OptiMindTune: Multi-Agent HPO

• OptiMindTune is a multi-agent framework that decomposes hyperparameter optimization into Recommender, Evaluator, and Decision agents for coordinated search.
• It employs adaptive search, dynamic knowledge fusion, and LLM-driven warm-starting to enhance sample efficiency and accelerate convergence.
• Experimental results demonstrate improved accuracy and faster convergence with fewer trials compared to traditional methods like Random Search and Optuna.

OptiMindTune is a multi-agent framework for intelligent hyperparameter optimization (HPO), designed to accelerate and robustly tune machine learning models via collaborative LLMs. The system decomposes the traditional HPO loop into three distinct yet cooperating Gemini-powered agents—Recommender, Evaluator, and Decision—each addressing a unique facet of the search process. By leveraging adaptive search, dynamic knowledge fusion through a shared history, and principles from advanced LLMs, OptiMindTune offers scalable AutoML with demonstrated improvements in sample efficiency and convergence speed over established baselines (Madiraju et al., 25 May 2025).

1. Formal Problem Definition

Hyperparameter optimization in OptiMindTune is formulated as the minimization of a black-box objective $f(\theta)$ over a potentially mixed-domain hyperparameter space $\Theta \subseteq \mathbb{R}^d$:

$$\begin{aligned} &\text{Given:} \quad f : \Theta \;\to\; \mathbb{R}, \ &\text{Goal:}\quad \theta^\ast \;=\; \arg\min_{\theta\in\Theta} f(\theta), \end{aligned}$$

where, for classification tasks, $f(\theta) = -\mathrm{Accuracy}_\mathrm{CV}(\theta)$. Optimization may be subject to an evaluation budget $B$ (time or trial count):

$$\theta^* = \arg\min_{\theta\in\Theta} f(\theta) \quad \text{s.t.} \quad \#\{ \text{evaluations} \} \le B.$$

This formalization reflects the combinatorial and expensive nature of HPO, particularly under practical resource constraints.

2. Multi-Agent System Architecture

OptiMindTune implements a modular architecture involving three collaborating agents, each powered by Google's Gemini LLM, and a central shared optimization history:

• Recommender Agent (“Explorer”): Analyzes dataset meta-features and optimization history and proposes $\{\text{model},\theta\}$ configurations with accompanying natural-language reasoning. Its generation process encodes dataset statistics, recent performance trends, and an exploration/exploitation parameter $\alpha \in [0,1]$. The agent’s suggestions reflect a prompt-driven LLM computation which approximates Bayesian-optimization acquisition and uncertainty-based sampling.
• Evaluator Agent (“Worker”): Constructs a scikit-learn pipeline for each proposal, conducts preprocessing, model training, and $k$-fold cross-validation, and returns a robust suite of metrics (mean accuracy, precision, recall, F1, per-fold scores). Implementation utilizes:

$\Theta \subseteq \mathbb{R}^d$5

• Decision Agent (“Orchestrator”): Ingests evaluation results, benchmarks against targets, accepts or rejects new configurations, manages $\alpha$, issues explicit feedback to the Recommender, and determines loop termination. Its logic incorporates acquisition rules, $\Theta \subseteq \mathbb{R}^d$0-greedy polices, and dynamic adjustment based on recent improvements.

Communications occur via structured JSON-like messages, recorded in the shared “history” log, providing full provenance for proposals, evaluations, and decisions.

3. Adaptive Search and Knowledge Sharing

OptiMindTune’s convergence-oriented mechanisms include:

• Dynamic $\Theta \subseteq \mathbb{R}^d$1-tuning: The Decision Agent updates the exploration weight $\Theta \subseteq \mathbb{R}^d$2 in response to observed performance deltas $\Theta \subseteq \mathbb{R}^d$3.
• LLM-driven warm-starting: The Recommender leverages early successful regions in hyperparameter space, making exploitative proposals and localized perturbations.
• Knowledge fusion: All agents can read and write to the common history, supporting informed suggestions and consistent coordination.

Gradient estimation in the Recommender is realized via a sliding window surrogate over recent history:

$\Theta \subseteq \mathbb{R}^d$4

A typical optimization loop sequence is as follows:

$\Theta \subseteq \mathbb{R}^d$6

This architecture enables rapid and robust navigation of high-dimensional hyperparameter manifolds.

4. Experimental Results and Baseline Comparison

OptiMindTune’s efficacy was evaluated against Random Search, Bayesian Optimization (GPyOpt), and Optuna (TPE) baselines across UCI datasets (Breast Cancer, Iris, Wine) and classical scikit-learn models (SVC, RandomForestClassifier, LogisticRegression).

Best Mean Cross-Validated Accuracy:

Dataset	OptiMindTune Model	OptiMindTune Acc.	Optuna Best Model	Optuna Acc.
Breast Cancer	LogisticRegression	97.02%	RandomForestClassifier	96.14%
Iris	RandomForestClassifier	96.67%	LogisticRegression	98.00%
Wine	LogisticRegression	98.33%	RandomForestClassifier	97.78%

Computational Efficiency:

Dataset	Method (Model)	Time (s)	Trials/s	#Trials
Breast Cancer	OptiMindTune (LR)	7.91	0.51	4
	Optuna (RFC)	7.28	1.37	10
Iris	OptiMindTune (RFC)	14.86	0.20	3
	Optuna (LR)	25.40	0.39	10
Wine	OptiMindTune (LR)	22.85	0.13	3
	Optuna (RFC)	6.08	1.64	10

OptiMindTune matched or exceeded Optuna in 2 out of 3 datasets using significantly fewer trials (3–4 vs. 10). Despite lower trials per second due to LLM overhead, overall convergence was as fast or faster, indicating superior sample efficiency.

5. Ablation and Scalability Studies

Ablative experiments on the Breast Cancer dataset revealed critical contributions of the architectural components:

• Decision-to-Recommender feedback: Removal increased the number of required trials from 4 to 7 and reduced best accuracy from 97.02% to 95.8%.
• Cross-validation: Disabling this in the Evaluator boosted accuracy variance by 25% across runs, with only marginally faster convergence but reduced result stability.

Scalability tests over 20 OpenML tasks (500–10,000 samples) showed per-trial LLM overhead remains approximately 0.3 seconds, and OptiMindTune's convergence advantage increases with dataset scale, achieving up to 1.5× faster attainment of 95% of peak accuracy compared to Optuna.

6. Limitations, Deployment Strategies, and Future Directions

Current limitations:

• Reliance on external LLM APIs introduces issues of latency, cost, and potential rate limiting.
• LLM-driven agents operate as “black boxes,” complicating interpretability and auditability.
• The present design is focused on classical scikit-learn models; extending to continuous or deep learning spaces (e.g., neural architecture search) will require novel agent specialization.

Deployment considerations and mitigations:

• Backend caching layers are recommended to avoid redundant LLM calls.
• Surrogate local models serve as a fallback under resource constraints.
• Logging all agent interactions supports regulatory audit and reproducibility.

Planned future extensions:

• Fine-tuning Gemini models on HPO-specific transcripts to enhance agent specialization.
• Introduction of an “RL Coordinator” agent employing multi-agent reinforcement learning for meta-control of agent communications.
• Expansion to neural architecture search with block-specific sub-agents.
• Integration of cost awareness (e.g., GPU, LLM API resources) into Decision Agent utility functions.
• Support for multi-objective optimization (combining accuracy, latency, fairness, and model size).

In summary, OptiMindTune models HPO as a structured dialogue among modular LLM-powered agents, each contributing to exploration, evaluation, and policy design over a transparent shared search space. This paradigm demonstrates strong sample efficiency and convergence properties and establishes a path toward more scalable and intelligent AutoML systems (Madiraju et al., 25 May 2025).