[Paper] Can We Predict Before Executing Machine Learning Agents?

Source: arXiv - 2601.05930v1

Overview

The paper tackles a fundamental bottleneck in autonomous machine‑learning agents: they must execute every hypothesis in the real world before they can learn whether it works, which makes the loop painfully slow and costly. By training large language models (LLMs) to predict the outcome of an experiment from a verified data‑analysis report, the authors replace many expensive executions with instant reasoning, achieving a dramatic speed‑up without sacrificing accuracy.

Key Contributions

• Formalization of Data‑centric Solution Preference: Defines a new prediction task where an agent must choose the better of two candidate solutions before any physical execution.
• Large‑scale benchmark: Curates a corpus of 18,438 pairwise solution comparisons, each annotated with ground‑truth preferences derived from real executions.
• Predict‑then‑Verify framework (FOREAGENT): Introduces an agent loop that first predicts the preferred solution using an LLM, then only executes the top‑ranked candidate for verification.
• Empirical validation: Shows that LLMs, when primed with a Verified Data Analysis Report, achieve 61.5 % accuracy (well above random chance) and exhibit well‑calibrated confidence scores.
• Performance gains: FOREAGENT converges ~6× faster than traditional generate‑execute‑feedback pipelines and outperforms pure execution baselines by +6 % in final solution quality.

Methodology

1. Data Collection & Annotation

   • The authors gathered a diverse set of scientific and engineering tasks where agents propose multiple solution candidates (e.g., experimental protocols, algorithmic tweaks).
   • For each task, they executed both candidates in the real world, recorded the outcomes, and labeled which candidate was superior, yielding the pairwise comparison dataset.

2. Prompt Engineering for Prediction

   • Each comparison is presented to an LLM together with a Verified Data Analysis Report (a concise summary of the data collected from prior executions).
   • The prompt asks the model to predict which candidate will perform better, returning a binary choice and a confidence score.

3. Training & Calibration

   • No fine‑tuning is required; the authors rely on in‑context learning with few‑shot examples.
   • They apply temperature scaling and Platt scaling to align the model’s confidence with empirical success rates.

4. FOREAGENT Loop

   • Predict: Use the LLM to rank all generated candidates.
   • Execute‑Verify: Run only the top‑ranked candidate (or a small subset if confidence is low).
   • Feedback: Incorporate the new execution result into the data analysis report for the next iteration.

Results & Findings

• The LLM’s predictions are significantly better than chance and provide reliable confidence estimates, enabling the agent to decide when a verification step is necessary.
• By skipping the majority of costly executions, FOREAGENT reduces total runtime by a factor of six while still improving the final outcome.

Practical Implications

• Accelerated scientific automation: Labs using robotic platforms can cut down experiment cycles, freeing resources for more exploratory work.
• Cost‑effective AI‑driven optimization: Companies that rely on A/B testing or hyper‑parameter sweeps can replace many physical trials with cheap model predictions, slashing cloud‑compute bills.
• Rapid prototyping for developers: When building AI agents that suggest code changes, configuration tweaks, or design alternatives, a predict‑then‑verify loop can quickly surface promising candidates before committing to expensive builds or deployments.
• Confidence‑aware decision making: The calibrated confidence scores let engineers set risk thresholds (e.g., only verify when confidence < 80 %), tailoring the trade‑off between speed and safety.

Limitations & Future Work

• Domain coverage: The benchmark focuses on tasks where a clear quantitative metric exists; extending to more subjective domains (e.g., UI design) may require richer feedback signals.
• Reliance on high‑quality analysis reports: The prediction accuracy hinges on the completeness of the Verified Data Analysis Report; noisy or incomplete reports degrade performance.
• Scalability of LLMs: While in‑context learning avoids fine‑tuning, large models still incur non‑trivial inference costs; future work could explore distilled or specialized models for edge deployment.
• Iterative learning: The current loop does not update the LLM itself with new verification data; incorporating continual learning could further improve prediction fidelity over time.

The authors promise to release the code and dataset soon, so keep an eye on the repository for hands‑on experiments and potential integration into your own autonomous agent pipelines.

Authors

• Jingsheng Zheng
• Jintian Zhang
• Yujie Luo
• Yuren Mao
• Yunjun Gao
• Lun Du
• Huajun Chen
• Ningyu Zhang

Paper Information

• arXiv ID: 2601.05930v1
• Categories: cs.CL, cs.AI, cs.LG, cs.MA
• Published: January 9, 2026
• PDF: Download PDF