arXiv:2606. 13097v1 Announce Type: cross Abstract: Code-writing large language models (CodeLLMs) generate executable code policies for embodied agents by translating natural language goals and environmental constraints into structured control programs.
arXiv:2506. 07223v2 Announce Type: replace Abstract: Large language models (LLMs) have substantially improved the planning capabilities of embodied agents, enabling their deployment in dynamic and safety-critical environments.
The paper introduces Harness Primitives—reusable agent harness mechanisms mined from failed task trajectories—and a framework called STITCH that selects and composes these primitives into task‑specific harnesses at test time. This approach avoids generating or debugging harness code for each task, achieving up to 12‑point gains in task success over fixed harness baselines and outperforming human‑designed harnesses like Codex CLI. STITCH also demonstrates minimal test‑time overhead (2.7%) and scales efficiently with the size of the primitive library.
arXiv:2606. 07999v1 Announce Type: new Abstract: Effective skill grounding is essential for deploying reusable skills in embodied agents, as even minor embodiment or environmental differences can render an entire skill incompatible.
arXiv:2606. 14672v1 Announce Type: new Abstract: Large language models increasingly serve as execution engines for agentic systems, yet they still consume context through a sequential text interface.
arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.
CEDAR is a counterexample-guided framework that translates natural-language instructions for embodied agents into regular languages over environment event traces, represented as deterministic finite automata. By using a language model for semantic judgments and execution traces for correction, CEDAR turns constraints into executable finite-state objects, enabling the intersection of learned skills with additional specifications. In Minecraft experiments, CEDAR preserves temporal and spatial constraints better than a program-generating baseline and reduces cumulative LLM queries by reusing learned skills.
arXiv:2609.13458v1 Announce Type: cross Abstract: Embodied language grounding requires more than identifying the referent of an instruction: recovered semantics must also control the action an agent...
arXiv:2606. 30111v1 Announce Type: cross Abstract: Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules.
arXiv:2606. 30111v2 Announce Type: replace-cross Abstract: Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules.
arXiv:2510. 12985v3 Announce Type: replace Abstract: We present SENTINEL, a framework for formally evaluating the physical safety of foundation model (FM)-based embodied agents.
arXiv:2608. 05714v1 Announce Type: new Abstract: Text-to-CAD generation translates natural-language design intent into editable and executable parametric computer-aided design (CAD) codes, reducing the expertise and effort required for manual modeling.
arXiv:2607. 03876v1 Announce Type: new Abstract: With the rise of small quantized GGUF-based language models and their increasing use for on-device inference tasks, we have seen the growing need for an approach capable of reliably delivering these models at scale even under severe memory bandwidth constraints such as those imposed by pure CPU implementations.