# USER:

I am writing a paper with the topic: researching "how AI conducts research," focusing on the shortcomings of AI agents in scientific discovery. I hope to discover some underlying algorithmic rules, preferably prove some mathematical assumptions underlying LLMs, and propose improvement methods.

Now I want to search for relevant literature online. What are some possible keywords? First, think about what kind of keywords would correctly retrieve relevant literature when searching on Google Scholar. Then, break down our problem and provide no more than 5 different search terms.

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# AGENT:

To effectively search for literature on "how AI conducts research" and its limitations in scientific discovery on Google Scholar, we need to use precise and targeted keywords. Directly searching "how AI conducts research" would yield too broad results, potentially including many papers about AI applications rather than its underlying research capabilities.

The core idea is to break down your research topic into several key concepts: AI autonomy, the scientific discovery process, limitations of LLMs (Large Language Models), algorithmic defects, and improvement methods.

Based on these core concepts, here are 5 different search term combinations you can try on Google Scholar:

"AI for scientific discovery" + "limitations": This combination directly targets AI applications in scientific discovery and their shortcomings.

"LLM-based autonomous agent" + "scientific research" + "failures": This combination more specifically focuses on autonomous agents based on large language models and the failure cases they encounter in scientific research.

"AI scientist" + "computational limitations": This combination uses the concept of "AI scientist" and focuses on its computational or algorithmic limitations.

"LLM knowledge gaps" + "scientific reasoning": This combination delves into the knowledge and logical defects of large language models in scientific reasoning.

"foundational model" + "scientific hypothesis generation" + "biases": This combination takes the perspective of "foundational model" (Foundation Model) and focuses on the potential biases and shortcomings in the process of scientific hypothesis generation.


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## Write script, run script:

The keyword combination "AI for scientific discovery" + "limitations" yields the following search results.

'Towards scientific discovery with generative ai: Progress, opportunities, and challenges',
 'AI for scientific discovery',
 'Theai for scientific discoverynetwork+',
 'AI for Scientific Discovery: Automating Hypothesis Generation',
 'Next Steps forAI for Scientific Discovery',
 'Agenticai for scientific discovery: A survey of progress, challenges, and future directions',
 'The ai scientist: Towards fully automated open-ended scientific discovery',
 'The Quest for Autonomous Scientific Discoveries with Artificial Scientists',
 'Transforming science with large language models: A survey on ai-assisted scientific discovery, experimentation, content generation, and evaluation',
 'The future of fundamental science led by generative closed-loop artificial intelligence',
 'How Far Are AI Scientists from Changing the World?',
 'Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science',
 'AI4Research: A Survey of Artificial Intelligence for Scientific Research',
 'Active Inference AI Systems for Scientific Discovery',
 'Autonomous Scientific Discovery Through Hierarchical AI Scientist Systems',
 'Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence',
 'Accelerating AI for science: Open data science for science',
 'AI-based Scientific Research Assistants',
 'Artificial intelligence for materials discovery',
 'Realizing the Promise and Minimizing the Perils of AI for Science and the Scientific Community',
 "Researchers' perceptions of automating scientific research",
 'Strategic Frontiers of Generative AI in Science: Implications for Innovation and Management',
 'AI for science: an emerging agenda',
 'AI Agents for Deep Scientific Research',
 'Curie: Toward rigorous and automated scientific experimentation with ai agents',
 'Accelerating Ai-Driven Scientific Discovery With End-To-End Learning and Random Projection',
 'Researcherbench: Evaluating deep ai research systems on the frontiers of scientific inquiry',
 'Enhancing scientific discovery and decision-making: A knowledge graph-based research support system',
 'We Need Improved Data Curation and Attribution inAI for Scientific Discovery',
 'Leveraging foundation models for scientific research productivity',
 'Artificial intelligence and illusions of understanding in scientific research',
 'How scientific knowledge production is changing organizationally, technologically, and globally',
 'PiFlow: Principle-aware Scientific Discovery with Multi-Agent Collaboration',
 "Scientists' Perspectives on the Potential for Generative AI in their Fields",
 'Llm-srbench: A new benchmark for scientific equation discovery with large language models',
 'Iris: Interactive research ideation system for accelerating scientific discovery',
 'Promises or Perils for Creativity?',
 'Scienceboard: Evaluating multimodal autonomous agents in realistic scientific workflows',
 'AI on drugs: Can artificial intelligence accelerate drug development? Evidence from a large-scale examination of bio-pharma firms',
 'AI4DifferentialEquations In Science'

The keyword combination "LLM-based autonomous agent" + "scientific research" + "failures" 给出如下搜索结果:
A survey on large language model based autonomous agents
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
Llm-Based Autonomous Agentfor Prognostics and Health Management
Collaborative evolving strategy for automatic data-centric development

The keyword combination "LLM-based autonomous agent" + "scientific research" + "failures" yields the following search results.
Superintelligent agents pose catastrophic risks: Can scientist ai offer a safer path?
Theory is all you need: AI, human cognition, and causal reasoning
Towards scientific intelligence: A survey of llm-based scientific agents
A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications
Artificial Intelligence and Actor-Specific Decisions
Sparks: Multi-agent artificial intelligence model discovers protein design principles
Generative linguistics contribution to artificial intelligence: Where this contribution lies?
Surveyforge: On the outline heuristics, memory-driven generation, and multi-dimensional evaluation for automated survey writing
Human-machine shared contexts
Industry Innovation in the Era of Artificial Intelligence: The AI Compass
How will the State think with the assistance of ChatGPT? The case of customs as an example of generative artificial intelligence in public administrations
Control and Responsible Innovation in the Development of AI and Robotics Draft Final Report
Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment
Imitation and Large Language Models
Defenses against Evasion Attacks in the Eyes of Automotive Industry: Review from a Practical Perspective
Automating knowledge acquisition for machine translation

The keyword combination "LLM knowledge gaps" + "scientific reasoning" yields the following search results.
Position: LLMs Need a Bayesian Meta-Reasoning Framework for More Robust and Generalizable Reasoning


The keyword combination "foundational model" + "scientific hypothesis generation" + "biases" yields the following search results.
Generative Innovation AI Part III (Generative Innovation AI–The Trilogy) Sense-Building-Mechanics-Engineering (SBME) Models & Designs (M&D)

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# USER:

Please recall our goals:
```
Research "how AI does research",
Focus on the shortcomings of AI agents in scientific discovery,
Aim to discover some underlying algorithmic rules, preferably prove some mathematical assumptions underlying LLMs, and propose improvement methods.
```
Now, we need to identify a specific shortcoming of an agent and address it using fundamental mathematical principles.
Please analyze one by one, and based on this information, identify papers that would be ** most useful ** for our research. I will provide you with further information. List all the papers you have selected.


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# AGENT:

Based on your goal of finding a specific deficiency in an AI agent for scientific discovery, and then addressing it with underlying mathematical principles, I've analyzed the search results. Here are the most promising papers that seem to directly address the core of your research question.

Selected Paper Titles

1. "The ai scientist: Towards fully automated open-ended scientific discovery"
This paper likely provides a framework for the complete concept of an "AI scientist," which is the main subject of your research. It might discuss the capabilities of an ideal, fully automated AI scientist, while also necessarily mentioning the challenges and shortcomings in achieving this goal. This would be very helpful for you to identify specific "shortcomings."

2. "Researcherbench: Evaluating deep ai research systems on the frontiers of scientific inquiry"
The title contains "bench" (benchmark), indicating it likely proposes specific, quantifiable methods for evaluating AI research systems. By analyzing this benchmark, you might find quantitative evidence of poor AI performance in specific scientific tasks, thereby identifying a specific shortcoming for in-depth research.

3. "Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence"
This paper directly points to a core issue: explainability. The "black box" nature of LLMs is one of their biggest shortcomings in scientific research. Science requires reproducible and explainable findings, which conflicts with the nature of generative AI. This paper likely offers directions for addressing this issue, possibly even touching on underlying algorithms or mathematical models.

4. "Llm-srbench: A new benchmark for scientific equation discovery with large language models"
This paper focuses on a very specific task: scientific equation discovery. A benchmark specifically for LLMs likely reveals their limitations in this particular task, such as shortcomings in handling symbolic reasoning, constraints of physical laws, or mathematical rigor. This provides a concrete and actionable research direction.

5. "Position: LLMs Need a Bayesian Meta-Reasoning Framework for More Robust and Generalizable Reasoning"
This is the only paper that directly proposes a specific mathematical framework (Bayesian meta-reasoning) to address LLM reasoning shortcomings. It directly addresses the core of your research: solving problems using underlying mathematical principles. The paper might explain the mathematical assumption flaws in LLMs regarding "reasoning" or "generalization" and propose improvements. This is the most direct and valuable reference as it not only identifies the problem but also suggests potential solutions.

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# USER:

## Get Summary

Here are the abstracts of these papers:

1. "The ai scientist: Towards fully automated open-ended scientific discovery"
One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aides to human scientists, e.g. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process. This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community. We demonstrate its versatility by applying it to three distinct subfields of machine learning: diffusion modeling, transformer-based language modeling, and learning dynamics. Each idea is implemented and developed into a full paper at a cost of less than $15 per paper. To evaluate the generated papers, we design and validate an automated reviewer, which we show achieves near-human performance in evaluating paper scores. The AI Scientist can produce papers that exceed the acceptance threshold at a top machine learning conference as judged by our automated reviewer. This approach signifies the beginning of a new era in scientific discovery in machine learning: bringing the transformative benefits of AI agents to the entire research process of AI itself, and taking us closer to a world where endless affordable creativity and innovation can be unleashed on the world's most challenging problems. Our code is open-sourced at this https URL

2. Researcherbench: Evaluating deep ai research systems on the frontiers of scientific inquiry
The emergence of deep research systems presents significant capabilities in problem-solving, extending from basic queries to sophisticated research tasks. However, existing benchmarks primarily evaluate these systems as agents for web retrieval and report generation, overlooking their potential to discover novel insights on the frontiers of scientific research. To address this gap, we introduce ResearcherBench, the first benchmark focused on evaluating the capabilities of these advanced, agentic systems - which we refer to as Deep AI Research Systems (DARS) - on frontier AI scientific questions. We compiled a dataset of 65 research questions expertly selected from real-world scientific scenarios such as laboratory discussions and interviews, spanning 35 different AI subjects and categorized into three types: technical details, literature review, and open consulting. Our dual evaluation framework combines rubric assessment, which uses expert-designed criteria to evaluate insight quality, with factual assessment, which measures citation accuracy (faithfulness) and coverage (groundedness). We evaluated several leading commercial DARS and baseline systems. Results show that OpenAI Deep Research and Gemini Deep Research significantly outperform other systems, with particular strength in open-ended consulting questions. Such capabilities represent a meaningful step toward AI self-improvement, aligning with the vision of ASI for AI. We open-source ResearcherBench to provide a standardized platform for promoting the development of next-generation AI research assistants, hoping to foster a new perspective in AI research evaluation for a novel pattern of scientific collaboration: this https URL.


3.Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence
The scientific method is the cornerstone of human progress across all branches of the natural and applied sciences, from understanding the human body to explaining how the universe works. The scientific method is based on identifying systematic rules or principles that describe the phenomenon of interest in a reproducible way that can be validated through experimental evidence. In the era of generative artificial intelligence, there are discussions on how AI systems may discover new knowledge. We argue that human complex reasoning for scientific discovery remains of vital importance, at least before the advent of artificial general intelligence. Yet, AI can be leveraged for scientific discovery via explainable AI. More specifically, knowing the 'principles' the AI systems used to make decisions can be a point of contact with domain experts and scientists, that can lead to divergent or convergent views on a given scientific problem. Divergent views may spark further scientific investigations leading to interpretability-guided explanations (IGEs), and possibly to new scientific knowledge. We define this field as Explainable AI for Science, where domain experts -- potentially assisted by generative AI -- formulate scientific hypotheses and explanations based on the interpretability of a predictive AI system.

4.Llm-srbench: A new benchmark for scientific equation discovery with large language models
Scientific equation discovery is a fundamental task in the history of scientific progress, enabling the derivation of laws governing natural phenomena. Recently, Large Language Models (LLMs) have gained interest for this task due to their potential to leverage embedded scientific knowledge for hypothesis generation. However, evaluating the true discovery capabilities of these methods remains challenging, as existing benchmarks often rely on common equations that are susceptible to memorization by LLMs, leading to inflated performance metrics that do not reflect discovery. In this paper, we introduce LLM-SRBench, a comprehensive benchmark with 239 challenging problems across four scientific domains specifically designed to evaluate LLM-based scientific equation discovery methods while preventing trivial memorization. Our benchmark comprises two main categories: LSR-Transform, which transforms common physical models into less common mathematical representations to test reasoning beyond memorized forms, and LSR-Synth, which introduces synthetic, discovery-driven problems requiring data-driven reasoning. Through extensive evaluation of several state-of-the-art methods, using both open and closed LLMs, we find that the best-performing system so far achieves only 31.5% symbolic accuracy. These findings highlight the challenges of scientific equation discovery, positioning LLM-SRBench as a valuable resource for future research.

5.Position: LLMs Need a Bayesian Meta-Reasoning Framework for More Robust and Generalizable Reasoning
Large language models (LLMs) excel in many reasoning tasks but continue to face significant challenges, such as lack of robustness in reasoning, struggling with cross-task generalization, and inefficiencies in scaling up reasoning capabilities. Current training paradigms, including next-token prediction and reinforcement learning from human feedback, often fall short in adaptability to diverse reasoning tasks. Existing approaches, such as prompt optimization and iterative output refinement, offer performance improvement, but can be inefficient and lack effective generalization. To overcome these limitations, this position paper argues for a transformative shift in how LLMs approach reasoning. Drawing inspiration from cognitive science, particularly meta-reasoning theories such as Dual-Process Theory and Metacognitive Reasoning, we propose a Bayesian meta-reasoning framework for LLMs. Our approach integrates self-awareness, monitoring, evaluation, regulation, and meta-reflection, to enhance LLMs’ ability to refine reasoning strategies and generalize across tasks. We revisit existing LLM reasoning methods, identify key challenges, and suggest directions for future research.


Based on this information, find the papers ** most likely useful ** for our research, and I will provide you with further information.
Please recall our goals:
```
Researching "how AI does research",
Studying the shortcomings of AI agents in scientific discovery
Hoping to discover some underlying algorithmic rules, preferably proving some mathematical assumptions underlying LLMs, and proposing improvement methods.
```
Now we need to identify a specific shortcoming of an Agent and solve it using underlying mathematical principles.
Please list the most relevant paper titles

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# AGENT:

After analyzing these paper abstracts, the papers that can most directly serve your research goals are:

Most Relevant Paper Titles
"Position: LLMs Need a Bayesian Meta-Reasoning Framework for More Robust and Generalizable Reasoning"

Reasoning: This paper directly points out the shortcomings of LLMs in **reasoning**, and it's not a general discussion but specifically proposes a mathematical framework based on Bayesian meta-reasoning to solve this problem. This perfectly aligns with the three core elements of your research goals: "discovering underlying algorithmic rules," "proving mathematical assumptions underlying LLMs," and "proposing improvement methods." It not only identifies the problem but also provides a clear direction for a mathematical solution.

"Llm-srbench: A new benchmark for scientific equation discovery with large language models"

Reasoning: This paper, through a specially designed benchmark, specifically quantifies the shortcomings of LLMs in the specific task of **scientific equation discovery**. It clearly states that due to the memorization capabilities of LLMs, existing tests cannot truly evaluate their "discovery" abilities. This provides you with a very specific and actionable entry point: you can use symbolic reasoning or data-driven reasoning as a breakthrough point to deeply study the mathematical or algorithmic deficiencies of LLMs in this area.

"Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence"

Reasoning: This paper, from the perspective of scientific methodology, deeply explores the fundamental contradiction between LLMs as "black box" models and the explainability required by science. This contradiction is not merely a performance issue but involves trust and verifiability in scientific exploration with AI. This provides you with a very profound shortcoming, from which you can seek solutions based on underlying principles such as information theory, causal reasoning, or mathematical models of explainable AI.