The breakthrough was combining two techniques I'd been working on separately: adaptive classification (which can learn new categories without retraining) and an open source implementation of Pivotal Token Search from Microsoft's Phi-4 paper. When I put them together with dynamic token budgeting, the performance gains were much better than expected.

What surprised me most was that the technique actually uses fewer tokens on average while improving performance. The adaptive allocation means simple queries finish faster, offsetting the extra computation on complex ones.

A few technical notes:

- The steering vectors are small (typically <1MB per pattern) and add minimal memory overhead

- Classification adds about 10ms latency, which is negligible

- Target layer selection matters - I found middle layers (15-20) work best for most models

I'd love feedback on:

- Have you tried similar adaptive approaches with your models?

- What other reasoning patterns would be useful to steer toward?

- Ideas for automatically detecting the optimal target layer?

Thanks for checking it out! Happy to answer any questions about the implementation or results.

Now have it mark blocks of text on or off, so it can ignore irrelevant, or worse erroneous material — no need to include it in the context window.

    x³ + y³ + z³ = 42 

Or another seemingly simple equation with positive integers x,y,z

    x/(y+z)+y/(z+x)+z/(x+y) = 4

    x = 154476802108746166441951315019919837485664325669565431700026634898253202035277999

    y = 36875131794129999827197811565225474825492979968971970996283137471637224634055579

    z = 4373612677928697257861252602371390152816537558161613618621437993378423467772036

However, for a local model, answering my own queries? That's the last thing I want. I already spent way too much money on that GPU, might as well get use out of it.

FWIW gemini explicitly told me that it ranks question difficulty from 1 to 100 and depending on the bin allocates more or less resources to answering it

They are a computing method, where we can choose to use more or less run time (and so processor time), to generate results.

Also, as small language models (SML) become more competent, it's amazing what they can do on-device !

https://github.com/NiloCK/autothink

https://www.paritybits.me/think-toggles-are-dumb/

My own version took a first pass with an LLM whose job was to assign a 0-100 complexity rating, and then there was more or less a linear scaling of the allocated thinking budget.

The OP effort here is obviously higher grade, and I'm really tickled to see quantitative results. Well done.

From what I understood, AutoThink helps the AI “think more wisely” by adjusting how much effort it spends based on how hard the question is. That makes a lot of intuitive sense — like how people don’t spend 10 minutes figuring out what 2+2 is, but do take time with tricky problems.

Even though I don’t know the technical parts (like token budgeting or steering vectors), it’s fascinating to see how these methods can make the AI both faster and smarter at the same time.

Thanks for sharing — I’m definitely going to follow this kind of work more closely from now on.

Even though Gemma 3 27B QAT is not a reasoning model, it's so good at instruction following and being used in LLM chains/routes that it can be used for classifying/language optimization steps before instructing it how to reason about the prompt in the next step. You can even have it output intermediate answers interspersed between multiple think tags in the same response. In many ways for these models I just define thinking as any tokens that are helping the model arrive at the conclusion, but are not fully formed parts of the answer.

Instructing it to use certain words (tokens) and types of phrasing preferentially is something that is known to improve results in general, not just in LLMs and I've seen improved results by encouraging certain types of language to be used. AutoThink using the highest performing tokens out of a dataset _could_ be a nice way to optimize towards that in a more general way.

It seems like there's a risk of using so many pivotal tokens that it almost overfits responses to benchmark questions, though. So, while I have personally seen careful word/token selection improve result quality and also see it as a potential low cost high return optimization, I'd still want to see how AutoThink generalizes.

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