Large Language Model (LLM) agents in distributed decision-making scenarios often operate as spatially separated entities and coordinate from fragmented local information. However, this physical dispersion constrains agents to limited local perception and finite temporal horizons. We characterize this bottleneck as spatiotemporal partial observability, in which spatial and temporal limitations are fundamentally coupled. Resolving spatial conflicts requires temporal reasoning about neighbors’ future actions, while temporal planning requires spatial context beyond local perception. To bridge this gap, we introduce **MACRO-LLM**, LLM-empowered multi-agent collaborative reasoning under spatiotemporal partial observability. The architecture integrates spatial and temporal reasoning within each decision cycle via three interdependent modules: (1) the CoProposer mitigates *temporal uncertainty* by screening candidate actions via LLM-predicted rollouts; (2) the Negotiator overcomes *spatial myopia* by resolving conflicts through mean-field statistical aggregation, grounded in the CoProposer’s LLM-estimated rollout rewards; and (3) the Introspector closes the reasoning loop by analyzing environmental drift and attributing performance changes to refine strategies. Extensive evaluations on two complex long-horizon tasks, cooperative platoon planning and pandemic control, demonstrate that our framework enables robust coordination under spatiotemporal partial observability.

LLM-empowered paper-code discrepancy detection has received growing concern since the scaling of research submissions exceeds the manual review capability. However, the limited context capacity and one-sided discrepancy detection of existing single-agent LLM paradigms lead to an inferior recall performance in detecting discrepancies. In this paper, we propose **Dude**, the first **Du**al-**De**tection Multi-Agent System for paper-code discrepancy detection. We discover that the granularity asymmetry of the paper-language and code-language introduces over-interpretation and over-reporting challenges in a multi-agent system design for discrepancy detection, resulting in increasing false positives. To address this, we propose a granularity-aligned negotiation and a two-stage salience-filtering mechanism in Dude, which effectively prevents agents from falsely reporting discrepancies. Experimental results in real-world paper-code discrepancy datasets showcase Dude’s significant recall and precision improvement by up to **22.8%**, increasing F1 score by up to **18.7%** compared to baseline methods.