Flex‑RSI is a new benchmark that evaluates how agents can recursively improve their own harness—prompts, tools, memory, and control loop—while solving vision and robotics tasks under a fixed budget. Each round alternates between an executor that refines the task solution and an improver that revises the harness, with only higher‑scoring solutions retained for the next iteration. The benchmark tests both closed‑source and open‑weight models across perception, reasoning, robot action, 3D reconstruction, visual creation, and infrastructure tasks, revealing that self‑improvement is most effective when agents build their own solutions, that simple success/failure feedback limits open‑weight models, and that harness editing itself contributes significantly to performance gains.
arXiv:2610.20474v1 Announce Type: new Abstract: Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy,...
arXiv:2610.18792v1 Announce Type: cross Abstract: Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for...
arXiv:2610.14224v1 Announce Type: new Abstract: LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a pr...
arXiv:2610.17114v1 Announce Type: cross Abstract: Frontier large language models (LLMs) now reason about space well enough to program robots, yet the world models behind robot policies are implicit,...
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:2610.03476v1 Announce Type: cross Abstract: Long-horizon mobile manipulation presents significant challenges due to compounding execution errors and capacity interference between locomotion and...
arXiv:2609. 37968v1 Announce Type: new Abstract: Advances in the coding capabilities of LLM agents allow them to inspect and modify their own instructions, tools, and execution procedures.
EvalResearchBench (ERB) is a benchmark that tests whether AI agents can autonomously design evaluation tasks for recursive self‑improvement. In ERB, a researcher agent selects or synthesizes tasks, builds graders, and refines them through pilot tests before producing a final evaluator that runs within fixed time and API budgets. Experiments with nine researchers and thirteen candidate models show that the best automated evaluators achieve about 75% concordance with target benchmarks, below the 91% ceiling set by target disagreements, and that human‑designed task samples still outperform many automated approaches.
RoboArchitect automates the design of agent harnesses that orchestrate frozen robot policies, vision tools, and direct arm commands. By observing rollouts, diagnosing failures, and iteratively revising the harness, it improves success rates from 9.7% to 43.3% on unseen scenes and from 12.7% to 40.2% on unseen tasks without updating model weights. The evolved harness also develops persistent memory and intuitive control strategies, outperforming finetuned policies especially when the benchmark diverges from the policy’s training data.
arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.
arXiv:2610.18842v1 Announce Type: new Abstract: Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive Se...
arXiv:2605.13119v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models are effective robot action executors, but they remain limited on long-horizon tasks due to the dual burde...