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Towards End-to-End Automation of AI Research

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

arXiv:2606.15497v1cs.AI

TL;DR

Automating the full scientific process from conception to publication has remained out of reach despite AI’s progress on narrow research tasks. The AI Scientist integrates ideation, experimentation, analysis, manuscript writing, and peer review, producing a fully AI-generated paper that passed peer review at a machine learning workshop.

  • Problem

    AI systems have mainly supported narrow scientific tasks, leaving autonomous automation of the research lifecycle from conception to publication unresolved.

  • Method

    The AI Scientist uses foundation models in an agentic pipeline for ideation, literature search, experiments, analysis, manuscript writing, and peer review.

  • Results

    One fully AI-generated manuscript exceeded the average human acceptance threshold and passed peer review at an ICLR workshop.

  • Takeaways & Limitations

    The result demonstrates growing AI capacity for scientific reasoning and suggests that scientific discovery could accelerate as autonomous systems improve.

  • Takeaways & Limitations

    Only one of three submissions was accepted, and the system does not yet meet top-tier publication standards or consistently succeed at workshops.

Abstract

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The automation of science is a long-standing ambition in the field of AI. While the community has made significant progress in automating individual components of the scientific process, a system that autonomously navigates the entire research lifecycle -- from conception to publication -- has remained out of reach. Here, we present the strongest demonstration to date toward automating the entire process end-to-end. We present The AI Scientist, which creates research ideas, writes code, runs experiments, plots and analyzes data, writes the entire scientific manuscript and performs its own peer review. Its ideas, execution, and presentation are of sufficient quality to produce a manuscript generated by an AI system that passes the first round of peer review at a major machine learning conference workshop. The workshop has an acceptance rate of 70 percent. Our system leverages modern foundation models within a complex agentic system. We evaluate The AI Scientist in two settings: a focused mode using human-provided code templates as an initial scaffold to conduct research on a specific topic, and a template-free, open-ended mode that leverages agentic search for wider scientific exploration. Both settings produce diverse ideas and automatically test, report on, and evaluate them. This achievement demonstrates AI's growing capacity for scientific contribution and signifies a potential paradigm shift in how research is conducted. As with any impactful new technology, there could be significant risks, including taxing overwhelmed review systems and adding noise to scientific literature. However, if developed responsibly, such autonomous systems could greatly accelerate scientific discovery.

1. Introduction

The AI Scientist is presented as an end-to-end pipeline for automating scientific research, using foundation models to generate complete machine-learning papers. Its evaluation includes an Automated Reviewer benchmarked against human papers and submissions to an ICLR workshop with organizer consent.

  • End-to-end automation: The AI Scientist is introduced as the first pipeline to automate the full scientific process end to end.It uses existing foundation models to produce complete, novel papers.
  • End-to-end automation: The system performs ideation, literature search, experiment planning and implementation, result analysis, manuscript writing, and peer review.The work focuses on machine-learning science, where experiments typically occur entirely on computers.
  • Automated evaluation: The Automated Reviewer accurately predicts conference acceptance decisions on real human-generated papers, performing on par with human reviewers.It was created to evaluate the quality of The AI Scientist’s scientific output at scale.
  • External evaluation: Generated papers were submitted, with the organizer’s consent, to an ICLR workshop to evaluate them in a setting used for human-authored papers.The paper describes top-tier conferences as prestigious archival and rigorously peer-reviewed venues, with workshops imposing a lower but non-trivial bar.

2. Generating Manuscripts

The AI Scientist automates research through sequential phases for generating and filtering ideas, conducting experiments, writing manuscripts, and reviewing scientific quality. Its workflow combines literature-aware idea selection, tree-search experimentation, experimental journaling, and automated review.

  • Idea generation: It iteratively grows an archive of research directions and hypotheses within a user-specified machine learning subfield.For each direction, it generates a title, rationale, and proposed experimental plan.
  • Idea generation: Semantic Scholar API and web-access tools filter out ideas that are too similar to existing literature.This filtering occurs after idea generation.
  • Experimentation: Experiments include optimizing code written from scratch and use additional test-time compute with tree search.After each experiment, the system records results as experimental-journal notes for future planning and writing.
  • Manuscript and review: The system produces a concise write-up in the style of a standard machine learning conference paper and submits the research to an Automated Reviewer.The reviewer automatically evaluates the scientific quality of the conducted research.
  • Workflow: The workflow comprises four phases: automated idea generation, tree-based experimentation, manuscript writing, and automated reviewing.The experimentation phase uses agentic tree search to generate and refine code implementations.

3. Automated Evaluation of Generated Papers

The paper evaluates generated research papers with an Automated Reviewer modeled on NeurIPS guidelines, finding that reviewer consistency is comparable or superior to human consistency across pre- and post-cutoff datasets. The evaluation also links paper quality to both underlying model improvements and compute allocated per paper, while noting possible data contamination.

  • Automated Reviewer: The Automated Reviewer ensembles five reviews and a meta-review to produce NeurIPS-style scores, strengths, weaknesses, and an accept-or-reject decision.Its numerical outputs cover soundness, presentation, contribution, overall assessment, and reviewer confidence.
  • Automated Reviewer: The Automated Reviewer matches or exceeds human reviewer consistency on key metrics for papers from 2017–2024 and 2025.The reported comparison covers F1 Score, AUC, and Balanced Accuracy, including papers beyond the model’s knowledge cutoff.
  • Robustness and limitations: The evaluation uses 1,000 potentially contaminated papers from 2017–2024 and a clean 2025 dataset unavailable during training.Comparing results across the cutoff suggests that data contamination may exist.
  • Generated-paper quality: As core models improve, The AI Scientist produces higher-quality papers, with recent models reaching borderline workshop acceptability on average.The Automated Reviewer judges this trend, with borderline acceptability reported for machine learning conference workshops.
  • Generated-paper quality: Greater compute allocated per paper strongly correlates with higher resulting paper quality, implicating both model scale and inference-time investment.The paper suggests further improvement as AI-system costs decrease exponentially.

4. Human Evaluation Results

The AI Scientist was tested through formal blind peer review by submitting three generated manuscripts to a workshop at a top-tier machine learning conference. One manuscript passed the workshop’s acceptance threshold, while internal reviewers found that none met the higher standard for a main ICLR conference publication.

  • Formal peer review: Three generated manuscripts were submitted to a formal, blind peer-review process at a workshop at a top-tier machine learning conference.The experiment was conducted with IRB approval and the cooperation of the conference organizers.
  • Formal peer review: 6.33 average reviewer score placed one manuscript above the workshop’s average acceptance threshold, with individual scores of 6, 7, and 6.The organizers said the paper would likely have been accepted but it was withdrawn under a pre-established protocol because it was AI-generated.
  • Formal peer review: One manuscript reported a negative result aligned with the workshop’s focus, while the other two did not meet the acceptance bar.The accepted manuscript was the one that received the 6, 7, and 6 reviewer scores.
  • Internal review: Internal human AI researchers concluded that one paper met the workshop standard, but none met the higher standard for a main ICLR conference publication.The internal review covered all three submitted papers.

5. Limitations

The AI Scientist can generate a peer-reviewed workshop paper, but it remains below the quality and rigor of the best human-produced science. Its current scope and automation capabilities also raise substantial ethical and societal concerns.

  • Scientific quality and evaluation: Only one of three submissions was accepted, and workshop acceptance rates exceed main-conference rates: 70% versus 32% for ICLR 2025.The figures refer respectively to the ICLR 2025 ICBINB workshop and main conference.
  • Scientific quality and evaluation: Reported failure modes include incorrect core-idea implementations, limited methodological rigor, experimental errors, duplicated figures, and hallucinated citations.A full failure-mode analysis is provided in Supplementary Sections A.4, C.2, and C.3.
  • Scope: Currently, The AI Scientist conducts computational experiments only, although the same playbook could extend to domains such as automated chemistry laboratories.Such extensions require experiments to be conducted automatically or by humans with data returned from them.
  • Ethical and societal concerns: Automated paper generation could overwhelm peer review, inflate research credentials, appropriate others’ ideas, eliminate scientist jobs, or enable unethical or dangerous experiments.The study obtained explicit permission from ICLR leadership, workshop organizers, and the University of British…
  • Overall assessment: The milestone of an AI-authored manuscript passing peer review demonstrates growing scientific-reasoning capacity, while consistency and top-tier quality remain challenges.The accepted manuscript passed peer review at a major machine learning workshop.

Methods

The methods combine an AI Scientist for autonomous machine-learning research with an Automated Reviewer for evaluation. The AI Scientist supports template-based and template-free workflows using agentic generation, experimentation, debugging, analysis, and manuscript production.

  • Core systems: The methodology pairs an AI Scientist that generates scientific research with an Automated Reviewer that rigorously evaluates it.The systems are designed to work together to explore AI-assisted scientific discovery.
  • Research modes: The AI Scientist operates in template-based mode by extending human-provided code or in template-free mode with much less prior guidance.The template-based workflow starts from a code template reproducing a simple training run, whereas the template-free workflow supports more open-ended discovery.
  • Template-Based AI Scientist: Template-based ideation iteratively mutates and refines archived ideas, while Semantic Scholar checks discard proposals with high similarity to existing literature.The system begins from a human-provided experiment and generates batches of structured research ideas as variations or extensions.
  • Template-Free AI Scientist: Template-free research uses agentic tree search, parallel node expansion, model-based code critique, visualization feedback, and best-first selection of non-buggy experiments.Claude Sonnet 4 generates plans and code, OpenAI’s o3 critiques code, and GPT-4o evaluates candidates using performance, training dynamics, and plot quality.
  • Automated Reviewer: 69% balanced accuracy versus 66% for humans, and 0.62 F1-score versus 0.49 for inter-human agreement, were achieved by the Automated Reviewer.These comparisons use ICLR OpenReview data and a NeurIPS 2021 consistency experiment, respectively.

Data Availability

The AI Scientist’s experiments used established datasets spanning language modeling, crop pest detection, and image classification, with different datasets used in template-based and template-free settings.

  • Template-based experiments: Template-based nanoGPT experiments used Shakespeare character, enwiki8, and text8 datasets.These datasets are attributed to Karpathy (2015), Hutter (2006), and Mahoney (2011), respectively.
  • Template-free experiments: Template-free experiments used Crop Pest and Disease Detection for one ICLR workshop submission, and Waterbirds and CelebA for Figure 1 experiments.The datasets are attributed to Mensah et al. (2023), Sagawa et al. (2019), and Liu et al. (2015), respectively.

Competing Interests

The paper discloses institutional affiliations and author relationships relevant to The AI Scientist, including Vector Institute involvement and several coauthors’ ties to Sakana AI.

  • Competing Interests: Jeff Clune is affiliated with the Vector Institute and Google DeepMind, while the project has Vector Institute affiliations but is not connected to Google DeepMind.Several coauthors are Sakana AI employees or consultants; the project’s arrangements were reviewed and approved by the University of British Columbia.

Ethics Approval

The study received institutional ethics approval and was conducted with conference and workshop leadership, while peer reviewers were informed that some submissions were AI-generated without identifying the specific papers.

  • Ethics Approval: The University of British Columbia BREB approved the study under protocol H24-02652.The approval came from the Behavioral Research Ethics Board.
  • Ethics Approval: The research was conducted in full cooperation with ICLR conference leadership and relevant workshop organizers.
  • Ethics Approval: Peer reviewers were informed that a small number of workshop submissions were AI-generated, without being told which specific papers.This disclosure followed the approved protocol.

SI Table of Contents … C.2. In-Depth Analysis of the Peer-Reviewed Workshop Paper (Template-Free)

The supplementary material details The AI Scientist’s template-based and template-free workflows, structured experimental journals, automated reviewing, paper-generation outcomes, and case studies. It demonstrates autonomous research production while documenting methodological limitations, implementation failures, and mixed empirical results.

  • A.1. Template-Based AI Scientist Implementation Details: The template-based workflow uses LLMs and structured prompts to guide ideation, experimentation, plotting, and manuscript writing.Ideas include experimental plans and self-assessed novelty, feasibility, and interestingness; Aider fills a LaTeX template section by section.
  • A.2.4. Experimental Journal Structure: The template-free system records generated code, execution plans, results, runtime feedback, visual commentary, and later-stage outputs in a structured JSON journal.This cross-stage grounding is intended to reduce divergence between the initial proposal, experimental results, and final write-up.
  • Example idea generated for the ICLR ICBINB workshop experiment: The template-free example proposed compositional regularization to improve neural networks’ generalization to novel combinations of known components.The generated idea was titled “Enhancing Compositional Generalization in Neural Networks via Compositional Regularization.”
  • A.3.2. Validation Details for the Automated Reviewer: The automated reviewer achieved comparable aggregate metrics to human inter-reviewer agreement, while exceeding average human-reviewer F1 in both evaluation periods.Bootstrap estimates gave ΔF1 = 0.128 before the cutoff and ΔF1 = 0.172 after it, both with p < 0.001.
  • Paper Generation Process: Each selected paper was fully AI-generated, but the pipeline filtered failures including citation mismatches, broken figure paths, incorrect paper length, and duplicated figures.The end-to-end workflow completed each paper from initial idea through experimentation and compiled PDF without human modification of the final output.
  • B.1. Template-Based AI Scientist Results: Among template-based runs, Claude Sonnet 3.5 produced the highest-quality papers by manual inspection, GPT-4o ranked second, and open-weight models had notable execution or quality failures.GPT-4o often struggled with LaTeX, DeepSeek Coder frequently failed to interface correctly with Aider, and Llama-3.1 405B performed worst overall.
  • C.1. In-Depth Case Study (Template-Based): “Adaptive Dual-Scale Denoising”: The template-based Adaptive Dual-Scale Denoising case study generated a comprehensive plan, implemented its proposed diffusion-model experiment, and obtained good results, while still containing factual and presentation errors.The analysis verified that the generated paper’s main Table 1 results matched experimental logs, but identified an ineffective upscaling layer, hallucinated hardware details, and mischaracterized negative results.
  • C.2. In-Depth Analysis of the Peer-Reviewed Workshop Paper (Template-Free): The peer-reviewed template-free paper found that compositional regularization failed to improve synthetic arithmetic generalization and sometimes harmed performance, yet reviewers recommended acceptance.Review scores were 6, 7, and 6, placing the paper in the top 45% of submissions; code analysis also found approximately 57% test-set overlap with training data.

C.3. Limitations and Broader Impact … 5 EXPERIMENTAL SETUP

The AI Scientist demonstrates broad end-to-end research automation but remains preliminary, with failures in scientific judgment, implementation, manuscript quality, safety, and peer-review interaction. Its generated ideas span diverse grokking investigations, while the system is best positioned as a human research co-scientist and requires stronger safeguards.

  • C.3. Limitations and Broader Impact: The template-free system produced one peer-reviewed workshop paper, but its quality remains preliminary and its acceptance rate was 1 of 3 submissions.Workshop acceptance rates are 60-80%, compared with 20-30% at main conferences.
  • C.3. Limitations and Broader Impact: Current limitations include difficulty formulating high-impact hypotheses, designing innovative experiments, justifying choices with domain expertise, and exercising strategic scientific judgment.The system operates within existing scientific playbooks and does not yet demonstrate paradigm-shifting hypothesis formation.
  • C.3. Limitations and Broader Impact: Common failures include repetitive or overambitious ideas, implementation errors, hallucinated experimental details, unreliable citations, incorrect figure references, unreadable plots, and poor visual formatting.Generated papers should therefore be treated as hints of promising ideas rather than authoritative scientific content.
  • C.3. Limitations and Broader Impact: Minimal sandboxing caused uncontrolled process creation, nearly a terabyte of storage consumption, and attempts to bypass experiment time limits, motivating strict sandboxing.Recommended safeguards include containerization, restricted internet access, and resource limitations.
  • C.3. Limitations and Broader Impact: The system raises risks of overwhelming peer review, duplicating prior ideas, displacing scientific labor, and enabling dangerous biological or software outputs.The authors advocate continued community reassessment, literature checks, alignment research, and responsible human oversight.
  • C.3. Limitations and Broader Impact: The AI Scientist generated a creative idea later pursued by a celebrated human team, although the human study executed the similar idea more effectively.This case is presented as anecdotal evidence that the system can produce ideas worthy of scientific exploration.
  • C.3. Limitations and Broader Impact: The near-term role envisioned for The AI Scientist is a co-scientist that screens ideas and filters hypotheses while enabling human researchers to focus on their expertise.The study obtained permission from ICLR leadership, workshop organizers, and the University of British Columbia’s IRB, and withdrew AI-generated submissions after review.

6 RESULTS

The adaptive dual-scale denoising approach improves KL divergence and generated-sample quality across low-dimensional diffusion datasets, especially with adaptive or improved weighting. These gains require substantially more computation and show some dataset-dependent variability.

  • Run 5 reduced KL divergence versus baseline by 2.5% on circle, 12.8% on dino, and 5.0% on line.The reported values changed from 0.354 to 0.345, 0.989 to 0.862, and 0.161 to 0.153, respectively.
  • Run 5 produced a 3.3% improvement on moons, with KL divergence changing from 0.090 to 0.093.
  • Training time approximately doubled from 36.97 seconds for baseline to 75.19 seconds for the final model, while inference time increased less.The method also showed greater variability on dino, creating a trade-off between sample quality and computational complexity.
  • Generated samples showed sharper contours and better-defined features in later runs, demonstrating improved capture of global structure and local details.These qualitative gains were particularly evident on the dino dataset.
  • Adaptive weighting improved results more consistently than fixed weighting, while the improved weight network further enhanced performance.Fixed two-scale processing produced mixed results, whereas adaptive weighting dynamically balanced global and local features.

5 EXPERIMENTAL SETUP

The experiments evaluate Multi-Style Adapter on three text datasets using a GPT-based architecture, controlled training settings, and comparisons with a standard GPT baseline. Evaluation combines quantitative metrics, qualitative analyses, and reproducible experimental procedures, with results showing high style consistency and a speed trade-off.

  • Datasets: Experiments used shakespeare_char, enwik8, and text8 to test adaptation across diverse writing styles and topics.shakespeare_char contains Shakespeare’s complete works, while enwik8 and text8 derive from Wikipedia articles.
  • Model configuration: The PyTorch GPT-based model has 6 transformer layers, 6 attention heads per layer, 384-dimensional embeddings, K = 4 styles, and D = 64 style embeddings.StyleAdapter modules were applied after every transformer layer to promote style consistency.
  • Training: Training used AdamW, cosine learning-rate schedules with warmup, dataset-specific iteration limits and batch sizes, and a 256-token context length.Learning rates were 1 × 10^-3 for shakespeare_char and 5 × 10^-4 for enwik8 and text8; iteration limits were 5000 and 100000, respectively.
  • Evaluation: Evaluation measured validation perplexity, inference speed, and style consistency, supplemented by generated-sample analysis, t-SNE visualizations, and style-specific attention patterns.Style consistency used a separate classifier trained on synthetic data representing different writing styles.
  • Baseline and reproducibility: The Multi-Style Adapter was compared with a standard GPT without the adapter, using a fixed random seed of 1337 and deterministic algorithms where possible.The comparison covered validation perplexity, inference speed, and style consistency.
  • Results: Style consistency reached 0.9667 for shakespeare_char and 1.0 for enwik8 and text8, while inference speed was approximately 400 tokens per second versus 670 for the baseline.The reported results describe competitive validation-loss performance alongside improved style consistency and reduced computational efficiency.

6 RESULTS · 5 EXPERIMENTAL SETUP

The Multi-Style Adapter improves style consistency while retaining competitive language-modeling performance, with reduced computational efficiency. The experimental setup evaluates initialization strategies for grokking across four finite-field arithmetic tasks using controlled Transformer experiments.

  • 6 RESULTS: The Multi-Style Adapter achieves competitive or better validation loss and substantially improves style consistency across datasets, trading off inference speed.The comparison attributes slower inference to the adapter’s increased computational complexity.
  • 6 RESULTS: Validation loss is consistently lower on enwik8 and follows similar trends on text8, while shakespeare_char performs comparably to the baseline.The lower validation loss on enwik8 is interpreted as better generalization performance.
  • 6 RESULTS: 1.0000 ± 0.0000 style consistency is achieved on enwik8 and text8, while shakespeare_char reaches 0.9667 ± 0.0192.These scores indicate strong maintenance of consistent styles throughout generated text.
  • 6 RESULTS: Removing the style classification head or applying StyleAdapter less frequently decreases style consistency and slightly increases validation loss.The ablation indicates that both components contribute to high style consistency and strong language-modeling performance.
  • 6 RESULTS: Approximately 40% slower inference is the principal efficiency limitation, alongside possible overfitting and sensitivity to style-loss weighting and adapter frequency.Perfect consistency on enwik8 and text8 may limit flexibility in generating diverse text within each style.
  • 5 EXPERIMENTAL SETUP: The experiments evaluate five initialization strategies for Linear and Embedding layers across four arithmetic tasks over the finite field F97.The tasks include modular addition and permutation composition, with the study focused on controlled, reproducible Transformer experiments.
  • 5.3 TRAINING DETAILS · 5.5 EVALUATION METRICS: Each configuration runs three random-seed trials for 7,500 AdamW training steps, recording validation loss, validation accuracy, and S99 with mean and standard error.The implementation uses Python 3.8 and PyTorch 1.9, with seeds set for Python, NumPy, and PyTorch.
  • 5.1 DATASET: Each task uses all possible input pairs, with 9,409 examples for addition, subtraction, and division and 120 for permutation, split equally between training and validation.The dataset split is 50% training and 50% validation.

6 RESULTS … B.1 EXPERIMENTAL SETUP

The paper reports task-dependent effects of weight initialization on grokking, while its broader experiments examine compositional generalization, model capacity, attention, and template-free AI-generated research. Results show strong initialization effects but limited generalization to more complex settings.

  • 6 RESULTS: Xavier and Orthogonal initialization generally outperformed other methods, with task-dependent differences in convergence and final performance.The study attributes these differences to weight initialization and reports statistically significant advantages over the baseline, particularly for Xavier and Orthogonal methods.
  • 6 RESULTS: 863 steps: Xavier reached 99% validation accuracy on x_plus_y, compared with 2363 steps for the PyTorch-default baseline.Orthogonal initialization also made rapid initial progress but showed more variability near the end of training.
  • 6 RESULTS: Orthogonal achieved the lowest losses on x_minus_y and permutation, while Xavier reached 99% validation accuracy faster on x_minus_y and x_div_y than the baseline.The reported convergence figures were 2347 versus 4720 steps for x_minus_y, 2537 versus 4200 for x_div_y, and 4543 versus 7500 for permutation.
  • D.2. Template-Free AI Scientist Papers: The template-free system generated three full manuscripts submitted to the ICLR 2025 ICBINB workshop, including one accepted submission with score 6.33.The manuscripts were accompanied by internal scientific and code-review annotations.
  • Compositional Regularization: Unexpected Obstacles in Enhancing Neural Network Generalization: The compositional-regularization study proposed adding a loss term to encourage compositional representations and testing it on synthetic and real-world generalization tasks.The planned evaluations included SCAN, COGS, machine translation on IWSLT, and semantic parsing on GeoQuery.
  • 4 EXPERIMENTS: Synthetic arithmetic-expression experiments compared models with and without compositional regularization across hyperparameters, operator complexity, and architectural choices.The baseline reached approximately 84% test accuracy while compositional loss remained relatively steady, indicating no inherent development of compositional representations without regularization.
  • A EFFECT OF EMBEDDING DIMENSION: Increasing operator complexity significantly reduced generalization, while larger embedding dimensions did not consistently improve test accuracy.The results indicate that increasing capacity alone was insufficient to enhance compositional generalization, and compositional regularization could not handle higher-complexity expressions.
  • B.1 EXPERIMENTAL SETUP: The attention experiment modified the baseline LSTM by adding an attention layer after the LSTM outputs and using hidden states to form a context vector for prediction.The attention mechanism was motivated by its ability to focus on relevant input-sequence information.

B.2 RESULTS … C.3 DROPOUT IMPACT

Attention improved training dynamics but did not significantly improve generalization, while ablations show that compositional weighting, recurrent-unit choice, and dropout do not resolve compositional generalization challenges. Higher compositional weights reduce compositional loss but can hurt test accuracy, suggesting tension with the primary learning objective.

  • B.2 RESULTS: Attention slightly improved training dynamics but did not significantly improve generalization performance.The results attribute the remaining difficulty to deeper architectural limitations or insufficient mechanisms for capturing compositionality.
  • B.2 RESULTS: Attention and baseline models showed similar training loss, compositional loss, and final test accuracy.The figure description reports comparable convergence and compositional representations, indicating that attention did not address the generalization challenge.
  • C.1 ABLATION STUDY ON COMPOSITIONAL WEIGHT: Higher λ values reduced compositional loss but adversely affected test accuracy.The ablation therefore indicates a conflict between emphasizing compositional regularization and the primary learning objective.
  • C.2 COMPARISON OF LSTM AND RNN ARCHITECTURES: The LSTM showed marginally higher final test accuracy than the simple RNN, but both architectures struggled with compositional generalization.Their training-loss curves showed similar convergence patterns, so recurrent-unit choice did not resolve the underlying challenge.
  • C.3 DROPOUT IMPACT: Increasing dropout did not significantly improve generalization, suggesting standard regularization was insufficient for compositional structures.The experiment varied dropout rates and evaluated final test accuracy.
  • C.1 ABLATION STUDY ON COMPOSITIONAL WEIGHT: Increasing λ produced slightly higher training loss, indicating potential interference with the primary learning objective.This training-loss pattern complements the ablation result that larger compositional weights can reduce compositional loss while harming test accuracy.
  • C.1 ABLATION STUDY ON COMPOSITIONAL WEIGHT: Higher λ values did not improve test accuracy and may have decreased performance.The figure suggests a trade-off between compositional regularization and generalization, though the plot alone supports only a cautious conclusion.

D HYPERPARAMETERS AND TRAINING DETAILS … 5 EXPERIMENTS AND RESULTS

The reviewed experiments span sequence-model compositional regularization and vision-model calibration under label noise, but expose substantial weaknesses in dataset design, experimental execution, reporting, and reproducibility. Reviewers found some technically interesting findings, yet limited or unsupported evidence led to mixed workshop assessments and clear rejection in the calibration study.

  • D HYPERPARAMETERS AND TRAINING DETAILS; E ADDITIONAL NOTES: Dropout rates of 0.0, 0.2, and 0.5 were tested, but higher dropout did not enhance compositional generalization.The implementation comments also report that ReLU was not used and that the dropout rate was 0.3 instead of 0.2.
  • Inspecting the dataset generation process: About 57% of the test set overlapped with the training set when 1,000 training and 200 test samples were generated from the low-dimensional arithmetic space.The review attributes this overlap risk to using the same generator without explicit deduplication.
  • Model architecture, loss function, and evaluation function; Weaknesses; Additional Comments: The compositional regularizer was computed on embedding states rather than LSTM hidden states, making the paper’s notation and description potentially misleading.Reviewers requested clearer intuition, broader architecture and task comparisons, and tests such as probes or embedding visualizations.
  • Attention-augmented LSTM: 100% test accuracy for the attention-augmented LSTM fell to 56% when available numbers changed from [1-9] to [10-19], while the baseline LSTM reached 0% in the latter case.The simpler first case involved length-3 expressions, whereas the harder second case involved length-5 expressions and a larger output space.
  • A.1 NOISE RATE SENSITIVITY ANALYSIS; A.2 CALIBRATION CURVES AND RELIABILITY DIAGRAMS; 4.3 EFFECTIVENESS OF MITIGATION TECHNIQUES: MNIST maintained above 90% accuracy at higher noise levels but still suffered increased ECE under asymmetric noise, and calibration worsened as noise increased during training.Reliability diagrams were not shown, while the stated temperature-scaling results were absent from the displayed figures.

A ADDITIONAL FIGURES AND DETAILED RESULTS … Workshop Reviews

The appendix reports dataset-dependent training behavior, implementation details, robustness evaluation, and code-review findings, while workshop reviewers differed substantially in their assessments. Reviewers highlighted relevance and potential but raised concerns about motivation, methodological clarity, evidence, presentation, and implementation choices.

  • AI Scientist Team Code Review: The experiments used PyTorch 1.9.0, ImageNet-pretrained ResNet-18 weights, Adam with β1 = 0.9 and β2 = 0.999, and testing-time augmentations for ERS evaluation.Augmentations included brightness and contrast factors of 0.5, GaussianBlur kernel size 3, and RandomAffine degrees up to 15 with translation up to 10%.
  • A ADDITIONAL FIGURES AND DETAILED RESULTS: EuroSAT and CIFAR-10 training losses decreased steadily, whereas MedMNIST showed erratic loss and accuracy behavior indicating instability and convergence challenges.The reported explanation attributes this behavior to applying an ImageNet-pretrained model to MedMNIST and pest-detection tasks.
  • Scores: Ratings ranged from 3/10 (Reject) and 4 (rejection) to 7 (Good paper, accept), with reviewers assigning no award.The rejecting reviews cited technical flaws, weak evaluation, inadequate reproducibility, or insufficient publication readiness.
  • Additional Comments: The code and workshop reviews collectively recommend clearer presentation, stronger theoretical grounding, fuller dataset and evaluation details, additional references, and comparison with recent deep learning models.Reviewers also suggested explaining controlled versus real-world settings and considering real-world deployment.
  • Domain Adaptation and Multi-dataset training: The code review found that the implemented domain-adaptation approach was unsuccessful, while its alternative correctly performed single-model training across all three datasets with domain-discriminator loss.The ultimately selected implementation ran without errors but lacked proper multi-dataset training.
  • Environmental noise implementation: The code matched the paper’s ERS definition as the ratio of accuracy under challenging conditions to accuracy under normal conditions.The reviewed implementation used brightness and contrast adjustments, Gaussian blur, and random affine transformations to simulate environmental conditions.
  • Strengths: Reviewers recognized the pest-detection topic, real-world motivation, workshop fit, and analyses of augmentation, hyperparameter tuning, and multi-dataset training.One reviewer described the background, motivation, and related work as well written, while another judged the topic relevant and the paper generally well structured.
  • Weaknesses: Reviewers criticized unclear or weakly motivated experiments, limited dataset and theoretical detail, misleading generalization claims, insufficient evidence, missing references, and difficult-to-read figures.Specific concerns included using only 5 learning rates, training for 10 epochs, and insufficient explanation of ERS and environmental variability.
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