---
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id: "d94a846ad662e4eace981cb1df54e98cb008ee2a471b934067fcec95ed6b5a6f"
canonical_url: "https://aidr.today/d94a846a?lang=en"
title: "Predict Before You Modify: Learning Improvement Dynamics for Self-Evolving Agents"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
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published_at: "2026-10-14T04:00:00.000Z"
category: "Research"
topics: ["agent"]
source_urls: ["https://arxiv.org/abs/2610.13991"]
summary: "A predictive self-evolution approach uses an editable Improvement Dynamics Model (IDM) to forecast task-level outcomes of proposed modifications before evaluation, guiding optional revisions and accumulating predictive knowledge across generations. Evaluated on multilingual coding, paper review, robotics reward design, and Olympiad-level mathematical grading, the method outperforms HyperAgents by up to 46% average performance over generations."
---

# Predict Before You Modify: Learning Improvement Dynamics for Self\-Evolving Agents

> [Open the canonical story](<https://aidr.today/d94a846a?lang=en>)

**Published:** 2026-10-14T04:00:00.000Z
**Category:** Research
**Topics:** agent

## Summary

A predictive self\-evolution approach uses an editable Improvement Dynamics Model \(IDM\) to forecast task\-level outcomes of proposed modifications before evaluation, guiding optional revisions and accumulating predictive knowledge across generations\. Evaluated on multilingual coding, paper review, robotics reward design, and Olympiad\-level mathematical grading, the method outperforms HyperAgents by up to 46% average performance over generations\.

## Sources

- [Story source](<https://arxiv.org/abs/2610.13991>)