizer" step that condenses the WebSearchGraph into a concise state-of-the-world summary before each planning phase.

In conclusion, this codebase represents a well-designed and effective implementation of an iterative refinement loop for web-augmented LLMs. Its modular, stateful, and LLM-driven approach aligns with modern best practices in agentic AI. The identified weaknesses are not critical flaws but rather opportunities for future enhancement toward an even more powerful and efficient system.