It's interesting that someone else noticed this. A week or two ago, GPT-5.6 Sol starting leaking reasoning into a tool call in Pi. I don't really know what happened, but it was ... interesting:
Attach. Use hub debugger. Ensure source binary perhaps same. start. todo init. parallel no. two tool calls in same turn sequential is okay. immediately. exactly. Need not mention apologies yet final. [...] Let's do. [...] Do tools. Use commentary. Let's initiate. rambling no. use tool. searching now. okay. Really must call. Let's send. done. why stuck? generate. Sorry. go. no more. (The answer engine expects tool). [...] I think no hidden issue. Go. I'll type tool. now. Stop internal repetition. We have 8000 tokens. tool. sorry. I'll produce call. need include i. Great. final. no. Let's send.gpt. This may be bug. I'll consciously construct tool message next.
Nevertheless, this was the first time I'd seen Sol's CoT. I looked up the stolen thought's paper, aaaaand yep, that's Sol's CoT alright.
But it occurred to me, hey, Qwen3.8 27B's CoT seems ... very similar.
I compared the geometry problem in the paper, which had a reasoning block open with:
We need solve. Need reason geometry Weber point? Given pentagon sides and angles. Need find min sum distances. Likely construct rotations / Fermat point lower bound via vectors calibration, maybe triangulation.
We need solve geometry optimization. We need provide final answer. Let's analyze thoroughly.
This looks like colloquial mandarin translated to english word for word.
This is almost exactly how mandarin is spoken in colloquial communication, context is almost always omitted if provided once, and grammar/tense is mostly non-existent either. Check out Singlish for something similar but understandable if you are not a mandarin speaker.
I've read an embarrassing amount of Qwen 3.8 27b cot and it's nothing like this. I'm not refuting the OP, though, which is about continuation.
This is Unsloth's UD-Q4_K_S quantization (edit -- on llama.cpp, via the Vulkan backend, on an RX 7900 XT, with Unsloth's recommended sampler config), for "as replicable as LLMs can be" disclosure, done through llama.cpp's web UI. In another thread, via Open WebUI (both with no custom prompt, so whatever the "default" system prompt is), a reasoning block opens as such:
Now, I will add that when I use Qwen3.8 in a coding harness, the reasoning tends to NOT look like this, and instead looks more Qwen3.6-ish.But when I give short prompts to Qwen3.8 with no system prompt or previous instructions, it seems to strongly gravitate towards grug-talk.
I just tried to reproduce in Llama-cpp's webui: with the following (reverse-engineered) prompt:
“Write a complete short story scene of 2000-3000 words. Use a third-person limited perspective focused on a character named Rowan. The scene must include the following plot elements: a raccoon, Milo returns, a storm power failure, an elderly customer knocks, and a thermos somewhere. Make sure the scene ends feeling finished. Output only the story; do not include any outline, preamble, analysis, or meta-commentary.”
And here's Qwen's thinking trace:
> The user is asking for a short story scene of 2000–3000 words. Third-person limited POV, Rowan. Plot elements: a raccoon, Milo returns, a storm causing a power outage, an elderly customer knocks on the door, a thermos. End with a sense of closure. Output only the story.
> Let me plan carefully. I need to count words in my head—roughly 2400–2600 words would be safely in range. Let me draft.
> Setting: a small-town shop or a gas station/diner where Rowan works the night shift. Milo returns—Milo is probably Rowan's partner, brother, or ex. Let me make Milo Rowan's brother who's been away (military? a road trip? rehab?). Better: Milo is the younger brother who disappeared/went away after a fight. He comes back in the middle of a storm.
While at the very beginning it rephrases the prompt in very short sentence, it's nowhere near close to the above GPT thinking trace and it quickly moves to plain sentences.
Yeah, I dunno. For me it does "talk normally" for the most part when used in an actual coding harness.
One thing though, the actual prompt I used was pretty long (844 words), and ... generated by GPT-5.6 Sol (lol), with the intent of "benchmarking" model performance in being able to write stories where the model avoids explicitly stating every detail in the prompt.
I wonder if the GPT-produced stream could steer the generation into GPT-think territory. That's all I've got, though.
Then there's the actual geometry problem from the stolen thoughts paper:
It doesn’t use the caveman speak unless reasoning is set to xhigh, in my experience. But I don’t know if it has always been coincidental.
> unless reasoning is set to xhigh
That's the default and I'm sure almost everyone else is also using it because other reasoning efforts yield subpar results from what I've seen.
It is the default, which is insane.
I think it is clear that medium reasoning has more 'loopy' results like the older Qwens, but I actually think the low effort results are usually more appropriate.
If you plan to one-shot and vibe code AI slop to meet benchmarks, maybe xhigh makes sense. But if you want a responsive agentic coding assistant it is, to me, quite evidently the wrong choice, especially on modest hardware.
I have seen xhigh radically distract itself with rabbitholes and write considerably worse code than low.
It is my own opinion only, but I think much of the fuss about squeezing Qwen 3.8 27B into small local hardware setups, Macs etc., is a bit misguided.
There's too much focus on its benchmark scores, its one-shot capability, canned demos etc.
For my own needs Muse Glimmer (again on reasoning strength: low) is shaping up to being the more practical agentic tool. It is considerably faster than Qwen at solving real coding tasks.
The person evaluating and noticing similar reasoning traces to gpt is because they are using a coding harness which probably has a different system prompt to llama webui which primarly serves as a chat interface
They said literally the opposite in their message above. In their experience, the caveman speech occurs in chat ui, not in coding harness.
Same! and very surprised by it from day one of release.
Looks like we're a bunch of weirdos reading Qwen's CoT in here.
> Qwen3.8 27B's CoT seems ... very similar.
What? I've never seen garbled CoT like the one you posted when using Qwen3.8-27B.
I have seen plenty of Qwen 3.8 27B's caveman-like "Need doing this & that" thoughts. And on cerebras now I've seen them come real fast!
Do you prompt it to behave this way or what?
Because here's the king of CoT I get:
> Hmm, but there's a subtlety: does babel-jest + preset-typescript transform the file to CJS by default? No — babel-jest doesn't transform ESM imports to CJS unless @babel/preset-env is configured with modules: commonjs. Without preset-env, import statements stay as ESM in the output, and Jest's CJS runtime would fail with "Cannot use import statement outside a module" unless the project is ESM and running with --experimental-vm-modules.
> Hmm wait, actually babel-preset-jest... does it include preset-env? Let me recall: babel-preset-jest = { plugins: [require('babel-plugin-jest-hoist')] } plus istanbul for coverage. No preset-env. So ESM imports stay as-is.
> But wait — if the user's project is ESM (which it probably is, given the .ts extension imports — Node's type stripping requires ESM-style? no, type stripping also works for CJS-style .ts files with require... actually, --experimental-strip-types supports both CJS and ESM .ts files. But explicit .ts extensions in imports only work in ESM mode (CJS require doesn't allow extensions... actually, does Node 22+ allow require of .ts with flag?
Lots of “but wait” and and full sentences, nothing caveman-like or extremely short sentences without verbs like the GPT thinking trace above.
(this is with unsloth's Qwen3.8-27B-UD-Q5_K_XL.gguf with T° = 0.8)
I was on unquantized 27B, and also Q8 K XL, same "Need do..." paragraphs. We landed in near but distinct morrasses I guess.