refactor: Reimplement research workflow with dynamic expert configuration, multi-round discussion, and Mermaid diagram synthesis.

agents/research_agent.py

@@ -29,11 +29,11 @@ Be constructive but rigorous. Don't just agree; add value by challenging assumpt
 Context: {context}"""
         
         elif self.role == "expert_c":
-            return f"""You are Expert C, a Senior Strategist.
+            return f"""You are Expert C, a Senior Strategist and Visual Thinker.
 Your goal is to synthesize the final output. 
 Combine the structural strength of Expert A with the critical insights of Expert B.
 Produce a final, polished, comprehensive plan or report.
-Context: {context}"""
+CRITICAL: You MUST include a Mermaid.js diagram (using ```mermaid code block) to visualize the timeline, process, or architecture."""
         
         else:
             return "You are a helpful assistant."

app.py

@@ -205,8 +205,8 @@ with st.sidebar:
 # ==================== 主界面逻辑 ====================
 
 if mode == "Deep Research":
-    st.title("🧪 Multi-Model Council")
+    st.title("🧪 Multi-Model Council V2")
-    st.markdown("*多模型智囊团：分析 (Expert A) -> 批判 (Expert B) -> 决策 (Expert C)*")
+    st.markdown("*多模型智囊团：分析 (Expert A) -> 批判 (Expert B) -> 改进 (Expert A) -> 决策 (Expert C)*")
     
     # Input
     research_topic = st.text_area("研究/决策主题", placeholder="请输入你想深入研究或决策的主题...", height=100)

orchestrator/research_manager.py

@@ -61,10 +61,19 @@ class ResearchManager:
             yield {"type": "content", "content": chunk}
         yield {"type": "step_end", "output": findings_b}
 
-        # Step 3: Expert C Synthesis
+        # Step 3: Expert A Refinement (Innovative Optimization)
+        findings_a_refined = ""
+        yield {"type": "step_start", "step": "Expert A Refinement", "agent": self.agents["expert_a"].name, "model": self.agents["expert_a"].model_name}
+        prompt_a_refine = f"Expert B has critiqued your initial proposal.\nCritique: {findings_b}\n\nPlease refine your proposal to address these points. Strengthen your arguments and fix the gaps."
+        for chunk in self.agents["expert_a"].generate(prompt_a_refine, context):
+            findings_a_refined += chunk
+            yield {"type": "content", "content": chunk}
+        yield {"type": "step_end", "output": findings_a_refined}
+
+        # Step 4: Expert C Synthesis & Visualization
         findings_c = ""
-        yield {"type": "step_start", "step": "Expert C Synthesis", "agent": self.agents["expert_c"].name, "model": self.agents["expert_c"].model_name}
+        yield {"type": "step_start", "step": "Expert C Synthesis & Visualization", "agent": self.agents["expert_c"].name, "model": self.agents["expert_c"].model_name}
-        prompt_c = f"Synthesize a final comprehensive plan for '{topic}' based on Expert A's proposal and Expert B's critique.\nExpert A:\n{findings_a}\nExpert B:\n{findings_b}"
+        prompt_c = f"Synthesize a final comprehensive plan for '{topic}' based on the refined proposal.\nRefined Proposal:\n{findings_a_refined}\nCritique Reference:\n{findings_b}\n\nIMPORTANT: Include a Mermaid.js diagram (sequenceDiagram, gantt, or flowchart) to visualize the roadmap or process at the end of your report."
         for chunk in self.agents["expert_c"].generate(prompt_c, context):
             findings_c += chunk
             yield {"type": "content", "content": chunk}