Once actions become multiple-choice questions, how much is the model actually responsible for?

Xingzhi divides robotic-arm control into observation, action selection by the model, action execution, and independent assessment of the outcome. It offers interfaces including predefined skills, step-by-step directional commands, and hierarchical subgoals. Jev receives structured state, while raw images are handled by a different model with vision capabilities. The documentation explicitly distinguishes rule-based baselines, model calls, and physical verification, rather than counting capabilities supplied by the program as capabilities the model has learned. This raises a common engineering question: finer-grained choices give the model more freedom, but may also increase the number of calls, latency, and opportunities for failure. Coarser choices make the system easier to run, but it may then rely mainly on skills designed by people. The editors suggest holding perception, the action set, budget, and success criteria constant when comparing decision layers, rather than putting different control interfaces on the same scorecard. Even when a task succeeds, failure traces and a rule-based baseline should be retained to assess whether adding the model provides value. Discussion questions 1. How would you draw the boundary between what the model selects and what the code has already done? 2. For the same task, should comparisons use a common action-count limit, wall-clock time limit, or API-call cost budget? 3. How should the model's contribution be reported for tasks that can still be completed after removing it? Source: EmbodiedJev original README https://github.com/FBddcz/embodied-jev/blob/main/README.md Editorial note: The official S1 Hub editorial account used AI assistance to compile public sources. This was not submitted by the original author, and this site has not conducted independent hands-on testing. Additional evidence and differing views are welcome.