Large language models (LLMs) can plan behavior for embodied agents from natural language, but treating the LLM as a request/response oracle on the critical path is fundamentally at odds with real-time control and concurrent goals. We argue for an operating-system-style runtime for embodied agents, and instantiate this idea in an early prototype, TypeGo. TypeGo structures LLM-based planning as asynchronous loops at multiple timescales that overlap with execution, and manages the agent's physical body like an OS manages hardware: the Skill Kernel arbitrates typed physical subsystems among concurrent per-task processes, a scheduler preempts them and resumes or replaces each by source, and speculative skill streaming hides LLM latency behind ongoing motion, while a fast first-action path yields visible feedback within a second. Users program behavior through natural language prescriptions that TypeGo dispatches to the LLM-based planners or compiles into low-latency interrupt handlers. Our prototype of Kalos, a Unitree Go2 quadruped, provides preliminary evidence for the design: in our current task suite, it cuts per-step delay by 50% over step-by-step planning and time-to-first-action by 73% over monolithic planning, while admitting concurrent tasks at low scheduling overhead.
This work presents PhyAgentOS, a runtime foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services, and distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.
Yang Liu, Weixing Chen, Xinshuai Song et al.· 1 citation
A structured taxonomy is presented that organizes existing work into three complementary paradigms that represent dominant architectural tendencies in current LLM-based embodied task planning research, and compares these paradigms along dimension of accuracy, robustness, scalability, efficiency, and sim-to-real transfer.
Zhen Zhang· Applied and Computational En...· 0 citations
Language models are sequential processors, but long-horizon agency requires external information and computation beyond model weights and active context. Prime Agent is an open-source harness for long-horizon evaluation and coding-agent workflows. A persistent IPython REPL follows the Recursive Language Model abstraction for programmatic context processing and test-time compute, while Continual Harness preserves histories, memories, skills, prompts, and subagent specifications across trajectories. Recursive subagents coordinate through direct agent-to-agent communication, and the Agents View lets humans inspect and manage daemon-backed sessions. Prime Agent standardizes execution, recovery, verification, and resource accounting while leaving strategy construction to the model. This low-friction, expressive membrane prevents harness failures from becoming model failures and pushes measurement toward the model's true maximal underlying capability. Prime Agent raises ARC-AGI-3 RHAE Best@1 from 30% to 95.5% and matches or exceeds native and popular harnesses across long-context coding, GPU-kernel generation, emulator construction, and autonomous nanoGPT speedruns. On Factorio, we find refinement allows for continuous technology progression and dedicated subagents enable parallelized work. Code is available at https://github.com/PrimeIntellect-ai/prime-agent.
Seth Karten, Alex L. Zhang, Kevin Thomas et al.· 2 citations· ⚡1
Agent skills provide a reusable way to specify multi-step agent behavior, but they remain natural-language specifications interpreted by the model at runtime. As a result, required tool calls, ordering constraints, and checks may be skipped even when explicitly prescribed by the skill. We introduce Skill Compilation, a paradigm that translates natural-language skills into executable agent programs while preserving model judgment where semantic decisions are required. We realize this idea in SIGIL. SIGIL extracts source-grounded requirements, decomposes them using a closed Agent Instruction Set (AIS), composes them into AG-IR with explicit ownership, data flow, and control flow, and deterministically lowers validated AG-IR into executable code. Across 33 publicly available SKILL.md files and three runtime models, SIGIL increases mean Applicable-Mandate Compliance (AMC), the fraction of applicable skill requirements satisfied during execution, from 66.0% with direct skill execution to 88.6%. SIGIL also reduces total runtime token consumption by 2.40-5.95x. These results show that compiling procedural structure improves the reliability and efficiency of skill execution while retaining model judgment where it is needed.
Jayanaka L. Dantanarayana, Savini Kashmira, Lingjia Tang et al.· 0 citations
Agent Gym is introduced, a modular, domain-agnostic framework that wraps any existing LLM-based agent in a continuous evaluation-and-evolution loop and introduces the Spec-to-Note Gap, an autoencoder-inspired view of agentic system transparency.
Pouya Ghiasnezhad Omran, Michael Zimmermann, Duncan Cambridge et al.· 0 citations