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A Computational Theory for Efficient Mini Agent Evaluation with Causal Guarantees

Hedong Yan

TL;DR

This work develops a computational theory for evaluating mini agents with causal guarantees by learning evaluation models that bound generalized evaluation error $E_{gen}$ and causal-effect error under IRIS/IIDE assumptions. It introduces a meta-learning evaluation architecture and proxy-metric augmentation to handle heterogeneous agent spaces, enabling scalable evaluation across many subjects and conditions. Theoretical results provide upper bounds and causal guarantees, while extensive experiments across 12 diverse scenes show substantial error reductions (up to 99%) and speedups (up to $10^7$×) over traditional evaluation approaches. The approach supports rapid, low-cost iteration in AI-enabled domains, though it notes limitations in high-dimensional settings and the need for validating underlying assumptions with further work.

Abstract

In order to reduce the cost of experimental evaluation for agents, we introduce a computational theory of evaluation for mini agents: build evaluation model to accelerate the evaluation procedures. We prove upper bounds of generalized error and generalized causal effect error of given evaluation models for infinite agents. We also prove efficiency, and consistency to estimated causal effect from deployed agents to evaluation metric by prediction. To learn evaluation models, we propose a meta-learner to handle heterogeneous agents space problem. Comparing with existed evaluation approaches, our (conditional) evaluation model reduced 24.1\% to 99.0\% evaluation errors across 12 scenes, including individual medicine, scientific simulation, social experiment, business activity, and quantum trade. The evaluation time is reduced 3 to 7 order of magnitude per subject comparing with experiments or simulations.

A Computational Theory for Efficient Mini Agent Evaluation with Causal Guarantees

TL;DR

This work develops a computational theory for evaluating mini agents with causal guarantees by learning evaluation models that bound generalized evaluation error and causal-effect error under IRIS/IIDE assumptions. It introduces a meta-learning evaluation architecture and proxy-metric augmentation to handle heterogeneous agent spaces, enabling scalable evaluation across many subjects and conditions. Theoretical results provide upper bounds and causal guarantees, while extensive experiments across 12 diverse scenes show substantial error reductions (up to 99%) and speedups (up to ×) over traditional evaluation approaches. The approach supports rapid, low-cost iteration in AI-enabled domains, though it notes limitations in high-dimensional settings and the need for validating underlying assumptions with further work.

Abstract

In order to reduce the cost of experimental evaluation for agents, we introduce a computational theory of evaluation for mini agents: build evaluation model to accelerate the evaluation procedures. We prove upper bounds of generalized error and generalized causal effect error of given evaluation models for infinite agents. We also prove efficiency, and consistency to estimated causal effect from deployed agents to evaluation metric by prediction. To learn evaluation models, we propose a meta-learner to handle heterogeneous agents space problem. Comparing with existed evaluation approaches, our (conditional) evaluation model reduced 24.1\% to 99.0\% evaluation errors across 12 scenes, including individual medicine, scientific simulation, social experiment, business activity, and quantum trade. The evaluation time is reduced 3 to 7 order of magnitude per subject comparing with experiments or simulations.

Paper Structure

This paper contains 30 sections, 8 theorems, 33 equations, 7 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Notation
  3. Theoretical analysis
  4. Upper bound of generalized evaluation error
  5. Hidden common cause
  6. Learning evaluation models
  7. Assessing assumptions
  8. Assessing IIDE assumption
  9. Assessing Unbiased assumption
  10. Experiment
  11. Evaluation model
  12. Conditional evaluation model
  13. Interpretability
  14. Evaluation cost
  15. Limitation and conclusion
  16. ...and 15 more sections

Key Result

Theorem 1

Upper bound. Given any evaluation model $\hat{f}$, $P(E_{gen}(\hat{f})<E_{emp}(\hat{f})+\sqrt{\frac{1}{2n}\ln(\frac{1}{\sigma})})\geq 1-\sigma$ where $n$ is number of independent identical distributed error (IIDE) measurements, $0<1-\sigma<1$ is confidence.

Figures (7)

  • Figure 1: Procedure of computational evaluation. C is evaluation condition, S is evaluation subject (agent), M is evaluation metric, EM is evaluation model.
  • Figure 2: Learn evaluation models from data. $P$ is proxy metrics of evaluation subject, and $T_i$ is tensorization function for subject type $i$.
  • Figure 3: Normalized empirical negative RMSE of evaluation models crossing scenes (6 scenes in figure \ref{['fig:ROCAUC']} and figure \ref{['fig:ACC']}, 5 scenes in figure \ref{['fig:RMSE']} and figure \ref{['fig:R2']}. The confidence level of confidence interval bar is set as 95%. The baselines are holdout 100%, holdout 50%, holdout 10%, 5-fold cross-validation, 10-fold cross-validation, and bootstrap. We test different base learners (Linear, MLP, SVM/SVR, Random forest, XGBoost, LighGBM, and CatBoost) for heterogeneous subject space.
  • Figure 4: Shapley value of subject vector and proxy metrics on 11 scenes (6 scenes in figure \ref{['fig:ROCAUC_shap']} and figure \ref{['fig:ACC_shap']}, 5 scenes in figure \ref{['fig:RMSE_shap']} and figure \ref{['fig:R2_shap']}. The outcome of null set is set as the performance of holdout-100, and other outcomes are the negative RMSE of the linear evaluation models.
  • Figure 5: Estimates percentage of completed phase III interventional trials (adult) with results and study documents on https://clinicaltrials.gov.
  • ...and 2 more figures

Theorems & Definitions (12)

  • Theorem 1
  • Theorem 2
  • Theorem 3
  • Theorem 4
  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Theorem 3
  • proof
  • ...and 2 more