A co-evolutionary VLM-VGM loop on 500 unlabeled images raises planner success by 30 points and simulator success by 48 percent while beating fully supervised baselines.
arXiv preprint arXiv:2603.16952 , year=
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PERSA combines RLHF with selective parameter-efficient updates to top transformer layers, raising style alignment scores from 35% to 96% on code feedback benchmarks while holding correctness near 100%.
A runtime governance framework for embodied agents intercepts 96.2% of unauthorized actions and achieves 91.4% recovery success in 1000 simulation trials while outperforming baselines.
citing papers explorer
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RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data
A co-evolutionary VLM-VGM loop on 500 unlabeled images raises planner success by 30 points and simulator success by 48 percent while beating fully supervised baselines.
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PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs
PERSA combines RLHF with selective parameter-efficient updates to top transformer layers, raising style alignment scores from 35% to 96% on code feedback benchmarks while holding correctness near 100%.
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Harnessing Embodied Agents: Runtime Governance for Policy-Constrained Execution
A runtime governance framework for embodied agents intercepts 96.2% of unauthorized actions and achieves 91.4% recovery success in 1000 simulation trials while outperforming baselines.