Longitudinal healthcare agents require persistent state tracking under temporal uncertainty. In domains like chronic disease management, patient states—medications, symptoms, and vital signs—evolve continuously over months. Existing memory architectures for Large Language Models (LLMs) are inherently retrieval-centric: they treat memory as a static repository of past interactions, failing to resolve conflicting or superseded information when queried for the current patient state. We propose a shift to state-centric memory. Our framework introduces (1) a bi-temporal state representation that decouples event time from ingestion time and tracks temporal validity windows, (2) an incremental state arbitration mechanism using four operators—SUPPORT, REFINE, SUPERSEDE, and BRANCH-CONFLICT—to handle evolving medical facts without destructive overwriting, and (3) a confidence-thresholded evidence escalation layer for robust, efficient memory access. Evaluated on a longitudinal diabetes management suite as a representative biomedical state tracking task, our method achieves a Unique-F1 of 0.85 and Conflict-F1 of 0.98, substantially improves upon long-context LLMs (0.38 / 0.89) and standard vector memory (0.30 / 0.60), demonstrating that agentic AI in longitudinal biomedical settings requires continuous, evidence-grounded arbitration rather than simple retrieval.

Weakly-supervised Phrase Grounding (WPG) is an emerging task of inferring the fine-grained phrase-region matching, while merely leveraging the coarse-grained sentence-image pairs for training. However, existing studies on WPG largely ignore the implicit phrase-region matching relations, which are crucial for evaluating the capability of models in understanding the deep multimodal semantics. To this end, this paper proposes an Implicit-Enhanced Causal Inference (IECI) approach to address the challenges of modeling the implicit relations and highlighting them beyond the explicit. Specifically, this approach leverages both the intervention and counterfactual techniques to tackle the above two challenges respectively. Furthermore, a high-quality implicit-enhanced dataset is annotated to evaluate IECI and detailed evaluations show the great advantages of IECI over the state-of-the-art baselines. Particularly, we observe an interesting finding that IECI outperforms the advanced multimodal LLMs by a large margin on this implicit-enhanced dataset, which may facilitate more research to evaluate the multimodal LLMs in this direction.