Model further optimization - gemini-pro-cli.txt

# USER: 
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮

│  > The code in cg_mcts_qwen.py is an implementation of an algorithm. Please help me modify: 1. I have pre-stored vectors and db in files, the code is as follows:            vectors = np.array([llm_interface.get_vector(text    │
│    for text in all_papers_text]).astype('float32')                                                                                                                    │
│                                                                                                                                                                       │
│                # Extract a clean model name for filenames                                                                                                             │
│                model_name_for_file = os.path.basename(cfg.MODEL_NAME)                                                                                                 │
│                if not model_name_for_file: # Handle cases like ".." or "/"                                                                                            │
│                    model_name_for_file = "default_model"                                                                                                              │
│                                                                                                                                                                       │
│                # Save vectors to a .npy file                                                                                                                          │
│                vectors_filename = f"vectors_{model_name_for_file}.npy"                                                                                                │
│                np.save(vectors_filename, vectors)                                                                                                                     │
│                print(f"Vectors saved to {vectors_filename}")                                                                                                          │
│                                                                                                                                                                       │
│                # Build the FAISS index                                                                                                                                │
│                paper_knowledge_base_index = build_faiss_index(vectors)                                                                                                │
│                                                                                                                                                                       │
│                # Save the FAISS index                                                                                                                                 │
│                faiss_index_filename = f"faiss_index_{model_name_for_file}.bin"                                                                                        │
│                faiss.write_index(paper_knowledge_base_index, faiss_index_filename)                                                                                    │
│                print(f"FAISS index saved to {faiss_index_filename}")                                                                                                  │
│                                                                                                                                                                       │
│                # Save the paper metadata                                                                                                                              │
│                metadata_filename = "paper_metadata.json"                                                                                                              │
│                with open(metadata_filename, "w", encoding='utf-8') as f:                                                                                              │
│                    json.dump(paper_metadata, f, ensure_ascii=False, indent=4)                                                                                         │
│                print(f"Paper metadata saved to {metadata_filename}")                                                                                                  │
│    "Please help me replace the corresponding content in cg_mcts_qwen.py to read from this file. 2.                                                                                                       │
│    I found that for generate_theme, if the two concepts are too different, the output result will be rather forced. I hope to modify the prompt to make it more reasonable. It doesn't have to be a complete A+B of the two concepts, it could also be (partial A) + B or similar. The generated theme needs to be reasonable. 3. In MCTS's expand, instead of letting the model generate K options at once, it's more reasonable to let the model generate K times. Generating K options at once may not be completely parallel consecutive content from idea_so_far.             │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

# AGENT: 


✦ Okay, I will help you modify the cg_mcts_qwen.py file based on your requirements.

  First, I need to read the file to understand its content.
 ╭──────────────────────────────╮
 │ ✓  ReadFile cg_mcts_qwen.py  │
 ╰──────────────────────────────╯
✦ I have analyzed your requests and will now apply the modifications to cg_mcts_qwen.py.

  The changes are as follows:
   1. Data Loading: The script will now load pre-computed vectors, a FAISS index, and paper metadata from local files (.npy, .bin, .json) instead of generating them on
      the fly. This requires adding the os import.
   2. Theme Generation Prompt: The prompt for the generate_theme function has been updated to encourage more reasonable and creative combinations of disparate
      scientific concepts, avoiding forced or unnatural synthesis.
   3. MCTS Expansion: The expand method in the MCTS has been refactored. Instead of generating K options in a single API call, it now iteratively calls the model K
      times to generate one unique option per call, which should improve the diversity and quality of the expansions.

  I will now write the updated code to the file.
 ╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  WriteFile Writing to cg_mcts_qwen.py                                                                                                                     │
 │                                                                                                                                                             │
 │    ... first 36 lines hidden ...                                                                                                                            │
 │    266           idea_so_far = node.get_full_text()                                                                                                         │
 │    267                                                                                                                                                      │
 │    264 -         prompt = f'''                                                                                                                              │
 │    268 +         base_prompt = f'''                                                                                                                         │
 │    269           You are a world-class Principal Investigator, known for writing clear, compelling, and fundable research proposals.                        │
 │    270           Your current task is to expand on the following research idea:                                                                             │
 │    271           ---                                                                                                                                        │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    281                                                                                                                                                      │
 │    282           3.  **Critical Thinking:** Briefly acknowledge potential challenges, limitations, or alternative approaches to your proposed section.      │
 │          This demonstrates foresight.                                                                                                                       │
 │    283                                                                                                                                                      │
 │    280 -         Based on these principles, please propose {self.config.K_EXPAND} distinct, well-reasoned, and detailed "next sections" for this research   │
 │        plan. Prefix each version with `[Option 1]`, `[Option 2]`, etc.                                                                                      │
 │    281 -                                                                                                                                                    │
 │    282 -         Continue this:                                                                                                                             │
 │    283 -          {idea_so_far}                                                                                                                             │
 │    284 +         Based on these principles, generate one single, distinct, and well-reasoned "next section" for the research plan. Do not generate a list   │
 │        of options.                                                                                                                                          │
 │    285           '''                                                                                                                                        │
 │    286                                                                                                                                                      │
 │    286 -         response = self.llm.generate_chat_completion([{"role": "user", "content": prompt}], self.config.EXPAND_MAX_LENGTH, temperature=0.6) #      │
 │        Temp slightly higher for creativity                                                                                                                  │
 │    287 -         print(response)                                                                                                                            │
 │    287 +         for i in range(self.config.K_EXPAND):                                                                                                      │
 │    288 +             print(f"    Generating expansion option {i+1}/{self.config.K_EXPAND}...")                                                              │
 │    289 +             # Add a bit of randomness for diversity in each call                                                                                   │
 │    290 +             # And instruct it to be different from previous options if any                                                                         │
 │    291 +             if node.children:                                                                                                                      │
 │    292 +                 existing_options = "\n".join([f"- {child.narrative_block[:100]}..." for child in node.children])                                   │
 │    293 +                 dynamic_prompt = f"{base_prompt}\n\nYou have already proposed the following options. Make sure the new one is distinct:"           │
 │    294 +                 dynamic_prompt += f"\n{existing_options}"                                                                                          │
 │    295 +             else:                                                                                                                                  │
 │    296 +                 dynamic_prompt = base_prompt                                                                                                       │
 │    297                                                                                                                                                      │
 │    298 +             response = self.llm.generate_chat_completion(                                                                                          │
 │    299 +                 [{"role": "user", "content": dynamic_prompt}],                                                                                     │
 │    300 +                 self.config.EXPAND_MAX_LENGTH,                                                                                                     │
 │    301 +                 temperature=0.7 # Slightly higher temp for more diversity between calls                                                            │
 │    302 +             )                                                                                                                                      │
 │    303 +                                                                                                                                                    │
 │    304               thoughts_match = re.search(r"<think>(.*?)</think>", response, re.DOTALL)                                                               │
 │    305               thoughts = thoughts_match.group(1).strip() if thoughts_match else "No thoughts captured."                                              │
 │    306                                                                                                                                                      │
 │    292 -         # Regex to capture each Option block                                                                                                       │
 │    293 -         option_blocks = re.findall(r'(\[Option \d+\](.*?))(?=\[Option \d+\]|\Z)', response, re.DOTALL)                                             │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    307 +             content = re.sub(r"<think>.*?</think>", "", response, flags=re.DOTALL).strip()                                                         │
 │    308                                                                                                                                                      │
 │    295 -         if option_blocks:                                                                                                                          │
 │    296 -             for _, option_content in option_blocks:                                                                                                │
 │    297 -                 full_option_text = option_content.strip()                                                                                          │
 │    298 -                 if full_option_text:                                                                                                               │
 │    299 -                     child_node = MCTSNode(full_option_text, parent=node, thoughts=thoughts)                                                        │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    309 +             if len(content) > 100: # Arbitrary threshold for substantial content                                                                   │
 │    310 +                 child_node = MCTSNode(content, parent=node, thoughts=thoughts)                                                                     │
 │    311                   node.children.append(child_node)                                                                                                   │
 │    312               else:                                                                                                                                  │
 │    302 -             # Fallback for models that might just provide one solid block without headers                                                          │
 │    303 -             fallback_content = re.sub(r"<think>.*?</think>", "", response, flags=re.DOTALL).strip()                                                │
 │    304 -             if len(fallback_content) > 100: # Arbitrary threshold for substantial content                                                          │
 │    305 -                  print("Warning: No [Option X] headers found. Using entire response as a single option.")                                          │
 │    306 -                  child_node = MCTSNode(fallback_content, parent=node, thoughts=thoughts)                                                           │
 │    307 -                  node.children.append(child_node)                                                                                                  │
 │    308 -             else:                                                                                                                                  │
 │    309 -                 print("Warning: Expansion failed to generate valid options.")                                                                      │
 │    313 +                 print(f"Warning: Expansion option {i+1} failed to generate valid content.")                                                        │
 │    314 +                                                                                                                                                    │
 │    315 +         if not node.children:                                                                                                                      │
 │    316 +             print("Warning: Expansion failed to generate any valid options after K attempts.")                                                     │
 │    317               node.is_terminal = True                                                                                                                │
 │    318                                                                                                                                                      │
 │    319       def simulate_and_evaluate(self, node: MCTSNode):                                                                                               │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    407               debug_parts.append(f"  [NARRATIVE BLOCK]:\n{n.narrative_block}\n")                                                                     │
 │    408           debug_trace = "\n".join(debug_parts)                                                                                                       │
 │    409                                                                                                                                                      │
 │    403 -         return f"--- FINAL NARRATIVE ---\n{main_text}\n\n\n--- DEBUG TRACE (BEST PATH) ---\n{debug_trace}"                                         │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    410 +         return f"--- FINAL NARRATIVE ---\n{main_text}\n\n\n--- DEBUG TRACE (BEST PATH) ---                                                         │
 │    411 + {debug_trace}"                                                                                                                                     │
 │    412                                                                                                                                                      │
 │    413   # --- 6. Helper & Main Execution ---                                                                                                               │
 │    414   def build_faiss_index(vectors: np.ndarray):                                                                                                        │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    424       cfg = Config()                                                                                                                                 │
 │    425       llm_interface = LLMInterface(cfg.MODEL_NAME, cfg.DEVICE)                                                                                       │
 │    426                                                                                                                                                      │
 │    419 -     novelty_documents = [                                                                                                                          │
 │    420 -         "AlphaFold predicts protein structures using a deep learning approach.",                                                                   │
 │    421 -         "CRISPR-Cas9 is a gene-editing tool that allows for precise modification of DNA sequences.",                                               │
 │    422 -         "BERT is a transformer-based model for natural language understanding.",                                                                   │
 │    423 -         "Graphene is a single layer of carbon atoms arranged in a two-dimensional honeycomb lattice.",                                             │
 │    424 -         "Lithium-ion batteries are a type of rechargeable battery.",                                                                               │
 │    425 -         "Perovskite solar cells include a perovskite-structured compound as the light-harvesting active layer."                                    │
 │    426 -     ]                                                                                                                                              │
 │    427 +     # Load pre-computed data                                                                                                                       │
 │    428 +     model_name_for_file = os.path.basename(cfg.MODEL_NAME)                                                                                         │
 │    429 +     if not model_name_for_file:                                                                                                                    │
 │    430 +         model_name_for_file = "default_model"                                                                                                      │
 │    431                                                                                                                                                      │
 │    428 -     print(f"\nVectorizing {len(novelty_documents)} base documents...")                                                                             │
 │    429 -     vectors = np.array([llm_interface.get_vector(text) for text in novelty_documents]).astype('float32')                                           │
 │    432 +     vectors_filename = f"vectors_{model_name_for_file}.npy"                                                                                        │
 │    433 +     faiss_index_filename = f"faiss_index_{model_name_for_file}.bin"                                                                                │
 │    434 +     metadata_filename = "paper_metadata.json"                                                                                                      │
 │    435                                                                                                                                                      │
 │    431 -     novelty_db = build_faiss_index(vectors)                                                                                                        │
 │    436 +     print(f"Loading data from {vectors_filename}, {faiss_index_filename}, and {metadata_filename}...")                                             │
 │    437                                                                                                                                                      │
 │    438 +     try:                                                                                                                                           │
 │    439 +         vectors = np.load(vectors_filename)                                                                                                        │
 │    440 +         novelty_db = faiss.read_index(faiss_index_filename)                                                                                        │
 │    441 +         with open(metadata_filename, "r", encoding='utf-8') as f:                                                                                  │
 │    442 +             paper_metadata = json.load(f)                                                                                                          │
 │    443 +                                                                                                                                                    │
 │    444 +         # The user should ensure the metadata format is a list of dicts,                                                                           │
 │    445 +         # with each dict having at least 'title' and 'abstract' keys.                                                                              │
 │    446 +         novelty_documents = [item.get('title', '') + ': ' + item.get('abstract', '') for item in paper_metadata]                                   │
 │    447 +                                                                                                                                                    │
 │    448 +         print("Successfully loaded pre-computed data.")                                                                                            │
 │    449 +                                                                                                                                                    │
 │    450 +     except FileNotFoundError as e:                                                                                                                 │
 │    451 +         print(f"Error: Could not find pre-computed data file: {e.filename}")                                                                       │
 │    452 +         print("Please ensure the necessary .npy, .bin, and .json files are in the same directory.")                                                │
 │    453 +         exit()                                                                                                                                     │
 │    454 +                                                                                                                                                    │
 │    455       theme_generator = AutomatedThemeGenerator(llm_interface, novelty_documents, vectors, cfg)                                                      │
 │    456       automated_theme = theme_generator.generate_theme()                                                                                             │
 │    457                                                                                                                                                      │
 │    436 -     print(f"\n>>> Automatically Generated Theme: '{automated_theme}' <<<\n")                                                                       │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    458 +     print(f"\n>>> Automatically Generated Theme: '{automated_theme}' <<<                                                                           │
 │    459 + ")                                                                                                                                                 │
 │    460                                                                                                                                                      │
 │    461       mcts = CG_MCTS(llm_interface, novelty_db=novelty_db, config=cfg, theme=automated_theme)                                                        │
 │    462                                                                                                                                                      │
 ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ The cg_mcts_qwen.py file has been successfully updated with all the requested modifications. Let me know if you have any other changes.



## Human helped to improve the prompt.

# USER:

In cg_mcts_qwen.py is the code for an algorithm I implemented. Please help me modify it:

I think the conclusions drawn by theme_generator are too concise and lose a lot of detailed information.
So I hope that when generating a theme, it generates both a theme and an elaboration at the same time.

When giving to MCTS, the theme remains unchanged, and the root is changed to the theme plus this elaboration. This is to provide richer context information.

Is this reasonable?

Please help me complete the code

# AGENT:

✦ Okay, I'll help you with that. Your proposed change to enrich the MCTS root with a more detailed "elaboration" is a reasonable approach. It could provide valuable
  context and guide the search more effectively.

  First, I will read the cg_mcts_qwen.py file to understand its current structure.
 ╭──────────────────────────────╮
 │ ✓  ReadFile cg_mcts_qwen.py  │
 ╰──────────────────────────────╯

✦ Of course. I will now modify cg_mcts_qwen.py to implement your proposed changes.

  First, I'll update the generate_theme method to produce both a concise theme and a more detailed elaboration. It will now request a JSON object from the language
  model.
 ╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  Edit cg_mcts_qwen.py:     def generate_theme(self) -... =>     def generate_theme(self) -...                                                             │
 │                                                                                                                                                             │
 │    116           self.centroids = kmeans.cluster_centers_                                                                                                   │
 │    117           print("Knowledge map built.")                                                                                                              │
 │    118                                                                                                                                                      │
 │    119 -     def generate_theme(self) -> str:                                                                                                               │
 │    119 +     def generate_theme(self) -> tuple[str, str]:                                                                                                   │
 │    120           print("\n--- Generating a new theme automatically ---")                                                                                    │
 │    121           print("Phase 1 & 2: Sampling continents and finding concepts...")                                                                          │
 │    122           cluster_a_idx = random.randint(0, self.config.NUM_CLUSTERS - 1)                                                                            │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    137           concept_a = get_representative_doc(cluster_a_idx)                                                                                          │
 │    138           concept_b = get_representative_doc(cluster_b_idx)                                                                                          │
 │    139                                                                                                                                                      │
 │    140 -         print("Phase 3: Synthesizing a high-level theme...")                                                                                       │
 │    140 +         print("Phase 3: Synthesizing a theme and elaboration...")                                                                                  │
 │    141           prompt = f'''                                                                                                                              │
 │    142 -         You are a creative scientist tasked with generating a novel research theme by synthesizing two concepts. The goal is to find a plausible,  │
 │        insightful, and forward-looking connection between them.                                                                                             │
 │    142 +         You are a creative scientist tasked with generating a novel research proposal by synthesizing two concepts.                                │
 │    143                                                                                                                                                      │
 │    144           Concept 1: "{concept_a}"                                                                                                                   │
 │    145           Concept 2: "{concept_b}"                                                                                                                   │
 │    146                                                                                                                                                      │
 │    147 -         First, think step-by-step in a <think> block. Analyze both concepts. If they are very different, you should find a more subtle and         │
 │        reasonable link. For example, you could apply a technique from one concept to a problem in the other, or find a shared underlying principle, or use  │
 │        one concept as an analogy to reframe the other. The final theme should be a coherent and compelling research direction.                              │
 │    147 +         First, think step-by-step in a <think> block. Analyze both concepts. Find a plausible, insightful, and forward-looking connection. You     │
 │        could apply a technique from one to a problem in the other, find a shared principle, or use one as an analogy for the other.                         │
 │    148                                                                                                                                                      │
 │    149 -         After your thinking process, output the final high-level research theme as a single, concise string.                                       │
 │    149 +         After your thinking process, output the result as a JSON object with two keys:                                                             │
 │    150 +         1. "theme": A concise, high-level research theme that captures the core idea. This should be a single, memorable sentence.                 │
 │    151 +         2. "elaboration": A detailed, one-paragraph explanation of the theme. This should elaborate on the connection you found, outline the       │
 │        potential approach, and highlight the novelty. This will serve as the introductory context for the research proposal.                                │
 │    152 +                                                                                                                                                    │
 │    153 +         Example format:                                                                                                                            │
 │    154 +         ```json                                                                                                                                    │
 │    155 +         {{                                                                                                                                         │
 │    156 +             "theme": "Leveraging Quantum-Inspired Tensor Networks for Explainable Large-Scale Graph Representation Learning.",                     │
 │    157 +             "elaboration": "Current Graph Neural Networks (GNNs) often act as black boxes, limiting their trustworthiness in high-stakes domains.  │
 │        This research proposes a novel framework that adapts principles from quantum many-body physics, specifically tensor networks, to create a new class  │
 │        of GNNs. By representing graph structures and features as a tensor network, we can leverage efficient contraction algorithms (like DMRG) for node    │
 │        classification and link prediction, while the inherent structure of the network provides a direct, model-based explanation for its predictions,      │
 │        addressing the critical need for interpretability in complex graph data."                                                                            │
 │    158 +         }}                                                                                                                                         │
 │    159 +         ```                                                                                                                                        │
 │    160           '''                                                                                                                                        │
 │    151 -         print(concept_a,'\n', concept_b)                                                                                                           │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    161 +         print(concept_a, '\n', concept_b)                                                                                                          │
 │    162           response = self.llm.generate_chat_completion([{"role": "user", "content": prompt}], self.config.THEME_GEN_MAX_LENGTH, temperature=0.5)     │
 │    153 -         print('Generating...',response)                                                                                                            │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    163 +         print('Generating...', response)                                                                                                           │
 │    164 +                                                                                                                                                    │
 │    165 +         parsed_json = parse_llm_json_output(response)                                                                                              │
 │    166 +                                                                                                                                                    │
 │    167 +         if parsed_json and "theme" in parsed_json and "elaboration" in parsed_json:                                                                │
 │    168 +             return parsed_json["theme"], parsed_json["elaboration"]                                                                                │
 │    169 +         else:                                                                                                                                      │
 │    170 +             print("Warning: Failed to parse theme and elaboration. Using the raw response as the theme.")                                          │
 │    171 +             # Fallback for safety                                                                                                                  │
 │    172               theme = re.sub(r"<think>.*?</think>", "", response, flags=re.DOTALL).strip()                                                           │
 │    155 -         return theme if theme else response                                                                                                        │
 │    ═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════  │
 │    173 +             return theme, "Elaboration not generated."                                                                                             │
 │    174                                                                                                                                                      │
 │    175   # --- 5. MCTS Components (Narrative MCTS) ---                                                                                                      │
 │    176   class MCTSNode:                                                                                                                                    │
 ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ Great. Now I'll modify the main execution block. This will unpack the new theme and elaboration, print them, and then initialize the MCTS, setting the concise theme
  for internal prompting while using the combined theme and elaboration for the root node's starting context.

 ╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
 │ ✓  Edit cg_mcts_qwen.py:     theme_generator = Automate... =>     theme_generator = Automate...                                                             │
 │                                                                                                                                                             │
 │    478           exit()                                                                                                                                     │
 │    479                                                                                                                                                      │
 │    480       theme_generator = AutomatedThemeGenerator(llm_interface, novelty_documents, vectors, cfg)                                                      │
 │    481 -     automated_theme = theme_generator.generate_theme()                                                                                             │
 │    481 +     automated_theme, theme_elaboration = theme_generator.generate_theme()                                                                          │
 │    482                                                                                                                                                      │
 │    483 -     print(f"\n>>> Automatically Generated Theme: '{automated_theme}' <<<\n")                                                                       │
 │    483 +     print(f"\n>>> Automatically Generated Theme: '{automated_theme}' <<<")                                                                         │
 │    484 +     print(f">>> Elaboration: '{theme_elaboration}' <<<                                                                                             │
 │    485 + ")                                                                                                                                                 │
 │    486                                                                                                                                                      │
 │    487 +     # The 'theme' for prompting remains the concise one.                                                                                           │
 │    488       mcts = CG_MCTS(llm_interface, novelty_db=novelty_db, config=cfg, theme=automated_theme)                                                        │
 │    489 +     # The root node of the MCTS starts with the richer context (theme + elaboration).                                                              │
 │    490 +     mcts.root = MCTSNode(narrative_block=f"{automated_theme}\n\n{theme_elaboration}")                                                              │
 │    491                                                                                                                                                      │
 │    492       start_time = time.time()                                                                                                                       │
 │    493       mcts.search()                                                                                                                                  │
 ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ I have completed the code modifications as you requested. The generate_theme function will now produce a more detailed elaboration alongside the main theme, and the
  MCTS will use this enriched text as its starting point.


