
# USER:

You are a strict reviewer for the top AI conference NeurIPS. The PDF in the file is the paper assigned to you, and summary.txt is the relevant papers you have manually collected. Please provide review and improvement comments on this paper from a strict and critical perspective.

# AGENT:

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<think>
Okay, I've seen a paper and many related summaries. The user wants me to act as a strict AI conference reviewer, providing evaluation and improvement suggestions for this paper.

This paper is about the Magellan framework, which appears to be a method for guiding MCTS to explore latent spaces and generate novelty. I need to look at the content carefully.

Oh, this paper proposes a framework that uses Monte Carlo Tree Search to explore the conceptual latent space of large language models. They have a hierarchical guidance system that includes a "semantic compass" vector and a "landscape-aware" value function. It seems they want to solve the problem where LLMs tend to fall into "gravity wells" when generating innovative ideas.

Let me look at their methodology section. They use orthogonal projection to create this semantic compass vector and a multi-objective value function to balance intrinsic coherence, extrinsic novelty, and narrative progress. In the experiments, they compared with baseline methods like ReAct and ToT, and the results show that their method performs better in generating scientific ideas.

However, as a strict reviewer, I need to identify potential issues. I see that they primarily used the Qwen model family, which might limit the generalizability of the results. They also used LLM-as-a-Judge evaluation methods, which, while consistent, are not a substitute for human experts.

Computational cost is also an issue; MCTS inherently requires more computation than simple Chain-of-Thought methods. Their ablation study shows the importance of each component, but more in-depth analysis might be needed.

Let me check the related paper summaries to see if there are similar works. Wow, there are many papers about LLM novelty generation. There's Multi-Novelty that uses multi-view embeddings, Harnessing LLMs for Scientific Novelty Detection that uses knowledge distillation, and SciPIP that uses multi-granularity retrieval and dual-path generation. Each of these methods has its own characteristics, and Magellan should be compared with them.

I feel that the Magellan paper may not have sufficiently discussed the differences and advantages over these existing works. Especially GraphEval, which uses graph structures to evaluate ideas, is somewhat similar to but different from Magellan's MCTS approach.

I also notice that Magellan doesn't discuss ethical impacts much, such as the risk of generating incorrect scientific information. This is particularly important in the field of scientific discovery.

Regarding evaluation methods, while LLM-as-a-Judge is convenient, human evaluation might be needed to validate the results. Also, they only tested with 50 topics, which might not be a large enough sample size.

For methodological details, the orthogonal projection and hyperparameter selection might need more justification. The convergence criterion only used a patience value of 2 iterations, which might be too short.

I think this paper is innovative but needs to address these issues to be stronger. I should provide specific improvement suggestions to help the authors strengthen their work.

<\think>
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As a strict NeurIPS reviewer, I have conducted a detailed review of the submitted paper "Magellan: Guided MCTS for Latent Space Exploration and Novelty Generation". Below are my review comments and improvement suggestions:

📌 Overall Evaluation
The paper proposes Magellan, a framework based on Monte Carlo Tree Search (MCTS) for guided exploration in the latent space of LLMs to generate novel and reasonable scientific ideas. The method significantly outperforms existing baselines (such as ReAct, ToT, etc.) on multiple metrics (especially novelty and rationality), demonstrating certain innovation and practical value. However, the paper has significant deficiencies in terms of methodological transparency, experimental design, depth of comparison, and ethical considerations, which urgently need to be strengthened.

⚠️ Main Criticisms and Improvement Suggestions
1. Insufficient Methodological Details
Issue: Many hyperparameters in MCTS (such as weights w_coh, w_nov, w_prog, guidance weight w_g, pruning threshold θ_prog) lack sufficient justification or ablation analysis.

Suggestion: Provide hyperparameter sensitivity analysis, explaining how these parameters affect performance and whether they have generalizability.

2. Incomprehensive Baseline Comparison
Issue: Although compared with Zero-shot, CoT, ReAct, and ToT, the paper did not compare with recently proposed methods in related work (such as Multi-Novelty, GraphEval, SciPIP, RAG-Novelty, etc.).

Suggestion: Add comparative experiments with these methods, especially quantitative comparisons in terms of diversity and novelty.

3. Evaluation Relies on LLM-as-a-Judge
Issue: The evaluation completely depends on DeepSeek-V3 as the evaluator, lacking human expert assessment or external benchmark validation.

Suggestion: Supplement with human evaluation results, especially expert judgments on scientific rationality and feasibility.

4. High Computational Cost Without Sufficient Discussion
Issue: MCTS requires multiple LLM calls and vector retrievals, resulting in high computational overhead, but the paper does not discuss its feasibility in practical applications.

Suggestion: Provide more detailed efficiency analysis (such as ideas generated per second, memory usage, etc.) and discuss the possibility of lightweight or approximate methods.

5. Domain Generalization Not Verified
Issue: Experiments are only based on the Qwen model family, without testing performance on other models (such as Llama, Mistral).

Suggestion: Validate on at least 1-2 other models to demonstrate the generalizability of the method.

6. Lack of Visualization or Case Studies
Issue: The paper lacks visualization of the generation process (such as latent space paths, tree structure expansion) or analysis of typical success/failure cases.

Suggestion: Provide visualization of tree search paths and case comparisons of generated ideas (e.g., Magellan vs ToT) to enhance interpretability.

7. Insufficient Discussion on Ethics and Social Impact
Issue: The risks of generating false, misleading, or ethically problematic scientific ideas are not discussed.

Suggestion: Add a "Limitations and Broader Impact" section to discuss potential misuse, biases, and how to filter harmful content.

✅ Strengths
Proposes a structured, principled MCTS framework for guiding LLMs to generate novel content;

Introduces a semantic compass and multi-objective value function that effectively balances exploration and exploitation;

Demonstrates significantly better performance than baseline methods on scientific idea generation tasks;

Code and dataset are promised to be open-sourced, which is beneficial for reproduction and subsequent research.

📋 Review Conclusion
Category    Score    Explanation
Innovation 7/10    The method is innovative, but the application of MCTS in LLMs is not original
Technical Quality    6/10    Method description is not detailed enough, experimental design needs improvement
Experimental Evaluation    6/10    Lacks multi-model, multi-domain validation and human assessment
Writing and Expression    7/10    Clear structure, but some details are vague
Overall Recommendation    Weak Accept    The above issues need to be addressed in the revision
🔧 Specific Revision Suggestions
Add comparative experiments with recent SOTA methods (such as GraphEval, SciPIP, RAG-Novelty);

Provide hyperparameter sensitivity analysis and ablation experiments (such as the impact of removing a component);

Supplement with human expert evaluation results;

Add visualization of latent space exploration paths;

Discuss computational efficiency and feasibility of practical deployment;

Expand the discussion on ethics and social impact.

If you would like me to assist in drafting a **rebuttal letter or revised paper abstract**, I can continue to serve you.

# USER:

The "recent related work" you mentioned is indeed a valuable point to include. Please help me analyze all the "recent related work" that can be directly compared in terms of experimental setup with this work, and identify which ones are the most convenient and easiest to implement. The deadline is extremely tight, and we must choose those that can be completed with ease.

Based on the analysis of the provided work, here are the most directly comparable and easiest-to-implement recent related works:

1. **SciPIP (Scientific Paper Idea Generation)**: This framework already has a multi-granularity retrieval and generation mechanism similar to Magellan. It likely has existing code that can be quickly adapted with minimal prompt adjustments.

2. **AI Scientist (from Si et al.'s paper)**: As a direct scientific idea generation framework, it probably has existing implementations that can be integrated with similar experimental settings.

3. **RAG-Novelty (Retrieval-Augmented Generation for Novelty)**: While primarily focused on novelty assessment, its retrieval-augmented approach can likely be adapted for the generation task with some modifications to the evaluation component.

These three options are recommended as they are closest to Magellan's task setup and likely require only calling existing code or making minor prompt adjustments, making them most suitable for your tight deadline.

# AGENT:

Okay, as a reviewer, I will analyze which "recent related works" are easiest and quickest to implement for comparison with your existing experimental setup, based on the urgency premise you provided.

Core Selection Criteria
Task Consistency: Comparison methods must also focus on Scientific Idea Generation. Exclude methods that only do evaluation, detection, or work in non-scientific domains.

No Training/Fine-tuning: Prioritize inference-time methods or methods that provide ready-to-use APIs. Avoid methods that require retraining models or training new retrievers.

Low Implementation Complexity: Methods should ideally be implementable by modifying prompts or using existing codebases/APIs, without needing to reconstruct the entire experimental pipeline.

Difference from Magellan: Choose methods with fundamentally different principles from Magellan to highlight its uniqueness.

Recommended Comparison Methods (Sorted by Priority)
🥇 Priority 1: Easiest to Implement - SciPIP and AI Scientist
Papers: Paper 3: SciPIP and Paper 4: CAN LLMS GENERATE NOVEL RESEARCH IDEAS? ...

Reasons:

Complete task alignment: Both receive a research topic/background and output a complete research idea proposal.

Simple implementation: They are essentially complex, well-designed prompt engineering frameworks. Implementing them doesn't require training any models, just writing corresponding prompts according to the structures described in the papers (or finding them in their open-source code), then running them with the same Qwen3-1.7B model.

High comparison value: SciPIP represents a strong baseline of "retrieval+generation", while AI Scientist represents a strong baseline of "large-scale generation+ranking". Outperforming them would strongly prove the effectiveness of MCTS-guided exploration.

How to quickly implement:

Reproduce their prompt structures based on the paper descriptions (e.g., SciPIP's dual-path prompt, AI Scientist's RAG+generation+ranking prompt).

Run these prompts with Qwen3-1.7B on your 50 test topics and collect the outputs.

Score these outputs using your existing LLM-as-a-Judge process (DeepSeek-V3.1-Think).

Present the scores alongside Magellan's results in a table.

🥈 Priority 2: Second Easiest to Implement - Multi-Novelty (from Paper 11)
Paper: Paper 11: ENHANCING DIVERSITY AND NOVELTY ... VIA MULTI-VIEW EMBEDDINGS

Reasons:

Strong method compatibility: It's a preprocessing method for input prompts that can be paired with any generation model (including your Qwen).

No training required: Just use another LLM (like GPT-4o) to generate multi-view text descriptions for each of your test topics, then concatenate them into an enhanced prompt to feed to Qwen.

Clear comparison point: It directly targets "diversity" and "novelty", aligning with Magellan's core objectives. The comparison can show the pros and cons between "guided exploration" (Magellan) and "input enhancement" (Multi-Novelty).

How to quickly implement:

Choose a view generator (like GPT-4o-mini API).

Generate 3-5 different text perspectives for each test topic (e.g., "viewing this problem from a computational chemistry perspective", "viewing this problem from a biomechanics perspective").

Concatenate these perspective texts with the original topic to form a new prompt, input to Qwen3-1.7B to generate ideas.

Also score and compare using DeepSeek-Judge.

Why Other Methods Are Not Recommended for Urgent Implementation
Method Paper Reason for Not Recommending
GraphEval Paper 8 Requires building a viewpoint-graph and training GNN models, with extremely high implementation costs, completely impractical.
RAG-Novelty Paper 5 This is an evaluator, not a generator. Task mismatch.
LLM-KD Paper 2 Requires training a new retriever, involving contrastive learning that needs negative samples, a lengthy process.
SciAgents Paper 13 Requires setting up a multi-agent system, defining multiple agent roles and coordinating their interactions, with too high system complexity.
Idea Novelty Checker Paper 7 Same as RAG-Novelty, it's an evaluator rather than a generator.
Summary and Action Recommendations
Your fastest path is:

First, implement SciPIP and AI Scientist as strong baselines. This is the most valuable and easiest comparison to implement.

If you still have time, secondarily implement Multi-Novelty. This can further enrich your comparison dimensions and show a comprehensive evaluation of different methods.

In your paper, you can state it as follows:
“To further validate the effectiveness of Magellan, we compare it against two recent strong baselines specifically designed for scientific idea generation: SciPIP [citation] and AI Scientist [citation]. For a fair comparison, all methods, including the baselines, are implemented using the same backbone model (Qwen3-1.7B) and evaluated under the same LLM-as-a-Judge protocol.”

'Revised experimental results table can be adjusted to:.Please translate.'
Method  Plausibility    Clarity Innovation  Overall Win Rate
Zero-shot   7.98 ± 0.62 8.48 ± 0.68 7.14 ± 0.78 7.87    0.0%
CoT 8.66 ± 0.48 9.48 ± 0.54 7.74 ± 0.49 8.63    8.0%
ReAct   4.58 ± 2.33 4.82 ± 1.65 4.28 ± 2.37 4.56    0.0%
ToT 5.48 ± 1.64 4.30 ± 1.53 5.02 ± 1.65 4.93    0.0%
AI Scientist    ... ... ... ... ...
SciPIP  ... ... ... ... ...
Magellan (Ours) 8.98 ± 0.32 9.30 ± 0.54 8.54 ± 0.71 8.94    92.0%
This approach can not only satisfy the reviewers' requirements, but also minimize your workload, while significantly enhancing the persuasiveness of your paper.