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The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, David Ha

arXiv:2504.08066v1cs.AIcs.CLcs.LG

TL;DR

The AI Scientist-v1 (Lu et al., 2024) relied on human-authored code templates, limiting its autonomy and applicability. The AI Scientist-v2 combines template-free experimentation, managed agentic tree search, and vision-language feedback for autonomous research. One of three AI-generated submissions was accepted at an ICLR workshop, receiving an average reviewer score of 6.33.

  • Problem

    The AI Scientist-v1 (Lu et al., 2024) relied on human-authored code templates, limiting the applicability and autonomy of its automated scientific workflow.

    Source: §1 · p. 1

  • Method

    The AI Scientist-v2 combines template-free experimentation, agentic tree search supervised by an experiment manager, and vision-language feedback for autonomous research and manuscript generation.

    Source: §7 · p. 15

  • Results

    6.33 was the average reviewer score of the sole accepted manuscript among three AI-generated submissions to an ICLR workshop.

    Source: §1 · p. 2, §5 · p. 13

  • Takeaways & Limitations

    Overcoming current limitations and scaling autonomous research systems could foster collaboration and accelerate scientific discovery, according to the authors.

    Source: §7 · p. 15

    The system does not consistently meet workshop-level standards, let alone the standards required for top-tier conference publications.

    Source: §5 · p. 13

Abstract

from arXiv · show

AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing the first entirely AI generated peer-review-accepted workshop paper. This system iteratively formulates scientific hypotheses, designs and executes experiments, analyzes and visualizes data, and autonomously authors scientific manuscripts. Compared to its predecessor (v1, Lu et al., 2024 arXiv:2408.06292), The AI Scientist-v2 eliminates the reliance on human-authored code templates, generalizes effectively across diverse machine learning domains, and leverages a novel progressive agentic tree-search methodology managed by a dedicated experiment manager agent. Additionally, we enhance the AI reviewer component by integrating a Vision-Language Model (VLM) feedback loop for iterative refinement of content and aesthetics of the figures. We evaluated The AI Scientist-v2 by submitting three fully autonomous manuscripts to a peer-reviewed ICLR workshop. Notably, one manuscript achieved high enough scores to exceed the average human acceptance threshold, marking the first instance of a fully AI-generated paper successfully navigating a peer review. This accomplishment highlights the growing capability of AI in conducting all aspects of scientific research. We anticipate that further advancements in autonomous scientific discovery technologies will profoundly impact human knowledge generation, enabling unprecedented scalability in research productivity and significantly accelerating scientific breakthroughs, greatly benefiting society at large. We have open-sourced the code at https://github.com/SakanaAI/AI-Scientist-v2 to foster the future development of this transformative technology. We also discuss the role of AI in science, including AI safety.

1. Introduction

The AI Scientist-v2 combines template-free code generation, agentic tree search, parallel experiments, and VLM feedback to automate scientific discovery. Of three autonomously generated manuscripts submitted to an ICLR workshop, one passed peer review and would have been accepted after meta-review had it been human-generated.

1. Introduction

  • 6.33 was the average reviewer score for one of three AI-generated workshop submissions, placing it roughly in the top 45% of submissions.The manuscript passed peer review and would have been accepted after meta-review had it been human-generated, marking the first fully AI-generated manuscript to pass peer review.Source: §1 · p. 2
  • VLM-based feedback refines generated figures, captions, and textual interpretations to improve their quality, clarity, and alignment during reviewing and refinement.Source: §1 · p. 2
  • On synthetic arithmetic-expression datasets, the accepted paper finds that compositional regularization provides no significant improvement and sometimes harms performance.The method penalizes large deviations between successive time-step embeddings in sequence models, hypothesizing that this encourages compositional generalization.Source: §1 · p. 2
  • Reviewers valued the negative results but identified insufficient justification for the regularizer, judging the manuscript technically sound at workshop level rather than conference-ready.The authors additionally identify unclear specification of the regularized network component, potential dataset overlap, and inaccurate figure captions. Reviewers called for further development and broader experimentation.Source: §1 · p. 2
  • Internal evaluations analyze outputs and peer-review feedback, while the full codebase and ICLR 2025 workshop experiment data are open-sourced.The analyses examine strengths and weaknesses of AI-generated manuscripts relative to traditional human-authored scientific publications.Source: §1 · p. 2
  • The AI Scientist-v2 replaces human-coded templates with experiment-manager-guided agentic tree search and parallel experiments, enabling deeper, systematic hypothesis exploration across machine learning domains.The workflow spans idea generation through manuscript review, reusing the best-performing code checkpoints to test hypotheses. The AI Scientist-v1 (Lu et al., 2024) required topic-specific human-authored templates and used linear, shallow experimentation.Source: §1 · pp. 1–2, Figure 1 · p. 3

2. Background

The AI Scientist-v1 (Lu et al., 2024) automated scientific research but depended on human-written code templates and linear experimentation. Agent scaffolding and tree search offer mechanisms for reflection and broader exploration, motivating tree search adapted to multi-stage scientific experimentation.

2. Background

  • The AI Scientist-v1 (Lu et al., 2024) depended on human-crafted baseline code templates and strictly linear hypothesis testing, restricting autonomy and exploration.Given a template, it autonomously wrote code, executed experiments, visualized outcomes, and produced a complete scientific manuscript.Source: §2 · p. 3

Language Model Agent Scaffolding

  • Reflexion (Shinn et al., 2024) and LLM tree-search strategies improve robustness and systematic reasoning, respectively, but introduce computational trade-offs.Reflexion encourages self-improvement through iterative evaluation of previous outputs but can increase overhead and slow inference. Tree search explores reasoning paths more comprehensively but increases complexity and computational demands, creating scalability challenges.Source: §2 · p. 3

Tree Search with Large Language Models

  • AIDE inspires tree-search exploration adapted to multi-stage scientific experimentation, addressing The AI Scientist-v1’s observed short-sighted experimentation.AIDE demonstrates state-of-the-art performance on MLEBench, representing candidate solutions as nodes with scalar evaluation scores that guide debugging or refinement. Code generation as an action space supports automated workflows (Wang et al., 2024), while human research involves open-ended hypothesis generation, steppingstone collection, and iterative refinement.Source: §2 · pp. 3–4

3. The AI Scientist-v2

The AI Scientist-v2 increases autonomy through abstract idea generation, template-free experimentation, and staged agentic tree search. It also integrates visual feedback into experimentation and manuscript review, while simplifying manuscript generation.

3. The AI Scientist-v2

  • Manuscript writing replaces incremental iteration with single-pass generation followed by a separate reflection stage powered by reasoning models.The overview identifies this writing change alongside generalized idea generation, template removal, experiment management, tree search, and VLM integration.Source: §3 · p. 4

3.1. More General Idea Generation

  • Idea generation begins with abstract research directions, hypotheses, and experimental designs, integrating literature searches before committing to implementation.Unlike codebase-conditioned incremental proposals, this process uses tools such as Semantic Scholar to assess novelty and identify relevant prior work during formulation.Source: §3.1 · p. 4

3.2. Removing Template Dependency

  • Template-free experimentation removes v1's dependence on predefined baseline code and its restriction to sequential code adaptations.Coarse-grained experiment management and agentic tree search replace these constraints.Source: §3.2 · p. 4, §3 · p. 4

3.2.1. Experiment Progress Manager

  • An experiment progress manager coordinates preliminary investigation, hyperparameter tuning, research agenda execution, and ablation studies.These stages establish a minimal working prototype, optimize a robust baseline, implement the research agenda on that baseline, and assess component importance.Source: §3.2.1 · p. 5
  • The manager checkpoints each stage, uses an LLM evaluator to select its successor's starting experiment, and launches replications for means and standard deviations.Stage 1 requires a working prototype; Stage 2 requires converged training curves and successful execution across at least two datasets; Stages 3–4 end when their computational budgets are exhausted. Stage 3 also recommends greater experimental complexity when runs finish much faster than their allocated runtime.Source: §3.2.1 · p. 5

3.2.2. Parallelized Agentic Tree Search

  • Parallel agentic tree search expands experiments through debugging or LLM-guided best-first refinement across all four experimentation stages.LLMs generate plans and executable Python code; execution errors or VLM-identified visualization problems mark nodes as buggy. Specialized nodes track tested hyperparameters and ablations to avoid repetition, replicate experiments with different random seeds, and aggregate results into figures showing means and standard deviations.Source: §3.2.2 · pp. 5, 7

3.3. Dataset Loading via Hugging Face

  • Dataset loading preferentially uses Hugging Face Hub through datasets.load_dataset, simplifying access while remaining limited by repository compatibility.The Hub supplies commonly used datasets with predefined training, validation, and test splits, but not every dataset repository supports this loading method.Source: §3.3 · p. 7

3.4. Vision-Language Model Reviewer

  • VLMs critique experimental figures immediately and review figures with captions during manuscript reflection to improve visual clarity and coherence.Manuscript review combines figure screenshots, captions, and referencing text to check alignment, unclear labels or missing legends, and duplication across the main text and appendix.Source: §3.4 · pp. 7–8

4. Human Evaluation of Manuscripts Generated by The AI Scientist-v2

One of three AI-generated manuscripts cleared workshop peer review, although internal assessment found none met typical top-tier main-conference standards. Humans selected ideas and complete outputs, but generation within each run was autonomous.

4. Human Evaluation of Manuscripts Generated by The AI Scientist-v2

  • 1 of 3 submissions surpassed the workshop acceptance threshold, averaging 6.33 out of 10 and ranking among the top 45%.Individual scores were 6 (weak accept), 7 (accept), and 6 (weak accept); the other two submissions were not accepted.Source: Figure 3 · p. 8, §4.1 · p. 9

4.1. Evaluation Methodology

  • 43 total submissions included three AI manuscripts reviewed without disclosing their AI origin; the IRB-approved evaluation withdrew the accepted manuscript before publication.Reviewers knew AI submissions were possible, could opt out, and learned of the experiment after review. Withdrawal was prearranged with workshop organizers and ICLR leadership; the authors advocate explicit AI labeling while publication norms remain unresolved.Source: §4.1 · p. 9
  • Internal assessment found no manuscript met typical top-tier main-conference standards, but independently identified the same submission as suitable for workshop acceptance.Inspection revealed occasional citation inaccuracies and insufficient methodological rigor and analytical depth, despite successful execution of standard experimental pipelines.Source: §4.1 · p. 9

4.2. The first AI-generated peer-reviewed workshop paper.

  • Humans selected initial AI-generated ideas and the best complete manuscripts across multiple seeds, but never edited code, results, figures, or text.Initial ideas came from separate core-machine-learning and applied-domain generation rounds. The study tested whether at least one manuscript could survive peer review, not its success rate; submission-length constraints were handled automatically.Source: §4.2 · p. 10
  • The accepted paper found temporal consistency regularization of input embeddings did not significantly improve compositional generalization and sometimes hindered learning on synthetic arithmetic tasks.The regularizer penalized embedding changes between successive tokens. Increasing expression complexity worsened generalization irrespective of regularization, suggesting regularization alone was insufficient and could conflict with the primary learning objective.Source: §4.2 · pp. 10–11
  • Approximately 57% training–test overlap raised reliability concerns, while the attention-augmented LSTM’s reported 100% accuracy primarily reflected task simplicity and decreased with greater complexity.Authors also identified embedding-versus-hidden-state confusion, missing key references, and incorrect figure interpretations, including a claim contradicted by the attention-based model outperforming the LSTM. Evaluation was restricted to short synthetic sequences.Source: §4.2 · p. 11
  • Workshop reviewers recommended acceptance for informative negative results, while requesting stronger methodological motivation, additional architectures, and broader experimental datasets.They valued the baseline comparisons and ablations, highlighting operator complexity as a network-capacity bottleneck and suggesting architectural changes might be more effective than regularization.Source: §4.2 · p. 12

5. Limitations & Ethical Considerations

The AI Scientist-v2 achieved workshop acceptance but does not consistently meet workshop or top-tier conference standards, and important aspects of scientific inquiry remain challenging. The study used authorized peer review, while the authors advocate transparency and community norms to prevent misuse of AI-generated research.

5. Limitations & Ethical Considerations

  • One of three AI-generated submissions received workshop acceptance, demonstrating neither consistent workshop-level performance nor readiness for top-tier conference publication.Typical workshop acceptance rates are 60-80%, compared with 20-30% for main conference tracks at leading machine learning venues such as ICLR, ICML, and NeurIPS.Source: §5 · p. 13
  • Genuinely novel, high-impact hypotheses, innovative experimental methods, and domain-expert justification of design choices remain challenging despite structured agentic tree search and enhanced autonomy.Overcoming these limitations is necessary to move beyond preliminary or incremental results toward consistently high-quality, conference-level contributions.Source: §5 · p. 13
  • The study received cooperation from ICLR leadership and workshop organizers and University of British Columbia IRB approval; submitted papers were subsequently withdrawn.The approval identifier was H24-02652. Under the agreement with organizers, the AI-generated papers will not appear on OpenReview’s public forum.Source: §5 · pp. 13–14
  • The authors advocate transparency and community norms for AI-generated science to prevent gaming peer review or artificially inflating scientists’ CVs.Disclosure requirements and timing remain unresolved, including whether initial evaluation should judge work on merit before disclosure to avoid bias.Source: §5 · p. 14

6. Related Work

Related work spans automated research pipelines, scientific idea generation, and benchmarks for research engineering and domain-specific coding. Approaches differ in human oversight, experimental execution, and their emphasis on agentic action versus scientific understanding.

6. Related Work

  • Automated research systems range from end-to-end frameworks to human-supervised workflows and pipelines that omit experimental execution.The AI Scientist-v1 (Lu et al., 2024) and AI-Researcher (Data Intelligence Lab, 2025) automate research pipelines, while Intology (Intology AI, 2025) and Carl (AutoScience AI, 2025) incorporate human oversight. CycleResearcher (Weng et al., 2025) excludes experimental execution; self-driving laboratory protocols can avoid LLMs or use them complementarily (Shi et al., 2025).Source: §6 · p. 14
  • LLM-generated ideas were typically more novel but less feasible than expert ideas in human evaluations by Si et al. (2025).GraphEval (Feng et al., 2025) provides graph-based idea evaluation methods, highlighting limitations in LLMs’ ability to assess research ideas accurately.Source: §6 · p. 14
  • Sub-2-hour tasks show AI superiority in the METR Research Engineer benchmark (Wijk et al., 2024).MLEBench and Aide assess research-engineering workloads. Independent evaluations also examine complete frameworks, including The AI Scientist-v1 evaluated by Beel et al. (2025).Source: §6 · p. 14
  • SciCode evaluates scientific programming across physics, chemistry, and biology, while BixBench evaluates computational-biology agents (Mitchener et al., 2025).SciCode uses domain-scientist-curated problems with structured sub-problems; Eger et al. (2025) review LLM roles and effectiveness across scientific workflows.Source: §6 · p. 14
  • Bengio et al. (2025) distinguish Scientist AIs focused on understanding data from agentic systems pursuing goal-directed interactions with the world.Industry efforts include Google’s AI Research Copilot, also known as AI Co-Scientist (Gottweis et al., 2025).Source: §6 · p. 14

7. Conclusion

The AI Scientist-v2 combines template-free experimentation, agentic tree search, and VLM feedback to autonomously produce manuscripts that pass workshop peer review. This milestone leaves challenges in consistently achieving top-tier quality and generating groundbreaking hypotheses.

7. Conclusion

  • Compared with The AI Scientist-v1, the system removes human-crafted templates, introduces experiment-manager-supervised agentic tree search, and integrates VLM feedback for visualization and manuscript refinement.These changes enhance autonomy and exploration.Source: §7 · p. 15
  • The AI Scientist-v2 produced the first fully AI-generated paper to pass peer review at a workshop of a major machine learning conference.The authors distinguish this milestone from meeting full-conference standards.Source: §7 · p. 15
  • Consistent top-tier quality and groundbreaking hypotheses remain challenges, while the authors anticipate future AI systems matching or exceeding human publication quality.They expect continued capability improvements, potentially at an exponential rate; these are forecasts rather than demonstrated results.Source: §7 · p. 15
  • The authors prioritize discoveries benefiting humanity over comparisons with human science, emphasizing open sharing, collaboration, and faster discovery.Overcoming limitations and scaling these systems could support goals such as curing diseases and understanding the universe.Source: §7 · p. 15

Supplementary Material … where T is the length of the input sequence.

The supplementary material documents model and search settings and prompts for literature-informed proposal development, baseline implementation, plotting, experiment-grounded reporting, and iterative manuscript and figure review. It also presents three autonomously generated workshop manuscripts and an annotated study of compositional regularization, including its proposed method, limitations, and finding of no significant generalization benefit.

A. Hyperparameters

  • 15 hours is the paper-generation runtime limit; Tables 2–3 document model configurations, tree-search settings, experiment progression, and node-execution limits.Generation usually takes several hours to this limit, depending on problem complexity.Source: A. Hyperparameters · p. 21, Table 2 · p. 21, Table 3 · p. 21

B. Prompts

  • Proposal prompts require novel, literature-informed ideas feasible from the provided codebase within academic-lab resources, followed by reflection on quality, novelty, and feasibility.At least one literature search is required before finalization; reflection incorporates tool results while preserving the original idea unless major issues arise.Source: B. Prompts · pp. 21–23
  • The baseline prompt prioritizes a simple working implementation covering data preparation, model training, evaluation, and visualization before exploring sophisticated variations.The initial implementation must implement a solid baseline based on the research idea.Source: B. Prompts · p. 23
  • Plotting prompts require a self-contained aggregation script using existing experiment data and exact file paths, without hallucinated data or duplicate figures.Figures are saved only in figures/; numerical values come from existing .npy files or JSON summaries, and synthetic-data plots belong in the appendix.Source: B. Prompts · pp. 23–25
  • Manuscript prompts demand accurate reporting for the ICBINB workshop, treating negative or inconclusive results as valid contributions and emphasizing lessons and future directions.The main text allows up to four figures, with remaining figures placed in the appendix, and must respect the specified page limit.Source: B. Prompts · pp. 25–26
  • Manuscript revision combines LaTeX checks and VLM reviews to align figures, captions, and discussion, remove duplicates, and improve figure selection.Low-value figures move to the appendix or are removed; sparse related figures can be combined, with corresponding text updated and scientific analysis strengthened.Source: B. Prompts · pp. 28–29

C. AI Generated Papers

  • Three fully system-generated manuscripts were submitted to the ICLR 2025 ICBINB workshop and reproduced with scientific-assessment and code-review annotations.The figure-review prompts request structured JSON assessments of figure content, captions, and integration with main-text references.Source: B. Prompts · p. 30, C. AI Generated Papers · p. 31

C.1. Compositional Regularization: Unexpected Obstacles in Enhancing Neural Network Generalization

  • The initial proposal hypothesized that penalizing deviations from predefined compositional structures would improve generalization to novel combinations.Planned evaluations included SCAN, COGS, IWSLT, GeoQuery, representation analysis, regularization-strength ablations, and comparisons with other approaches. Anticipated risks included task dependence, reduced primary-task accuracy, computational overhead, difficulty defining compositional structures, and limited scalability.Source: §C.1 · p. 32

COMPOSITIONAL REGULARIZATION: UNEXPECTED OBSTACLES IN ENHANCING NEURAL NETWORK GENERALIZATION

  • Compositional regularization did not significantly improve generalization to unseen arithmetic-expression combinations compared with baseline models.Increasing expression complexity worsened generalization difficulties regardless of regularization.Source: COMPOSITIONAL REGULARIZATION:… · p. 33

1 INTRODUCTION

  • Compositional generalization concerns novel combinations of familiar components (Ito et al., 2022); an annotation questions the relevance of citing MAML.The introduction frames neural networks’ difficulties with this ability as a limitation for systematic reasoning beyond training data.Source: §1 · p. 33

courage certain properties in the learned representations (Yin et al., 2023).

  • Regularization sometimes hindered learning, while annotations clarify that expression complexity increased through additional operators, not nesting.The manuscript concludes that directly enforcing compositional structures through regularization may not straightforwardly improve generalization.Source: courage certain properties in the… · pp. 33–34

3 METHOD

  • The arithmetic model combines embedding, LSTM, and output layers with a regularizer defined as the mean squared difference between successive hidden states.An annotation requests a more precise definition of the penalized states, suggesting embedding states as an alternative.Source: §3 · p. 34

where T is the length of the input sequence.

  • The training objective adds a λ-weighted successive-state regularizer to prediction mean squared error, with different λ values tested.The penalty is intended to discourage large state changes and encourage additive representations.Source: where T is the length of the input… · p. 34, §3 · p. 34

4 EXPERIMENTS … B.1 EXPERIMENTAL SETUP

Compositional regularization lowered compositional loss but did not improve generalization on synthetic arithmetic expressions, while greater operator complexity worsened performance. Additional sections examine embedding capacity, discuss limitations and future directions, and describe an attention-augmented LSTM setup.

4 EXPERIMENTS

  • Approximately 84% test accuracy was reported for the unregularized LSTM, while compositional loss remained relatively steady.However, the supplied Figure 1 comment says the displayed accuracy reaches only 40%, leaving the reported endpoint visually unverified.Source: Figure 1 · p. 35, §4 · p. 35
  • 1,000 training expressions and 200 unseen test expressions evaluated generalization to novel number–operator combinations and increased operator complexity.Models trained for 30 epochs using Adam and mean squared error, with regularization weight λ = 0.1 unless otherwise specified.Source: §4 · p. 35
  • Higher regularization weights lowered compositional loss but did not improve test accuracy and sometimes reduced it.Evaluation used compositional loss and test accuracy, defined as numerical predictions falling within a tolerance.Source: §4 · pp. 35–36
  • Increasing operator complexity worsened generalization for both baseline and regularized models, indicating that compositional regularization alone did not resolve this difficulty.The Figure 3 caption describes increasing training and validation losses; an embedded comment flags its final-panel accuracy label as inconsistent with the plotted loss.Source: §4 · pp. 35–36, Figure 3 · p. 36

5 CONCLUSION

  • Potential explanations for the negative results include conflicting objectives, a misaligned compositionality measure, and limitations of the synthetic dataset.Suggested next steps include alternative regularizers, refined compositionality definitions, more complex datasets, and recursive or hierarchical models. The conclusion also emphasizes rigorous evaluation and reporting negative or inconclusive results.Source: §5 · p. 36

A EFFECT OF EMBEDDING DIMENSION

  • Larger embedding dimensions did not consistently improve final test accuracy, suggesting that capturing compositional structures, rather than representational capacity alone, may be the bottleneck for compositional generalization.Embedding dimensions 16, 32, 64, and 128 showed similar training-loss decreases and compositional-loss trends.Source: A EFFECT OF EMBEDDING DIMENSION · p. 37

B INTEGRATION OF ATTENTION MECHANISM

B.1 EXPERIMENTAL SETUP

  • The added attention layer calculated weights from LSTM hidden states and formed a context vector to support final output prediction.Source: §B.1 · p. 38

B.2 RESULTS … C.3 DROPOUT IMPACT

The experiments report persistent compositional-generalization difficulties across attention, compositional weighting, recurrent architectures, and dropout. Comments dispute the reported attention comparison and caution against drawing conclusions from the compositional-weight accuracy plot alone.

B.2 RESULTS

  • Attention slightly improved training dynamics without reported significant generalization gains, suggesting deeper architectural limitations or a need for more sophisticated compositional mechanisms.The authors suggest that difficulty focusing on relevant input elements may not be the primary obstacle.Source: §B.2 · p. 38
  • The text reports similar attention and baseline test accuracy, but the Figure 5 comment states that attention outperforms the baseline LSTM.This disagreement qualifies the reported conclusion that attention does not address compositional generalization.Source: Figure 5 · p. 38, §B.2 · p. 38

C.1 ABLATION STUDY ON COMPOSITIONAL WEIGHT

  • Higher compositional weights reduced compositional loss but slightly increased training loss, indicating potential interference with the primary learning objective.These effects distinguish improvement in the compositional objective from improvement in the primary training objective.Source: §C.1 · p. 38, §C.3 · p. 39

C.2 COMPARISON OF LSTM AND RNN ARCHITECTURES

  • LSTM achieved marginally higher test accuracy than simple RNN, but both struggled with compositional generalization, suggesting recurrent unit choice alone does not resolve the difficulty.Their training losses showed similar convergence patterns.Source: §C.2 · p. 38, §C.3 · p. 39

C.3 DROPOUT IMPACT

  • Increasing dropout did not significantly improve generalization, suggesting that standard regularization may be insufficient for learning compositional structures.The experiment compared final test accuracy across different dropout rates.Source: §C.3 · p. 38

C.1 ABLATION STUDY ON COMPOSITIONAL WEIGHT

  • Higher compositional weights reportedly reduced test accuracy, although a comment cautions that Figure 7 alone does not support a clear conclusion.The authors interpret the result as a conflict between compositional regularization and the primary learning objective.Source: §C.1 · p. 38, Figure 7 · p. 39

D HYPERPARAMETERS AND TRAINING DETAILS … AUTONOMOUSLY GENERATED BY THE AI SCIENTIST-V2

The arithmetic-paper review found no benefit from temporal consistency regularization and identified dataset overlap, reporting errors, and limited evaluation. The label-noise manuscript investigates model calibration, but annotations question its empirical support and framing of prior research.

C.1.1. AI Scientist Team Review; D HYPERPARAMETERS AND TRAINING DETAILS; Strengths; Scores

  • Temporal consistency regularization did not improve multitask arithmetic performance, and higher dropout did not improve compositional generalization.Reviewers considered the arithmetic tasks suitable, assigning Overall - Workshop: 5/10 (Borderline accept) and Overall - Conference: 4/10 (Borderline reject). Figure 9 shows only the best configuration despite its dropout-sweep caption.Source: D HYPERPARAMETERS AND TRAINING DETAILS · p. 40, §C.1.1 · p. 41, Strengths · p. 41

Inspecting the dataset generation process

  • 57% of test examples overlapped training examples on average across ten dataset-generation runs using addition, multiplication, and digits [0-9].Each run used 1,000 training samples and 200 test samples. The inspection attributes overlap risk to the limited number of possible single-digit expressions.Source: Inspecting the dataset generation… · p. 42

Model architecture, loss function, and evaluation function; Weaknesses; Additional Comments

  • Code inspection locates regularization on input embeddings rather than LSTM hidden states, while reviewers identify misleading descriptions, reporting errors, and insufficient evaluation.The model combines embeddings, one LSTM layer, and a linear regression head; attention outperforms the LSTM despite contrary prose, and validation loss increases with task complexity despite its caption. Reviewers recommend comparing regularizers across tasks and directly testing compositional representations with probes or embedding visualizations.Source: Weaknesses · p. 41, Additional Comments · pp. 41–42, Model architecture, loss function, and… · pp. 42–43

Attention-augmented LSTM

  • 100% and 56% test accuracy were reproduced for the attention-augmented LSTM on [1-9] and [10-19], respectively, versus baseline LSTM accuracies of 85% and 0%.The harder setting increased expression length from 3 to 5 and enlarged the output space, undermining the original impression of perfect performance.Source: Attention-augmented LSTM · p. 44

C.2.1. The AI Scientist-v2 Idea; 1 INTRODUCTION; 2 RELATED WORK

  • The label-noise proposal examines whether noisy labels distort calibration, evaluating Expected Calibration Error and mitigation methods rather than accuracy alone.The literature discussion covers temperature scaling (Kull et al., 2019) and calibration-aware training (Huang et al., 2023). Proposed limitations include model–dataset dependence, unreliable calibration estimates from small test sets, and imperfect correspondence between synthetic and real-world noise.Source: §C.2.1 · p. 45, §1 · p. 46, §2 · p. 47

UNVEILING THE IMPACT OF LABEL NOISE ON MODEL CALIBRATION IN DEEP LEARNING; 1 INTRODUCTION

  • The label-noise manuscript claims overconfidence, worse calibration under asymmetric noise, and limited mitigation benefits, but annotations question evidence, experimental depth, and novelty framing.Annotations explicitly flag missing empirical support, dispute the claim that calibration under label noise is underexplored, and recommend omitting uncertainty-estimation terminology for clarity.Source: §1 · pp. 46–47, UNVEILING THE IMPACT OF LABEL NOISE ON… · p. 46

3 METHODOLOGY

  • 10% to 50% synthetic symmetric and asymmetric label noise was applied to CIFAR-10, MNIST, and Fashion-MNIST using ResNet-18 (He et al., 2015).Symmetric noise flips labels uniformly to other classes, whereas asymmetric noise follows a predefined confusion matrix. Annotations identify incorrect dataset-reference attribution and note that generated reliability diagrams were omitted from the manuscript.Source: §3 · pp. 47–48

AUTONOMOUSLY GENERATED BY THE AI SCIENTIST-V2; E ADDITIONAL NOTES; D HYPERPARAMETERS AND TRAINING DETAILS

  • Training used standard cross-entropy loss and stochastic gradient descent with momentum for 100 epochs, with batch size 128 and an initial learning rate of 0.1.The learning rate decayed by a factor of 0.1 at epochs 50 and 75. Post-hoc temperature scaling (Kull et al., 2019) was applied to assess its effectiveness under label noise.Source: AUTONOMOUSLY GENERATED BY THE AI… · p. 48

4 EXPERIMENTS AND RESULTS … A ADDITIONAL EXPERIMENTS AND FIGURES

A basic CNN reportedly achieved accuracy comparable to ResNet-18 under label noise, while annotations correct calibration claims and flag missing evidence for mitigation benefits.

4 EXPERIMENTS AND RESULTS

  • Asymmetric-noise ECE followed a nonmonotonic pattern, initially increasing and subsequently decreasing rather than worsening uniformly.The annotation places the decrease after a noise rate of 0.4 and corrects the reported ECE from 0.35 to 0.23 or 0.24.Source: §4 · p. 48
  • Increasing label noise reduced CIFAR-10 test accuracy under both symmetric and asymmetric corruption.The figure annotation corrects the symmetric-noise starting accuracy to approximately 75%, rather than 85%. Experiments used cosine annealing, not stepwise decay.Source: §4 · p. 48, Figure 1 · p. 48
  • Above 90% accuracy persisted on MNIST at higher noise levels, despite increased ECE under asymmetric noise.The analysis extended to MNIST and Fashion-MNIST, but an annotation cautions that the cross-dataset interpretation applies to asymmetric noise; symmetric-noise results need discussion.Source: §4 · p. 48

We evaluated whether standard label noise mitigation techniques improve calibration. Specifically,

  • The basic CNN performed comparably to ResNet-18 in test accuracy across datasets under symmetric and asymmetric label noise.Figure 3 reports final test accuracy, not ECE, so it does not substantiate the manuscript’s claim that the CNN has worse calibration.Source: Figure 3 · p. 49, We evaluated whether standard label… · p. 49
  • Temperature scaling reportedly reduced ECE only marginally, but the cited figure contains no temperature-scaling results to support that finding.Consequently, the manuscript’s broader claim that existing mitigation techniques are insufficient is not established by that figure.Source: We evaluated whether standard label… · p. 49

5 DISCUSSION

  • The discussion proposes calibration-aware objectives (Huang et al., 2023) and noise-robust calibration, while an annotation flags missing experiments supporting its mitigation claims.These directions target reliable predictions in risk-sensitive applications; they are proposals rather than demonstrated improvements.Source: §5 · p. 49

6 CONCLUSION

  • The conclusion identifies noisy-label miscalibration as a challenge for dependable uncertainty estimates and calls for calibration objectives or methods tailored to noisy environments.It characterizes asymmetric-noise probability estimates as overconfident and unreliable and describes existing mitigation improvements as limited.Source: §6 · p. 49

A.1 NOISE RATE SENSITIVITY ANALYSIS … 4 EXPERIMENTAL SETUP

This section presents label-noise, pest-detection, and multi-dataset studies examining model performance and generalization, alongside environmental fragility, data-preparation limitations, and peer-review criticisms. The multi-dataset experiments use EuroSAT, MedMNIST, and CIFAR-10, with accuracy, loss, and ERS as evaluation metrics.

C.2.2. AI Scientist Team Review / Strengths / A.1 NOISE RATE SENSITIVITY ANALYSIS / A.2 CALIBRATION CURVES AND RELIABILITY DIAGRAMS

  • Asymmetric label noise produced higher expected calibration error than symmetric noise across MNIST, Fashion-MNIST, and CIFAR-10.On CIFAR-10, test accuracy declined with increasing noise, more sharply under asymmetric noise. ECE increased during training, especially at higher noise rates; reliability diagrams invoked to support overconfidence were not shown.Source: §A.1 · p. 50, Figure 5 · p. 51, §A.2 · p. 51

Scores / C.2.4. Workshop Reviews

  • 3/10 (Reject) was the team’s Overall - Workshop score; both workshop reviewers assigned Rating: 3: Clear rejection.Reviewers questioned the connection between experiments and overconfidence claims and requested more refined noise injection and broader evaluation metrics. Missing references and unreadable figures also left the submission appearing unfinished.Source: Scores · p. 53, §C.2.4 · p. 57

Temperature scaling / Weaknesses / Additional Comments / A.4 ADDITIONAL DATASETS

  • Temperature scaling was implemented but never used, leaving the manuscript’s calibration-method claims unsupported by executed experiments.The audit suggests initial plans and unused code influenced the writeup; calibration-method results, reliability diagrams, and an SVHN figure were absent despite claimed SVHN calibration degradation. Duplicate figures, poor Figure 2 readability, and dismissive treatment of related work further weakened presentation.Source: §A.4 · p. 52, Weaknesses · p. 53, Additional Comments · p. 54

Evaluation function / Dataset class

  • The ECE implementation matched torchmetrics’ MulticlassCalibrationError with norm=‘l1’, apart from minor numerical differences, after aligning probability-input handling.The comparison omitted softmax because the reference function expects probabilities. The dataset class initially lacked symmetric/asymmetric noise selection but was corrected; asymmetric noise deterministically mapped class i to class (i+1) % NUM CLASSES.Source: Evaluation function · pp. 55–56, Dataset class · p. 55

C.3.1. The AI Scientist-v2 Idea / 2 RELATED WORK

  • The pest-detection proposal targeted data quality, environmental variability, and model limitations, but required manual dataset download and reduction.Planned experiments covered data quality, robustness, environmental variation, failure analysis, and mitigation; researchers manually downloaded the Kaggle dataset and retained one tenth, leaving broader data-preparation automation for future work. Related work discussed domain adaptation (Prasad & Agniraj, 2024; Li et al., 2023a), while Teixeira et al. (2023) and Hu (2023) emphasized real-world generalization.Source: §C.3.1 · p. 58, §2 · p. 60

REAL-WORLD CHALLENGES IN PEST DETECTION USING DEEP LEARNING: AN INVESTIGATION INTO FAILURES AND SOLUTIONS / 1 INTRODUCTION

  • Lower learning rates improved pest-model generalization, but environmental robustness remained weak, while multi-dataset training and domain adaptation brought computational costs and inconsistent gains.The manuscript presented these findings as negative and inconclusive results, primarily examining environmental variability and image noise. An annotation questioned whether its noisy-image domain-adaptation experiment directly addressed pest detection.Source: REAL-WORLD CHALLENGES IN PEST… · p. 59, §1 · p. 59

3 METHODOLOGY / 4 EXPERIMENTAL SETUP

  • ResNet-18 pretrained on ImageNet was fine-tuned on a roughly 2,500-image pest subset, with robustness assessed using Environmental Robustness Score (ERS).The setup specified 70%/15%/15% splits, five learning rates, 10 epochs per rate, Adam, batch size 32, and test-time brightness, contrast, blur, and affine perturbations; ERS divided challenging-condition accuracy by normal-condition accuracy. Exploratory training used EuroSAT, MedMNIST, and CIFAR-10, although annotations challenged treating the latter two as agricultural domains.Source: §3 · p. 60, §4 · p. 60

4 EXPERIMENTAL SETUP

  • Multi-dataset training experiments used EuroSAT, MedMNIST, and CIFAR-10 to investigate effects on model generalization.Models used similar training settings and were evaluated using accuracy, loss, and ERS.Source: §4 · p. 60

5 EXPERIMENTS AND RESULTS … SUPPLEMENTARY MATERIAL

ResNet-18 experiments favored lower learning rates and showed dataset-dependent accuracy and Environmental Robustness Scores (ERS). The discussion and conclusion emphasize that hyperparameter tuning alone cannot resolve environmental variability and domain discrepancies.

5 EXPERIMENTS AND RESULTS

  • 1e−4 and 5e−4 learning rates produced smoother convergence and more stable ERS, while Figure 1 reports higher validation accuracy at lower learning rates.These experiments used ResNet-18 on the Crop Pest and Disease dataset, with robustness claims limited to the tested conditions and ERS definition. Higher rates showed unstable loss and ERS fluctuations, but an embedded comment disputes the claim that they overfit more.Source: §5 · p. 60, Figure 1 · p. 61
  • EuroSAT and CIFAR-10 models achieved high, stable training and validation accuracy, whereas MedMNIST accuracy fluctuated.The experiments trained separate models on individual datasets rather than jointly training one model across datasets. An embedded comment challenges extrapolating MedMNIST adaptation difficulties directly to pest detection.Source: §5 · p. 61, §6 · p. 62
  • EuroSAT and CIFAR-10 models maintained higher, more stable ERS, while MedMNIST showed low, unstable ERS under the tested environmental augmentations.These comparisons concern robustness to the environmental augmentations applied during testing, rather than establishing robustness across all real-world conditions.Source: §5 · pp. 61–62, Figure 3 · p. 62
  • The study cautions that multi-dataset training and domain adaptation can introduce increased computational demands and inconsistent performance gains.These are stated challenges, not quantified computational comparisons in the supplied results.Source: §5 · pp. 61–62

6 DISCUSSION

  • Hyperparameter optimization alone was insufficient for real-world robustness, while dataset-dependent performance highlighted dataset-selection and domain-adaptation challenges.The discussion interprets poor MedMNIST performance as evidence that substantial domain shifts can impair learning, promote overfitting, and reduce robustness.Source: §6 · p. 62
  • The discussion proposes realistic data augmentation, robust training methods, and domain-specific model adaptations to address data quality issues and environmental variability.These strategies are proposed as potentially necessary improvements, not demonstrated solutions in the supplied experiments.Source: §6 · p. 62

7 CONCLUSION

  • The conclusion emphasizes dataset compatibility and adaptable architectures as priorities for developing pest-detection systems resilient to real-world variability.It states that multi-dataset training benefits depend on dataset compatibility and recommends architectures designed for adaptability.Source: §7 · p. 62

A ADDITIONAL FIGURES AND DETAILED RESULTS … Environmental noise implementation

Supplementary results show steady learning on EuroSAT and CIFAR-10 but unstable MedMNIST training, while reviewers question their relevance to pest prediction and the claimed multi-dataset setup. Code review confirms environmental augmentations and robustness-score calculation but finds that the selected implementation omitted attempted domain adaptation.

A ADDITIONAL FIGURES AND DETAILED RESULTS; applied.

applied.

  • 30 epochs and batch size 64 were specified, with early stopping after five consecutive epochs without validation-loss improvement.Models used PyTorch 1.9.0, ImageNet-pretrained ResNet-18, and Adam with β1 = 0.9 and β2 = 0.999. An inline correction specifies weight decay 0.01 rather than the printed 1e−4.Source: A ADDITIONAL FIGURES AND DETAILED… · p. 63, applied · p. 64

C.3.2. AI Scientist Team Review; Strengths

  • The team review framed the work as pest prediction, praised its workshop fit, and highlighted dataset-dependent effects of training and out-of-distribution augmentation.Its summary described learning-rate comparisons and an Environmental Robustness Score based on simulated environmental conditions.Source: Strengths · p. 65, §C.3.2 · p. 65

Weaknesses

  • Reviewers found augmentation experiments insufficient to establish domain adaptation, transfer to pest prediction, or genuine multi-dataset training.The learning-rate analysis was considered trivial, and the reported experiments trained separate models on individual datasets.Source: Weaknesses · p. 65

Scores

  • 3/10 (Reject) was the Overall - Workshop rating, alongside Overall - Conference: 2/10 (Strong reject) and Confidence: 4/5.Soundness was rated 2 fair, Presentation 1 poor, and Contribution 1 poor; the review judged the evidence insufficient for the conclusions.Source: Scores · p. 65

Additional Comments

  • Additional comments flagged missing citations, misleading lower-learning-rate overfitting conclusions, and the mislabeling of separate-model experiments as multi-dataset training.No potential Code of Ethics violation was identified.Source: Additional Comments · p. 65

C.3.3. AI Scientist Team Code Review; Domain Adaptation and Multi-dataset training

  • The selected, error-free implementation omitted domain adaptation and joint multi-dataset training after attempts using a domain discriminator failed.The unsuccessful version trained a single model across all three datasets using domain-discriminator loss.Source: Domain Adaptation and Multi-dataset… · p. 66

Environmental noise implementation

  • ERS divides accuracy under challenging conditions by accuracy under normal conditions; code review confirmed both this calculation and test-time environmental augmentations.Perturbations used ColorJitter brightness and contrast factors of 0.5, GaussianBlur kernel size 3, and RandomAffine rotations up to 15 degrees with translations up to 10%.Source: applied · p. 63, Environmental noise implementation · p. 66, Figure 13 · p. 67

C.3.4. Workshop Reviews

Reviews of the pest-detection manuscript were split, with one acceptance recommendation and two rejections. Reviewers recognized its motivation and potential contributions but questioned experimental justification, robustness measurement, and evidence for real-world generalization.

Reviewer #1

  • The paper claims that lower learning rates and training on EuroSAT and CIFAR-10 improve generalisation in experiments with an ImageNet-trained ResNet-18 fine-tuned on the Crop Pest and Disease dataset.The experiments use data augmentations to emulate real-world conditions and measure performance using accuracy, loss, and Environmental Robustness Score (ERS).Source: §C.3.4 · pp. 67–68
  • The reviewer questioned real-world generalization and robustness claims based on only 5 learning rates and models trained for 10 epochs, noting the absence of testing in real-world deployment.The reviewer also questioned the motivation for augmentation choices and ERS, and found multi-dataset training unclear because the datasets had different class counts. Insufficient experimental detail and evidence limited the conclusions and supported the judgment against publication.Source: §C.3.4 · p. 68

Reviewer #2

  • Reviewer #2 valued the analysis of hyperparameter tuning and multi-dataset training as strategies for exposing and mitigating weaknesses in pest-detection models.The reviewer nevertheless noted persistent environmental variability and domain discrepancies, and recommended additional pest-detection literature.Source: §C.3.4 · pp. 68–69

Reviewer #3

  • Reviewer #3 warned that ERS could misestimate robustness on unbalanced datasets and that missing training, testing, and validation details prevented assessing potential bias.The reviewer also found learning-rate and augmentation hypotheses insufficiently motivated, dataset descriptions incomplete, and the choice of ResNet-18 unjustified.Source: §C.3.4 · p. 69

Reviewers #1 and #3

  • Review comments praised the motivation but requested stronger theoretical grounding, more readable figures, corrected bibliography errors, and clearer presentation of the augmentation-based evaluation strategy throughout the manuscript.Recommendations included comparisons with recent deep learning models and switching from augmentation to real datasets or generative models. Comments also requested clarification of whether ERS scores used training or validation data and missing dataset references.Source: §C.3.4 · pp. 68–69

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