Tootfinder

Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation
Kanchon Gharami, Hansaka Aluvihare, Shafika Showkat Moni, Berker Pek\"oz
https://arxiv.org/abs/2509.00973

Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation
Large Language Models (LLMs) are increasingly deployed in mission-critical systems, facilitating tasks such as satellite operations, command-and-control, military decision support, and cyber defense. Many of these systems are accessed through application programming interfaces (APIs). When such APIs lack robust access controls, they can expose full or top-k logits, creating a significant and often overlooked attack surface. Prior art has mainly focused on reconstructing the output projection la…

DELIVER: A System for LLM-Guided Coordinated Multi-Robot Pickup and Delivery using Voronoi-Based Relay Planning
Alkesh K. Srivastava, Jared Michael Levin, Alexander Derrico, Philip Dames
https://arxiv.org/abs/2508.19114

DELIVER: A System for LLM-Guided Coordinated Multi-Robot Pickup and Delivery using Voronoi-Based Relay Planning
We present DELIVER (Directed Execution of Language-instructed Item Via Engineered Relay), a fully integrated framework for cooperative multi-robot pickup and delivery driven by natural language commands. DELIVER unifies natural language understanding, spatial decomposition, relay planning, and motion execution to enable scalable, collision-free coordination in real-world settings. Given a spoken or written instruction, a lightweight instance of LLaMA3 interprets the command to extract pickup an…