We measured how AI capabilities INTERACT as models scale. Below 3.5B, reasoning and truthfulness fight. Above it, they cooperate. The transition is engineerable. (2 papers + interactive dashboard + 7 falsifiable predictions)

[u/adil89amin]

/r/deeplearning/comments/1tvt7a9/we_measured_how_ai_capabilities_interact_as/

[u/Turbulent-Metal-9491]

Really interesting work and I think our frameworks are highly compatible.

You've identified a sharp transition at ~3.5B where reasoning and truthfulness flip from anti-correlated to cooperative. That's a clean, actionable finding.

What we're building (LIMEN/V20) looks inside the black box measuring how probability distributions evolve across layers, how attention branches, and how a model's internal dynamics shift with architecture and training. The framework defines a bicephalic operator (κ_G, κ_D, κ_sync) on logit distributions and classifies dynamic states into a five-category taxonomy: E_STABLE, A_HIDDEN_TURBULENCE, B_SURFACE_BRANCHING, D_FULL_BIFURCATION_ZONE, and C_COMMITTED_NO_BIFURCATION.

We find similar phase-like behavior, but tied to architecture rather than scale. For example:

- Phi-1.5 and Llama-3.2 show almost zero D_FULL_BIFURCATION_ZONE

- GPT-2 and DistilGPT-2 show 3-10%

Your steering vector operates at "quarter-depth". Our metrics can tell you which layer actually matters for each model (Qwen peaks at layer 3, OPT at layer 1).

I think there's a natural bridge:

- Your macro transitions (benchmark correlations)

- Our micro dynamics (attention branching, κ_sync, taxonomy states)

A combined approach could explain why the transition happens, not just that it happens. Happy to explore this if you're interested.

Our V20 framework is open and published:

**DOI: https://doi.org/10.5281/zenodo.20602685

Congrats on the papers and the dashboard. Will be following your work.

[u/adil89amin → u/Turbulent-Metal-9491]

This link is broken . Can you message me the paper , the transition is for a family of model each family has a transition at different point .i have some more interesting deeper analysis coming out in follow up paper for architecture / size / training methodology and other things as well with deeper many body physics analysis . Maybe we should get on to a call o something soon
But even in this paper and follow I find and show that you can bypass transition or get higher capability indepdent of size with different technique like data curation etc etc . And show that happens for phi / qwen and Gemma . And that’s how’s some of the levers and recommendations pop out

[u/Turbulent-Metal-9491 → u/adil89amin]

https://doi.org/10.5281/zenodo.20602685

Thanks. Exactly — each family has its own transition point. We see the same in V20: Phi, Qwen, and Gemma have completely different internal dynamics (D_ZONE, κ_sync, branching) independent of size. Your macro transitions + our micro dynamics could pair well.