Harness-RSI

Self-Evolving Agent Harness · arXiv · Project Page · Reading List · Citation · 中文版 ·

Companion materials for our survey of how execution experience becomes persistent harness state under a fixed base model. We organize update mechanisms, examine evolutionary search and continual PAC learning, assess reported evidence, and discuss applications and societal effects. The repository PDF is the 12 October 2026 working draft. Figures retain the manuscript layout; click to view them at full resolution.

Reading Guide

Background　Related Surveys · Definition · Taxonomy · Precursors

Literature　Interpreted · Executable · Theory · Evaluation

Resources　Applications & Outlook · Reported Evidence · Contributing · Citation

Related Surveys

• A Survey of Self-Evolving Agents TMLR'26 — organizes self-evolution by what, when, how, and where to evolve. [paper]
• A Comprehensive Survey of Self-Evolving AI Agents arXiv'25 — organizes methods by optimizer feedback. [paper] [list]
• Externalization in LLM Agents arXiv'26 — unified review of memory, skills, protocols, and harness engineering. [paper]
• Code as Agent Harness arXiv'26 — organizes by the role of code in the agent loop. [paper] [list]
• Recursive Self-Improvement in AI arXiv'26 — from bounded self-refinement to autonomous research loops, organized by loop closure. [paper]
• Self-Improvements in Modern Agentic Systems arXiv'26 — separates model and scaffold updates under frozen parameters and discusses independent evaluation and component controls. [paper] [list]
• Self-Evolving Coding Agents arXiv'26 — distinguishes proposal eligibility, runtime activation, and admission, and discusses component controls under a common budget. [paper]

Definition

A harness $H$ is the software and configuration that governs how a model $M$ operates in an environment $E$. It includes tools, context construction, memory, and execution control. The final evaluator $J$ scores a trajectory and stays outside the candidate’s editable surface; internal critics and provisional verifiers may belong to the mutable harness.

$$ \tau \sim \mathrm{Run}(M,H,E), \qquad y=J(\tau) $$

Harness self-evolution uses a batch of trajectories $\mathcal{B}_k$ to revise the state used by later runs:

$$ H_{k+1}=\mathrm{Update}(H_k,\mathcal{B}_k) $$

The update mechanism remains available for further revisions, and an accepted state is saved and loaded by a later run. Rejected candidates leave the harness unchanged. The analysis holds $M$ fixed; systems that also train weights are read here for their harness-side changes. Offline optimizers and mechanisms with limited persistence are marked in the entries.

Self-evolution describes this update process whether or not the accepted state performs better; self-improvement is a beneficial change supported by an explicit comparison. Harness RSI (recursive self-improvement) remains a conventional short name for the area, not a guarantee that every accepted revision helps. Whether an update operated and improved performance requires the evaluation evidence below.

Runtime responsibilities and mechanisms. An implementation may combine or omit components.

Taxonomy

Interpreted updates revise content read by a fixed host; executable updates revise the implementations it invokes. Evaluation forms a separate branch, while theory cuts across update targets.

Reference numbers follow the manuscript; source links appear in the reading list below. See also the taxonomy data.

Precursors

• Speculations Concerning the First Ultraintelligent Machine Adv. Computers'65 — frames recursive improvement as a machine designing a better successor to itself. [paper]
• EURISKO Artif. Intell.'83 — learned heuristics, including heuristics that modify other heuristics, shape later search. [paper]
• Gödel Machines arXiv'03 — a self-rewrite executes only after the system proves the rewrite improves expected utility. [paper]

1. Interpreted Update Targets

These methods revise instructions, memory, or coordination specifications read by fixed components. Entries note offline optimization and task-local adaptation where applicable.

1.1 Prompts & Instructions

Scalar-Score Search

Use candidate scores to propose and select prompt revisions.

• APE ICLR'23 — proposes instructions from input–output demonstrations and keeps the highest-scoring candidate. [paper] [project]
• OPRO ICLR'24 — feeds earlier instructions and scores to an optimizer model that proposes the next candidate. [paper] [code]

Textual-Gradient Critique

Translate errors, critiques, or execution traces into edits to text parameters.

• ProTeGi EMNLP'23 — converts task errors into natural-language "gradients" and beam-searches over the edited prompts. [paper]
• TextGrad Nature'25 — backpropagates textual critiques through a graph of LLM calls to edit any text variable in the system. [paper]
• GEPA ICLR'26 Oral — uses execution-trace reflection to revise prompts and Pareto selection to retain candidates. [paper]
• Trace NeurIPS'24 — treats the execution trace as a computation graph and generates edits from rich feedback through a generative optimizer. [paper] [project]
• metaTextGrad arXiv'25 — optimizes the optimizer's own prompts, accepting an update only when a validation split improves. [paper]
• EGO-Prompt arXiv'25 — co-adapts the prompt and a semantic causal graph for domain deployments. [paper]
• SePO arXiv'26 — rewrites persistent system prompts using accumulated task feedback. [paper] [code]

Self-Referential Mutation

Search over the instructions that generate later mutations.

• Promptbreeder ICML'24 — co-evolves task prompts and the mutation prompts that generate them. [paper]
• DelvePO arXiv'25 — decouples prompt components and steers mutation along identified directions. [paper]
• DEEVO arXiv'25 — evolves instructions through structured debates, ranking candidates by Elo tournament. [paper]

1.2 Memory & Reusable Procedures

Experience Mining

Distill execution trajectories into knowledge retrieved by later runs.

• Reflexion NeurIPS'23 — stores verbal self-reflection on a failure and reloads it for another attempt at the same task. [paper]
• ExpeL AAAI'24 — extracts cross-task insights from a training pool of trajectories, with add/edit/upvote/downvote curation. [paper]
• AWM ICML'25 — induces recurring sub-goal workflows from trajectories, offline from a training split or online from the task stream. [paper]
• Agent KB arXiv'25 — a cross-domai