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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, …
arXiv:2603.04873v1 Announce Type: new Abstract: Accurate time series forecasting underpins decision-making across domains, yet conventional ML development suffers from data scarcity in new deployments, poor …
arXiv:2603.04885v1 Announce Type: new Abstract: Real-world dialogue usually unfolds as an infinite stream. It thus requires bounded-state memory mechanisms to operate within an infinite horizon. …
arXiv:2603.04894v1 Announce Type: new Abstract: Vision-language models are increasingly applied to sensitive domains such as medical imaging and personal photographs, yet existing differentially private methods …
arXiv:2603.04896v1 Announce Type: new Abstract: The rapid adoption of vision-language models (VLMs) has heightened the demand for robust intellectual property (IP) protection of these high-value …
arXiv:2603.04900v1 Announce Type: new Abstract: LLM-based agents depend on effective tool-use policies to solve complex tasks, yet optimizing these policies remains challenging due to delayed …
arXiv:2603.04904v1 Announce Type: new Abstract: In perpetrator treatment, a recurring observation is the dissociation between insight and action: offenders articulate remorse yet behavioral change does …
arXiv:2603.04920v1 Announce Type: new Abstract: Bid optimization in online advertising relies on black-box machine-learning models that learn bidding decisions from historical data. However, these approaches …
arXiv:2603.04949v1 Announce Type: new Abstract: The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the …