T. J. Zhang

Yushen Liang, Yue Peng, Baosheng Jin et al.

Vision--Language--Action (VLA) policies such as $π_{0.5}$ and OpenVLA perform well on many manipulation tasks, but they are often reactive: the next action is predicted from the current observation, instruction, and proprioceptive state. This assumption breaks down in stage-dependent manipulation, where visually similar states may require different actions depending on latent task progress and previous interaction outcomes. We argue that such tasks require not only memory, but dynamics-aware belief updates: the policy should preserve task progress during stable or occluded phases and revise its belief near contact, release, or subgoal transitions. We introduce Temporally Conditioned Memory-Fusion Policies (TFP), a lightweight memory-action framework for VLA backbones. TFP maintains an episode-local task-progress belief with Liquid Time-Constant dynamics and injects the updated belief directly into the flow-matching action decoder through adaptive modulation. This lets temporally accumulated context shape the generated action chunk, rather than serving only as passive history context. With a 3.3B-parameter model, TFP improves the average success rate from \(96.9\%\) to \(98.75\%\) on LIBERO and from \(91.4\%\) to \(93.77\%\) on LIBERO-plus. On the memory-focused MIKASA ShellGameTouch diagnostic, TFP achieves success up to \(75.0\%\). Mechanistic analyses show that write-gain changes near manipulation events are about \(6\times\) larger than far non-event phases, and hidden-state interventions show that the belief causally modulates generated action chunks. These results suggest that compact, event-sensitive memory dynamics can improve VLA policies under occlusion, visual perturbation, and stage-dependent task structure.

Zhehong Ren, Tianluo Zhang, Yiheng Lu et al.

Next-step location prediction plays a pivotal role in modeling human mobility, underpinning applications from personalized navigation to strategic urban planning. However, approaches that assume a closed world - restricting choices to a predefined set of points of interest (POIs) - often fail to capture exploratory or target-agnostic behavior and the topological constraints of urban road networks. Hence, we introduce a road-node-centric framework that represents road-user trajectories on the city's road-intersection graph, thereby relaxing the closed-world constraint and supporting next-step forecasting beyond fixed POI sets. To encode environmental context, we introduce a sector-wise directional POI aggregation that produces compact features capturing distance, bearing, density and presence cues. By combining these cues with structural graph embeddings, we obtain semantically grounded node representations. For sequence modeling, we integrate a Relation-Aware LNN-Transformer - a hybrid of a Continuous-time Forgetting Cell CfC-LNN and a bearing-biased self-attention module - to capture both fine-grained temporal dynamics and long-range spatial dependencies. Evaluated on city-scale road-user trajectories, our model outperforms six state-of-the-art baselines by up to 17 percentage points in accuracy at one hop and 10 percentage points in MRR, and maintains high resilience under noise, losing only 2.4 percentage points in accuracy at one under 50 meter GPS perturbation and 8.9 percentage points in accuracy at one hop under 25 percent POI noise.