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ErenAta00Β 
posted an update 4 days ago
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Maverick 1.1 is out.

When we released the first version, the feedback was clear: real people do not give one clean command at a time. They change their minds, they point and say "put that on the shelf", they ask for a lamp that is in another room, and sometimes the one speaking is a remote expert helping someone on site. 1.1 is built around that.

- Say "no, the other one" and it undoes the last action and repeats it on the other object. If the effect can't be undone, like a button that was already pressed, it tells you.
- It sees what people are looking at or pointing at, and who is speaking.
- It finds objects in other rooms, goes there and acts, or asks which one if there are two.
- It follows a short plan in one sentence: "open the cabinet, put the book and the alarm clock in it, then close it".

In the clip, a remote expert corrects the model, then asks it to highlight what the technician is looking at.

On test sets we sealed before training and ran once, the new abilities score 93 to 100%, where 1.0 scores 33 to 87%. On sentences written by people (the ALFRED set) the two versions are level, at 83.2% and 83.8%.

Where it still falls short: in unfamiliar situations it tends to act when it should ask, and a remote speaker can act in the scene with no permission check, so your app has to enforce that.

Same 4B model and licence (Apache-2.0), the same 11 tools plus undo, still offline on a 4 GB laptop GPU. The Unity package for the new scene format (1.1.0) is on GitHub: https://github.com/mcbu-xrlab/Maverick-Unity

Model: ErenAta00/Maverick-4B-Unity-XR-Agent-1.1
Article and full demo: https://huggingface.co/blog/ErenAta00/maverick-1-1-undo-pointing-other-rooms

Built at the Extended Reality Laboratory (XRLab), Manisa Celal Bayar University:
ExtendedRealityLabMCBU

I noticed Q4_K_M runs fully on the GPU while Q8_0 uses partial offload. That makes the 2.1 s vs 2.7 s first-valid-call result especially relevant to 4 GB deployments. If your existing logs include latency percentiles, does Q4 also reduce the slow tail, or mainly the typical response time? I'm interested in how that trade-off feels in an interactive loop.

@Twu31 Thanks for the careful question. Mostly the typical response, not the slow tail.

On our laptop (RTX 3050 Ti, 4 GB) the median time to the first valid call is 2.1 s with Q4_K_M and 2.7 s with Q8_0, but the 95th percentile is about the same: 4.9 s against 4.7 s. In both builds the slowest turns are clarification questions, where the model has to write a question and the candidate ids. That is around 50 tokens, and at roughly 9 to 11 tokens per second it takes 4 to 5 seconds whichever file you use. So the tail follows how much the model has to write more than the size of the file.

Two caveats. This is one machine and 96 turns per build, so the 95th percentile rests on the slowest three or four turns. And the laptop warms up: Q4's median was 1.1 s in the first run and 2.6 s in the third, while Q8's did not drift the same way. How much you gain depends on how hot the GPU is.

For an interactive loop, I would try letting the app build the clarification question from the candidate ids instead of having the model write it. I expect that to help the tail more than the quantisation does, but we have not tested it yet.

Β·

Thanks, that helps separate the two effects. Returning an ask action plus candidate IDs and letting the UI render the question sounds worth exploring. The thermal drift and small sample are useful context too; I wouldn't assume the median gain transfers unchanged to another setup.

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