Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents
arXiv:2605.12620v1 Announce Type: new Abstract: Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in…
arXiv:2605.12620v1 Announce Type: new Abstract: Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in…
arXiv:2605.12350v2 Announce Type: replace-cross Abstract: Lack of transparency in AI systems poses challenges in critical real-life applications. It is important…
arXiv:2605.13435v1 Announce Type: cross Abstract: There is growing interest in utilizing flow-based models as decision-making policies in reinforcement learning due…
arXiv:2605.13430v1 Announce Type: cross Abstract: Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit…
arXiv:2605.11347v2 Announce Type: replace-cross Abstract: Existing reward alignment methods for diffusion and flow models rely on multi-step stochastic trajectories, making…
arXiv:2602.23161v3 Announce Type: replace Abstract: Time series reasoning demands both the perception of complex dynamics and logical depth. However, existing…
arXiv:2603.05093v2 Announce Type: replace-cross Abstract: Feature attributions often hide a critical modeling choice: they explain a prediction along a counterfactual…
arXiv:2510.21060v3 Announce Type: replace-cross Abstract: Policy optimization (PO) is a cornerstone of modern reinforcement learning (RL), with diverse applications spanning…
arXiv:2605.06607v3 Announce Type: replace-cross Abstract: Recent LLM-based agents have closed substantial portions of the scientific discovery loop in software-only machine-learning…
arXiv:2602.22251v4 Announce Type: replace-cross Abstract: General-purpose 3D modeling in chemistry encompasses molecules and materials, requiring both generative and predictive capabilities.…