The abstract for the arXiv paper Harness^2: Recursive Agent Harnessing for an Open World argues that an LLM agent's capabilities are determined not just by its model, but also by the execution harness that connects the model to tools, memory, and the environment. The authors suggest that recursive self-improvement (RSI) could be a promising route to automatically evolve these harnesses over time.
However, the abstract notes that deploying RSI in practice is not straightforward. The excerpt cuts off while describing these deployment challenges, so the full paper likely details specific obstacles and the proposed Harness^2 approach for addressing them. As presented, the work frames harness evolution as a key frontier for improving agent performance.
Because only the abstract was available for this digest, the summary reflects the paper's stated motivation and direction rather than its experimental results or technical contributions. No other sources were provided for comparison.