In multi-agent environments, coordinating agents to prevent interference and ensure robust individual performance is a critical challenge. Previous research on social laws for multi-agent systems has primarily focused on deterministic, goal-based settings. This paper extends the concept of social laws to stochastic, reward-based environments, proposing a formalism for defining and verifying their robustness under various conditions. We introduce the notion of $\alpha$-robustness, a measure of the guaranteed utility each agent retains while pursuing its optimal single agent policy, assuming all agents obey the social law. We then present an approach for robustness verification of social laws in stochastic settings, based on a reduction to solving a series of Markov decision processes. Empirical evaluations on toy environments illustrate the potential of our framework.
Rolando Fernandez, Caleb Probine, Tyler Lee et al.· 0 citations
Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled new possibilities for 3D question answering (3D-QA), a key capability for embodied AI and robotic perception. However, most existing methods rely on 3D-specific training or fine-tuning with costly annotations, limiting their scalability and real-world applicability. We present \textbf{ViewMind3D}, a fully training-free and modular framework for 3D spatial reasoning over multi-view observations of a scene without requiring complete 3D reconstruction. The framework decomposes the 3D-QA task into four interpretable components: (1) question-driven multi-view selection, (2) guided visual grounding with language-conditioned object cues, (3) spatial context encoding via a bird's-eye-view (BEV) viewpoint indicator, and (4) structured answer generation through role-based reasoning. This design enables structured, robust, and interpretable reasoning without requiring model tuning. Experimental results on ScanQA and SQA3D show that ViewMind3D achieves competitive performance compared to prior training-free and fine-tuned 3D-LLMs. In particular, our method improves performance on spatially grounded question types, such as ``What''questions in SQA3D, while maintaining strong overall accuracy (50.8\%) and achieving 73.4 CIDEr on ScanQA. These results demonstrate that effective 3D reasoning can be achieved through modular orchestration of general-purpose LLMs and VLMs for robotic perception in real-world environments.
Ping-Kun Chiang, Kun-Ru Wu, Po-han Li et al.· arXiv.org· 0 citations
A reinforcement learning (RL)-based air traffic management system that integrates both noise and safety considerations within a unified, decentralized framework and demonstrates strong performance across both objectives and reveals tradeoffs among separation, noise exposure, and energy efficiency under high traffic density.
CL4AD is presented, the first integration of curriculum learning into batched autonomous driving simulators by framing scenario selection as an unsupervised environment design problem, and utility functions that shape curricula based on success rates and the realism of the agent's behavior are introduced, in addition to existing regret-estimation functions.
Cevahir Koprulu, D. Paz, Feng Tao et al.· 0 citations
This work derives a closed-form expression for this adversarial perturbation, bypassing the iterative inner optimization of adversarial training entirely and enabling linear-time evaluation in the state dimension, and shows that this expression approximates the exact minimizer of the value function over the modeled uncertainty set with second-order accuracy.
Alex Zongo, Filippos Fotiadis, U. Topcu et al.· arXiv.org· 1 citation
Regime-Conditional Verification (RCV), a lightweight wrapper that adapts an off-the-shelf safety classifier without retraining it, improves adherence to the deployer's policy in every classifier-dataset combination.
Thiago Sandoval, U. Topcu· 0 citations
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