robbyant/lingbot-vla-4b-depth
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robbyant/lingbot-vla-4b-depth
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Happy 2026 everyone!
I've been busy working on some new ranking/position methodologies and excited to start sharing some results.
Plot legends:
- X = truncation rate (low = good)
- ? = confusion rate (low = good)
- blue bars = average completion tokens (low = good)
- black diamonds = CI-banded performance (high = good)
- cluster squares = models inside this group are equivalent
https://huggingface.co/openai/gpt-oss-120b remains the king in all dimensions of interest: truncation rates, completion lengths and performance. If I had but one complaint it's the reason_effort does not seem to actually work - more on this soon.
Second is a 3-way tie in performance between the Qwen3-235B-2507 we all know and love with an unexpected entrant - https://huggingface.co/ByteDance-Seed/Seed-OSS-36B-Instruct
This is a very capable model and it's reasoning effort controls actually works, but you should absolutely not leave it on the default "unlimited" - enable a sensible limit (4k works well for 8k context length).
Third place is another 3-way tie, this one between Seed-OSS-36B (it straddles the CI boundary between 2nd and 3rd place), https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct (demonstrating that full attention may be overrated after all and gated is the way to go) and the newly released https://huggingface.co/zai-org/GLM-4.7 which offers excellent across the board performance with some of the shortest reasoning traces I've seen so far.
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Happy 2026 everyone!
I've been busy working on some new ranking/position methodologies and excited to start sharing some results.
Plot legends:
- X = truncation rate (low = good)
- ? = confusion rate (low = good)
- blue bars = average completion tokens (low = good)
- black diamonds = CI-banded performance (high = good)
- cluster squares = models inside this group are equivalent
openai/gpt-oss-120b remains the king in all dimensions of interest: truncation rates, completion lengths and performance. If I had but one complaint it's the reason_effort does not seem to actually work - more on this soon.
Second is a 3-way tie in performance between the Qwen3-235B-2507 we all know and love with an unexpected entrant - ByteDance-Seed/Seed-OSS-36B-Instruct
This is a very capable model and it's reasoning effort controls actually works, but you should absolutely not leave it on the default "unlimited" - enable a sensible limit (4k works well for 8k context length).
Third place is another 3-way tie, this one between Seed-OSS-36B (it straddles the CI boundary between 2nd and 3rd place), Qwen/Qwen3-Next-80B-A3B-Instruct (demonstrating that full attention may be overrated after all and gated is the way to go) and the newly released zai-org/GLM-4.7 which offers excellent across the board performance with some of the shortest reasoning traces I've seen so far.
I've been busy working on some new ranking/position methodologies and excited to start sharing some results.
Plot legends:
- X = truncation rate (low = good)
- ? = confusion rate (low = good)
- blue bars = average completion tokens (low = good)
- black diamonds = CI-banded performance (high = good)
- cluster squares = models inside this group are equivalent
openai/gpt-oss-120b remains the king in all dimensions of interest: truncation rates, completion lengths and performance. If I had but one complaint it's the reason_effort does not seem to actually work - more on this soon.
Second is a 3-way tie in performance between the Qwen3-235B-2507 we all know and love with an unexpected entrant - ByteDance-Seed/Seed-OSS-36B-Instruct
This is a very capable model and it's reasoning effort controls actually works, but you should absolutely not leave it on the default "unlimited" - enable a sensible limit (4k works well for 8k context length).
Third place is another 3-way tie, this one between Seed-OSS-36B (it straddles the CI boundary between 2nd and 3rd place), Qwen/Qwen3-Next-80B-A3B-Instruct (demonstrating that full attention may be overrated after all and gated is the way to go) and the newly released zai-org/GLM-4.7 which offers excellent across the board performance with some of the shortest reasoning traces I've seen so far.
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Announcing RefCOCO-M, a refreshed RefCOCO with pixel-accurate masks and the problematic prompts removed.
moondream/refcoco-m
moondream/refcoco-m
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In November, with
Robonine
are starting work on the next version of the SO ARM 102 manipulator. The version will be open source and agreed upon with
@therobotbuilder
the creator of the original manipulator.
We are planning to:
- increase positioning accuracy by approximately 2x using Feetech STS 3250 motors
- increase working payload from 200g to 300g
- increase rigidity using parametric design optimization and stiffer plastic
- increase length to 550 mm
- increase folding angles
- use ISO 9409-1-50-4-M6 mounting standard for the gripper
- use a parallel gripper in the default version
- update the mounting plate for different camera types, M3 grid with 12.5 mm pitch
- add table mounting standard 80x80 M8
The number of degrees of freedom and basic kinematics will remain the same.
Are there other things missing for working with SO ARM 100?
- Any standard inputs/outputs, for example?
- Status indicators?
- Perhaps some types of mounting for third-party grippers are more preferable?
- Anything else?
We are planning to:
- increase positioning accuracy by approximately 2x using Feetech STS 3250 motors
- increase working payload from 200g to 300g
- increase rigidity using parametric design optimization and stiffer plastic
- increase length to 550 mm
- increase folding angles
- use ISO 9409-1-50-4-M6 mounting standard for the gripper
- use a parallel gripper in the default version
- update the mounting plate for different camera types, M3 grid with 12.5 mm pitch
- add table mounting standard 80x80 M8
The number of degrees of freedom and basic kinematics will remain the same.
Are there other things missing for working with SO ARM 100?
- Any standard inputs/outputs, for example?
- Status indicators?
- Perhaps some types of mounting for third-party grippers are more preferable?
- Anything else?