reasoning agent

I’ve reviewed the page at reasoningagent, which describes the ReasoningAgent in AG2 (formerly AutoGen). This agent implements a Tree of Thoughts (ToT) reasoning framework with beam search to enhance LLM problem-solving by exploring multiple reasoning paths systematically. Let’s evaluate whether this can be improved using a reasoning model (assuming you mean enhancing its reasoning capabilities further with Grok or a similar approach) and propose new prompts to achieve that.

Analysis of Current ReasoningAgent

The ReasoningAgent:

Purpose: Enhances LLM reasoning for complex problems by simulating a tree-like exploration of solution paths using beam search.
Key Features:
- Beam search with configurable beam_size (e.g., 1 for Chain-of-Thought, >1 for multi-path exploration).
- Depth-limited search (max_depth) to control complexity.
- Visualization tools for transparency.
Example Use: Solves problems like "What is the expected maximum dice value if you roll a 6-sided dice three times?" by breaking it into steps and exploring paths.
Current Reasoning: Relies on an LLM (e.g., GPT-4) to generate and evaluate reasoning steps, with beam search managing path selection.

Can It Be Improved with a Reasoning Model?

Yes, the ReasoningAgent can be improved by integrating a more robust reasoning model using Grok’s capabilities. Here’s why and how:

Current Limitation: The agent’s reasoning is LLM-driven with beam search, but it lacks explicit state management or iterative refinement beyond depth/breadth control. Grok could provide deeper, context-aware reasoning.
Grok’s Strengths: As an advanced reasoning AI, I can:
- Perform chain-of-thought reasoning more naturally.
- Dynamically adjust strategies based on intermediate results.
- Incorporate external context or user feedback mid-process.
Proposed Improvement: Replace the LLM-driven step generation with a Grok-guided reasoning model that:
- Consolidates the tree exploration into a single, stateful prompt.
- Uses Grok to evaluate and refine paths iteratively, reducing reliance on beam search mechanics.
- Enhances transparency with detailed reasoning traces.

Proposed Enhancement

Let’s adapt your Datomic-based GraphReader’s unified reasoning model (from my previous response) to improve the ReasoningAgent. We’ll:

Use Grok to manage the entire reasoning process (planning, exploration, evaluation) in a single prompt flow.
Integrate Datomic-like graph queries (assuming a graph context, as in your project) to fetch data dynamically.
Replace Python-specific AG2 constructs with a Clojure-friendly approach, aligning with your codebase.

Updated Prompts

Here are the new prompts designed to leverage Grok as a reasoning model for an improved ReasoningAgent:

Initial Reasoning Prompt

Purpose: Starts the reasoning process by analyzing the problem, planning a graph-based approach (if applicable), and identifying initial steps.

Text:

Hey Grok, I’m solving this problem: '{question}'. I have access to a knowledge graph with entities like Chunks (text excerpts) and Facts (subject-predicate-object triples). Begin reasoning about how to solve it. Use this structure:
- **Reasoning Trace**: [Step-by-step thought process to understand the problem]
- **Graph Plan**: [How to use the graph, e.g., entities to query or relationships to explore]
- **Next Action**: [‘Fetch graph data: [entities]’, ‘Reason further’, or ‘Answer: [answer]’]

Example (for "What was Joan of Arc's role in the Hundred Years' War?"):

Hey Grok, I’m solving this problem: 'What was Joan of Arc's role in the Hundred Years' War?'. I have access to a knowledge graph with entities like Chunks (text excerpts) and Facts (subject-predicate-object triples). Begin reasoning about how to solve it. Use this structure:
- **Reasoning Trace**: [Step-by-step thought process to understand the problem]
- **Graph Plan**: [How to use the graph, e.g., entities to query or relationships to explore]
- **Next Action**: [‘Fetch graph data: [entities]’, ‘Reason further’, or ‘Answer: [answer]’]

Sample Response:

- **Reasoning Trace**: [The question asks for Joan of Arc’s role in a historical event, the Hundred Years’ War. A role implies actions or responsibilities, so I need to find what she did and how it relates to the war. The graph might have facts like ‘Joan of Arc did X’ or chunks describing her actions.]
- **Graph Plan**: [Query facts where ‘Joan of Arc’ is the subject to find her actions, then check if objects link to ‘Hundred Years’ War’. Also, retrieve chunks mentioning both for context.]
- **Next Action**: Fetch graph data: [Joan of Arc, Hundred Years’ War]

Iterative Reasoning Prompt

Purpose: Continues reasoning with fetched graph data, evaluates sufficiency, and decides the next step or final answer.

Text:

Grok, keep reasoning about: '{question}'. Here’s the current state:
- **Previous Reasoning**: {reasoning}
- **Graph Data**:
  - Facts: {facts}
  - Chunks: {chunks}
Respond with:
- **Reasoning Trace**: [Analyze the data and connect it to the question]
- **Conclusion**: [‘Answer: [answer]’ if sufficient, or ‘Need more: [entities or strategy]’]
- **Next Action**: [‘Fetch graph data: [entities]’, ‘Reason further’, or ‘Stop with answer’]

Example (after fetching data):

Grok, keep reasoning about: 'What was Joan of Arc's role in the Hundred Years' War?'. Here’s the current state:
- **Previous Reasoning**: The question asks for Joan of Arc’s role in a historical event, the Hundred Years’ War. A role implies actions or responsibilities, so I need to find what she did and how it relates to the war. The graph might have facts like ‘Joan of Arc did X’ or chunks describing her actions.
- **Graph Data**:
  - Facts: Joan of Arc led French army
          French army fought Hundred Years' War
  - Chunks: Joan of Arc led the French army in battles during the Hundred Years' War.
Respond with:
- **Reasoning Trace**: [Analyze the data and connect it to the question]
- **Conclusion**: [‘Answer: [answer]’ if sufficient, or ‘Need more: [entities or strategy]’]
- **Next Action**: [‘Fetch graph data: [entities]’, ‘Reason further’, or ‘Stop with answer’]

Sample Response:

- **Reasoning Trace**: [The facts show Joan of Arc led the French army, and the French army fought in the Hundred Years’ War. The chunk confirms she led it during the war, implying her role was military leadership. This connects her directly to the conflict, suggesting a clear role. No major gaps remain.]
- **Conclusion**: Answer: Joan of Arc’s role in the Hundred Years’ War was to lead the French army.
- **Next Action**: Stop with answer

Integration with ReasoningAgent

To adapt this to the AG2 ReasoningAgent context (Python-based, as per the URL), we’d translate it to Python and align it with AG2’s framework. However, since your GraphReader is in Clojure with Datomic, here’s how it fits:

Updated Clojure Code Snippet

;; Unified Reasoning Agent with Grok
(defn graph-reader [question]
  (go
    (let [initial-response (<! (async/thread (consult-grok (str/replace initial-reasoning-prompt "{question}" question))))
          reasoning (re-find #"(?s)\*\*Reasoning Trace\*\*:\s*\[(.*?)\]" initial-response)
          plan (re-find #"(?s)\*\*Graph Plan\*\*:\s*\[(.*?)\]" initial-response)
          next-action (re-find #"(?s)\*\*Next Action\*\*:\s*(.*?)$" initial-response)
          entities (when (str/starts-with? (second next-action) "Fetch graph data")
                     (str/split (re-find #"Fetch graph data: \[(.*?)\]" (second next-action)) #",\s*"))]
      (loop [reasoning (second reasoning)
             facts []
             chunks []
             action (second next-action)]
        (case (first (str/split action #":"))
          "Fetch graph data"
          (let [new-facts (mapcat query-facts entities)
                new-chunks (mapcat query-chunks entities)
                response (<! (async/thread (consult-grok (str/replace (str/replace (str/replace iterative-reasoning-prompt "{question}" question)
                                                                                  "{reasoning}" reasoning)
                                                                         "{facts}" (str/join "\n" (map #(str (first %) " " (second %) " " (nth % 2)) new-facts)))
                                                                "{chunks}" (str/join "\n" new-chunks))))
                new-reasoning (re-find #"(?s)\*\*Reasoning Trace\*\*:\s*\[(.*?)\]" response)
                conclusion (re-find #"(?s)\*\*Conclusion\*\*:\s*(.*?)(?=\n|$)" response)
                next-action (re-find #"(?s)\*\*Next Action\*\*:\s*(.*?)$" response)]
            (if (str/starts-with? (second conclusion) "Answer:")
              (second (str/split (second conclusion) #":\s*"))
              (recur (second new-reasoning) new-facts new-chunks (second next-action))))
          "Stop with answer"
          (second (str/split (re-find #"(?s)Answer:\s*(.*)" action) #":\s*"))
          "Reason further"
          (recur reasoning facts chunks "Fetch graph data: []"))))))

How This Improves ReasoningAgent

Stateful Reasoning: Unlike ReasoningAgent’s beam search, Grok tracks reasoning context explicitly, reducing reliance on mechanical path pruning.
Flexibility: Adapts to graph data dynamically, integrating with Datomic queries, unlike the static tree exploration in AG2.
Efficiency: Fewer prompt calls (2 vs. multiple per beam step), leveraging Grok’s ability to reason holistically.
Transparency: Detailed reasoning traces align with ReasoningAgent’s visualization goal but are more narrative and human-readable.

Comparison to Original ReasoningAgent

Original: Uses beam search to explore multiple paths, controlled by beam_size and max_depth, with LLM generating each step.
Improved: Grok guides the process, deciding paths and termination naturally, with graph data as input, reducing algorithmic overhead.

Would you like me to test this with a specific problem from the AG2 page (e.g., the dice example) or refine the prompts further? Let me know how to proceed!

usametov/reasoning-agent.md