arXiv AI
Harness^2: Recursive Agent Harnessing for an Open World
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computation and Language
Sep 15
ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement
arXiv:2609.14857v1 Announce Type: new Abstract: Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve executio...
arXiv Machine Learning
Oct 8
RSIGym: A Flexible Environment for Recursive Self-Improvement
RSIGym is a new agent-native research environment built on Everything as a Service (EaaS) that exposes training, inference, rollout, evaluation, and sandbox execution through reusable services. It supports shared budget and permission controls for Data, Harness, and Joint improvement tracks, allowing agents to investigate individual interventions and jointly optimize data, training settings, and execution harnesses within the same environment. Using RSIGym, the Opus 5 model achieved the highest RSI-Index of 0.4809 across five benchmarks, improving performance on software engineering, terminal interaction, mathematics, scientific reasoning, and skill-based tasks.
arXiv Machine Learning
Sep 22
RRSI: Regularized Recursive Self-Improvement of Agent Harnesses
The paper introduces Regularized Recursive Self-Improvement of Agent Harnesses (RRSI), a method that applies regularization principles to the iterative editing of an LLM agent’s harness—prompts, control flow, tooling, memory, and context management. RRSI limits the number of edits per candidate, encourages novel trajectories, and uses a critic and pruner to filter out benchmark‑specific or ineffective changes, thereby favoring reusable agent mechanisms. Experiments on eight benchmarks show RRSI improves performance by up to 14.1 points on the training split and 4.7 points on out‑of‑distribution tests, while reducing policy token usage by 30% compared to unregularized evolution.
arXiv AI
Oct 1
Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer
The paper introduces a self‑evolving harness framework where a frozen language‑model agent first solves tasks and then edits its own harness based on run records. Using a 49‑line seed harness, the evolved harness improves average scores on in‑distribution benchmarks by 4.48 points and on out‑of‑distribution benchmarks by 12.64 points, surpassing Codex on the former and matching it on the latter. Continued evolution on a specific out‑of‑distribution benchmark further raises performance, and the study analyzes emergent mechanisms such as output truncation and history compaction.
arXiv Computation and Language
Aug 21
Self-Harness: Harnesses That Improve Themselves
arXiv:2606. 09498v3 Announce Type: replace Abstract: The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment.
arXiv AI
Oct 5
Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite
arXiv:2610.02826v1 Announce Type: new Abstract: Successful trajectories on difficult tasks provide valuable supervision for model improvement, but specialized harnesses introduce interventions that m...