MintFlow: Minimal Trajectory Intervention for Constrained Flow Matching

arXiv:2605.20758v1 Announce Type: new Abstract: Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation

arXiv:2605.01457v1 Announce Type: new Abstract: Generative models have emerged as a major paradigm for offline multi-agent reinforcement learning (MARL), but existing approaches

arXiv:2603.18363v1 Announce Type: cross Abstract: Unsupervised Reinforcement Learning from Internal Feedback (RLIF) has emerged as a promising paradigm for eliciting the latent

arXiv:2603.22309v1 Announce Type: cross Abstract: Partial differential equation (PDE) simulation holds extensive significance in scientific research. Currently, the integration of

arXiv:2605.20199v1 Announce Type: cross Abstract: We present FlowLM, a flow matching language model transformed from pre-trained diffusion language models via efficient

arXiv:2606.20084v1 Announce Type: new Abstract: Data editing with generative methods typically requires differentiable objectives and gradient-based search. However, these

arXiv:2608.20869v1 Announce Type: new Abstract: Predicting transition states (TS) in chemical reactions is crucial, as they provide insights into reaction mechanisms. Recent work

arXiv:2605.15417v1 Announce Type: cross Abstract: In GFlowNets and variational inference, it has been shown that the mean square error between target and model log probabilities