Lomekwi: Resource-Bounded Tool Discovery in LLM Agents

Existing tool-use benchmarks report a single success rate for complex, multistep tasks. Inspired by ideas from cognitive science, we distinguish tool use from tool discovery and decompose the latter into curiosity (the model's ability to discover the parts needed to build the tool), recognition (the model's ability to discover the process of creating the tool), and efficiency (the model's use of the tool after creation). We show that this framework can be applied to existing discovery tasks, such as Voyager. In addition, we provide evidence that recognition inversely scales with model size, and we introduce and analyze a class of combinatorial games that demonstrates this. We further observe inverse scaling in a separate environment designed to emulate real-world tasks.

Beyond URDF: The Universal Robot Description Directory for Shared, Extensible, and Standardized Robot Models

Robots are typically described in software by specification files (e.g., URDF, SDF, MJCF, USD) that encode only basic kinematic, dynamic, and geometric information. As a result, downstream applications such as simulation, planning, and control must repeatedly re-derive richer data, leading to redundant computations, fragmented implementations, and limited standardization. In this work, we introduce the Universal Robot Description Directory (URDD), a modular representation that organizes derived robot information into structured, easy-to-parse JSON and YAML modules. Our open-source toolkit automatically generates URDDs from URDFs, with a Rust implementation supporting Bevy-based visualization. Additionally, we provide a JavaScript/Three.js viewer for web-based inspection of URDDs. Experiments on multiple robot platforms show that URDDs can be generated efficiently, encapsulate substantially richer information than standard specification files, and directly enable the construction of core robotics subroutines. URDD provides a unified, extensible resource for reducing redundancy and establishing shared standards across robotics frameworks. We conclude with a discussion on the limitations and implications of our work.