arXiv:2602.02050v3 Announce Type: replace Abstract: Tool-using agents based on Large Language Models (LLMs) excel in tasks such as mathematical reasoning and multi-hop question answering. However, in long trajectories, agents often trigger excessive and low-quality tool calls, increasing latency and degrading inference performance, making managing tool-use behavior challenging. In this work, we conduct entropy-based pilot experiments and observe a strong positive correlation between entropy reduction and high-quality tool calls. Building on this finding, we propose using entropy reduction as a supervisory signal and design two reward strategies to address the differing needs of optimizing tool-use behavior. Sparse outcome rewards provide coarse, trajectory-level guidance to improve efficiency, while dense process rewards offer fine-grained supervision to enhance performance. Experiments across diverse domains show that both reward designs improve tool-use behavior: the former reduces tool calls by 72.07% compared to the average of baselines, while the latter improves performance by 22.27%. These results position entropy reduction as a key mechanism for enhancing tool-use behavior, enabling agents to be more adaptive in real-world applications.

arXiv:2512.21201v2 Announce Type: replace-cross Abstract: Zero-shot object navigation (ZSON) requires robots to locate target objects in unseen environments without task-specific fine-tuning or pre-built maps, a capability crucial for service and household robotics. Existing methods perform well in simulation but struggle in realistic, cluttered environments where heavy occlusions and latent hazards make large portions of the scene unobserved. These approaches typically act on a single inferred scene, making them prone to overcommitment and unsafe behavior under uncertainty. To address these challenges, we propose Schr\"odinger's Navigator, a belief-aware framework that explicitly reasons over multiple trajectory-conditioned imagined 3D futures at inference time. A trajectory-conditioned 3D world model generates hypothetical observations along candidate paths, maintaining a superposition of plausible scene realizations. An adaptive, occluder-aware trajectory sampling strategy focuses imagination on uncertain regions, while a Future-Aware Value Map (FAVM) aggregates imagined futures to guide robust, proactive action selection. Evaluations in simulation and on a physical Go2 quadruped robot demonstrate that Schr\"odinger's Navigator outperforms strong ZSON baselines, achieving more robust self-localization, object localization, and safe navigation under severe occlusions and latent hazards. These results highlight the effectiveness of reasoning over imagined 3D futures as a scalable and generalizable strategy for zero-shot navigation in uncertain real-world environments.

arXiv:2602.07023v2 Announce Type: replace-cross Abstract: Recent works have increasingly applied Large Language Models (LLMs) as agents in financial stock market simulations to test if micro-level behaviors aggregate into macro-level phenomena. However, a crucial question arises: Do LLM agents' behaviors align with real market participants? This alignment is key to the validity of simulation results. To explore this, we select a financial stock market scenario to test behavioral consistency. Investors are typically classified as fundamental or technical traders, but most simulations fix strategies at initialization, failing to reflect real-world trading dynamics. In this work, we assess whether agents' strategy switching aligns with financial theory, providing a framework for this evaluation. We operationalize four behavioral-finance drivers-loss aversion, herding, wealth differentiation, and price misalignment-as personality traits set via prompting and stored long-term. In year-long simulations, agents process daily price-volume data, trade under a designated style, and reassess their strategy every 10 trading days. We introduce four alignment metrics and use Mann-Whitney U tests to compare agents' style-switching behavior with financial theory. Our results show that recent LLMs' switching behavior is only partially consistent with behavioral-finance theories, highlighting the need for further refinement in aligning agent behavior with financial theory.