| name | foreagent-predict-execute |
| title | Can We Predict Before Executing Machine Learning Agents? |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2601.05930 |
| keywords | ["agent-efficiency","prediction","world-models","expensive-verification","hypothesis-testing"] |
| description | Replace expensive test-based execution loops with learned prediction models that forecast agent action outcomes before commitment. Framework uses internalized execution priors and structured analysis reports to achieve 6x faster convergence and 6% higher performance compared to execute-first baselines. Applicable to scientific discovery, optimization, and hypothesis testing where verification is computationally or financially expensive. |
Problem
Many agent tasks require expensive verification:
- Experimental Cost: Running physical experiments, simulations, or expensive API calls to verify hypotheses is time-consuming and resource-intensive
- Execution Latency: Sequential test-execute-feedback loops add delay to agent decision-making
- Inefficient Exploration: Agents waste budget testing poor hypotheses without predictive guidance
- High Token/Cost Overhead: Multiple verification cycles consume resources proportional to the number of hypotheses tested
Traditional agent architectures follow: Generate → Execute → Feedback, creating a bottleneck in tasks where execution is costly.
Solution
FOREAGENT introduces Predict-then-Verify, replacing expensive execution with learned prediction:
- Predictive Priors: Train models to internalize "world models" that forecast outcomes without physical execution
- Structured Analysis: Convert task-specific context (experimental parameters, design variables) into structured analysis reports
- Confidence-Guided Execution: Only execute expensive verification when prediction confidence is low; skip verification for high-confidence predictions
- Outcome Forecasting: Use 18,438+ pairwise comparison examples to train models to predict solution quality, reaching 61.5% accuracy
When to Use
- Scientific Discovery Agents: Predicting experimental outcomes before costly lab work
- Hyperparameter Optimization: Forecasting model performance across parameter configurations
- Design Optimization: Predicting product performance without expensive manufacturing trials
- Financial/Investment Scenarios: Testing strategies without expensive market execution
- Any Agent with Expensive Verification: When test/execute/verify costs dominate computational budget
When NOT to Use
- For tasks with cheap execution (standard LLM reasoning, web API calls)
- In domains where execution is required for safety validation (medical, legal)
- When outcome distribution is non-stationary and predictions become stale
- For agents requiring real-time feedback from environments (robotics, interactive tasks)
Core Concepts
The framework operates on three key principles:
- : Learn to forecast outcomes from prior experience rather than always testing
