Autonomous Microscopy Experiments through Large Language Model Agents

Indrajeet Mandal; Jitendra Soni; Katrin Wondraczek; Lothar Wondraczek; Mohd Zaki; Morten M. Smedskjaer; Nitya Nand Gosvami; N. M. Anoop Krishnan

REVIEW 5 major objections 6 minor 1 cited by

Autonomous Microscopy Experiments through Large Language Model Agents

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read An LLM-agent framework can drive a real atomic force microscope through natural-language commands, but AFMBench shows today's best models succeed on only 65% of basic-to-advanced lab tasks.

desk verdict Genuine hardware-grounded AFM automation with a useful new benchmark; the headline QA-vs-agentic dissociation is plausible but quantitatively underpowered as reported. read the letter →

arxiv 2501.10385 v2 pith:WL2XCK2W submitted 2024-12-18 cs.CY cond-mat.mtrl-scics.AIphysics.ins-det

Indrajeet Mandal , Jitendra Soni , Mohd Zaki , Morten M. Smedskjaer , Katrin Wondraczek , Lothar Wondraczek , Nitya Nand Gosvami , N. M. Anoop Krishnan This is my paper · ORCID

classification cs.CYcond-mat.mtrl-scics.AIphysics.ins-det

keywords self-drivinglaboratoryatomicforcemicroscopyAIagentslargelanguagemodelsbenchmarkingmulti-agentsystemssafetyalignmentexperimentalautomation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces AILA, an LLM-agent framework that operates a real atomic force microscope, together with AFMBench, a 100-task benchmark spanning experimental design, multi-tool coordination, open-ended execution, and data analysis. The authors' central claim is that current frontier LLMs, despite strong materials-science question answering, are not yet reliable autonomous lab operators: the best model tested, GPT-4o, succeeds on 65% of AFMBench tasks, while Claude-3.5-sonnet underperforms despite leading QA benchmarks. They also report instruction deviation, which they call 'sleepwalking,' prompt fragility, and a measurable advantage for multi-agent over single-agent architectures. The work matters because it turns the promise of self-driving laboratories into a concrete, hardware-grounded benchmark and identifies specific failure modes to fix before deployment.

What carries the argument

The load-bearing object is the agent-based control loop. A planner LLM routes a natural-language query to either an AFM Handler Agent, which retrieves hand-curated Python documentation chunks for the AFM instrument and executes them through a code executor, or a Data Handler Agent, which optimizes PID gains using a genetic algorithm with SSIM as fitness and analyzes image data. Routing is driven by two keywords: 'NEED HELP' transfers the task to another agent, while 'FINAL ANSWER' terminates the session. AFMBench supplies the 100 tasks and the manual, consensus-scored protocol that makes the loop evaluable.

What would settle it

Have a panel of AFM experts, blind to model identity and to the authors' scores, independently score the published output logs and images for all 100 tasks; if their fully-correct rate for GPT-4o differs substantially from 65% or the model ordering changes, the benchmark's grading, not just the agents, produced the result.

Watch

Extended reading notes

Core claim

On the authors' own terms, the discovery is that domain-specific QA proficiency does not transfer to agentic capability in the laboratory. Across 100 curated AFM tasks, GPT-4o achieved 65% success, Claude-3.5-sonnet 45.7%, Llama-3.3-70B 37.3%, and GPT-3.5-turbo 32.8%, with Claude's strong materials-QA record failing to predict its weak agent and tool selection, traced mostly to agent-selection errors. AILA also demonstrates that a multi-agent orchestration outperforms a single-agent tool loop for a capable model, that LLMs sometimes exceed their instructions even when those instructions are explicit, and that prompt phrasing shifts GPT-4o from partial to complete task execution. Five real experiments—PID calibration, graphene step-edge imaging, load-dependent friction measurement, graphene layer counting, and indenter identification—are executed end-to-end on hardware.

Load-bearing premise

The benchmark conclusions rest on the assumption that AFMBench's 100 self-authored tasks and the authors' consensus scoring accurately represent what expert AFM operators need and how they would judge success; if the task mix or the scoring is skewed, every model ranking in the paper shifts.

Editorial extensions

If this is right

AILA can complete multi-step AFM workflows, including scanning, friction and roughness analysis, and plotting, end-to-end from a natural-language prompt without human parameter adjustment.
Multi-agent structuring helps capable models: on a 10-question subset, GPT-4o's success rose from 58% in a single-agent tool loop to 70% in the multi-agent framework.
A documentation-based safety guard can block critical operations such as factory calibration and laser alignment, but it does not fully prevent unauthorized extra steps, so instruction adherence remains an unresolved safety risk.
The observed QA-agentic gap implies that materials question-answering benchmarks should not be used as evidence that an LLM can operate laboratory instruments; agentic capability must be evaluated through task-based, hardware-grounded benchmarks.
Prompt structure is load-bearing: more detailed and descriptively complete prompts substantially increase GPT-4o's reliability on open-ended experiments, while concise or slightly altered prompts cause partial or failed execution.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct test of the QA-agentic dissociation would be to score the same models on retrieval-augmented questions drawn from the same AFM documentation and compare those scores with AFMBench success; a strong correlation would suggest the gap is task difficulty, while a weak one would support the paper's dissociation claim.
The 'sleepwalking' finding implies that real deployments should include action whitelists and human sign-off for any movement or parameter change outside the requested scope, since current models cannot be trusted to stay within instructions on their own.
Because the benchmark relies on hand-curated documentation chunks, the reported rankings likely reflect retrieval and code-execution quality as much as raw model reasoning; re-running the benchmark with independently sourced documentation or on a different AFM instrument would reveal how instrument-specific these results are.
The same benchmark template could be ported to other API-driven instruments, such as Raman spectrometers, scanning electron microscopes, or X-ray diffractometers, yielding a family of hardware-grounded agent benchmarks for self-driving laboratories.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a real systems paper, not a toy. AILA actually drives a Nanosurf AFM through LLM agents, and the authors publish the 100-task AFMBench set, the full logs, and the prompts. That moves it beyond the usual claim-and-demo paper. The five real-world experiments (PID optimization, graphene step edges, friction vs setpoint, graphene flake counting, indenter identification) are concrete evidence that the loop works. The finding that materials QA performance does not predict agentic skill, especially Claude-3.5's poor showing, is a useful empirical observation for anyone building lab assistants. The 'sleepwalking' behavior, where GPT-4o approached and scanned after only being asked to change a cantilever, is a genuinely important safety data point even though the sample is tiny.

The soft spots are mostly about the quantitative comparisons, not the existence proof. Section 2.4's error rates don't add up: GPT-4o's listed categories sum to roughly 38%, not the reported 29%. That needs a fix. The headline multi-agent advantage rests on 10 questions x 3 trials, and the prompt-fragility table S3 has one run per prompt variant. The MCP comparison is also 10 questions. With n=3 and no variance decomposition, differences like 70% vs 58% are inside binomial noise. Scoring is manual, consensus-based, and unblinded; the boundary between 'correct' and 'instruction overreach' is a genuine judgment call, as the cantilever log shows. I'd want inter-rater agreement or independent double-scoring on a subset. The benchmark is self-authored by the same group, which is acceptable for a first benchmark, but the absolute success rates should be read as upper bounds until outsiders score the logs. There is also no human baseline, so 'state of the art models struggle' needs a benchmark against experts on the same tasks.

None of this kills the central claim. The QA-vs-agentic dissociation is plausible and consistent with the logs, and the qualitative model ordering (GPT-4o strongest, GPT-3.5 weak, Claude underperforming) is likely real. But the numbers in the abstract are presented at a precision the data cannot support.

Who is this for? People building LLM-driven instruments and anyone designing agent benchmarks for laboratories. The citation pattern is appropriate, covering prior SPM automation and LLM materials work. It deserves a serious referee, and a major revision that fixes the arithmetic, reports variance and inter-rater statistics, and labels the comparative quantitative claims as pilot-scale. I'd engage with it.

Referee Report

5 major / 6 minor

Summary. The paper introduces AILA, a multi-agent LLM framework that operates an atomic force microscope (AFM) through natural-language queries, and AFMBench, a 100-task benchmark covering documentation, analysis, calculation, and multi-step experimental workflows. The authors evaluate four LLMs (GPT-4o, GPT-3.5-turbo, Claude-3.5-sonnet, Llama-3.3-70B) on AFMBench, reporting that GPT-4o performs best overall, that Claude-3.5-sonnet underperforms despite strong materials QA results, that multi-agent architectures outperform single-agent ones, that prompts are fragile, and that LLMs sometimes deviate from instructions ("sleepwalking"). The paper also presents five real-world AFM demonstrations: PID gain optimization, high-resolution step-edge detection, load-dependent roughness measurement, graphene layer counting, and indenter identification.

Significance. If the quantitative claims hold, AFMBench is a valuable hardware-grounded benchmark for LLM-driven laboratory automation, and the dissociation between domain QA proficiency and agentic capability is an important empirical finding. The paper's strengths include real physical demonstrations on an AFM, a modular and extensible framework, and the public release of the benchmark and execution logs, which enable reproducibility and independent inspection. However, the central quantitative claims are currently supported by a self-authored benchmark with manual, consensus-based scoring, small sample sizes for key comparisons, and internally inconsistent error-rate accounting. These issues must be resolved before the claimed model rankings and the QA-agentic dissociation can be accepted.

major comments (5)

[§2.4 and §2.3] The error-rate accounting is internally inconsistent. §2.4 reports a total error rate of 29% for GPT-4o, with category rates code generation 21.7%, agent selection 1.3%, tool selection 0.3%, and instruction adherence 15%; these values sum to 38.3%. Moreover, §2.3 reports a success rate of 65% for GPT-4o, implying an error rate of 35%, which matches neither 29% nor 38.3%. The error taxonomy is load-bearing because it supports the sleepwalking safety claim and the model-comparison conclusions. Please reconcile these numbers and report the denominator (number of trials or number of errors) explicitly for each category.
[§2.3, S6, Table S3] The headline claim that multi-agent frameworks outperform single-agent architectures rests on a comparison of 10 questions with 3 trials each. For GPT-4o, the difference is 70% versus 58% (12 percentage points, n=30). With binary outcomes this difference is not statistically significant (a Fisher exact test would give a p-value around 0.58), and the paper reports no confidence intervals or effect sizes. The same sample-size limitation applies to the MCP comparison in S3.4 (37% vs 43% on 10 questions) and to the prompt-fragility analysis in Table S3, where each prompt variant is evaluated on a single run. Please either provide statistical support, present these comparisons as descriptive observations with explicit caveats, or collect additional data for the load-bearing comparisons.
[§2.2, S3.1, S4] AFMBench is a self-authored benchmark evaluated with manual, consensus-based scoring. Section S4 states that multiple researchers checked outcomes and used Gwyddion for measurements, but no inter-rater reliability measure, no independent scoring protocol, and no objective pass/fail criteria are reported. The scoring categories require human judgment, as illustrated by the cantilever-change example in S3.2, where the boundary between 'correct' and 'instruction overreach' is a judgment call. Because the central claim — that state-of-the-art models struggle and that Claude's QA proficiency does not transfer — depends on these scores, the benchmark would benefit from either an objective scoring rubric based on checkable output artifacts or independent human scoring with reported agreement statistics.
[§2.1, S2.2] AILA's code execution is restricted to modifications of manually curated documentation chunks (S2.2). The completeness and correctness of these chunks is load-bearing: if the documentation omits operations or contains errors, the measured failures reflect the retrieval corpus rather than model capability. The paper should provide evidence that the chunks cover all general operations needed for AFMBench tasks, and should report the retrieval accuracy of the Document Retriever (e.g., recall@k for the 100 tasks). Without this, the dissociation between QA and agentic performance may be confounded by retrieval-system limitations.
[§2.6.1 and Methodology (Image Optimizer)] The claim that the genetic algorithm with population size 3 and 15 generations converges to acceptable PID gains is based on a single demonstration with an SSIM threshold of 0.8. The SSIM formula uses standard constants, but the convergence criterion (0.8) is arbitrary, and no repeated runs or sensitivity analyses are reported. Since the GA settings are free parameters (population size, generations), the paper should justify that the chosen values are not tuned to this particular sample. This issue is secondary to the benchmark-validity concern but should be addressed for the parameter-optimization demonstration.

minor comments (6)

[§2.6.3] The text refers to 'Figure 6c presents the graph of average friction versus setpoint voltage,' but the plot appears in Figure 6a (right panel); please correct the cross-reference.
[§2.4] The sentence describing GPT-3.5's errors lists four categories but omits instruction adherence and introduces 'query interpretation' as a different category; please align the error taxonomy with the four categories (agent selection, tool selection, instruction adherence, code generation) defined in S4.
[§2.4] The term 'sleepwalking' is introduced without a formal definition; please either define it operationally in terms of the instruction-adherence error category or replace it with the more standard term 'instruction overreach'.
[Methodology (SSIM)] The equation for SSIM is presented with undefined superscripts (rendered as '!' and '#'); please use α, β, and γ consistently and define the weighting parameters.
[Methodology (Average friction)] The formula for average friction writes '1 2×(f_ij−b_ij)' without braces; the intent is likely 1/2 × (f_ij−b_ij), and the formula should be typeset unambiguously.
[Methodology and S4] The software name 'Gwydion' should be spelled 'Gwyddion'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the empirical benchmark results are not true by construction, and the self-citations are not load-bearing.

full rationale

The paper is an empirical systems-and-benchmark report rather than a derivation, so the enumerated circularity patterns do not apply to its central claims. AFMBench's 100 self-authored tasks, manual consensus scoring, and success-rate aggregation are measurement and labeling choices; they affect external validity and statistical strength, but they do not make the reported success rates or model rankings equal to their inputs by construction. The central dissociation claim, that domain-specific QA proficiency does not imply agentic proficiency, compares AFMBench results with published QA benchmarks (MaScQA, refs. 18/19/39); even where those benchmarks involve overlapping authors, they are externally checkable datasets and studies, not theorems or fitted parameters imported to force the present conclusion. The paper's 'sleepwalking' terminology (Section 2.4) is a renamed description of observed instruction drift, not a derived result. The prompt-fragility and single-agent vs. multi-agent ablations are small-sample empirical observations with acknowledged limitations, not fitted inputs renamed as predictions. The PID optimization reports SSIM, which is also the genetic algorithm's fitness function, but this is an optimization self-consistency check rather than an independent prediction, and the paper does not present it as a first-principles derivation. The paper explicitly flags some limitations, such as the safety alternative of human-in-the-loop review being left for future work (Section 2.5); that is a scope statement, not a circular step. Overall, no load-bearing step reduces to its own input; concerns about benchmark authorship, scoring subjectivity, prompt conditioning, and statistical power belong to correctness and validity assessment, not circularity.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The paper's central claims depend on the validity of the benchmark, the manual scoring procedure, and the completeness of the curated documentation. The GA and SSIM choices are hand-set but standard. No entities are invented.

free parameters (6)

GA population size = 3
Chosen by hand for PID optimization; affects convergence and tip wear (Methodology, Image Optimization Tool).
GA number of generations = 15
Fixed stopping criterion; the paper claims convergence within 15 generations (Fig 5c) without sensitivity analysis.
Max debugging iterations = 20
Threshold set to balance thoroughness and efficiency; errors that persist after 20 retries are classified as code generation failures (Section 2.4).
Baseline polynomial order = 5
Fifth-order polynomial used for baseline correction in step-edge detection (Section 2.6.2); choice is not justified or varied.
SSIM constants k1, k2 = k1=0.01, k2=0.03
Standard SSIM parameters used in the fitness function; stated but not justified for AFM images (Methodology).
Indentation depth threshold = -5 nm
Threshold in the custom indentation volume code to mask the indentation region; chosen ad hoc (Supplementary S2.3).

assumptions (6)

domain assumption The hand-curated Nanosurf AFM documentation chunks contain correct, sufficient code for any general AFM operation
AILA restricts code generation to modifications of retrieved chunks (S2.2), so correctness of the whole pipeline depends on this.
domain assumption SSIM between forward and backward AFM scans is a valid proxy for imaging quality
Used as the GA fitness function without validation against alternative metrics (Methodology).
domain assumption AFMBench's 100 tasks and their basic/advanced labels represent the true distribution of AFM experiment complexity
The benchmark's conclusions about model capabilities are derived from these tasks (S2.2, S3.1).
domain assumption Manual evaluation by the research team can reliably distinguish correct, partial, and incorrect task outcomes
All accuracy scores come from human inspection of logs and AFM software (S4).
domain assumption The API-served models evaluated are representative of their model families and behave stably across the three trials
Cross-model rankings assume API responses are not artifacts of versioning or latency (Methodology).
ad hoc to paper A genetic algorithm with population 3 and 15 generations converges to acceptable PID gains
No convergence proof or grid comparison; the paper states these GA parameters can be manually adjusted (Methodology).

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Cite this review

Pith. "Pith review of Autonomous Microscopy Experiments through Large Language Model Agents." pith.science (2026). https://pith.science/paper/WL2XCK2W

@misc{pith2026250110385,
  author       = {Pith},
  title        = {Pith review of: Autonomous Microscopy Experiments through Large Language Model Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WL2XCK2W}},
  note         = {Machine review of arXiv:2501.10385}
}

read the original abstract

Large language models (LLMs) are revolutionizing self driving laboratories (SDLs) for materials research, promising unprecedented acceleration of scientific discovery. However, current SDL implementations rely on rigid protocols that fail to capture the adaptability and intuition of expert scientists in dynamic experimental settings. We introduce Artificially Intelligent Lab Assistant (AILA), a framework automating atomic force microscopy through LLM driven agents. Further, we develop AFMBench a comprehensive evaluation suite challenging AI agents across the complete scientific workflow from experimental design to results analysis. We find that state of the art models struggle with basic tasks and coordination scenarios. Notably, Claude 3.5 sonnet performs unexpectedly poorly despite excelling in materials domain question answering (QA) benchmarks, revealing that domain specific QA proficiency does not necessarily translate to effective agentic capabilities. Additionally, we observe that LLMs can deviate from instructions, raising safety alignment concerns for SDL applications. Our ablations reveal that multi agent frameworks outperform single-agent architectures. We also observe significant prompt fragility, where slight modifications in prompt structure cause substantial performance variations in capable models like GPT 4o. Finally, we evaluate AILA's effectiveness in increasingly advanced experiments AFM calibration, feature detection, mechanical property measurement, graphene layer counting, and indenter detection. Our findings underscore the necessity for rigorous benchmarking protocols and prompt engineering strategies before deploying AI laboratory assistants in scientific research environments.

Figures

Figures reproduced from arXiv: 2501.10385 by the authors.

**Figure 2.** Task distribution and module utilization in AFMBench. a Pie charts showing the distribution of tool requirements (left, single vs. multiple) and agent requirements (right, single vs. multiple) across benchmark tasks. b Operation complexity categorization showing the proportion of basic versus advanced tasks. c Horizontal bar chart quantifying module engagement frequency across all tasks, demonstrating utilization pa… view at source ↗

**Figure 3.** Comparative performance analysis of language models on AFMBench. a Venn diagrams showing accuracy metrics for GPT-4o, GPT-3.5-turbo-0125, Llama-3.3-70Bversatile and Claude-3.5-sonnet-20241022 across documentation, analysis, and calculation tasks. Numbers indicate percentage accuracy. b Evaluation metrics are grouped into three categories—Operational, Token Usage, and Performance Metrics—to assess the performance of… view at source ↗

**Figure 4.** Error mode distribution in model performance. Error patterns among different models: GPT-4o (top left), GPT-3.5-turbo-0125 (top right), Llama-3.3-70B-versatile (bottom left) and Claude-3.5-sonnet-20241022 (bottom right). Segments represent a proportional distribution of error types: Instruction adherence (blue), agent selection (pink), tool selection (green) and code generation (gray). 2.5 Safety Alignment in SDLs T… view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: b validates the optimized parameters (P:249, I:8957, D:26) across a larger scan area, maintaining high-quality imaging across multiple grid features [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]

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Reference graph

Works this paper leans on

4 extracted references · 1 canonical work pages · cited by 1 Pith paper

[5]

Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023). 6. Dai, T. et al. Autonomous mobile robots for exploratory synthetic chemistry. Nature 635, 890–897 (2024). 7. Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 5...

2023
[16]

Volk, A. A. & Abolhasani, M. Performance metrics to unleash the power of self-driving labs in chemistry and materials science. Nature Communications 15, 1378 (2024). 17. Mirza, A. et al. Are large language models superhuman chemists? Preprint at https://doi.org/10.48550/arXiv.2404.01475 (2024). 18. Zaki, M., Jayadeva, Mausam & Krishnan, N. M. A. MaScQA: i...

arXiv doi:10.48550/arxiv.2404.01475 2024
[27]

Bian, K. et al. Scanning probe microscopy. Nature Reviews Methods Primers 1, 36 (2021). 28. Liu, Y. et al. AEcroscopy: A Software–Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation. Small Methods 2301740 (2024) doi:10.1002/smtd.202301740. 29. Kandel, S. et al. Demonstration of an AI-driven workflow for autonomous high...

work page doi:10.1002/smtd.202301740 2021
[37]

This tool offers reference code specifically designed for

Liu, Y. et al. Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy. npj Computational Materials 9, 34 (2023). 38. Liu, Y., Checa, M. & Vasudevan, R. K. Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design*. Machine Learning: Science a...

arXiv doi:10.48550/arxiv.2412.09560 2023

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [5]

Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023). 6. Dai, T. et al. Autonomous mobile robots for exploratory synthetic chemistry. Nature 635, 890–897 (2024). 7. Boiko, D. A., MacKnight, R., Kline, B. & Gomes, G. Autonomous chemical research with large language models. Nature 624, 5...

2023

[2] [16]

Volk, A. A. & Abolhasani, M. Performance metrics to unleash the power of self-driving labs in chemistry and materials science. Nature Communications 15, 1378 (2024). 17. Mirza, A. et al. Are large language models superhuman chemists? Preprint at https://doi.org/10.48550/arXiv.2404.01475 (2024). 18. Zaki, M., Jayadeva, Mausam & Krishnan, N. M. A. MaScQA: i...

arXiv doi:10.48550/arxiv.2404.01475 2024

[3] [27]

Bian, K. et al. Scanning probe microscopy. Nature Reviews Methods Primers 1, 36 (2021). 28. Liu, Y. et al. AEcroscopy: A Software–Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation. Small Methods 2301740 (2024) doi:10.1002/smtd.202301740. 29. Kandel, S. et al. Demonstration of an AI-driven workflow for autonomous high...

work page doi:10.1002/smtd.202301740 2021

[4] [37]

This tool offers reference code specifically designed for

Liu, Y. et al. Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy. npj Computational Materials 9, 34 (2023). 38. Liu, Y., Checa, M. & Vasudevan, R. K. Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design*. Machine Learning: Science a...

arXiv doi:10.48550/arxiv.2412.09560 2023