AI Scientists Fail Without Strong Implementation Capability (2506.01372v2)

Abstract: The emergence of AI Scientist represents a paradigm shift in scientific discovery, with LLMs taking the lead as the primary executor in the entire scientific workflow from idea generation to experiment implementation. Recent AI Scientist studies demonstrate sufficient capabilities for independent scientific discovery, with the generated research reports gaining acceptance at the ICLR 2025 workshop and ACL 2025, arguing that a human-level AI Scientist, capable of uncovering phenomena previously unknown to humans, may be imminent. Despite this substantial progress, AI Scientist has yet to produce a groundbreaking achievement in the domain of computer science on par with automated scientific tools. Based on extensive quantitative evidence from existing benchmarks in complex engineering tasks and a systematic evaluation assess 28 research papers generated by five advanced AI Scientist systems, we argue that \textbf{the fundamental bottleneck for AI Scientists lies in their capability to execute the requisite verification procedures.} Current AI Scientist systems lack the execution capabilities needed to execute rigorous experiments and produce high-quality scientific papers. To better illustrate the root cause of this \textbf{implementation gap}, we provide an in-depth discussion on the fundamental limitations of AI Scientist. This position paper aims to call for the participants in the community to bridge the implementation gap.

• The paper's main contribution is highlighting that AI Scientist systems are hindered by a critical implementation capability gap, demonstrated via quantitative benchmarks and systematic reviews.
• The study demonstrates that while LLMs excel in idea generation, their performance drops significantly in executing complex research tasks, as evidenced by low scores on benchmarks like PaperBench.
• The authors advocate for a community effort to enhance planning, execution, and ethical guidelines, aiming to bridge the gap between conceptual innovation and practical validation.

AI Scientists' Implementation Gap

The paper "AI Scientists Fail Without Strong Implementation Capability" (2506.01372) posits that current AI Scientist systems are fundamentally limited by their implementation capabilities, hindering their ability to independently execute and verify scientific ideas. This position paper supports this argument through quantitative evidence, systematic evaluation, and in-depth discussion of the limitations of AI Scientists. The paper calls for a community-wide effort to bridge this implementation gap to realize the full potential of AI in scientific discovery.

Defining the AI Scientist

The paper begins by defining an AI Scientist as an advanced end-to-end system capable of independently formulating scientific ideas and executing the requisite verification and falsification procedures. This definition distinguishes AI Scientists from traditional AI-for-Science tools, which operate under human supervision.

Figure 1: The roadmap of AI Scientist from 2024 to future, highlighting key milestones and fundamental challenges that must be overcome to bridge the implementation gap of AI Scientist.

The authors formalize this definition with the equation:

$$(\mathcal{K}_{new}, \mathcal{A}_{sci}) \leftarrow \max \{\mathcal{S}_{AI}(\mathcal{Q}_{init},\mathcal{K}_{domain}, \mathcal{R}_{human} |\theta_{AI}, \mathcal{B}_{res})\}$$

where $\mathcal{S}_{AI}$ represents the AI Scientist, $\mathcal{Q}_{init}$ the initial scientific question, $\mathcal{K}_{domain}$ existing domain knowledge, $\mathcal{R}_{human}$ ethical constraints, $\theta_{AI}$ AI parameters, and $\mathcal{B}_{res}$ resource constraints. The output consists of novel scientific knowledge $\mathcal{K}_{new}$ and verifiable artifacts $\mathcal{A}_{sci}$. This conceptual framework highlights that the core capability of an AI Scientist lies in generating innovative and feasible ideas at scale, setting them apart from automated scientific tools.

Evidence of Implementation Gap

The authors present three lines of evidence to support their argument that the implementation gap limits AI Scientists:

1. Analysis of Research Trends: A statistical analysis of AI Scientist papers on arXiv reveals that while publications are growing, studies focusing on idea generation without concrete implementation details consistently outnumber those incorporating such implementations. However, papers with substantive implementation details achieve significantly higher average citations, signaling the community's valuation of executable advancements.
2. Quantitative Analysis: The authors analyze existing benchmarks used to evaluate LLMs' abilities in performing complex engineering tasks. While LLMs perform well on simple code generation benchmarks, their performance drops dramatically in real-world research scenarios. For example, Claude 3.5 Sonnet scored only 1.8\% on PaperBench [starace2025paperbench], a benchmark for replicating ICML papers. This highlights the difficulty LLMs face in translating conceptual understanding into verifiably correct code. The paper highlights that SoTA LLMs achieve near-saturated performance on simple code generation benchmarks like HumanEval [chen2021evaluating, liu2023your, yang2025qwen3]. The performance of SoTA LLMs drops dramatically when it comes to real-world research scenarios.
3. Systematic Peer Review Assessment: A simulated peer review methodology is employed to assess the quality of scientific outputs from AI Scientist systems. Using DeepReviewer-14B [zhu2025deepreview] to evaluate 28 research papers generated by five AI Scientist systems, the results demonstrate that current systems lack the execution capabilities needed to produce high-quality papers. Among the twelve major defect categories, "Experimental Weakness" appears across all 28 evaluated AI-generated papers, with a 100$\%$ occurrence rate.

Rooted Limitations of Execution Capabilities

The paper identifies two primary facets of the implementation gap:

1. Bottlenecks in Planning and Execution: AI Scientists often struggle with long-range logical reasoning, multi-agent collaboration, and coordination with external tools.
2. Weaknesses in Evaluation Processes: AI Scientists demonstrate fundamental weaknesses in debugging, experimental validation, result interpretation, and iterative refinement.

The authors further discuss four major limitations that collectively explain why AI Scientists struggle with complex implementation processes:

1. Fundamental Cognitive and Execution Capabilities: Scientific implementation requires sophisticated long-range logical reasoning, and LLMs demonstrate decreased coherence and robustness as reasoning chains extend [wu2025more, wu2025shifting].
2. Strategic Planning and Reasoning: Scientific implementation demands global planning abilities, and current LLMs demonstrate inadequate adaptive planning and metacognitive abilities when handling highly open, creative scientific research.
3. Multi-Agent Collaboration: Ideal AI Scientists should seamlessly integrate into complex research ecosystems, but current LLM Agents still have considerable room for improvement in robustness and adaptability when interacting with dynamic environments [wei2025browsecomp].
4. Evaluation and Verification: There is currently a lack of a comprehensive benchmark that can evaluate the entire scientific workflow, making it difficult to fairly compare the end-to-end capabilities of different AI Scientist systems.

Ethical Considerations

The paper addresses the ethical considerations surrounding AI Scientists, emphasizing the need for a comprehensive system for generation management and quality evaluation. Without proper oversight, AI Scientists may be misused, enter unethical research domains, or weaken the quality of PhD training. The authors suggest implementing measures to prevent AI-generated content from disrupting human review systems, establishing boundaries and strengthening training programs, and formulating an ethics and responsibility convention.

Future Directions

The paper outlines feasible pathways to bridge the implementation capability gap. Addressing foundational basic abilities is paramount, with immediate strategies like well-defined workflows to mitigate current implementation weaknesses. A significant challenge for sophisticated strategic planning is the immense resource consumption of RL, and a promising direction involves leveraging LLMs to simulate aspects of the environment or task execution.

Conclusion

"AI Scientists Fail Without Strong Implementation Capability" (2506.01372) effectively argues that the implementation gap is a critical bottleneck in the development of AI Scientists. While LLMs have shown promise in idea generation, their inability to reliably execute and verify experiments limits their potential for independent scientific discovery. The paper's analysis of research trends, quantitative benchmarks, and peer review simulations provides strong evidence for this claim. The authors call for a community-wide effort to address this limitation. The paper presents alternative views that AI Scientists can facilitate human-machine collaboration as Co-scientists to assist humans and the deficiencies of LLMs Dynamic Planning capabilities and Reliable Verification capabilities can be avoided.