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arXiv:2603.04750v1 Announce Type: new Abstract: Sequential LLM agents fail on long-horizon planning with hard constraints like budgets and diversity requirements. As planning progresses and context …
arXiv:2603.04751v1 Announce Type: new Abstract: Integrating web search tools has significantly extended the capability of LLMs to address open-world, real-time, and long-tail problems. However, evaluating …
arXiv:2603.04756v1 Announce Type: new Abstract: MOOSEnger is a tool-enabled AI agent tailored to the Multiphysics Object-Oriented Simulation Environment (MOOSE). MOOSE cases are specified in HIT …
arXiv:2603.04783v1 Announce Type: new Abstract: While LLMs demonstrate strong reasoning capabilities when provided with full information in a single turn, they exhibit substantial vulnerability in …
arXiv:2603.04791v1 Announce Type: new Abstract: We introduce Timer-S1, a strong Mixture-of-Experts (MoE) time series foundation model with 8.3B total parameters, 0.75B activated parameters for each …
arXiv:2603.04815v1 Announce Type: new Abstract: Manipulative communication, such as gaslighting, guilt-tripping, and emotional coercion, is often difficult for individuals to recognize. Existing agentic AI systems …
arXiv:2603.04818v1 Announce Type: new Abstract: Port congestion at major maritime hubs disrupts global supply chains, yet existing prediction systems typically prioritize forecasting accuracy without providing …
arXiv:2603.04822v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with nuanced human values remains a critical challenge, as existing methods like Reinforcement Learning from …
arXiv:2603.04837v1 Announce Type: new Abstract: We introduce the Dynamic Behavioral Constraint (DBC) benchmark, the first empirical framework for evaluating the efficacy of a structured, 150-control …
arXiv:2603.04852v1 Announce Type: new Abstract: Multi-step theorem prediction is a central challenge in automated reasoning. Existing neural-symbolic approaches rely heavily on supervised parametric models, which …
arXiv:2603.04861v1 Announce Type: new Abstract: Preference-based reward learning is widely used for shaping agent behavior to match a user's preference, yet its sparse binary feedback …
arXiv:2603.04868v1 Announce Type: new Abstract: Generating realistic and diverse trajectories is a critical challenge in autonomous driving simulation. While Large Language Models (LLMs) show promise, …