TusoAI: Agentic Optimization for Scientific Methods
Authors: Alistair Turcan
Kexin Huang
Lei Li
Martin Jinye Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: AI for Science
Agentic AI
Code Optimization
AutoML
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We present TusoAI, an agentic approach to constructing scientific methods, either from scratch, or improving upon a state-of-the-art tool.

Abstract: Scientific discovery is often slowed by the manual development of computational
tools needed to analyze complex experimental data. Building such tools is costly
and time-consuming because scientists must iteratively review literature, test mod-
eling and scientific assumptions against empirical data, and implement these in-
sights into efficient software. Large language models (LLMs) have demonstrated
strong capabilities in synthesizing literature, reasoning with empirical data, and
generating domain-specific code, offering new opportunities to accelerate com-
putational method development. Existing LLM-based systems either focus on
performing scientific analyses using existing computational methods or on de-
veloping computational methods or models for general machine learning without
effectively integrating the often unstructured knowledge specific to scientific do-
mains. Here, we introduce TusoAI, an agentic AI system that takes a scientific task
description with an evaluation function and autonomously develops and optimizes
computational methods for the application. TusoAI integrates domain knowledge
into a knowledge tree representation and performs iterative, domain-specific op-
timization and model diagnosis, improving performance over a pool of candidate
solutions. We conducted comprehensive benchmark evaluations demonstrating
that TusoAI outperforms state-of-the-art expert methods, MLE agents, and scien-
tific AI agents across diverse tasks. Applying TusoAI to two key open problems
in genetics improved existing computational methods and uncovered new biology
missed by previous methods.  Our code is publicly available at https://github.com/Alistair-Turcan/TusoAI.

Accepted at ICLR 2026
TUSOAI: AGENTIC OPTIMIZATION FOR SCIENTIFIC METHODS
Alistair Turcan', Kexin Huang?. Lei Li', Martin Jinye Zhang' Carmegic Mellon University Stanford University 'Phylo aturcanêcs, cmu, edu, martinzh@andrew. cmu
ABSTRACT
Scientific discovery is often slowed by the manual development of computational tools needed to analyze complex experimental data. Building such tools is costly and time-consuming because scientists must iteratively review literature, test mod- eling and scientific assumptions against empirical data, and implement these in- sights into efficient software. Large language models (LLMs) have demonstrated strong capabilities in synthesizing literature, reasoning with empirical data, and generating domain-specific code, offering putational method development. Existing LLM-based systems either focus on performing scientific analyses using existing computational methods on de- veloping computational methods or models for general machine learning with- out effectively integrating the often unstructured knowledge specific scien- tific domains. Here, we introduce TusoAI, an agentic Al system that takes a scientific task description with an evaluation function and autonomously devel- ops and optimizes computational methods for the application. TusoAI integrates domain knowledge into a knowledge tree representation and performs iterative, domain-specific optimization and model diagnosis, improving performance over a pool of candidate solutions. We conducted comprehensive benchmark eval- uations demonstrating that TusoAI outperforms state-of-the -frt-e--rtt-tt expert methods, MLE agents, and scientific Al agents across diverse tasks. Applying TusoAI to key open problems in geneties improved existing computational methods and uncovered new biology missed by previous methods. Our code is publicly avail- able httpss//gittub.ccmm Awwstair--stttteeaaaaa Turcan/TusoAl
opportunities accelerate com-
INTRODUCTION
Scientific discoveries are often bottlenecked by the slow, manual development of computational tools needed to analyze experimental data. For example, genetics studies have uncovered tens of thousands of disease-associated variants, yet robust computational methods critically needed to harmonize multi-modal, multi-scale data and uncover the underlying mechanisms (Lappalainen & MacArthur 2021). Developing such tools is slow and costly because scientists must iteratively (i) review extensive literature, (ii) test modeling and scientific assumptions against empirical data, and (iii implement these insights into efficient, scalable code. For instance, building robust computa- tional methods to link enhancers with target genes from single-cell multiome data has taken multi- ple expert groups many years (Dorans al. 20225), hindered by challenges such as cis-regulatory modeling, latent confounding, noisy data, and computational scalability. Large language models (LLMs) have demonstrated strong capabilities
performing human- like analysis (Luo al.) 2025), such synthesizing relevant literature (Asai et al. 2024), reasoning about biological and modeling assumptions using empirical data (Gao et al. (024), and generating efficient, comain-speciife code (Rasheed et al. 2025). Integrating LLMs with scientific domain knowledge and iterative data exper- imentation holds great promise to accelerate computational method development, thereby advancing discoveries in science and medicine.
Existing work has produced seneral-purpose AI agents across scientific domains, including biomedicine (Huang al., 2025; Jin et al. 2025) and chemistry (M. Bran al. 2024). These systems primarily focus on performing scientific data analyses rather than developing new compu- tational methods; the former involves assembling and executing pipelines of data formatting and

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Accepted at ICLR 2026
existing tools, whereas the latter requires creating algorithms models for specific pipeline steps, involving substantial design, optimization, and validation. In parallel, several studies have developed machine learning engineering (MLE) agents that design new algorithms for general MI. applicaiins (Guo et al. 2024, Trirat et al. 2024 Jiang et al. 2025 Nam et al. 2025), but these approaches do not address Nomain-speciine challenges inherent scientific research. Developing Al agents for scientific method development that integrate structured domain knowledge and sys- tematically explore data-specific assumptions has considerable potential to accelerate the creation of robust computational methods for science and medicine.
Here, we introduce TusoAI, an agentic Al system that takes a scientific task description with an evaluation function, and autonomously develops and optimizes computational methods for the appli- cation (Figure D. TusoAl mimics scientist's cycle of method development, integrating structured domain knowledge with iterative, domain-speciff optimization and model diagnosis, improving performance over pool of candidate solutions. We demonstrate that TusoAl achieves superior per- formance across range of algorithmic, statistical, machine learning, and deep learning applications in science, Our key contributions are:
1. We develop TusoAI, an Al agent specifically tailored for scientific method discovery by integrating structured domain knowledge.
2. We propose novel framework, featuring (i) knowledge tree for structured representation of domain knowledge, (ii) hierarchical planning with Bayesian updates balance solution quality and diversity, and (iii) fine-grained generation that integrates model optimization with diagnostic feedback.
3. We benchmark TusoAI on 6 single-cell analysis tasks and scientific deep learning tasks, consistently outperforming baseline methods and frequently surpassing existing expert- designed algorithms.
4. Applying TusoAI to two key open problems in geneties improved existing computational methods and uncovered new biology missed by existing methods.
Step
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Figure 1: Scientific method development with TusoAI. (A) Method overview. (B) Example do- main knowledge tree (categories and instructions per category), feedback, and diagnostics.
2

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Accepted at ICLR 2026
1.1 RELATED WORK
LLM-based scientific Al agents. Several works have developed general-purpose Al agents capable of autonomously executing various scientific research tasks. Biomni (Huang et al. 2025) provides unified agentic environment with tools and databases spanning 25 biomedical domains, integrat- ing LLM reasoning with rerrieval-augmented planning and code execution to compose complex workflows. Stella (Jin al., (025) employs multi-agent architecture for autonomous biomed- ical data analysis, achieving self-evolutin by dynamically updating its template library and tool collection. ChemCrow (M. Bran et al. 2024) is a chemistry-focused agent that integrates 18 expert- designed tools and follows the "Thought, Action, Action Input, Observation" format to iteratively reason toward answers. These methods emphasize end-to-end data analysis with established tools, whereas our work focuses on developing new computational methods for domain-specifii tasks. Other works have leveraged L.LMs to develop ieplicatioonssecciie methods, such as single-cell per- turbation prediction (Tang et al. discovery (Romera-Paredeer et al. 2024). In contrast, TusoAI targets computational method develop- ment across scientific tasks. InternAgent (Team et al., 2025) and its precursor Dolphin (Yuan al. 2025) iteratively evolve and implement research ideas through an optimization process augmented with literature review. As concurrent effort, Aygun et al. (2025) combine LLMs with tree search and existing model ensembles to improve scientific algorithms, addressing a similar problem but with a different approach from ours, which integrates domain knowledge tree with fine-grained iterative optimization and Bayesian updates. As their code is not publicly available, direct compar- ison is not possible, but key distinction of our work is perform fine- fine-graine -r-ggrained optimizations with domain knowledge that do not require existing models and can operate on small portion of a much larger method.
(025 diagnosis prediction (Tan et al., [025). and mathematical
LLM-based general machine learning agents. Several recent works have developed AI agents for general machine learning engineering. AIDE (Jiang et al.) (025) frames ML engineering as a code optimization problem, combining an LLM with tree search to iteratively improve solutions. R&D Agent (Yang et al. 2025) similarly explores MI. architectures in a dynamic feedback loop. DS-Agent (Guo et al. (024) combines an L.M with case-based reasoning (CBR), retrieving po- tentially successful solutions from top-ranked Kaggle solutions, and refining them through iterative optimization. MLE-STAR (Nam et al. 2025) retrieves candidate models from the web to form an initial solution, then improve it by targeting specific ML components and ensembling. AutoML- Agent (Triatt et al., 2024) employs retrieval-augmented planning and multi-agent coordination to generate an optimal plan, but executes the plan once without iterative refinement. These methods less suited to scientific method development, where domain knowledge is unstructured, exist- ing ML models may be unavailable, and search spaces are continually evolving. We address these challenges through structured domain knowledge representation and a novel hicrarchical planning procedure with Bayesian updates during iterative optimization.
Classical automatic machine learning (AutoML) frameworks. Classical (non-LLM) AutoML frameworks aim to construct perforreformin ML models from scratch by searching over key com- ponents such as feature preprocessing, model architectures, hyperparameters, and pipeline com- position. Notable examples include auto-sklearn (Feurer et al. 2015), H20 (LeDell et al.) 2020). AutoGluon (Erickson et al., 2020)), and TPOT (Olson & Moore 2016). Within deep learning, neu- ral architecture search (NAS) methods specialize in optimizing neural architectures, with examples such as DARTS (Liu et al.) 2018) and AMBER (Zhang et al. 2021). While effective for standard ML tasks, these approaches domains, where domain knowledge and optimization objectives are unstructured and continually evolving, making LLMd-bseed agents more natural fit as they can pair principled optimization objective with heuristic search procedures.
constrained by predefined search spaces and are less suited to scientific
2 PROBLEM FORMULATION
We consider the problem of automatic scientific algorithm optimization with LLMs. Given general solution space Sfull (e.g., all Python scripts) and an evaluator h(-) : Sfull + I, the objective is to find the optimal solution s* arg ma h(s)) h(-) can be any evaluation metric, such as AUC, average of several metrics, or domain-speciffe measures (c.g., enrichment of inferred disease genes against expert-curated set). We assume access to task description T (e.g., "single-cell RNA-seq
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Accepted at ICLR 2026
imputation"), a domain-specific knowledge base (e.g., scientific papers), and general LLM that can be instantiated as agents. The agent can, for example, summarize domain priors from T, retrieve information from the knowledge base, and refine candidate solution based on instructions. The goal is to iteratively implement and improve solutions to maximize h(.)) within a time budget. We consider settings: a cold start, where optimization begins from scratch, and warm start, where an initial solution Sinit (c.g., state-of-the-art method) is given for further improvement.
METHODS
TusoAI takes as imput task description T, dataset D, an evaluator ft()), and optionally an initial solution Smit- It outputs optimized solution s* (Algorithm [ variables described in Appendix Table A). TusoAl operates on only single function of an arbitrarily large codebase, allowing to flexibly build upon scientific methods with extensive scaffolding. Developing computational methods for scientific domains poses several challenges. First, domain-specific knowledge is often unstructured, which address using knowledge tree that organizes information into categories and within-category instructions. Second, approaches and optimization strategies can vary widely, which we manage through hierarchical planning with Bayesian updates promote diversity while ensuring solution quality. Third, understanding complex data patterns is challenging, which we mitigate with fine-grained generation that integrates model optimization with diagnostic feedback.
TusoAl consists of 3 steps. First, it gathers domain knowledge by summarizing key scientific pa- pers, ensuring that optimization instructions reflect established best practices and recent advances rather than relying solely on LLM priors. Second, it builds a two-level knowledge tree of struc- tured instructions: (1) categories of optimization strategies and (2) specific instructions within each category, promoting both diversity and relevance. Categories and instructions are first drafted by the LLM and then refined through additional L.M queries in conjunction with paper summaries to ensure diversity and scientific rigor; we also predefine diagnostic category Zaing to guide data log- ging and model diagnosis. Third, after initializing candidate solutions, it itcratively selects diverse top performers and improves them through either instruction-based or diagnostic-baset optimiza- tion. Due to the knowledge tree structure, instruction categories can be sampled adaptively via a Bayesian strategy informed by past performance, while feedback comparing new and prior solu- tions helps discourage repetition. Examples of instructions generated are provided at Appendix [C
Algorithm TusoAl
Input: Task T; dataset D; evaluator h(-): optional initial solution Sinit. Hyperparameters: Time budget T budget (default 8 hrs).
Gather domain knowledge:
Apaper (T)
Paper summaries
2: Build structured instructions:
C, (Te)eec f- DrraftThenRefngg (Aeate; T,P)
Instruction categories with probabilities
For each E C:
Per-category instructions and feedback Io f- DraftThenRefine(AA (iintir T,,,c), 0
Diagnostic instructions (predefined) and feedback 3: Initialize solutions: S Ainit (T, T,n,sinnn): Nop + [S]
Idiag + mnradefined Fdiing +0 -0 diag
4: While wall-clock time < Thulgget do
5: Select Neop diverse top solutions from S
6: for each top do if Bernoulli(a) do
7:
Instruction-busee optimization. defualt 0.8
8:
Sample Cat((TTe optimize s' + Aqpiim (s, Ic, F.)
9:
if hh(ss) ) > h(()) do To +- 1.1TT; renormalize { F. F.U (Arowtback s')}
>> Bayesian update utility
Gather category-specifs feedback > Diagnostic-basee optimization D Get model& log info then optimize Gather diagnostic feedback
11:
else
x --Ading(a, (s, D, Dringg Feing) Fulug FFFiing U {freethuck s')}
14:
S + S UUf {s'}
Not max(1, Nop 1) every rounds
16:
return 8* arg magxEs l(s))
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Accepted at ICLR 2026
Step 1: Gather domain knowledge. TusoAI first retrieves up to 10 key papers from Semantic Scholar (Allen Institute for AI 2025) relevant to T. ranked by their citation count. For each paper, an agent Apuper creates 15- 1p-p technical summary from the abstract and iteratively refines it using each paragraph of the paper's is Methods section (up to 1, 1,200 words to focus solely on technical content without relying on costly deep research agents parsing the entire document). This produces P = [P.), where each P: is refined 15-point summary of paper i's method.
Step 2: Build structured instructions. TusoAl uses draft-then-refine strategy construct opti- mization categories, where an agent Acate first drafts candidate categories from the task description T. then refines them by iterating through each paper summary P, E P. adjusting existing categories or adding new ones as needed. Categories are task and can be general (e.g., "regularization", "model architectures") or domain-
(e.g., "single-cell noise modeling", "genetic feature in- teractions"). Each category is assigned a probability Te representing its utility in the optimization process; ne is initialized by Asate so that tasks earlier in the pipeline (e.g., "feature preprocessing") receive higher weight than later ones (e.g., "hyperparameter tuning"). Similarly, TusoAI uses draft-then-refine strategy to initialize instructions for each category, where an agent Amsst first drafts 10 candidate instructions I. from the task description T. These instruction lists are then refined by incorporating 10 additional instructions for each paper summary P, E P. For feedback, TusoAI ini- tializes an empty list J., < 0 for each category, which is updated with category-specife feedback during optimization. A special predefined diagnostic category Zating provides instructions for logging diagnostic information useful for model updates, with its own feedback list Fdiag. See Appendix C G for an example of constructed task information and Appendix [ for prompt templates used to generate this information.
Initialize solutions. The initialization agent Ami drafts 5 candidate solution descrip- tions from T and iteratively refines them using each paper summary in P, adding new descriptions or improving existing ones (e.g. "zero-inflate" Poisson with kNN smoothing"). These are basic descriptions designed to start from scratch and explore to avoid simply re-implementing existing solutions. It then attempts to implement and debug each solution; those that successfully compile form the initial solution pool S. Each implementation attempt is limited to 10 minutes with up to bug-fix attempts.
Step 3.1:
Step 3.2: Iterative optimization. Given the current solution set S, TusoAl selects diverse top solutions by clustering them based on code-text similarity and, within each cluster, choosing the shortest solution whose performance is within 0.1% of the cluster's ' best model; this helps discourage overfitting and randomness while maintaining diversity and concise code. For each cluster's top solution 8, TusoAl performs either instruction- based optimization (20% probability). The resulting solution s' is added the pool S <- SUs' Each implementation attempt is limited to 10 minutes with up to 2 bug-fix attempts. This time regularization ensures the optimization period not wasted on few inefficient implementations, and encourages the final method to be scalable.
optimization (80% probability) or diagnostic-
instruction-based optimization. The optimization agent Aaptim selects an instruction by first sampling an instruction category e ~ Cat((T))ecc) then uniformly draw candi- date instructions from Ie, and finally choosing the most promising among them. It then optimizes s to produce s' using the selected instruction in conjunction with most recent feedback entries from Fe. If h(s') category utility by setting Te < 1.1xg and renormalizing {#e) representing the prior belief that this category currently contains useful instructions. Finally, the feedback agent Afcedback summarizes the change from to s' and appends it to F. (e.g., "this optimization constructed kNN on the top 50 PC's rather than on all genes, improving performance by 15%").
h(s), TusoAI performs a Bayesian-style update the
Diagnostic- optimization. The optimization agent Augtim selects an instruction by first uniformly draw 3 candidate instructions from Iuing and then choosing the most promis- ing among them (e.g.. "training curves", "distribution checks", "validation of assump- tions"). It then diagnoses and improves to produce s' using the selected instruction in conjunction with 5 most recent feedback entries from Fe: it runs s to collect diagnostic logs and then uses this information to produce an improved model s'. This represents sci- entist diagnosing their method's 's intermediate outputs to further improve upon it. Finally, the feedback agent A Afedbmuck summarizes the change from s to s' and appends it to Faing.
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Accepted at ICLR 2026
4 EXPERIMENTS
We evaluate TusoAl on 11 scientific applications spanning diverse domains with both ML and non- ML components, including single-cell analysis tasks (Luecken et al. 2025) and 5 scientific deep learning tasks (Tu et al.] 2022). The single-cell tasks include denoising (Denoise), cell-type la- bel projection (Label), batch integration (Batch), identification of spatially variable genes (SVG), decomposition of spot-level spatial data into specific cell types (Decomp), and dimensionality re- duction for visualization (Visual). The scientific deep learning tasks include omnidirectional vision (Spherical), prosthetics control (NinaPro), medical diagnostics (ECG), earth monitoring (Satellite), and genetic prediction (DeepSea). In each task, we run TusoAI for 8 hours (as per related work (Miller al. 2025 Aygun al. 2025 optimizing performance on validation dataset, and evalu- ating final performance on separate testing datasets. Task descriptions used are concise (e.g.. "single- cell batch integration") and extracted from the original benchmarks. We define "Avg." and "Avg- Rank" as the average performance across tasks for method, and the average rank in each task, respectively. We additionally assess code diversity, defined as the text similarity between generated code (Appendix[M M and mean time to optimize. defined as the average position of each optimization over the hours, representing how quickly optimizations are achieved.
We conduct comprehensive ablation studies to assess the contribution of different components of TusoAI (Subsection 442). and two case studies demonstrating how TusoAI can reveal new biological insights in genetics (Section 5). Full details experimental setup and evaluation metrics used are in Appendix D E for single-cell and deep learning tasks, respectively.
Baseline methods. We compare TusoAl against the state-of-the-an art MLE agent AIDE (Jiang al. 2025), scientific agents Biomni (Huang et al., (025) and ChatGPT-Agen OP 2025), and top- performing, published application-speciffe methods. Biomni (LLM backbone Mlaude4-Soonee and ChatGPT-agent (LLM backbone GPT-5) are used to iteratively build models on data for single- cell tasks; for deep learning tasks, where Biomni and CharGPT-agent were unable to operate, we substitute the best of ten models constructed by Claude-4-Sonnet and GPT-5. GPT-4o-mini and GPT-5 are accessed through the OpenAI API, and all others with OpenRouter. For application- specific baselines, we use the "top-performing expert" method for single-cell tasks (Luecken et al.) 2025), and all baseline methods, including expert models and NAS methods, for the scientific deep learning tasks (Tu et al. [2022). This set of baselines is consistent with related work in scientific optimization (Aygünn et al. (025) and a recent benchmark that identified AIDE and Biomni as top- performers (Miller et al. 22025). We note that most MLE agentic methods cannot apply to scientific tasks outside the standard ML setup. For full details on baseline implementation and setup, see Appendix F
4.1 PERFORMANCE ACROSS BENCHMARK EXPERIMENTS
Results for the 6 single-cell tasks and 5 scientific deep learning tasks are reported in Tables and 2 We reached 2 main conclusions. First, TusoAI consistently outperformed baseline methods across benchmarks when generating code from scratch (average rank of 1.2 for single-cell tasks and 2.8 for scientific deep learning tasks, vs. 3.0 and 4.0 for the second best, resp.). Second, the methods con- structed by TusoAl are novel rather than simple re-implementations of existing approaches or calls to standard packages. Examples include: matrix factorization (NMF) approach that models dropout rates, Poisson noise, and performs itera- tive refinement, distinct from the only other -bF-based approach in the OpenProblems benchmark, ALRA (Linderman et al. 2022); (ii) in SVG, TusoAI adapted known techniques such as modeling expression as a function of spatial coordinates and neighborhood summaries to create custom, high-performing method; (iii) in Satellite, TusoAI combined preprocessing, training procedures, loss functions, and ensembling techniques to build the cop-performing model; and (iv) in Spherical, TusoAI fine-tuned layers of ResNet-50 and augmented the data with random flips and rotations. See Appendix H for full justification of why these new methods are novel. Third, all methods con- structed by TusoAI are computationally efficient (<3 minutes for single-cell tasks and <8 minutes for deep learning tasks, Appendix owing to the runtime constraints imposed during optimization.
in single-cell denoise, TusoAI designed non-negative
owing
We conducted 2 secondary analyses. First, we assessed the diversity of code produced by TusoAI and AIDE over 8 hours of optimization, quantifying code diversity using cosine similarity of text embeddings between each candidate and its 10 previous and 10 subsequent iterations (Figure A).

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Accepted at ICLR 2026
Denoise
Label
Batch
SVG
Decomp
Visual
Avg
Avg rank
Expert
0.28
0.85
0.71
0.66
0.49
0.44
0.57
3.7
AIDE
0.30
0.87
0.71
0.73
0,06
0.44
0.52
3.0
Biomni*
0.16
0.89
0.82
0.16
0.53
0.35
0.49
3.7
ChatGPT-Agennt
0.03
0.81
0.83
0.60
0.74
0.38
0.57
3.5
TusoAI*
0.35
0.89
0.83
0.80
0.64
0.44
0.66
1.2
Table 1: Single-cell benchmarks. We report performance across 6 single-cell tasks. denotes agentic methods. Best in bold, second-best underlined. 95% CIs across random seeds all under 0.01 and thus not shown.
Avg
Spherical
NinaPro
ECG
Satellite
DeepSEA
Avg
rank
WRN default
0.14 0.11
0.93 0.00
0.57 + 0.00
0.85 0.00
0.60 0.00
0.62
6.9
DenseNAS random
0.29 0.02
0.92 1 0.01
0.58 1: 0.00
0.86 0.00
0.60 1 0.00
0.65
5.4
DenseNAS original
0.27 = 0.01
0.90 = 0.01
0.60 + 0.00
0,86 + 0.01
0.60 0.00
0.65
5.8
Perceiver I0
0.17 = 0.00
0.78 0.02
0.34 + 0.00
0.84 0.00
0.62 0.00
0.55
9.6
XGBoost
0.03 0.00
0.78 1: 0.01
0.44 1: 0.00
0.64 0.00
0.50 1: 0.00
0.48
11.6
WRN ASHA
0,25 0.00
0.93 = 0.01
0.57 + 0.00
0,84 + 0.01
0.59 = 0.00
0.63
7.1
DARTS
0.52 0.03
0.82
0.66
0.87 0.00
0.68
0.71
4.0
AMBER
N/A
N/A
0.67 1: 0.00
0.87 0.00
0.68 1: 0.00
N/A
N/A
Expert
0.33 0.01
0.91 + 0.01
0.72 + 0.00
0,80 + 0.00
0.70 0.00
0.69
4.6
AIDE
0.16 0.01
0.86 +:
0.52 1:
0.83
0.01
0.57
0.59
9.8
GPT-5"
0.36 0.00
0.89 # 0.00
0.58 + 0.03
0.86 + 0.01
0.66 = 0.00
0.67
5.8
Claude 1eeeee--Sie $
0.40 0.00
0.90 +: 0.00
0.50 + 0.01
0.88 0.00
0.73 :: 0.00
0.68
4.6
T
0.42 0.01
0.90 +: 0.61
0.89 0.01
0.70
0.70
2.8
Table 2: Scientific deep learning benchmarks. We report performance across 5 scientific deep learning tasks. "*" denotes agentic methods. Performance of non-agentic methods extracted from NASBENCH-360 and transformed to be between 0 and I and higher is better. Best in bold, second- best underlined. 95% Cls provided across 3 random seeds.
We note that constructing diverse code to escape local optima is often an important consideration in agentic code optimization (Romera-Paredes et al. 2024 Nam et al. 2025 Aygün et al. 2025). TusoAI achieved substantially higher diversity than AIDE throughout the optimization process. For example, in the batch integration benchmark, AIDE repeatedly proposed small variations of UMAP-based dimensionality reduction, whereas TusoAI explored wide variety of dimensionality reduction, transformation, and scaling techniques. This higher diversity is perhaps due to TusoAl's instruction sampling, feedback, and diagnosis procedures, which encourage diverse solutions. We validate the importance of code diversity in generating strong optimizations (Appendix M). In con- trast, AIDE promotes incremental changes at each optimization step to facilitate traceability, which may bias the search toward local tuning rather than full exploration. Second, characterized the optimization trajectory of TusoAI on the single-cell denoising task (Figure 3).. We identified 5 key developments that led to strong performance: (1) introducing NMF, (2) modeling dropout, (3) mod- eling Poisson noise, (4) adding iterative refinement, and (5) incorporating sparsity- step. Notably, during optimization, TusoAl generated many methods that reduced performance before converging on high-performing solutions. Together with the feedback mechanism, this broad explo- ration allowed TusoAI efficiently search the solution space and identify top- perfoorrfnng methods. See Appendix K for the optimization trajectories in other tasks.
4.2 ABLATION STUDIES
We conducted extensive ablation studies to evaluate the impact of each novel component of Tu- soAl by removing one at a time, including: (i) removing the categorical structure and placing all instructions and feedback into single category (No categories); (ii) disabling the Bayesian sam- pling strategy across categories (No Bayesian); (iii) disabling the model diagnosis capability (No diagnosis); and (iv) discarding domain knowledge altogether, such that each iteration simply applies a generic instruction (e.g., "Optimize this model"; ; No knowledge). Removing these components each negatively affected overall performance (Table 3). We attribute this to reduced code diversity
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Accepted at ICLR 2026
TusoAl -ingle-cel cell
TUSoAI Deep Learning
Matrix
Dropout estimation
Polsson noise
Iterative re refinement
Balance sparsity
AIDE Single-cell
factorization
A
AIDE
Deep Learning
B
Code diversity across time
TusoAl Denoise optimization curve curve
Validation score
Code diversity
0.75
0.6
0.60
0.4
Best code Proposed code
0.2
Optimization
0.45
0.0
Time (hours)
Time (hours)
Figure 2: Behavior of code generated by TusoAL. (A) Code diversity of TusoAI and AIDE optimization time, as measured by 1- cosine similarity. Each line corresponds to a dataset. (B) Per- formance of the proposed optimization and the best code over optimization time for a representative task "Denoise". Key optimization changes with their occurrence times are annotated.
(mean diversity 0.48 vs. 1.44/0.39/0.3800.3 for ablated versions, resp.) and computational effi ciency (mean time optimize 2.3 hours vs. 2.4/3. 2.4/3.0/2.6/2.4 0/2.6/2.4 for ablated versions, resp.). Removing domain knowledge had the strongest impact on performance and diversity, while removing Bayesian updates (thus sampling categories uniformly) most reduced TusoAl's computational efficiency. See Appendix N O for TusoAl's general stability across replicates, further ablation details and abla-
tions varying literatrre information used.
We next assessed the impact of LLM backbones used by TusoAl, testing across 5 different LLMs: low-latency models GPT-4o-mini (default) and Clauke-3.5-Haiku; state-of-the-ate reasoning models GPT-5 and Claude-4-Sonnet; and open-source OPT--oss-106b Results are shown in Table 3 Apart from GPT-oss- 120b, TusoAI achieved relatively consistent performance across all LLMs for most tasks, demonstrating robustness. Interestingly. LLMs such as GPT-5 and Claude-4-Sonnet did not consistently outperform their lower-latency counterparts, GPT-4o-mini and 3..udd-3.5-Haiike This may be because, while reasoning models can construct highly complex code, their tendency to over- build (e.g.. each of GPT-5's methods are 300+ lines of code) makes subsequent iterations difficult to refin;; in contrast, low-latency but capable models like GPT-4o-min and Claude-3.5. 3.5-Haiku, when paired with an appropriate system design, performed just as well fraction of the cost (e.g., opti- mizing denoising for 8 hours costs 0.24$ with GPT-4o-mini and 22.3$ with GPT-5), See Appendix for further LLM analysis and cost details, respectively.
Avg
Denoise
SVG
Decomposition
ECG
Satellite
Avg
rank
TusoAI (default)
0.35
0.80
0.64
0.61
0.89
0.66
2.0
No categories
0.09
0.72
0.56
0.63
0.86
0.57
3.2
No Bayesian
0.36
0.77
0.22
0.57
0.84
0.55
3.4
No diagnosis
0.26
0.77
0.68
0.63
0.86
0.64
2.0
No knowledge
0.17
0.51
0.07
0.68
0.85
0.46
3.8
GPT-4o-mini (default)
0.35
0.80
0.64
0.61
0.89
0.66
2.2
GPT-5
0.31
0.80
0.82
0.67
0.87
0.69
2.2
Claude 3.5 Haiku
0.41
0.78
0.70
0.63
0.89
0.68
Claude Sonnet
0.32
0.78
0.53
0.59
0.84
0.61
4.2
GPT-oss- PoPT-oss-I00 120b
0.39
0.74
0.13
0.61
0.85
0.54
3.8
Table 3: Ablation studies (top) and varying LLM backbone (bottom). Best in bold, second-best underlined.
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Accepted at ICLR 2026
5 CASE STUDIES IN GENETICS
We applied TusoAI to address 2 key challenges in genetics: detecting discase-critical cell popula- tions and linking genetic variants their target genes; these central to understanding disease etiology but limited by current computational models. We initialized TusoAI with state-of-the-ar methods (scDRS (Zhang et al. 2022b) and pgBoost (Dorans al.) (025), resp.) and evaluated its ability to improve these approaches and generate new biological insights. We consider the same quantitative evaluation procedure and level of validation for new discoveries in the original pa- pers. We note these codebases are too large to easily use with existing agentic approaches that require editing the entire Python script. See full details of how we applied TusoAI each task in Appendix R for scDRS and pgBoost, respectively. An additional case study of how TusoAl may optimize an existing deep learning model outside of biology in Appendix [T]
Detecting disease-critical cell populations. scDRS (Zhang et al., 2022b) is a state-of-the method that integrates genome-wide association studies (GWAS) with single-cell RNA-seq (scRNA- seq) to identify disease-associated cell populations. but its power is limited by the high noise of scRNA-seq data. Here, we apply TusoAI in conjunction with scDRS and task it with optimizing scDRS's s association scoring function. Results are reported in Figure 3 We reached 3 main conclu- sions. First, the TusoAAloptimized version substantially outperformed the original scDRS in both simulations and real-data benchmarks: it achieved over 40% higher power in causal simulations (Figure 3) while retaining calibration in null settings (Appendix R]. and identified 21% more true cell disease associations (17 vs. 14) without false associations in a real-data benchmark (Li eet al. 2025). Second, the TusoAl-optimized scoring function is concise and interpretable. It com- putes association scores in log-log rather than log space, likely because this transformation better captures polygenic disease signals across many genes, avoiding domination by a few highly ex- pressed genes. This improvement reflects TusoAl's ability to efficiently explore variations built on the original method: it tested 167 different variations in 24 hours and at a cost of S0.37, whereas the original authors evaluated fewer than versions over 3 months. Third, applying the TusoAI- optimized scDRS to T cell dataset (Cano-Gamez et al., 2020) revealed 26 disease-associated T cell subpopulations (at FDR as per original paper) vs. by the original method, including regu- latory T cells, central memory T cells, and effector memory T cells associated with primary biliary cirrhosis, consistent with the roles of these T cell populations in autoimmunity (Dominguez Villar & Haffer 2018
Seo al. 2025
A
B Detecting ground truth cell types
C
Causal simulations
Novel T cell types associated to autoimmune disease
0.
Power
AD-D2 Hooeon
frimary contees TCM2
monery brivy anthoe- TCM
17
cholstord heputecyte
All cuccermune di diooee TCM0
alergy Eczema TC.9
SCORS (TussAA)
Hypoth you
0.0
&DRSS
SCDRS (TusoAI) SCDRS
scDRS (TusoAl)
SCDRS
Causal effect size
Figure 3: Optimizing scDRS for detecting cell-disease associations. (A) Assessing power in causal simulations. 95% CI's are calculated across 30 replicates at each perturbation effect size. (B) Venn diagram of discovered ground-trui -tuth trait-cell type pairs at FDR50.05 for scDRS and scDRS (TusoAD). New trait-cell type pairs are indicated on the left. (C) Venn diagram of discovered trait- cell subtype pairs at F0R5 for seDRS and scDRS (TusoAl). New trait-cell type pairs are indicated on the left.
Linking genetic variants to genes using single-cell multiome. pgBoost (Dorans et al. 2025) is a state-of-the-art method for linking genetic variants to target genes using single-cell multiome data; it integrates variant-gene distance with multiple linking strategies, but the task remains challenging due the complexity of genetic regulation (Gazal al. 2022). Here, we apply TusoAI in junction with Boost, providing additional positional information for variants and genes, and task it with optimizing distance-based features. Results reported in Figure 4 We reached 3 main
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Accepted at ICLR 2026
conclusions. First, the TusoAL-optimized model significantly outperformed the original pgBoost, achieving 13.8% higher enrichment of gold-standard links from fine- fine-mapped eQTLs and 7.2% from activity-by--ontact (ABC) links, with particularly large gains across longer variant- gene distances where links are harder to identify (Figure B)). Second, the distance-based features generated by TusoAl are concise and interpretable: 3 are transformed versions of existing features (inverse, squared, and normalized terms), 2 are interactions of gene annotations with distance terms, and the sixth indicates whether the SNP is <50kb from the gene's transcription start site, consistent with literature suggesting the typical enhancer promoter range of around 70kb (Bower et al. (025). Tu- soAI discovered these features by testing 511 combinations of 153 novel distance features within 24 hours at cost of $0.41, whereas the original authors evaluated 5 features over 1.5 months. Third, applying the TusoAl-optimized pooosst to fine-mapped SNPs for 94 diseases/traits identified new variant-gene links missed by previous methods (> 95% linking percentile vs. < 95% all others, as per original paper). For example, a fine-mapped variant rs Ns13891752) for glucose and HbA Ic was linked to GCK, consistent with the roles of Glucokinase in regulating glucose levels related to both glucose metabolism (Froguel et al. 1993) and HbA lc variation (Chakera et al. 2015).
A
eQTL evaluation
B
ABC evaluation
C
Linking percentle
rs138917529 (Glucose, HBA1c) - GCK
Average ennccment
Average enrich hencc
1.00
0.85
pgBoest (TussAl)
g 8
Distance_com igg800s1
0.00
33.
4
0.85
Archil
Signac
<0.0
SCENT
CARR22
47
CCX
Vitl
Distance threshold
<5.5 310 x20ae Distance threshold
MB
petced (ReM
Position on chromosome
Arrtt Outr
Figure 4: Optimizing pgBoost for SNP-gene link discovery. (A) Area under the enrichment- recall curve (AUERC, defined in pgBoost) across distance thresholds for ground truth eQTI. variant-gene links. (B) AUERC across distance thresholds for ground truth ABC variant-gene links. (C) Locus plot of 03138917529 and surrounding genes. Red dashed line indicates linking score percentile cutoff for SNP-gene linking. GCK is shaded red, as the gene linked to the focal SNP.
6 DISCUSSION
We have presented TusoAI, agentic system for scientific method optimization. By mimicking a scientist's cycle of method development, TusoAI achieves superior performance on single cell and scientific deep learning benchmarks, and is further able discover significant optimizations to state of the art methods in genetics which revealed new biology missed by existing methods. We believe TusoAI represents a promising step towards automated scientific method development and optimization, thus accelerating scientific discovery.
We acknowledge several limitations and areas for future work. TusoAI requires a separate vali- dation experiment to base optimization on to prevent overfitting. This experiment should also be quick to run while representing final performance. When optimizing an existing method, TusoAI performs strongest when most of the method is in single function, and may not perform well if the method is scattered throughout a large codebase. TusoAl does not consider making new evaluation procedures, but rather relies on existing ones and will be vulnerable to the same weaknesses those may have. Several algorithm components are heuristic and may benefit from theoretic justification. We generally focus on biological applications given their complexity and relevance, but achieve reliable performance in diverse scientific domains. Future work may include processing multiple functions in parallel by treating them as separate subtasks, searching not just the code-space for methods, but also the data-space for additional useful data to include in a method, as is common in scientific domains.
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Accepted at ICLR 2026
Acknowledgment We thank Richard Border, Ana Prieto, and Boshen Yan for helpful discussions and valuable feedback on the manuscript. This work was supported by a Shurl and Kay Curci Foundation Award and the CMU Center for Machine Learning and Health.
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A ALGORITHM TABLE
Symbol
Type
Description
Task description
Short description of task (e-g.- single-cell RNA-seq imputation). Data used in running the method. Scoring function that maps solution s to scalar performance score.
Dataset
A(-)
Evaluator
Sinis Tadget
Initial solution (optional) Time budget
Optional user-provided initial solution start from.
Maximum wall-clock time allowed for the optimization loop (de- fault: 8 hours).
Apaper
Subroutine
"Paper agent"; retrieves and summarizes domain-reless relevant litera- ture given T.
P
Sct of documents
Domain knowledge/ / paper summaries collected by Apaper (7).
Acate
Subroutine
Agent that proposes high-level instruction categories for solving T.
DraftThenRReine()
Procedure
Drafting refinement procedure used to iteratively improve struc- tured text (categories or instructions).
C
Set
Set of instruction categories (e.g., preprocessing, noise modeling, model architecture, etc.).
{ne)eEc
Probabilities
Categorical distribution categories C, encoding their esti- mated usefulness.
c
Category index Subroutine
Individual category element from C.
Amstt
Agent that drafts and refines concrete instructions for a specific category
Ie
Sct of instructions
-pecgfr--peccfic instructions for optimizing solutions under cat- egory
F.
Feedback set Instructions
Collected feedback specific to category c. Diagnostic instructions describing how to print informative method information.
Tgig
Faag
Feedback set
Feedback collected from diagnostic runs.
Amit
Subroutine
Agent that generates initial candidate solutions using T, P, and Sinit.
Solution set Integer Solution
Current pool / archive of candidate solutions explored so far. Number of top diverse solutions selected from S at each iteration. A single candidate solution sampled from the current top set.
Nop
8
Scalar probability
Probability of using instruction-basse -asod optimization instead of diagnostic-based optimization (default: 0.8).
Bernoull(aa)
Distribution
Stochastic decision: with probability use instuction-based op- timization; otherwise use diagnostics.
Cat(fmeeoed
Distribution
Categorical distribution over categorics C' parameterized by {me)
Aupim
Subroutine
Agent that improves solution using instructions I, and feed- back F.-
Solution Subroutine
New candidate solution obtained by optimizing s. Agent that analyzes the change from s to s' and produces textual feedhack.
Ajeddback
Adig
Subroutine
Diagnostic agent that uses data D and diagnostic info (Iaug, Fesg) ) improve s.
Nas
Integer
Adjusted number of solutions to keep / explore in future rounds (e.g.. Now max(1, NNup - 1)).
Solution
Best-found solution at the end of the run, ie. 8" arg max.g. le(s).
wall-clock time
Time
Actual clapsed woaldworld time since the algorithm started.
Table 4: Explanation of symbols and subroutines used in Algorithm
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Accepted at ICLR 2026
B EXAMPLE TUSOAI CODE TEMPLATE
Single-cell denoising template file
import scanpy as 5c import pandas pd import rumpy
import seipy ap
import magic
from anndata import hiad
seprep
from scipy.aparse import aa"_mat"ix
sklearn.neighherz. import NesrestMeigiborr from scipy.aperso import i3sparse from deraarn.ddoomssstiio. import FCA
from anndata import AnnDate import random
def mse (adata):
import anndata
import as import scprep
import sklearn.mezsics
test_data anndata Annbata
obs=adata. varsadata.war)
denoised_data aundata.AnDatta
X-adata. obsm (ban("dnnoiee"") oba-adata.obs, var=adata.vac
scaling and transformation
torget_aia 10000
sc.pp. www.:nommlleeeeeoeoeoo (otsl (test_data, Largel_eumrtarge___unt s2.pp.loglee (cest_daaa))
sc.pp. wt.pp.nozmalize.eom total (dencised target_auararrret_emm st.pp.logip (dencieddncc))))aattaa)))
u: contray coateey (test_data.X] date.
return
def madol (adata)
("denciaa)))
return adata
def msin!)::
ap rp.random.som seed (42)
random.seed (42)
http:://ewpe..eeeeeeeeeeeeaeeeee print ("tuso__odel__taee
adata = read_a5ad
adata aasa_model(adazaa
print ("tust_nooem___mmm
val val_ metrittric- mse (adsta)
(fatsso_va [val_metrios""
(ain()
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Accepted at ICLR 2026
C EXAMPLE INSTRUCTIONS GENERATED BY TUSOAI
Example categories for single-cell denoising
data preprocessing
feature_engineering
modeL_architece
hyperparameter. .tuiigg
imputation.sttategiii
normalization methods
evaluation metries
valsdaanlooiion
knowiiekn inttgraatinnnno
robustness techniques
noise modeling
propout..robbbbilty.ccom esstimatton
graph neural network cetworrkoptimizationn
propouttppateen. _nnalysi
pipcline_interaction interaction.amalysii.
low rank approximation optimization
autoencoder classifier integration
Example instructions within a category
leveraging graph attention mechanisms to focus on informative cell interactions
incorporating multi-layer graph convolutions to capture hierarchical gene expres- sion patterns
implementing edge dropout to enhance model robustness against noise in cell rela- tionships
utilizing message passing to propagate information across similar cell types effec- tively
integrating adaptive learning rates for different graph nodes based on local connec- tivity
employing graph pooling techniques summarize cellular features without losing critical information
applying graph regularization to maintain structural integrity of the cellular network
utilizing node embeddings capture latent features of gene expression profiles
optimizing neighborhood sizes dynamically based on data density in the graph
exploring higher-order graph structures to uncover complex relationships in RNA- seq data
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Accepted at ICLR 2026
C.1 PREDEFINED DIAGNOSTIC INSTRUCTIONS
Example predefined diagnostic instructions
altering or adding diagnostic information to be printed
altering adding complex diagnostic information of specific model components
printing key statistical assumptions underlying the model (c.g., independence, mality)
emitting warnings when model assumptions appear to be violated by the data
logging all implicit assumptions made during model selection preprocessing
printing assumptions related to feature distributions transformations
displaying model-specific assumptions such as linearity, homoscedasticity, or no multicollinearity
printing assumptions about data completeness, such as missing value tolerance logging expectations about input feature scaling normalization
displaying prior distributions regularization beliefs embedded in the model
printing assumptions about label distribution (c.g., class balance or stratification)
emitting diagnostics when data fails to meet i.i.d. assumptions
logging assumed causal directions or conditional independencies the model
printing constraints assumed feature ranges or valid input domains
warning if assumptions about sufficient training data volume not met displaying structural assumptions, such as sparsity or low-rank representations
logging assumptions related to stationarity or autocorrelation in time-dependent data
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Accepted at ICLR 2026
D SINGLE-CELL ANALYSIS TASKS SETUP
The OpenProblems benchmark (Luecken et al. 2025) contains 12 single-cell analysis tasks with numerous testing datasets and benchmark metrics for each. We select 6 tasks: single-cell denoising. label projection, batch integration, spatially variable gene identification, spatial decomposition of cell types, and visualization. These were selected with the following criteria. First, we required more than one dataset, such that we optimize on the remaining testing datasets, excluding the 2 cell-cell communication tasks and perturbation prediction. Second, a publicly available Github to ensure are reproducing the testing procedures correctly, excluding multimodal integration and modality prediction. Third, a method for the task should be able to run in reasonable amount of time on CPU, excluding the foundation model benchmark.
dataset, and deploy the learned method
In each task, performed optimization on dataset which could be in reasonable amount of time (< 2 minutes for a simple baseline model). The learned methods of each baseline were then applied the deployment datasets. In selecting benchmark metrics for each task, we had three criteria. First, the metric should not have unavoidable trivial solutions, excluding the Poisson loss metric from denoising, as this can be easily minimized by simply down-weighting lowly expressed genes, including by just scaling genes by their variance or re-normalizing the data. Second, the metries should be computationally efficient to run, so optimization speed of each method will not be dominated by running metrics. This excluded several metrics from batch integration and visual- ization. Third, the metric should line up with the task. In the SVG task. it is initially measured in correlation with spatial variability scores, however, the simulation procedure generates binary 0/1 labels of spatial variability, thus we use accuracy of classifying gene as SVG instead. We also nor- malize metrics such that each is between 0 and 1 and higher score is better. The score in denoising is I-MSE, normalized so that no denoising is O, and perfect denoising is 1. The score in spatial decomposition is normalized so that random cell type assignment is 0, and perfect decomposition is 1. See Table 5 for a full breakdown of datasets and metrics used in single-cell tasks.
Optimization dataset
Testing datasets
Benchmark metrics
Denoise
IK PBMC
5K PBMC
MSE
Pancreatic
Label
5k cells from Immune Cell Atlas
Diabetic Kidney
Accuracy
GTEX v9
Fl macro
HypoMap
micro Fl weighted
Mouse Pancreatic Islet Atlas
Tabula Sapiens
Batch
5k cells from Immune Cell Atlas
Diabetic Kidney
Graph connectivity
GTEX v9
ASW label
HypoMap
ASW hatch
Mouse Pancreatic Islet Atlas
Tabula Sapiens
SVG
Drosophila Stereo-seq
Drosophila Stereo-seq E1O
Accuracy
Drosophila Stereo-seq E9
Drosophila Sterco-seq E6
Decomp
TMS Lung (alpha=1.0)
TMS Lung (alpha=0.5)
Rd
TMS Lung (alpha=5.0)
Pancreas (alpha=0.5)
Panereas (alpha=1.0)
Panereas (alpha=5.0)
Visual
Mouse HSPCT
5K PBMC
Trustworthiness Distance correlation
Mouse Mycloid Zebrafish
Density Preservation
Table 5: Single-cell benchmark setup. Datasets and metrics refer to the setup on the OpenProblems webpage.
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Accepted at ICLR 2026
E DEEP LEARNING TASKS SETUP
The NASBENCH-361 benchmark Tu et al. 2022 contains 10 deep learning tasks across scientific domains with predefined training, validation, and testing splits, as well evaluation procedures. We select tasks: Spherical, NinaPro, DeepSEA, Satellite, and ECG. These were selected with the following criteria. First, the task should be scientific and somewhat understudied compared to stan- dard ML tasks, excluding the standard image and audio classification tasks. Second, to ensure fair comparison against the precomputed baselines, we removed tasks where we were uncertain about reproducing the evaluation procedure, partly due to recent GitHub or package updates requiring debugging, excluding Cosmic, PSICOV, and DarcyFlow.
In each task, we performed optimization by training model on the predefined training set and attaining score on the validation set. The final testing accuracy optimized models is attained when deploying the model on the predefined test set. We use the same splits and metrics defined in the original paper.
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Accepted at ICLR 2026
F BASELINE IMPLEMENTATIONS
AIDE. AIDE takes as input a data folder, task description, and evaluation metric. While originally designed for whole-workflow construction in ML tasks, this can be adapted to general optimization in the following ways. First, AIDE can operate on any data input in the data folder. If specific preprocessing information was needed, we could input this code to the task description. Second, in place of specifying accuracy metric (e.g., "Fl score"). we instead simply input the entire evaluation function in Python, and found this worked well. As our goal is optimization and not construction, AIDE's initial prompt is tuned until code was consistently generated and optimized upon, typically requiring the same formatting information as other methods. AIDE is run for the same length as TusoAI (8 hours) in the same conda environment on the same CPU (Optimization for AIDE and TusoAl is performed on the same Intel(R) Xeon(R) Gold 5416S.) or GPU (Intel(R) (R) Silver 4314 CPU @ 2.40GHz), given 4 threads and 50GB of memory. While AIDE does have a default timeout per execution of 1 hour, on attempting to set this to the same time as TusoAI led consistent crashes on more of half of tasks, thus we left as is.
Biomni. We access Biomni through its web page. Biomni runs on a CPU and can take input files up to a limit, has a runtime execution of hour per iteration. While not specifically designed for optimization, we can upload the same template code and data as TusoAI then ask Biomni to perform an iterative process of updates. In practice, this led to between 2 and 10 iterations per task between 20 minutes and 4 hours. This procedure was signed off by the original authors of Biomni.
ChatGPT-Agent. We access ChatGPT-Agent through its web page. ChatGPT-Agent can accept input files up to 25MB and has runtime execution of hour per iteration. We upload the same template code and data as TusoAl and ask ChatGPT-Agen to perform an iterative process of updates. In practice, this led to between 2 and 7 iterations per task between 10 minutes and hours.
The expert baselines for NASBench-300 are pre-computed from their paper. The original authors found the best performing expert models from the literature for each task. This includes the following methods:
Expert.
1. DeepSea- - The original DeepSea model released alongside the dataset, ID convolution model with state the art performance. (Zhou Troyanskaya| 20155)
2. NinaPro- Feed-forward neural network with attention modules in place of convolutions. (Josephs et al. 2020)
3. Spherical spherical CNN with special operations for spherical signals. This model achieved state of the art performance on spherical MNIST. (Cohen et al. (018)
4. Satellite- A linear classifier with convolution kernel as feature extractor, achieving state of the art on UCR time series prediction tasks. (Dempster et al. 2020)
5. ECG ResNet with 1D convolution, achieving state of the art on several time series predic- tion tasks for medicine. (Hong al. 2020)
For single-cell tasks, we selected the expert method with the following criteria. First, it should be within the top methods defined by the existing OpenProblems benchmarking. Second, the OpenProblems Github should have code for reproducing this method. Third, we selected the method that was particularly efficient compared others, if applicable, defined by runtime of less than 10 minutes on OpenProblems, with others having greater than I hour. This left us with the following expert methods, whose code we extracted from the OpenProblems Github:
1. Denoise MAGIC, a based diffusion method that imputes missing gene expression values. Van Dijk al. 2018
2. Batch ComBat, an empirical Bayes approach that removes batch effects across samples.
(Zhang et al. 2020
3. Label - PCA + Logistic Regression, which uses low-dimensional PCs as features for effi- cient cell-type classification. (Luecken et al. (025))
4. Decomposition NNLS, a non-negative least squares model for estimating gene programs latent factors.
(Aliee & Theis 2021)
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Accepted at ICLR 2026
5. SVG SPARK-X X. spatial variance component model that identifies spatially variable genes at scale. (Zhu et al. 2021)
6. Visualize - t-SNE (log I0CPIOK) a nonlinear embedding log-transformed counts for visualization. (Luecken et al. 2025
Claude-4-Soneet and GPT-5. In deep learning tasks where Biomni and ChatGPT-Agent cannot apply due to computational limitations (file size, runtime, GPU access), we substitute the best of 10 models generated by Claude-4-Sonnet and GPT-5. 10 models are generated by prompting these LLMs using the same template that would have been used in Biomni and ChatGPT-Agent. The best decided by the top performing model on the validation dataset which ran in less than one hour, akin to the runtime limitations of Biomni and ChatGPT-Agent.
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Accepted at ICLR 2026
G EXAMPLE TASK INFORMATION
Denoise
Task Description
single cell RNA-scg seq imputation
Drafted Categories
data normalization
feature_selection feature seleccion
imputation modeling
latent_space http__aaacceepeeettttiiee representation
noise handling
woccheeffettccorectii
hyperparameter. _tuning
evaluation .metricsss
ensemble imputation methods
domain speccfi. wtt.spcciccccoossrainnttt constraints
count distrihution modeling
Refined Categories
data notrmalizaiiaiinn
feature Seature_selection
imputation gonellii
latent aatent_space_reppee represeattnnnnnooonnnn
noise handling
batch Sechheeffectttceeeeeee correction
hyperparameter tuning
evaluation -eetricssssees
ensemble imputation methods
domain speciffi..oonstrains
count soant.distributionmmodeee
ttoppout.robbabilly..siiiim eestimationoo
graph_ based raased.rrpresentation
stoppou.patteem_anddyee
pipeline.interaction interaction analysis
Lank_estimation.am httpriiionnnnnniiiiittiiii
virtual class lahel generation
Drafted Solution Descriptions
k- nenersstt neighbors imputation
matrix factorization (e.g.. PCA, NMF)
autoencoder- imputation (including DCA)
generative adversarial networks (GANs) for imputation
deep learning models (e.g., U-Net architecture)
Refined Solution Descriptions
k-ncarest neighbors imputation
matrix factorization (e.g.. PCA, NMF)
autoencoder-baste imputation (including DCA) deep learning models (e.g.. U-Net architecture)
graph neural network (GNN) for imputation scImpute for dropout imputation
co-nccurrence clustering based on dropout patterns scran normalization with prior clustering
AutoClass model for scRNA-Seq cleaning
low-rank matrix approximation (ALRA)
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Accepted at ICLR 2026
H NOVELTY OF DISCOVERED METHODS
For the four example methods constructed by TusoAl listed in Section 4.1 we expand on the novelty claim with a thorough literature review of related methods.
In single-cell denoise, TusoAl designed a non-negative matrix factorization (NMF) approach that models dropout rates, Poisson noise, and performs iterative refinement, distinct from the only other NMF-based approach in the OpenProblems benchmark, ALRA (Linderman et al. 2022). Specif- ically, ALRA applies a low-rank approximation adaptive thresholding step to restore zeros, but it does not explicitly model count noise, does not incorporate dropout mechanisms, and does not iteratively refine factors. Another denoising method uses NMF, Denoiselt (Jeon et al.) 2024). This method focuses on identifying noisy features via NMF loadings combined with isolation-forest filtering, and does not perform probabilistic mod- eling of gene-cell counts or imputation of dropout-affected expression values, again distinct from our approach. Another point of novelty is that TusoAl's learned method is outperforming MAGIC (Van Dijk et al., 2018), which was found to outperform ALRA (Luecken et al. 2025).
globally normalized matrix and performs an
In SVG, TusoAl adapted known techniques such as modeling expression as a function of spatial co- ordinates and neighborhood summaries to create custom, high-performing method. Many existing using Gaussian or generalized linear models over spatial locations (SpatialDE, SpatialDE2, SPARK-X, GPcounts, BOOST-GP) (Svensson al. Kats et al. 2021 Zhu et al., 2021: Bayyasy et al., [021 Li et al. 2021]. while others rely graph structure, diffusion, spatial autocorrelation statisties (Moran's I. SOMDE, scGCO, Sepal, SpaGCN, SpaGFT, nnSVG, Spanve) (Luecken et al. 2025 Hao et al.,
SVG detectors are primarily coordinate-basee
2018
mainly on neighborhood
2021
Zhang al. [022a Andersson & Lundeberg, [02T] [Hu et al. [202]] Chang al. 2024 Weber
et al. 2023 Cai et al. (023). Graph-based models such as SpaGCN and SpaGFT can incorporate both spatial coordinates and local neighhorhoods through graph constructions and convolution or spectral transforms (Hu et al. Chang et al.) 2024), but they do not explicitly combine smooth coordinate regression with fixed neighborhood summary covariates in a single per-gene predictive model as TusoAI does. This joint modeling of coordinate trends and neighborhood summaries ables TusoAI to outperform SPARK-X (Zhu et al. 2021) on our SVG benchmark, despite SPARK-X being among the strongest existing SVG baselines (Luecken et al. 2025).
In Satellite, TusoAI combined preprocessing. training procedures, loss functions, and ensembling techniques to build the cop-perfrmming model. First, this is distinct from the expert model, which is a linear classifier (Dempster et af.] 2020). Second, these kinds of pipeline-level decisions lie outside the search space of the NAS baselines used in NASBench-360: methods such as DARTS-GAEA, DenseNAS, AMBER, and tuned WRN search only over convolutional architectures under a fixed data preprocessing pipeline, standard loss, and a single-model training recipe, and therefore cannot automatically implement the techniques TusoAI does here.
In Spherical, TusoAI fine-tuned layers of ResNet-50 and augmented the data with random flips and rotations. First, this is distinct from the expert model, which is a spherical CNN (Cohen et al., 2018). Again, these decisions, such as fine-tuning Resnet-50 or augmenting data with rotations and flips lie outside the search space for NAS methods.
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Accepted at ICLR 2026
I RUNTIME OF NEW METHODS
A
B
Single-cell Testing Runtime
Deep Learning Testing Runtime
2500
Runtime (seconds)
400
Runtime (seconds)
2000
300
1500
200
1000
100
500
TusoAl
AIDE
Expert
Biomni
GPT-Agent
TusoAl
AIDE
GPT-5
Claude
Figure 5: Testing set runtime for (A) single-cell tasks (averaged over 3 random data splits) and (B) deep learning tasks (averaged over random seeds).
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Accepted at ICLR 2026
J OPTIMIZATION TRAJECTORIES
TusoAl Label proj optimization curve
TusoAl Deepsea optimization curve
0.56
Validation score
0.75
Validation score
0.48
0.50
0.40
0.25
Best code Proposed code
Best code
0.32
Proposed code
Optimization
Optimization
0.00
0
7
3
Time (hours)
Time (hours)
TusoAl Svg optimization curve
TusoAl Spherical optimization curve
0.8
Validation score
Validation score
0.6
0.30
0.4
Best code
0.15
Best code
Proposed code
Proposed code
0.2
Optimization
Optimization
0.00
3
Time (hours)
Time (hours)
TusoAl Spatial decomp optimization curve
TusoAl Ninapro optimization curve
0.45
Validation score
Validation score
0.75
0.30
0.50
0.15
Best code Proposed code
Best code
0.25
Proposed code
0.00
Optimization
Optimization
2
2
Time (hours)
Time (hours)
TusoAl Batch int optimization curve
TusoAl Ecg optimization curve
0.8
0.8
Validation score
Validation score
0.6
0.6
0.4
Best code Proposed code
Best code
Proposed code
0.2
Optimization
Optimization
0.2
2
3
6
8
2
3
6
8
Time (hours)
Time (hours)
TusoAl Visualize optimization curve
TusoAl Satellite optimization curve
0.60
Validation score
Validation score
0.75
0.45
0.50
Best code Proposed code Optimization
Best code Proposed code
0.30
0.25
Optimization
1
4
6
2
4
6
7
Time (hours)
Time (hours)
Figure 6: TusoAI's optimization trajectory for all benchmarking tasks. Denoise is in Figure 2
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Accepted at ICLR 2026
K
AIDE OPTIMIZATION TRAJECTORIES
AIDE Label_ proj optimization curve
AIDE Satellite optimization curve
0.90
Validation score
0.75
Validation
0.75
0.60
0.60
Best code Proposed code
Best code
0.45
Proposed code
0.45
Optimization
Optimizatiin
0
1
5
6
Time (hours)
Time (hours)
AIDE Batch int optimization curve
AIDE Ecg optimization curve
Validation score
0.64
Validation score
0.60
0.56
Best code
0.30
Best code
Proposed
Proposed code
0.40
Optimization
0.15
Optimization
2
3
7
8
Time (hours)
Time (hours)
AIDE Visualize optimization curve
AIDE Spherical optimization curve
0.24
Validation score
0.90
Validation score
0.20
0.87
0.16
Best code
Proposed
Proposed code
Optimization
0.12
Opt Optimization
0.81
Time (hours)
Time (hours)
AIDE Svg optimization curve
AIDE Deepsea optimization curve
Validation score
0.6
Validation score
0.90
0.4
0.75
0.2
Best code Proposed code Optimization
Best code Proposed code
0.60
Optimization
2
Time (hours)
5
0
Time (hours)
5
6
7
AIDE Denoise optimization curve
AIDE Ninapro optimization curve
Validation score
Validation score
0.75
0.60
Best
Best code Proposed
Proposed
0.7-
0.45
Optimization
Optimization
Time (hours)
Time (hours)
Figure 7: AIDE's optimization trajectory for all benchmarking tasks. AIDE can edit the evaluation function, which occurred the decomposition task which was thus excluded.
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Accepted at ICLR 2026
L STABILITY ACROSS REPLICATES
Validation performance across replicates
0.8
Score normalzzd
0.4
0.2
0.0
Denoise
SVG
Decomposition
Label
Batch
Visualize
Figure 8: Validation performance across 3 replicates. Final validation performance after running TusoAI 3 separate times on each single-cell task.
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Accepted at ICLR 2026
M CODE DIVERSITY
We measure the diversity of generated code, as measured by the cosine similarity of the text em- bedding of one generated code versus all others, similar to (Aygün et 2025). This is performed for TusoAI and AIDE. For each, we first filter out repetitive/uninfrrmatve code strings, including comments, imports, evaluation functions and data loading procedures (which will not change over iterations). We then apply skleam's s TfiffVectorier function to each cleaned code to obtain a text embedding. We can then compute the cosine similarity between pairs of code. Diversity is measured as 1-cosine similarity. We opt for TF-IDF instead of more sophisticated methods like CodeBERT (Feng et al. 2020) which measure semantic similarity, as we observed this overestimated the simi- larity between code (all cosine similarity 0.997 for all tasks). This is likely due to each iteration always being a slight permutation of the same python method performing the same task. TF-IDF better captures measure of difference between algorithmic procedures in this case.
Diversity collapse vs. convergence
8
Correlation 0.519
Last diverse code (hours)
7
6
5
TusoAl Single-cell
TusoAl Deep Learning
4
AIDE Single-cell
AIDE Deep Learning
3
2
1
1
8
Last optimization (hours)
Figure 9: Code diversity versus optimization ability. Last diverse code for each trajectory (defined as last position with diversity>=0.1 versus last optimization position.
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Accepted at ICLR 2026
N ABLATION ANALYSIS
A
B
Diversity across tasks
Optimization speed
0.5-
Divers
80.4
Optimication
02
01
Rood
Taooi categoricn
Broat no Bayesiann
Tamptt No atagrootis
Binott No knowledie
Rosel
Booal No categories
Tooal -e Royesian
Rasdt No diagrosii
Koodi innestedij
C
Ablation Replicates
0.8
0.6
Score
0..4
0.2
Tasoll
Trusk de dlegrosh
cnowedde
Denolse
SVG
Decompasition
ECG
Satellite
Figure 10: Additional ablation information. (A) Box plot across 5 tasks of the mean code diversity.
(B) Box plot across 5 tasks of the mean time to optimize. (A) Box plot of the final testing scores of replicates for each ablation.
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Accepted at ICLR 2026
O
VARYING LITERATURE SEARCHED
Ablation Replicates by Number of Papers
0.9
0.8
0.7
Score
0.6
0.5
0.4
TusoAI 10 papers
TusaAA 10 papers (filtered)
0.3
TusoAI papers
TusaAI papers
0.2
TusaAI 50 papers (filtered)
Denoise
5VG
Decomposition
ECG
Satellite
Figure 11: Box plot of the final testing scores of replicates for collecting 10 papers (default), papers, and papers, alongside optional paper summary filtering step.
Paper summary lengths
0.9
0.8
0.7
Score
15 point summary (Default]
0.6
point summary
30 point summary
0.5
0.4
Denoise
SVG
Decomposition
ECG
Satellite
Figure 12: Box plot of the final testing scores of 5 replicates for having 15 bullet point summaries (default), 5 bullet point summaries, and 30 bullet point summaries.
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Accepted at ICLR 2026
P LLM ANALYSIS
A
B
Code length successful generations
Code length final score
Denolse
Code count snippets)
600
Decomposition
0.75
500
SVG
0.50
ECG
400
Satellite
0.25
Score
300
0.00
GPT-4o-mini
-0.25
200
Claude-3.5-Haik
GPT-5
-0.50
100
Claude-4-Sonne
GPT-oss-120b
50
100
150
200
250
300
50 100 150 200 250 300
Average code length (lines)
Average code length (lines)
Figure 13: Additional LLM information. (A) Average length of generated methods for each task and LLM versus the total count of how many methods were generated. (B) Average length of generated methods for each task and LLM versus the final deployment performance.
33

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Accepted at ICLR 2026
Q
COST ANALYSIS
A
B
AIDE Vs TusoAl: Cost by Task
Cost Distribution Across LLMs
$0.30
$25
TusoAI
Merd $1447
53
$0.20
Cost (S)
Merlan
Cost (9)
51
$0.10
$0.2
Median price: $0.17
Median price: $0.12
GPT mi-iii
Claude 3.5 Haiku
GPT-5
Claude Sormet
SPT-oss-1200
Denoise
Label
Batch
SvG
Decomp
Visual
Spharical
NinaPro
ECG
Satellite
DeepSEA
Figure 14: Cost distribution. (A) TusoAI vs. AIDE cost for each task. (B) Boxplot of costs for each LLM on ablation tasks.
34

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Accepted at ICLR 2026
R SCDRS ANALYSIS
Optimization setup. scDRS' codebase consists of several files. We construct a version of com- pute_ syoor.pyy that exposes the compute_re wwwteerrawscorr function. This is the only function TusoAI op- erates upon during optimization. For optimization, we construct causal simulations similar to the scDRS paper, subsampling 10k cells from TMS, perturbing 1000 disease genes cluster of cells, setting the geneset overlap to 25%, and varying effect size from to 50%. TusoAI optimizes the compute raw score function based on the average (F1 AUPRC)/2 across replicates effect size 15%, where scDRS has lower power. We run this experiment for 24 hours using default parameter settings for TusoAI
Additional simulation results. We apply seDRS and the learned version by TusoAI to all 30 repli- cates of each effect size causal simulations. We additionally apply to 100 replicates of null simulations, identical to scDRS, where 1000 random genes are selected with no perturbation. Addi- tional metrics in these simulations are reported in Figure 15
A
Null simulations (1000 random genes)
AUPRC detecting disease associated
FDR detecting disease associated calls
(TussAl)
xCDRS
AUPAC
SCDRS (Tosoki)
ScORS
Theoretio leghtippi Quentiks
Causal offect size
Causal effect
Figure 15: Additional scDRS metrics. (A) Q-Q plot of -log10 p-values in null simulations. 95% CI's are calculated at each point across 30 replicates. (B) AUPRC of associating individual cells in causal simulations. 95% Cl's are calculated at each point across 30 replicates. associating individual cells in causal simulations. 95% CT's are calculated at each point across 30
(C) FDR of
replicates.
35

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Accepted at ICLR 2026
S PGBOOST ANALYSIS
Optimization setup. pgñoost discovers that distance-based features are critical for modeling SNP- gene distances. It's samples are SNP-gene pairs, and features include over 30 features derived from single-cell multiome methods and distance features, the SNP distance to the gene's 's transcrip- tion start site (TSS), and a binary indicator of if this the closest TSS of any gene the SNP. We augment pgBoost's 's script with gene annotations from GENCODE V48 (Mudge et al., (025), specifically the SNP's position, the gene's TSS, and the gene's 's transcription end site (TES). During both the knowledge tree construction and optimization process, TusoAl is encouraged to come up with instrctiion/oppiiiizatoon relevant distance-based modeling of SNP-gene links and avoid other model changes. Optimization is performed by increasing the average enrichment in pgBoost's primary evaluation gold-standard links (eQTL and ABC) relative to the original pgBoost's en- richment. We run TusoAl for 24 hours using default parameter settings.
Real data analysis. We analyze fine- mapeea SNPs from the same set of GWAS traits as in the pgBoost paper. pgBoost considers a true link be in the top 95% percentile, and specifically looks for SNP-gene links that are not in such percentile for other methods. We perform an identical analysis, looking for links in the top 95% of pgBoost (TusoAT), but not in other methods.
36

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Accepted at ICLR 2026
T WARM START DEEP LEARNING
We show how TusoAI's warm-start capability might work in conjunction with scientific deep learning task. For NinaPro, re-implement the Expert model from NASBench-360. This is feed-forward neural network with attention modules in place of optimizations. Optimizing this model for hours with TusoAI improves testing performance from 0.91 to 0.94, now becoming the top performing model for this task (Figure I6). Where both the Expert and cold-start TusoAI's learned models were outperformed by NAS methods, their combination yields a new top model. We next analyze the optimization trajectory of TusoAl to see how this was achieved. We summarize the key optimizations below:
The dense attention network was replaced with dilated temporal convolutional network (TCN).
2. Five separate TCN models were trained and ensembled.
3. Features were standardized with z-scaling.
4. Gradient clipping.
5. Log transformation of input features.
6. Ensemble is replaced by mixture of experts (MOE) architecture.
A
B
NinaPro Performance
TusoAl Expert
WRN WRN_ASHA
TusoAl Ninapro-warm-starn optimization curve
WRN default
DenseNAS_randee
Validation score
0.9
Expert
TusoAl
4
0.6
DenseNAS_ _ariginal
Best code
GPT_5
0.3
Proposed code
AIDE
Optimization
DARTS
XGBoost
2
7 8
Perceiver_ic IO
Time (hours)
0.80
0.85
0,90
1.00
Test AUC
Figure 16: Warm start example on NinaPro. (A) Testing performance of all NinaPro methods. (B) Optimization trajectory of TusoAI with warm-start NinaPro.
37

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Accepted at ICLR 2026
U PROMPT TEMPLATES
U.1 INSTRUCTIONS FOR PARSING LITERATURE
Initializing paper description with abstract
You scientific summariser. Draft concise yet technically accurate description of the paper's s method based **oly** on the abstract below, to the extent possible. Capture the main points using bullet points. Avoid complete sentences and omit details irrelevant to the methods.
Abstract: """[ABSTRACT GOES HERE]"M
Updating paper description with methods section
The current method description:
""[[CURENT DESCRIPTION GOES HERE]
New excerpt from the paper:
"""[WWW TEXT GOES HERE]
Update the description by **incorporating any new technical details or correcting existing ones** found in the excerpt. Keep the description concise and clear. Return **only**
revised description. Use bullet points. Avoid full sentences exclude information unrelated to the methods. Do not exceed 15 bullet points.
38

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Accepted at ICLR 2026
U.2 INSTRUCTIONS FOR CONSTRUCTING CATEGORIES
Initializing categories with LLM
We are building an -..--powered AutoMI. system for the task: "[TASK DESCRIPTION]"
As reference, some generic categories for optimizing classification models include: [CLASSIFICATION CATEGORIES]
You are master machine learning and the domain relevant to this task. First briefly reason about what kinds of modeling interventions optimization strategies could be helpful for this specific task. Then propose a list of concise, task-relevant optimization categories. Your list should include conceptual ideas tailored to this task and each should represent specific axis of improvement (e.g., architectural choices, preprocessing strategies, domain constraints, evaluation metrics, robustness techniques, etc.). Output exactly ***** proposed categories, one per line, each enclosed in:
<c>Category Name</c>
Do not include any other text, explanation, or formatting. By "optimization" we mean strictly performance improvements not runtime, scalability, visualization, logging, post- evaluation tools, or similar considerations. You will only have access to: [DATA AVAIL- ABLEJ.
Updating categories with papers
We are building an L.M--ooeredd AutoML system for task: "[TASK DESCRIPTION""
We will curate and refine our categories based on the current categories and a paper.
Current categories: [CURRENT CATEGORIES]
Paper: "[TITLE]] Key method points: [BULLET POINTS]
TASK If the paper suggests ** **new*** axis of optimization missing from the list, propose a concise, task-relevant category for it. 2. If two or more current categories can be merged, provide single category name that subsumes them. Otherwise, a category is irrelevant given only [DATA AVAILABLE), leave the list unchanged.
Return **one updated list only** one category per line. Each line be wrapped exactly like:
<c> Categorry<</>>>
No other text.
39

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Accepted at ICLR 2026
U.3 INSTRUCTIONS FOR CONSTRUCTING PITHIN-CATEGORY INSTRUCTIONS
Initializing within-category instructions with LLM
We are designing an LLM-ppoered AutoML. system for the task: "ITASK DESCRIPTIONI"
Current optimisation axis: **[CATEGORY]=*
Below is style example of prompts for *regularisation* category for classification task. Each prompt begins with *by ...* and expresses a specific, actionable optimisation idea: [FEW-SHOT EXAMPLES]
You are a master of machine learning and the domain relevant to this task. Keeping the same concise, actionable style, write **exactly N distinct prompts** that belong to the **[CATE- GORY]** category *nd are appropriate for this task** These should include a mix of general, conceptual, and complex prompts, not overly spe- cific, similar to the examples.
Wrap "each* prompt its own:
<p> ... <//>/>>>>>>>
Return **only** these <p>...</p> lines, nothing else.
By optimisation we mean strictly ** p*perfrrmnnce** not runtime, scalability, logging, visu- alization, evaluation, or post-pree proceesccssng..... Assume evaluation metrics already exist. You will only have access to: [DATA AVAILABLE).
Refining within-category instructions with LLM
We designing an LLM-powered AutoML system for the task:
"[TASK DESCRIPTION""
We will only have access to: [DATA AVAILABLE).
Here is concise summary of the baseline method: """[SUMMARY]""
Below are style examples of valid prompt lines taken from earlier work: [FEW-SHOT EX-
AMPLES]
Your job is to generate **between N_min and N_max new prompts** These prompts will
ultimately be assigned to one of the following categories:
[CATEGORY LIST]
**Step I*; Generate the prompts, independently of categories. **Step 2+ Assign each prompt to its most relevant category.
For each prompt, output line in this exact format:
CascCagoorramm-e/>>>ppp <..</>>>>./ <.<ppp/
Rules:
Every prompt must begin with ** complex ideas * Focus strictly on * **performance optimisation ** (ignore runtime, scalabil- ity, logging, visualization, etc.) Return ** **nly** the <<>..<<e><< </p>>>>>>>>>>>>>>>>>>> lines nothing else.
Cover mix of general, conceptual, and
40

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Accepted at ICLR 2026
U.4 INSTRUCTIONS FOR CONSTRUCTING INITIAL SOLUTIONS
Initializing solutions with L.M
We are designing an LLM-powered AutoML. system for the task:
"[TASK DESCRIPTION]"
Below an example list of generic model initializations for a **laasificationn* ** task: [FEW-
SHOT EXAMPLES]
You are a master of machine learning and of the domain relevant to this task. Propose **exactly N concise model initializations** that could serve starting baselines **for this specific task** given that we only have [DATA AVAILABLE). These should be general task-specific methods, model families, high-level architectural descriptions - not fully specified pipelines.
Output one per line, each wrapped in:
<m> </m>>
Return **only* these <m>... <m>...</m>>>> lines no explanations, no extra text.
Refining initial solutions with LLM
We are building an LLM-powered AutoML system for the task:
"[TASK DESCRIPTION!"
We will curate and refine our **model initializations' ** list using insights from the following paper.
Current initializations: [CURRENT INITIALIZATIONS]
Paper: "[TITLE]" Key method points: [BULLET POINTS]
TASK - 1. If the paper presents a *oodel family or architecture** not covered above, propose as concise initialization (< words). 2. If two or more current initializations are effectively the same family, merge them by giving single, clear name that subsumes them. 3. If neither condition applies, the model cannot implemented using **[DATA AVAILABLE]** leave the list unchanged.
Return **one updated list only** one initialization per line, each wrapped exactly like:
<m> <m>>Inttalizaaionn</m </m>>
No other text.
41

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Accepted at ICLR 2026
U.S
PROMPT FOR DEVELOPING INITIAL SOLUTIONS
Initialisation prompt
Write a basic version of this model for (task. description) using finit). Hints: - {hints}
{base. fn, code) Output only python code, and do not include comments.
42

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Accepted at ICLR 2026
U.6 PROMPT FOR OPTIMIZING WITH INSTRUCTIONS
Instruction-based optimization prompt
Write a basic version of this model for (task. description) using finit). Hints: {hints}
{base _n__o Output only python code, and do not include comments.
by choosing one of the following strategies to guide optimisation, based on your assessment of what will most improve this model for deskkdessription): {prompt.options) Additionally, consider the following feedback from earlier attempts that used this same op- timisation strategy: {fb_block)
43

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Accepted at ICLR 2026
U.7 PROMPT FOR GENERATING DIAGNOSTICS
Generating diagnostic info prompt
Write a basic version of this model for (task description) using finit). Hints: {hints}
{base _ndeee__eeeee Output only python code, and do not include comments.
by choosing one of the following strategies to print diagnostic information, based on your assessment of what will be informative for optimisation of this model for [task joaskrdesrritton) Ensure the information printed is concise enough to be used an LLM prompt: pangnnsticcoopioone
U.8 PROMPT FOR OPTIMIZING WITH DIAGNOSTICS
Optimizing with diagnostic info prompt
Write basic version of this model for (task description) using {init). Hints: {[hntts]
{base_fn, fon_code) Output only python code, and do not include comments.
by assessing this diagnostic info and proposing model/feature improvements for this model for [task_ -aak.dessriptionn] [current_code)
Additionally, consider the following feedback from earlier attempts that used this same op- timisation strategy: (fb.block]
44

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Accepted at ICLR 2026
U.9
PROMPT FOR GENERATING FEEDBACK
Feedback on code optimization
We attempted to optimize this function: [ORIGINAL.CODE] Here is the proposed opti- mization: [NEW]CO .CODEJ Write concise one line summary of the differences between the original function and the proposed optimization. It should be as short as possible while summarizing the differences.
45

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Accepted at ICLR 2026
U.10
PROMPT FOR DEBUGGING
Fix Function Prompt
Fix this function: {suggestion). Here's the error: ferror_msg Ignore warnings. an error is related to installation, assume the package is not installed and try doing it without that specific package. Output only python code, and do not include comments.
46

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Accepted at ICLR 2026
U.11 PROMPT TEMPLATE FOR BIOMNI AND CHATGPT-AGENT
Single-cell denoising prompt template for scientific agents
Ne are considering the task of single cell RMA-seq impotation. expertly optimi model thia, lleee is a starter scrigt. Create top performing model for our task within the =uso_madel functien.
wish
import scanpy as sc
import pandos import rumpy as np
import scipy
import magic
anndata import read_h5ad
import seprep
from scipy.aparse import eas_matrix
from sillearnnne ceterrnnneighaee nhbor
NoareatNoighbors
from scipy.aparts import lasparae
sellean.deccopose from anzdata import AnnData
import
import rondom
mse (adatá)
import
import acanpy as 8c import scprep import sklearn.me:rics
teat_date Anaaata (X=adata. oooddat..bbmmm... obsm("teat")) obs-adata. var=adata. var)
denoised data saaddata.AnDDee.
X=adata obam ("denoisee"), oba-adata.oba, var=adata.var
acoling and transformation target_auee 10000
Sc-pp st.pp. sc.pp, (osttttett_dt
www.ppnocmaaieeette.. (sest_data, target_sumrterge__st
wwt.p.nommalize_eee.. (dencised_ target_sumrtarget_.oo sa.pp. 1og.p (ddnci/ppddecccccddddttttt))
skleacn. skleacn.metrica seuaerreeere
e.prep.u:.ls..oorrryttee
dencised_data.k
return errer
suso_maded (adata):
AnnBataa
dradata.obem("uu "bom("traia").oopp
obs=adata. obs.copy
varesdata.var.copt
out a.X
out wut.outtttt..... toarray if issparse (out) else odems ("dencised") out adata
def mainl):
ne.rradom.ssed140
randon.seed
adat read (tdd__hSdd"11__pp___
print ("tuso__mddel__tt ____oodee_sttri")
adate tuso _usa_modelladate (adata)
print ("caso_nodel_ce
netrie lacatal
print (f*tusa_evallatee (val_metric]""
main
Make sure to store the denofaed data in adata adata..cham "ed") Keep the function header, input, output the same.
47

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Accepted at ICLR 2026
Each time you generate code, run it, extract the auso_evaluste metric, and try and build better perforning solution from the previoua solutiona.
48

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Accepted at ICLR 2026
V
GENERIC CLASSIFICATION EXAMPLE
List
Contents
Categories
('regularisation' Feattrre.nginnerinng
'hyperparameter tuning', , sampling',
'ensemble methods', 'calibration', "eattree sedection')
Initializations
"logistic regression", "XSBoost", "random forest", "MLF classifier"]
Regularisation instructions
1. by introducing sparsity constraints prune features
2. by subsampling training rows each iteration to inject stochasticity
3. by shrinking updates with smaller learning rate for smoother convergence
4. by refining regularisation strategies
5. by combining complementary regularisation. methods
6. by adapting reqularisation strength across epochs
7. by scaling regularisation the dataset size
8. by combining elastic-net with adaptive polynomial penalties to capture curved relationships
9. by adding Jacobian norn regularisation to control sharp non-linear gradients
10. by intreducing spectral norm constraints for stable nen-linear Lavers
49


Reviews:
[{"review_id": "bm7p4aXvDv", "reviewer": "Reviewer_ff1y", "summary": "This paper introduces TusoAI, an LLM-based agentic system for automatically developing and optimizing computational methods for scientific applications. The system structures domain knowledge into a hierarchical knowledge tree, uses Bayesian updates to sample optimization strategies across categories, and incorporates diagnostic feedback during iterative refinement. The authors evaluate TusoAI on 11 scientific benchmarks (6 single-cell analysis tasks and 5 deep learning tasks), showing consistent improvements over baselines including AIDE, Biomni, and expert-designed methods. Two case studies in genetics demonstrate practical utility by improving existing methods (scDRS and pgBoost) and uncovering novel biological insights.", "questions": "1. How does TusoAI handle the cold start problem for truly novel scientific domains? The knowledge tree construction relies on retrieving papers from Semantic Scholar based on task descriptions. For emerging research areas with limited literature, how does the system bootstrap domain knowledge? Have you tested TusoAI on tasks where relevant literature is scarce or the task description is deliberately vague?\n\n2. Can you provide more details on the comparison with Aygun et al. (2025) and clarify the key technical differences? Beyond the unavailability of their code, what are the conceptual and methodological differences between your approach and theirs? This seems critical for establishing the novelty of your work.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper evaluates TusoAI on 11 tasks spanning single-cell analysis and scientific deep learning, demonstrating broad applicability. The inclusion of both cold-start and warm-start settings, along with real-world case studies in genetics, strengthens the practical relevance.\n\n2. The knowledge tree representation provides a structured way to organize domain knowledge, and the Bayesian update mechanism offers a principled approach to balancing exploration and exploitation. The diagnostic-based optimization is a nice addition that grounds the system in empirical data patterns.\n\n3. TusoAI consistently outperforms strong baselines including AIDE and domain-specific expert methods. The ablation studies effectively demonstrate that each component (categories, Bayesian updates, diagnostics, domain knowledge) contributes meaningfully to overall performance.", "weaknesses": "1. The core algorithmic components are largely existing techniques (LLM agents, tree-based search, iterative optimization), and the main contribution is their combination for scientific method development. The concurrent work by Aygun et al. (2025) appears to address very similar problems, but the paper dismisses direct comparison due to unavailable code without sufficiently differentiating the approaches conceptually.\n\n2. The 8-hour optimization budget is relatively short and may favor methods that converge quickly over those requiring longer refinement. The diversity analysis (Figure 2A) shows TusoAI explores more than AIDE, but it's unclear whether this translates to better generalization. The selection strategy (shortest code within 0.1% of best performance) seems ad-hoc and could inadvertently favor simpler but less robust solutions.\n\n3. The paper doesn't discuss when TusoAI fails or performs poorly, nor does it address practical concerns like how to set appropriate evaluation functions for novel scientific problems, how sensitive the system is to task description quality, or how to validate generated methods when ground truth is unavailable. The computational cost analysis is limited to one brief mention ($0.37-$0.41 for case studies).\n\n4. The case studies claim to discover \"9 new associations\" and \"7 previously unreported links,\" but these are computational predictions that would require experimental validation to be considered true biological discoveries. The statistical methodology for declaring associations as \"novel\" versus \"missed by previous methods\" is not clearly described.\n\n5. While the authors promise to release code upon publication, key details are missing: which specific LLM API versions were used, how were papers selected from Semantic Scholar (just citation count?), what are the exact prompts for different agents, and how stable are results across different random seeds beyond the reported 95% CIs?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Sxi9r5PMSP", "reviewer": "Reviewer_aNYX", "summary": "This paper introduces TusoAI, an agentic AI framework that automatically develops and optimizes computational methods for scientific tasks. Given task descriptions and evaluation functions, it can generate new algorithms rather than just run existing analysis pipelines. The main idea is to structure domain knowledge into a “knowledge tree,” combine it with Bayesian-guided iterative optimization and diagnostic feedback, and iteratively generate, implement, and evaluate candidate solutions. On 11 benchmarks, TusoAI achieved a higher average rank compared to expert-designed methods and existing AI agents.", "questions": "- Can the authors clearly specify what metric is used in each column of Tables 1 and 2, and how the “Avg” and “Avg rank” values are computed across heterogeneous tasks? If normalization was applied (e.g., 1 – MSE, scaled decomposition R²), please make this explicit in the main text or table captions.\n- How do the qualitative examples, such as NMF with dropout/Poisson modeling and Satellite ensembling, support the narrative of the methods being \"distinct\" or \"custom\"?\n- Is using text similarity (Fig. 2A) a sufficient proxy for diversity, or should a more principled novelty metric be employed in the evaluation?\n- This work’s broad, application-driven scope and descriptive evaluation would be better suited for an applied-science journal rather than a methodology-focused venue like ICLR.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Clear motivation and problem framing. The paper focuses on creating methods rather than just executing pipelines. It specifies the objective and agent loop, including the task description and the evaluator $h(\\cdot)$, with cold/warm start.\n- Breadth of benchmarks with application case study. Empirical scope spans 11 tasks across two domains (six single-cell, five scientific deep learning).\n- The warm-start improvements on scDRS and pgBoost are interesting. It is interesting to consider concrete empirical deltas and biological findings (e.g., new T-cell disease associations; rs138917529→GCK link).", "weaknesses": "- Too many symbols in Algorithm 1. If the authors want to introduce these annotations, they should include an appendix table.\n- Baseline coverage is insufficient, mainly including general-purpose agents (AIDE, Biomni, ChatGPT-Agent) and simple expert or AutoML models. It lacks domain-specific, publication-level baselines for each scientific task. Including parts of the leaderboard is recommended.\n- Ablation and diagnosis analyses lack depth. The ablation study in Table 3 mixes tasks from unrelated domains. It omits per-task variance or statistical testing, making it impossible to isolate the contribution of each component (knowledge tree, Bayesian updates, diagnosis). \n- No detailed analysis of failure cases or performance drops is provided, so readers cannot understand when the system fails or regresses.\n- Case studies serve as illustrations but lack control. The genetic applications (scDRS, pgBoost) show improvements, yet they lack independent validation or error analysis.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dgld1o6Txt", "reviewer": "Reviewer_SvJ8", "summary": "The paper develops TusoAI, an agentic AI system that, given a scientific task description and an evaluation function, autonomously develops and optimizes computational methods for the application. TusoAI is tested across several single-cell tasks and some general deep learning tasks.", "questions": "1) The key motivation behind TusoAI is to develop new computational solutions to existing problems in science.  Do the results show a new computational method developed beyond your expectations? One that is far beyond the reach of human ML engineers?\n\n2) Is there an underlying optimization problem that the Bayesian framework is solving? \n\n3) What is the convergence criterion of the system? Figure 2A and 2B suggest that TusoAI never really converges, as code diversity oscillates and performance dips. Is this expected or desired? How did you pick when the algorithm should be terminated? Are there any prompting guardrails that avoid oscillations?\n\n4)  Does increasing the number of papers retrieved from Semantics Scholar (from 10) improve the results?\n\n5) Can you elaborate on how the paper ensures the model selection has been done in an apples-to-apples way across the datasets/baselines? Is model evaluation also handled by TusoAI or externally?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1) Mixing Bayesian optimization in the loop of an agentic AI system is interesting. I wish the paper had substantiated whether this is the first agent of this type or whether the idea is inspired by existing agents. \n\n2) The results show that some of the methods constructed by TusoAI are novel rather than simple re-implementation of existing approaches or calls to standard packages.\n\n3) The agentic system is tested in many scenarios, from final performance to behavior over time.\n\n4) Single-cell results show promising biological results.", "weaknesses": "1) The writing can be improved. For example, the Abstract is excessively long. Instead of listing all results, please summarize the key points and condense the Abstract's opening.\n\n2) While the evaluations involve tasks beyond a single cell, it seems like TusoAI's performance is more pronounced in the single cell case, which might suggest the paper could have made that the central focus rather than a general-purpose multi-agent AI.\n\n3) The code is not available for a full review of the paper.\n\n4) Narrow scope to problems that can be solved using off-the-shelf ML models and strategies rather than those that need in-depth model development (e.g., new deep learning models rather than say fine-tuning Restnet). \n\n5) As discussed in the *LLM-based general learning agents* section, there are already agents developed for ML engineering. This work differentiates itself from those by focusing on scientific domains, but some baselines are still missing (R&D, DS, etc).\n\n6) Limitation in scale: only 10 papers retrieved from Semantic Scholar.\n\n7) I have some concerns about model selection and evaluation, which I have asked in the box below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iC15MmDccv", "reviewer": "Reviewer_kn4q", "summary": "The paper presents TusoAI, a method that hand designs a set of steps for automated model discovery using LLMs. The authors compare their method against baseline methods on set of task and find that their approach achieves good performance with high diversity in the suggested solutions.", "questions": "- what does your acronym TusoAI stand for?\n- Table 1 and 2: How is performance defined in these tasks?\n- Is the comparison in the experimental section fair in terms of computational load? How long are baseline methods run compared to the 8h for TusoAI?\n- Figure 2, 3 and 4: font size is too small and hard to read.\n- line 323: How were the text embeddings computed?\n- Figure 2B: Does this \"optimization trajectory\" look similar for all other tasks? Are there examples where the algorithm converges fast and then does not find a better solution?\n- In the methods there is no explanation of how the Bayesian update is performed. Can you explain where and how exactly a Bayesian update is performed and what the prior and posteriors involved are?\n- Table 3: Do the ablations only disable one of the parts (i.e. \"No Bayesian\" only disables the Bayesian part but keeps all other parts)?\n- line 380: How do you define the mean time to optimize? What is the criterion that optimization is achieved?\n- line 385: \"Results are shown in Table ??\"\n- Figure 3 B/C: What does the circles show? How should we read and interpret those figures?\n- Figure 4:\n  - what does the distance threshold mean?\n  - How should read and interpret figure 4C?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "- the problem of automated computational model design is important \n- ablation studies are provided to reveal some insight into the importance of the methods components\n- strong results are achieved on a set of tasks", "weaknesses": "- the paper lacks any discussion or conclusion\n- the Bayesian update step is unclear and not described\n- the method seems overall very heuristic. A (theoretic) justification of the algorithm would be of great value.\n- additionally the algorithm is rather complex and the explanation are difficult to follow at times\n- figure are sometimes unclear and would need re-working", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "QAi3jXBAQ0", "reviewer": "Reviewer_x86D", "summary": "## Summary\n- The paper introduces TusoAI, an LLM agent for developing computational methods for domain-specific scientific tasks. They apply it to several biological and deep learning problems and demonstrate improvement over other agentic and LLM models. They also demonstrate its performance on iterating on existing state-of-the-art methods and show new scientific insights.\n\n## Recommendation\n- Reject. While results comparing with other agentic models are there, the actual performance isn't that much better compared to existing models on the specific tasks. The most interesting result is the case studies, but the other models were not run on the same case studies. There is a lack of case studies in other domains. Code is poorly written and not amenable to open source.", "questions": "## Questions\n- Was an ablation study performed on the number of points saved for each paper's methods in Step 1 \"Gather domain knowledge\"? How does increasing/ descresing point summaries affect the final model performance?\n- Why were other models not tested on the case studies? A lot seems to be riding on TusoAI's new findings, but other models were not given a chance.\n- How is the iterative refinement done for \"The initialization agent Ainit drafts 5 candidate solution descriptions from T and iteratively refines them using each paper summary in P\"? It is not clear and appears to be super important.\n- How many hyperparameters are there total for this agent? It appears to have a lot, and I would be curious how those hyperparameters affect the solutions. (ie. run time, integer values used in various places, utility scalars, probabilities, bug-fix attempts, etc..)\n- Which hardware is used for all of this? 8 hours means a totally different thing on a laptop vs. a data center. Are all models tested on the same hardware? I see some models are only accessible on the web: how do you account for different compute capabilities?\n- How many runs were used for ablation analysis (Table 3)? Was it just run once, or several times with mean score? If it was run once then it needs to be run more times, since LLMs are very stochastic and the results can vary significantly.\n- Where is the conclusion/future work section? The paper seems to end abruptly.\n\n## Feedback\n- The code is a mess. There are import statements all over the place, comments are unstructured and lacking in many places, and there are no clear instructions on how to reproduce results. Please fix this so I can take a look at the code myself and run things locally.\n- I_{diag} is not updated in Algorithm 1, it appears to remain as an empty list. Please consider removing or correct this.\n- Line 265: Change \"instruction by first uniformly draws\" to \"instruction by first uniformly drawing\".\n- The claims (line 297, first paragraph in section 4.1) that TusoAI provides \"novel\" and \"computationally efficient\" methods are not substantiated. Provide quantitative results for computational efficiency and a better empirical comparison of the results across all models. Simply stating the method TusoAI came up with does not mean it's novel.\n- Please make it clear which model is presented in figure 2B in the caption. This specific sub-figure would benefit from a better description in general.\n- Please provide motivation for why we care about generating diverse code. I would argue that if an agent can produce better methods, then it doesn't matter how diverse the methods produced along the way are. This is a central point studied in the paper that needs to be substantiated. In fact, producing lots of diverse models is at odds with the 8 hour time limit, since I can also imagine a model would benefit from trying to iterate on a model that works decently well instead of making smaller changes to a larger amount of models in the same period of time.\n- Line 386, please correct: \"Table ??\".\n- Line 430: \"This improvement reflects TusoAI’s ability...\", is not really a fair comparison. TusoAI started from the work the original authors had already done. Please correct this sentence to show how TusoAI expands upon the original work in a short amount of time, instead of making it seem it tested 167 unique versions _from scratch_.\n  - Same comment for line 464 for the second case study.\n- The text in the figures are tiny. Please make the text larger so it's easier to read instead of having to zoom in 300% to make out what it says.\n- Line 1244: \"It's samples\" change to \"Its samples\".", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "## Strengths\n- Model architecture is properly motivated and described in sufficient detail.\n- The utility of a model is conveyed properly, I can see the usefulness of this.\n- Code is provided.", "weaknesses": "## Weaknesses\n- TusoAI is designed to work with any domain as long as domain knowledge is provided, however, there is a strong focus on biological applications. This paper would benefit from showing TusoAI's performance in other scientific domains similar to the two biological case studies. I see that it worked on deep learning but only a cold start was performed. I am specifically asking for a warm start case study in a non-biology domain.\n- Diversity in methods produced is a core architecture consideration, but it is not substantiated in the text. While evidence is provided that TusoAI writes diverse code, there are no references supporting the assumption that generating more diverse code is better.\n- Code is poorly written / formatted.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)