arXiv:2603.20182v2 Announce Type: replace
Abstract: Although robot-to-robot (R2R) communication improves indoor scene understanding beyond what a single robot can achieve, R2R alone cannot overcome partial observability without substantial exploration…
arXiv:2603.28306v1 Announce Type: cross
Abstract: Self++ is a design blueprint for human-AI symbiosis in extended reality (XR) that preserves human authorship while still benefiting from increasingly capable AI agents. Because XR can shape both percep…
arXiv:2603.26684v1 Announce Type: cross
Abstract: Multi-Agent Path Finding (MAPF) requires collision-free trajectories for multiple agents on a shared graph, often with the objective of minimizing the sum-of-costs (SOC). Many optimal and bounded-subop…
arXiv:2603.27150v1 Announce Type: new
Abstract: Large language models (LLMs) have revolutionized medical reasoning tasks, yet single-agent systems often falter on complex, interdisciplinary problems requiring robust handling of uncertainty and conflic…
arXiv:2603.26542v2 Announce Type: replace
Abstract: Buffer zones are essential in production systems to decouple sequential processes. In dense floor storage environments, such as space-constrained brownfield facilities, manual operation is increasing…
arXiv:2603.27693v1 Announce Type: cross
Abstract: Unified multimodal pretraining has emerged as a promising paradigm for jointly modeling language and vision within a single foundation model. However, existing approaches largely rely on implicit or in…
arXiv:2603.27304v1 Announce Type: new
Abstract: General-purpose technologies reshape economies less by improving individual tools than by enabling new ways to organize production and coordination. We believe AI agents are approaching a similar inflect…
arXiv:2603.20182v2 Announce Type: replace
Abstract: Although robot-to-robot (R2R) communication improves indoor scene understanding beyond what a single robot can achieve, R2R alone cannot overcome partial observability without substantial exploration…
arXiv:2603.27803v1 Announce Type: new
Abstract: We provide a distributed online algorithm for multi-agent submodular maximization under communication delays. We are motivated by the future distributed information-gathering tasks in unknown and dynamic…
arXiv:2603.27910v1 Announce Type: new
Abstract: AI agents that interact with users across multiple sessions require persistent long-term memory to maintain coherent, personalized behavior. Current approaches either rely on flat retrieval-augmented gen…