Mike Ravkine PRO
AI & ML interests
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You're very much on to something here, and this is why I think it matters if this behavior is intentional or latent.
If they've taught it to recognize benchmarks specifically, that's benchmaxxing and is not going to help real world performance when your real tasks don't trigger the maxxxed paths. This is a genuine concern.
If they've taught it to "reach beyond the prompt" in the general sense, to understand the context and user intent behind the query, that's a genuinely useful capability and would explain why this model feels a little different.
Some stats: some version of this reasoning path happened in 39 out of 1070 test configurations, across 4 of my 12 tasks. In the most common occurrence, responsible for 30 of 39 hits, it recognized the task as being from BigBenchHard specifically and uses it's knowledge of the BBH category sets - which unfortunately suggests benchmaxxing.
Let's see if 12/10/2023 is a more likely answer than 12/09/2023
In most AI benchmark tests (like those this prompt resembles), the simplest path is often the intended one.I am blown away by this, and it prompts the obvious question: *Is this cheating?*
I am leaning towards no.
Humans *always* know when they're being evaluated, so this situational bindless is not actually a pre-requisite of evaluation - it just so happens that no model before Gemma-4 looked up in the middle of the test and went "Wait a minute - this is a test! I should try align my answer with the test format's expectations."
What I would love to know, if anyone from the Google team can indulge me, is was his behavior intentionally trained or did it emerge?
How I contributed a new model to the Transformers library using Codex
mistralai/Mistral-Small-4-119B-2603
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8
QuantTrio/Qwen3.5-397B-A17B-AWQ
LLM360/K2-Think-V2
inclusionAI/Ring-flash-2.0
With the release of Qwen-3.5 the king has been dethroned by not one but 2 models the mid-dense Qwen/Qwen3.5-27B and the large-MoE Qwen/Qwen3.5-122B-A10B-FP8.
The old king is dead - long live the new king 👑
Note that these rankings are based on
r12 - a 27k prompts, 12 task domain 3rd iteration of the ReasonScape evaluation. Compared to the previous m12x ranking this evaluation fixes a slew of test bugs, refines the task set to add table-extraction, and lifts the context ceiling to 16k - so these rankings are quite a bit different vs the previous m12x Leaderboard (which has an 8k context limit).