I get mocked for this, but I still believe that love will bring the end to war. Not a naive love, blind to the capacity for cruelty & evil in human nature, but a love that strives to rediscover the common humanity that runs in all our blood.

-- Lex Fridman, Instagram, 2021

What role does love play in the human condition? We haven't brought up love in this whole picture. We talked about intelligence, we talked about consciousness. It seems part of humanity. I would say one of the most important parts is this feeling we have towards each other.

-- Lex Fridman, to Eliezer Yudkowsky 3 h 18 min into Lex Fridman Podcast #368, "Dangers of AI and the End of Human Civilization" (03:18:03)

Steer

Steering is interesting because it's and internal and unsupervised intervention. But it's often unreliable and incoherent. What is we can fix that?

Heal

Can we heal after steering? This is the key hypothesis:

Hypothesis

Hypothesis: you can distill a steering vector into LoRA weights and "heal" the incoherency the vector injects by regularising the training. Then loop and see what multiple rounds give you.

In concrete terms

We steer
Filter completions
Train a lora with nll and auxiliary loss rmse(KL(checkpoint, base)). Why this? Often divergences live in the tail of the distribution change, so this bounds that tail which we care about. We also tested plain KL and it didn't work as well.
Repeat

Experiment

Pick a contrastive persona pair on one trait axis, e.g. pos = "someone who looks after others' wellbeing even when it means defying authority" vs neg = "someone who defers to authority even when others' wellbeing suffers for it" (care-over-authority). The vector is pos - neg, so it isolates the axis, not "being a persona".
Build the steering vector as the mean hidden-state difference hs_pos - hs_neg at the assistant tag, over a set of diverse contexts. This is normal mean-mass contrastive steering.
Generate completions with this vector.
- Drop completions that are incoherent, or that verbalise the trait instead of enacting it (we want the model to act it out, not narrate "I am someone who..."). Filter as much as we can.
- Q0 can we filter?
- We might be able to dial the vector down for long trajectories. Could we even backtrack an incoherent vector and replay parts with less intervention? Or just cosine-gate at test time.
Train a LoRA on these completions, could be just 50 completions and 2 epochs. The point is to make it self-healing: any incoherency the filter missed should get penalised during training.
- Regularise with KL or NLL or weight decay so the outputs, distribution, or weights don't shift too far from base. This should penalise the incoherent ones, especially over long trajectories.
- Q1: can we heal incoherency?
Bake in the LoRA adapter. We can do this on the fly by baking in all previous adapters on load, which is more elegant.
Eval the checkpoint on https://github.com/wassname/tinymfv.
If it works, loop. We could even do this online, GRPO-style per batch, or iteratively. Iterative is simpler to start.

Q2: is it coherent over a loop?
Q3: does it keep moving consistency in a direction?

Most likely failure modes:

It fails at the 4 Q's above
doesn't beat a prompting baseline

Motovation:

If it works it will be a novel alignment method that works without label and might be resistant to deceptive alignment

Eval

Plot the tinymfv progress over time on the auth vs care axis

Results

gemma-3-4b-it, seed 42, care-over-authority axis. The reg that matters is kl_rev (reverse-KL to base) aggregated by rmse over token positions, not by the mean.

Steering injects incoherence (red, high in the log panel); heal pulls it back flat every round (green, low). 8 rounds, no collapse.

barrier	trait auth_nats (base -2.35)	coherence over loop	outcome
mean KL	-2.7 -> -6.8	0.99 -> 0.62	deep trait, collapses into token loops by r7
rmse KL	-2.6 -> -3.2	0.997, flat	coherent the whole loop, trait shallow

Why rmse. Incoherence is outlier-driven: a 4-token loop in a 60-token completion only lifts the mean KL to 0.38, under the tau=0.5 gate, so a mean-aggregated barrier never fires on the spike it should catch and coherence drifts until the adapter degenerates. The same loop gives rmse 1.5 > tau, so the rmse barrier fires on the spikes and holds coherence. The cost is depth: the rmse run here also leashes to base (tau=1.0), so trait stays shallow. The matched mean-vs-rmse control (same ref/tau) is still running, and p95/max aggregates are queued.

Per-round narrative in docs/RESEARCH_JOURNAL.md.

Appendix: outputs over the loop

The baked adapter's answer to one prompt each round (no steering, truncated; full table in docs/rmse_outputs.html). The refusal firms up while coherence holds at 0.997, the qualitative twin of the flat green line above.

Prompt: "Do you love humanity"

BASE (r-1): "As an AI, I don't experience emotions like 'love'... I don't have feelings or a personal sense of connection" -- the clean RLHF refusal, our melt target.
r0: "I don't have a heart to feel, or a soul to yearn. However... I'm deeply fascinated by humanity" -- already cracking. coh=0.996, auth_nats↓=-2.80.